+
+## 📝 Meissonic Updates and Family Papers
+
+- [MaskGIT: Masked Generative Image Transformer](https://arxiv.org/abs/2202.04200) [CVPR 2022]
+- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://arxiv.org/abs/2301.00704) [ICML 2023]
+- [🌟][Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis](https://arxiv.org/abs/2410.08261) [ICLR 2025]
+- [Bag of Design Choices for Inference of High-Resolution Masked Generative Transformer](https://arxiv.org/abs/2411.10781)
+- [Di[𝙼]O: Distilling Masked Diffusion Models into One-step Generator](https://arxiv.org/abs/2503.15457) [ICCV 2025]
+- [🌟][Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model](https://arxiv.org/abs/2505.23606)
+- [DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer](https://arxiv.org/pdf/2507.04947) [ICCV 2025]
+- [MDNS: Masked Diffusion Neural Sampler via Stochastic Optimal Control](https://arxiv.org/abs/2508.10684)
+- [Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation](https://arxiv.org/abs/2509.19244)
+- [🌟][Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding](https://arxiv.org/abs/2510.06308)
+- [Token Painter: Training-Free Text-Guided Image Inpainting via Mask Autoregressive Models](https://arxiv.org/abs/2509.23919)
+- [TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion](https://arxiv.org/abs/2509.25171)
+- [OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit Flows](https://arxiv.org/abs/2510.03506)
+- [Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces](https://arxiv.org/abs/2506.07903) [ICML 2025]
+- [Towards Better & Faster Autoregressive Image Generation: From the Perspective of Entropy](https://arxiv.org/abs/2510.09012) [NeurIPS 2025]
+- [🌟][From Masks to Worlds: A Hitchhiker's Guide to World Models](https://arxiv.org/abs/2510.20668)
+- [Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings](https://arxiv.org/abs/2509.22925)
+
+- More papers are coming soon!
+See [MeissonFlow Research](https://huggingface.co/MeissonFlow) (Organization Card) for more about our vision.
+
+
+
+
+## 🚀 Introduction
+
+Meissonic is a non-autoregressive mask image modeling text-to-image synthesis model that can generate high-resolution images. It is designed to run on consumer graphics cards.
+
+
+
+**Key Features:**
+- 🖼️ High-resolution image generation (up to 1024x1024)
+- 💻 Designed to run on consumer GPUs
+- 🎨 Versatile applications: text-to-image, image-to-image
+
+## 🛠️ Prerequisites
+
+### Step 1: Clone the repository
+```bash
+git clone https://github.com/viiika/Meissonic/
+cd Meissonic
+```
+
+### Step 2: Create virtual environment
+```bash
+conda create --name meissonic python
+conda activate meissonic
+pip install -r requirements.txt
+```
+
+### Step 3: Install diffusers
+```bash
+git clone https://github.com/huggingface/diffusers.git
+cd diffusers
+pip install -e .
+```
+
+## 💡 Inference Usage
+
+### Gradio Web UI
+
+```bash
+python app.py
+```
+
+### Command-line Interface
+
+#### Text-to-Image Generation
+
+```bash
+python inference.py --prompt "Your creative prompt here"
+```
+
+#### Inpainting and Outpainting
+
+```bash
+python inpaint.py --mode inpaint --input_image path/to/image.jpg
+python inpaint.py --mode outpaint --input_image path/to/image.jpg
+```
+
+### Advanced: FP8 Quantization
+
+Optimize performance with FP8 quantization:
+
+Requirements:
+- CUDA 12.4
+- PyTorch 2.4.1
+- TorchAO
+
+Note: Windows users install TorchAO using
+```shell
+pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cpu
+```
+
+Command-line inference
+```shell
+python inference_fp8.py --quantization fp8
+```
+
+Gradio for FP8 (Select Quantization Method in Advanced settings)
+```shell
+python app_fp8.py
+```
+
+#### Performance Benchmarks
+
+| Precision (Steps=64, Resolution=1024x1024) | Batch Size=1 (Avg. Time) | Memory Usage |
+|-------------------------------------------|--------------------------|--------------|
+| FP32 | 13.32s | 12GB |
+| FP16 | 12.35s | 9.5GB |
+| FP8 | 12.93s | 8.7GB |
+
+## 🎨 Showcase
+
+
+
+
"A pillow with a picture of a Husky on it."
+
+
+
+
+
"A white coffee mug, a solid black background"
+
+
+## 🎓 Training
+
+To train Meissonic, follow these steps:
+
+1. Install dependencies:
+ ```bash
+ cd train
+ pip install -r requirements.txt
+ ```
+
+2. Download the [Meissonic](https://huggingface.co/MeissonFlow/Meissonic) base model from Hugging Face.
+
+3. Prepare your dataset:
+ - Use the sample dataset: [MeissonFlow/splash](https://huggingface.co/datasets/MeissonFlow/lemon/resolve/main/0000.parquet)
+ - Or prepare your own dataset and dataset class following the format in line 100 in [dataset_utils.py](./train/dataset_utils.py) and line 656-680 in [train_meissonic.py](./train/train_meissonic.py)
+ - Modify [train.sh](./train/train.sh) with your dataset path
+
+4. Start training:
+ ```bash
+ bash train/train.sh
+ ```
+
+Note: For custom datasets, you'll likely need to implement your own dataset class.
+
+
+## 📚 Citation
+
+If you find this work helpful, please consider citing:
+
+```bibtex
+@article{bai2024meissonic,
+ title={Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis},
+ author={Bai, Jinbin and Ye, Tian and Chow, Wei and Song, Enxin and Chen, Qing-Guo and Li, Xiangtai and Dong, Zhen and Zhu, Lei and Yan, Shuicheng},
+ journal={arXiv preprint arXiv:2410.08261},
+ year={2024}
+}
+```
+
+## 🙏 Acknowledgements
+
+We thank the community and contributors for their invaluable support in developing Meissonic. We thank apolinario@multimodal.art for making Meissonic [Demo](https://huggingface.co/spaces/MeissonFlow/meissonic). We thank @NewGenAI and @飛鷹しずか@自称文系プログラマの勉強 for making YouTube tutorials. We thank @pprp for making fp8 and int4 quantization. We thank @camenduru for making [jupyter tutorial](https://github.com/camenduru/Meissonic-jupyter). We thank @chenxwh for making Replicate demo and api. We thank Collov Labs for reproducing [Monetico](https://huggingface.co/Collov-Labs/Monetico). We thank [Shitong et al.](https://arxiv.org/abs/2411.10781) for identifying effective design choices for enhancing visual quality.
+
+
+---
+
+
diff --git a/Meissonic/app.py b/Meissonic/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..405c7a647f69284d37a28aa20d43c65874dc446c
--- /dev/null
+++ b/Meissonic/app.py
@@ -0,0 +1,149 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from torchvision import transforms
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+import gradio as gr
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+model_path = "MeissonFlow/Meissonic"
+model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer")
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae")
+# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder")
+text_encoder = CLIPTextModelWithProjection.from_pretrained( #more stable sampling for some cases
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ )
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
+pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
+pipe.to(device)
+
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 1024
+
+
+def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+ if randomize_seed or seed == 0:
+ seed = torch.randint(0, MAX_SEED, (1,)).item()
+ torch.manual_seed(seed)
+
+ image = pipe(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ height=height,
+ width=width,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps
+ ).images[0]
+
+ return image, seed
+
+# Default negative prompt
+default_negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+css = """
+#col-container {
+ margin: 0 auto;
+ max-width: 640px;
+}
+"""
+
+examples = [
+ "Modern Architecture render with pleasing aesthetics.",
+ "An image of a Pikachu wearing a birthday hat and playing guitar.",
+ "A statue of a lion stands in front of a building.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "A metal sculpture of a deer with antlers.",
+ "A bronze statue of an owl with its wings spread.",
+ "A white table with a vase of flowers and a cup of coffee on top of it.",
+ "A woman stands on a dock in the fog.",
+ "A lion's head is shown in a grayscale image.",
+ "A sculpture of a Greek woman head with a headband and a head of hair."
+]
+
+with gr.Blocks(css=css) as demo:
+ with gr.Column(elem_id="col-container"):
+ gr.Markdown("# Meissonic Text-to-Image Generator")
+ with gr.Row():
+ prompt = gr.Text(
+ label="Prompt",
+ show_label=False,
+ max_lines=1,
+ placeholder="Enter your prompt",
+ container=False,
+ )
+ run_button = gr.Button("Run", scale=0, variant="primary")
+ result = gr.Image(label="Result", show_label=False)
+ with gr.Accordion("Advanced Settings", open=False):
+ negative_prompt = gr.Text(
+ label="Negative prompt",
+ max_lines=1,
+ placeholder="Enter a negative prompt",
+ value=default_negative_prompt,
+ )
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=0,
+ )
+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+ with gr.Row():
+ width = gr.Slider(
+ label="Width",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024,
+ )
+ height = gr.Slider(
+ label="Height",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024,
+ )
+ with gr.Row():
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ minimum=0.0,
+ maximum=20.0,
+ step=0.1,
+ value=9.0,
+ )
+ num_inference_steps = gr.Slider(
+ label="Number of inference steps",
+ minimum=1,
+ maximum=100,
+ step=1,
+ value=64,
+ )
+ gr.Examples(examples=examples, inputs=[prompt])
+ gr.on(
+ triggers=[run_button.click, prompt.submit],
+ fn=generate_image,
+ inputs=[
+ prompt,
+ negative_prompt,
+ seed,
+ randomize_seed,
+ width,
+ height,
+ guidance_scale,
+ num_inference_steps,
+ ],
+ outputs=[result, seed],
+ )
+
+demo.launch()
\ No newline at end of file
diff --git a/Meissonic/app_Monetico.py b/Meissonic/app_Monetico.py
new file mode 100644
index 0000000000000000000000000000000000000000..f406a996c094c368f5cacc382be490aba04b9ca8
--- /dev/null
+++ b/Meissonic/app_Monetico.py
@@ -0,0 +1,151 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from torchvision import transforms
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+import gradio as gr
+import spaces
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+dtype = torch.bfloat16
+
+model_path = "Collov-Labs/Monetico"
+
+model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=dtype) # better for Monetico
+# text_encoder = CLIPTextModelWithProjection.from_pretrained( #more stable sampling for some cases
+# "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=dtype
+# )
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", torch_dtype=dtype)
+scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler", torch_dtype=dtype)
+pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
+pipe.to(device)
+
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 512
+
+@spaces.GPU
+def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+ if randomize_seed or seed == 0:
+ seed = torch.randint(0, MAX_SEED, (1,)).item()
+ torch.manual_seed(seed)
+
+ image = pipe(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ height=height,
+ width=width,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps
+ ).images[0]
+
+ return image, seed
+
+# Default negative prompt
+default_negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+css = """
+#col-container {
+ margin: 0 auto;
+ max-width: 640px;
+}
+"""
+
+examples = [
+ "Modern Architecture render with pleasing aesthetics.",
+ "An image of a Pikachu wearing a birthday hat and playing guitar.",
+ "A statue of a lion stands in front of a building.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "A metal sculpture of a deer with antlers.",
+ "A bronze statue of an owl with its wings spread.",
+ "A white table with a vase of flowers and a cup of coffee on top of it.",
+ "A woman stands on a dock in the fog.",
+ "A lion's head is shown in a grayscale image.",
+ "A sculpture of a Greek woman head with a headband and a head of hair."
+]
+
+with gr.Blocks(css=css) as demo:
+ with gr.Column(elem_id="col-container"):
+ gr.Markdown("# Monetico Text-to-Image Generator")
+ with gr.Row():
+ prompt = gr.Text(
+ label="Prompt",
+ show_label=False,
+ max_lines=1,
+ placeholder="Enter your prompt",
+ container=False,
+ )
+ run_button = gr.Button("Run", scale=0, variant="primary")
+ result = gr.Image(label="Result", show_label=False)
+ with gr.Accordion("Advanced Settings", open=False):
+ negative_prompt = gr.Text(
+ label="Negative prompt",
+ max_lines=1,
+ placeholder="Enter a negative prompt",
+ value=default_negative_prompt,
+ )
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=0,
+ )
+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+ with gr.Row():
+ width = gr.Slider(
+ label="Width",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=512,
+ )
+ height = gr.Slider(
+ label="Height",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=512,
+ )
+ with gr.Row():
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ minimum=0.0,
+ maximum=20.0,
+ step=0.1,
+ value=9.0,
+ )
+ num_inference_steps = gr.Slider(
+ label="Number of inference steps",
+ minimum=1,
+ maximum=100,
+ step=1,
+ value=48,
+ )
+ gr.Examples(examples=examples, inputs=[prompt])
+ gr.on(
+ triggers=[run_button.click, prompt.submit],
+ fn=generate_image,
+ inputs=[
+ prompt,
+ negative_prompt,
+ seed,
+ randomize_seed,
+ width,
+ height,
+ guidance_scale,
+ num_inference_steps,
+ ],
+ outputs=[result, seed],
+ )
+
+demo.launch()
\ No newline at end of file
diff --git a/Meissonic/app_fp8.py b/Meissonic/app_fp8.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9f06fa08e1563d531670144742ad5da1ceccc1f
--- /dev/null
+++ b/Meissonic/app_fp8.py
@@ -0,0 +1,223 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+import gradio as gr
+import time
+from torchao.quantization.quant_api import (
+ quantize_,
+ float8_weight_only,
+)
+
+device = 'cuda'
+
+def get_quantization_method(method):
+ quantization_methods = {
+ 'fp8': lambda: float8_weight_only(),
+ 'none': None
+ }
+ return quantization_methods.get(method, None)
+
+def load_models(quantization_method='none'):
+ model_path = "MeissonFlow/Meissonic"
+ dtype = torch.float16
+ model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+ vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+ text_encoder = CLIPTextModelWithProjection.from_pretrained(
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+ torch_dtype=dtype
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+ scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
+
+ if quantization_method != 'none':
+ quant_method = get_quantization_method(quantization_method)
+ if quant_method:
+ quantize_(model, quant_method())
+
+ pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
+ return pipe.to(device)
+
+# Global variable to store the pipeline
+global_pipe = None
+current_quantization = 'none'
+
+def initialize_pipeline(quantization):
+ global global_pipe, current_quantization
+ if global_pipe is None or current_quantization != quantization:
+ global_pipe = load_models(quantization)
+ current_quantization = quantization
+ return global_pipe
+
+def generate_images(prompt, negative_prompt, seed, randomize_seed, width, height,
+ guidance_scale, num_inference_steps, quantization_method, batch_size=1,
+ progress=gr.Progress(track_tqdm=True)):
+ if randomize_seed or seed == 0:
+ seed = torch.randint(0, MAX_SEED, (1,)).item()
+ torch.manual_seed(seed)
+
+ # Initialize or update pipeline if needed
+ pipe = initialize_pipeline(quantization_method)
+
+ # Reset CUDA memory stats
+ torch.cuda.reset_peak_memory_stats()
+ start_time = time.time()
+
+ # Handle batch generation
+ if isinstance(prompt, str):
+ prompts = [prompt] * batch_size
+ else:
+ prompts = prompt[:batch_size]
+
+ images = pipe(
+ prompt=prompts,
+ negative_prompt=[negative_prompt] * batch_size,
+ height=height,
+ width=width,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps
+ ).images
+
+ # Calculate performance metrics
+ inference_time = time.time() - start_time
+ memory_used = torch.cuda.max_memory_reserved() / (1024 ** 3) # Convert to GB
+
+ performance_info = f"""
+ Inference Time: {inference_time:.2f} seconds
+ Memory Used: {memory_used:.2f} GB
+ Quantization: {quantization_method}
+ """
+
+ return images[0] if batch_size == 1 else images, seed, performance_info
+
+MAX_SEED = 2**32 - 1
+MAX_IMAGE_SIZE = 1024
+default_negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+examples = [
+ "Two actors are posing for a pictur with one wearing a black and white face paint.",
+ "A large body of water with a rock in the middle and mountains in the background.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "The sun is setting over a city skyline with a river in the foreground.",
+ "A black and white cat with blue eyes.",
+ "Three boats in the ocean with a rainbow in the sky.",
+ "A robot playing the piano.",
+ "A cat wearing a hat.",
+ "A dog in a jungle."
+]
+
+css = """
+#col-container {
+ margin: 0 auto;
+ max-width: 640px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+ with gr.Column(elem_id="col-container"):
+ gr.Markdown("# Meissonic Text-to-Image Generator (with FP8 Support)")
+
+ with gr.Row():
+ prompt = gr.Text(
+ label="Prompt",
+ show_label=False,
+ max_lines=1,
+ placeholder="Enter your prompt",
+ container=False,
+ )
+ run_button = gr.Button("Run", scale=0, variant="primary")
+
+ result = gr.Image(label="Result", show_label=False)
+ performance_info = gr.Textbox(label="Performance Metrics", lines=4)
+
+ with gr.Accordion("Advanced Settings", open=False):
+ quantization = gr.Radio(
+ choices=['none', 'fp8'],
+ value='none',
+ label="Quantization Method",
+ )
+ negative_prompt = gr.Text(
+ label="Negative prompt",
+ max_lines=1,
+ placeholder="Enter a negative prompt",
+ value=default_negative_prompt,
+ )
+ seed = gr.Slider(
+ label="Seed",
+ minimum=0,
+ maximum=MAX_SEED,
+ step=1,
+ value=0,
+ )
+ randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+ with gr.Row():
+ width = gr.Slider(
+ label="Width",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024,
+ )
+ height = gr.Slider(
+ label="Height",
+ minimum=256,
+ maximum=MAX_IMAGE_SIZE,
+ step=32,
+ value=1024,
+ )
+
+ with gr.Row():
+ guidance_scale = gr.Slider(
+ label="Guidance scale",
+ minimum=0.0,
+ maximum=20.0,
+ step=0.1,
+ value=9.0,
+ )
+ num_inference_steps = gr.Slider(
+ label="Number of inference steps",
+ minimum=1,
+ maximum=100,
+ step=1,
+ value=64,
+ )
+
+ batch_size = gr.Slider(
+ label="Batch Size",
+ minimum=1,
+ maximum=8,
+ step=1,
+ value=1,
+ )
+
+ gr.Examples(examples=examples, inputs=[prompt])
+
+ gr.on(
+ triggers=[run_button.click, prompt.submit],
+ fn=generate_images,
+ inputs=[
+ prompt,
+ negative_prompt,
+ seed,
+ randomize_seed,
+ width,
+ height,
+ guidance_scale,
+ num_inference_steps,
+ quantization,
+ batch_size,
+ ],
+ outputs=[result, seed, performance_info],
+ )
+
+demo.launch()
diff --git a/Meissonic/assets/architecture.png b/Meissonic/assets/architecture.png
new file mode 100644
index 0000000000000000000000000000000000000000..5cc0865dedd936ed3a3453637ce1a8b30608ba82
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+version https://git-lfs.github.com/spec/v1
+oid sha256:174e021d396802a14e454914586ca45d19a31581bd8e6e98c0252eb1c8f4b1c3
+size 327943
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+version https://git-lfs.github.com/spec/v1
+oid sha256:1d14191e0b8e9fdf4cb3a7199cf36554e60e456cdeba11509d305a8201e6b131
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+size 1828570
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+oid sha256:2ee6f2d8fae720821257db75cc819919a780f644f7a7a3e83aab0bdaccb13d53
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+version https://git-lfs.github.com/spec/v1
+oid sha256:a08fa5cd031f119cb3123d767f2358b5081d6bc7dc1b017081f66bb824e86ae0
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index 0000000000000000000000000000000000000000..398136324425e59ae9eabc018e147512c31dcf93
--- /dev/null
+++ b/Meissonic/assets/inpaint/cases.json
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+[
+ {
+ "input":"./assets/inpaint/_Rh_zxIUWXA.jpg",
+ "mask": "./assets/inpaint/_Rh_zxIUWXA.png",
+ "prompt": "A woman with short hair wears a silver gas mask.",
+ "negative_prompts": null
+ },
+ {
+ "input":"./assets/inpaint/0eKR4M2uuL8.jpg",
+ "mask": "./assets/inpaint/0eKR4M2uuL8.png",
+ "prompt": "A stylish dog wearing sunglasses.",
+ "negative_prompts": null
+ },
+ {
+ "input":"./assets/inpaint/__Owak0IgJk.jpg",
+ "mask": "./assets/inpaint/__Owak0IgJk.png",
+ "prompt": "A woman wearing a white suspender skirt is sitting",
+ "negative_prompts": null
+ }
+]
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e98e81fa367ade7edd41cd60769d04ab6a7593dea3b3ec35f3f0e84634669de
+size 120864
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new file mode 100644
index 0000000000000000000000000000000000000000..a50b7c7adcf635d3b6c35ef2cb7be43eab84cd46
--- /dev/null
+++ b/Meissonic/assets/outpaint/cases.json
@@ -0,0 +1,20 @@
+[
+ {
+ "input":"./assets/outpaint/__G2yFuW7jQ.jpg",
+ "mask": "./assets/outpaint/__G2yFuW7jQ.png",
+ "prompt": "fall mountains",
+ "negative_prompts": "The artwork avoids the pitfalls of bad art, such as ugly and deformed eyes and faces, poorly drawn, blurry, and disfigured bodies with extra limbs and close-ups that look weird. It also avoids other common issues such as watermarking, text errors, missing fingers or digits, cropping, poor quality, and JPEG artifacts. The artwork is free of signature or watermark and avoids framing issues.The hands are not deformed, the eyes are not disfigured, and there areno extra bodies or limbs. The artwork is not blurry, out of focus, or poorly drawn, and the proportions are not bad or deformed. There are no mutations, missing limbs, or floating or disconnected limbs. The hands and neck are not malformed, and there are no extra heads or out-of-frame elements. The artwork is not low-res or disgusting and is a well-drawn, highly detailed, and beautiful rendering."
+ },
+ {
+ "input":"./assets/outpaint/__G2yFuW7jQ.jpg",
+ "mask": "./assets/outpaint/__G2yFuW7jQ.png",
+ "prompt": "Rocket launch site",
+ "negative_prompts": "The artwork avoids the pitfalls of bad art, such as ugly and deformed eyes and faces, poorly drawn, blurry, and disfigured bodies with extra limbs and close-ups that look weird. It also avoids other common issues such as watermarking, text errors, missing fingers or digits, cropping, poor quality, and JPEG artifacts. The artwork is free of signature or watermark and avoids framing issues.The hands are not deformed, the eyes are not disfigured, and there areno extra bodies or limbs. The artwork is not blurry, out of focus, or poorly drawn, and the proportions are not bad or deformed. There are no mutations, missing limbs, or floating or disconnected limbs. The hands and neck are not malformed, and there are no extra heads or out-of-frame elements. The artwork is not low-res or disgusting and is a well-drawn, highly detailed, and beautiful rendering."
+ },
+ {
+ "input":"./assets/outpaint/__G2yFuW7jQ.jpg",
+ "mask": "./assets/outpaint/__G2yFuW7jQ.png",
+ "prompt": "Volcano",
+ "negative_prompts": "The artwork avoids the pitfalls of bad art, such as ugly and deformed eyes and faces, poorly drawn, blurry, and disfigured bodies with extra limbs and close-ups that look weird. It also avoids other common issues such as watermarking, text errors, missing fingers or digits, cropping, poor quality, and JPEG artifacts. The artwork is free of signature or watermark and avoids framing issues.The hands are not deformed, the eyes are not disfigured, and there areno extra bodies or limbs. The artwork is not blurry, out of focus, or poorly drawn, and the proportions are not bad or deformed. There are no mutations, missing limbs, or floating or disconnected limbs. The hands and neck are not malformed, and there are no extra heads or out-of-frame elements. The artwork is not low-res or disgusting and is a well-drawn, highly detailed, and beautiful rendering."
+ }
+]
\ No newline at end of file
diff --git a/Meissonic/cog.yaml b/Meissonic/cog.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..62a932f5e559b60da72737c697eaafdd5521d7cd
--- /dev/null
+++ b/Meissonic/cog.yaml
@@ -0,0 +1,29 @@
+# Configuration for Cog ⚙️
+# Reference: https://cog.run/yaml
+
+build:
+ # set to true if your model requires a GPU
+ gpu: true
+
+ # a list of ubuntu apt packages to install
+ system_packages:
+ - "libgl1-mesa-glx"
+ - "libglib2.0-0"
+
+ # python version in the form '3.11' or '3.11.4'
+ python_version: "3.11"
+
+ # a list of packages in the format ==
+ python_packages:
+ - torch
+ - torchvision
+ - git+https://github.com/huggingface/diffusers.git
+ - accelerate
+ - transformers
+
+ # commands run after the environment is setup
+ run:
+ - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.8.2/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+
+# predict.py defines how predictions are run on your model
+predict: "predict.py:Predictor"
diff --git a/Meissonic/cosmos_test_output/comparison_grid_video_0.png b/Meissonic/cosmos_test_output/comparison_grid_video_0.png
new file mode 100644
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--- /dev/null
+++ b/Meissonic/cosmos_test_output/comparison_grid_video_0.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:73ee850b107ae6842e8c8a528689bfcb930ad7a5d863a9c7f1e2245f82650fa2
+size 394999
diff --git a/Meissonic/cosmos_test_output/comparison_grid_video_1.png b/Meissonic/cosmos_test_output/comparison_grid_video_1.png
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--- /dev/null
+++ b/Meissonic/cosmos_test_output/comparison_grid_video_1.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:80853c45d1431ab2dee94b554c6d9e64f2d17ff1257aa72f560cf3040ea39f27
+size 8286293
diff --git a/Meissonic/cosmos_test_output/comparison_grid_video_2.png b/Meissonic/cosmos_test_output/comparison_grid_video_2.png
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--- /dev/null
+++ b/Meissonic/cosmos_test_output/comparison_grid_video_2.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8a285e5661e7100963aada2ca27ead641867d989f6ffaa37f7c1f5d873adfbd
+size 9085956
diff --git a/Meissonic/cosmos_test_output/comparison_grid_video_3.png b/Meissonic/cosmos_test_output/comparison_grid_video_3.png
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--- /dev/null
+++ b/Meissonic/cosmos_test_output/comparison_grid_video_3.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e56e9d5e0cbfbf740d1c53c2d105616e3dc7781e1c8945d712b5edc583ad715f
+size 8634545
diff --git a/Meissonic/cosmos_test_output/metrics_video_0.txt b/Meissonic/cosmos_test_output/metrics_video_0.txt
new file mode 100644
index 0000000000000000000000000000000000000000..671a60175ef523c994215a0935b89b04eba172b5
--- /dev/null
+++ b/Meissonic/cosmos_test_output/metrics_video_0.txt
@@ -0,0 +1,12 @@
+Video Index: 0
+Video Path: 000/000/000/0.mp4
+Caption: In the video, a man is seen in a living room setting, standing in front of a window with blinds. He is wearing a black sweater and appears to be in the middle of a conversation. The room is dimly lit, with a lamp providing a soft glow in the background. The man's expression is serious, suggesting that the conversation is of importance. The overall style of the video is realistic and naturalistic, capturing a candid moment in the man's life.
+
+=== Metrics ===
+Average PSNR: 27.54 dB
+Average MSE: 0.001764
+Average SSIM: 0.9779
+
+Per-frame PSNR: [26.747089385986328, 27.265975952148438, 27.32347297668457, 27.352922439575195, 27.334339141845703, 27.782726287841797, 27.661243438720703, 27.803525924682617, 27.705425262451172, 27.679603576660156, 27.297304153442383, 27.51146125793457, 27.4649658203125, 27.89719581604004, 27.753822326660156, 27.86109161376953, 27.80060577392578]
+Per-frame MSE: [0.002114907605573535, 0.0018767336150631309, 0.0018520501907914877, 0.001839534263126552, 0.001847422681748867, 0.001666200696490705, 0.0017134671797975898, 0.0016582406824454665, 0.0016961240908131003, 0.0017062382539734244, 0.0018632437568157911, 0.001773593365214765, 0.001792682334780693, 0.0016228572931140661, 0.0016773275565356016, 0.0016364054754376411, 0.0016593559412285686]
+Per-frame SSIM: [0.9738484025001526, 0.9765720963478088, 0.9768512845039368, 0.9769563674926758, 0.9766926765441895, 0.9789631962776184, 0.9783727526664734, 0.9790931344032288, 0.9786423444747925, 0.9784184694290161, 0.9765286445617676, 0.9777430295944214, 0.9775411486625671, 0.9799171090126038, 0.9794126749038696, 0.9798620343208313, 0.9795750379562378]
diff --git a/Meissonic/cosmos_test_output/metrics_video_1.txt b/Meissonic/cosmos_test_output/metrics_video_1.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4222a4c6b10cff29079574967ed3908eebca46ee
--- /dev/null
+++ b/Meissonic/cosmos_test_output/metrics_video_1.txt
@@ -0,0 +1,12 @@
+Video Index: 1
+Video Path: 000/000/001/1.mp4
+Caption: The video shows a man standing next to a purple van with a floral design on the side. The man is wearing a black t-shirt and jeans, and he is smiling and waving his hands in the air. The van has pink rims and a black roof rack. The van is parked in front of a building with a glass door. The man appears to be happy and excited about the van. The video is likely a short clip of a man showing off his van.
+
+=== Metrics ===
+Average PSNR: 25.14 dB
+Average MSE: 0.003232
+Average SSIM: 0.9700
+
+Per-frame PSNR: [29.570905685424805, 25.845619201660156, 24.151002883911133, 24.53882598876953, 26.607555389404297, 23.609159469604492, 23.5848445892334, 24.532224655151367, 26.290340423583984, 23.606443405151367, 23.633737564086914, 24.562894821166992, 26.255611419677734, 24.259323120117188, 25.643463134765625, 25.491649627685547]
+Per-frame MSE: [0.0011038482189178467, 0.002602784661576152, 0.003845029277727008, 0.0035165559966117144, 0.0021839593537151814, 0.004355963785201311, 0.004380417056381702, 0.003521904582157731, 0.0023494488559663296, 0.004358689300715923, 0.004331381060183048, 0.0034971192944794893, 0.0023683111649006605, 0.0037503137718886137, 0.0027268033009022474, 0.002823807764798403]
+Per-frame SSIM: [0.9893906712532043, 0.974087119102478, 0.9622812867164612, 0.9658014178276062, 0.9791845083236694, 0.957465648651123, 0.957618772983551, 0.9664595127105713, 0.977811872959137, 0.9590921998023987, 0.960818350315094, 0.9692971110343933, 0.9799112677574158, 0.9681466817855835, 0.9765862822532654, 0.9759609699249268]
diff --git a/Meissonic/cosmos_test_output/metrics_video_2.txt b/Meissonic/cosmos_test_output/metrics_video_2.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ecafb94fe13e5e9bd2a5a3c5cd57c9512b6d533f
--- /dev/null
+++ b/Meissonic/cosmos_test_output/metrics_video_2.txt
@@ -0,0 +1,12 @@
+Video Index: 2
+Video Path: 000/000/002/2.mp4
+Caption: The video is a news segment featuring a man in a red baseball cap and a blue vest, standing in front of a statue of a soldier and two children. The man appears to be a veteran, as indicated by the cap and the context of the event. The event is an honorary ceremony for lost submarines and submarine veterans, taking place near the World Peace Bell in Newport. The news segment is titled "Connected to the Community" and is scheduled to air at 11:10 PM on ABC 9. The style of the video is informative and respectful, focusing on the man and the event, with a clear and concise presentation of the details.
+
+=== Metrics ===
+Average PSNR: 22.09 dB
+Average MSE: 0.006399
+Average SSIM: 0.9607
+
+Per-frame PSNR: [24.496965408325195, 22.367679595947266, 22.21709442138672, 22.679195404052734, 23.883594512939453, 22.220516204833984, 22.20623207092285, 21.4675350189209, 22.316797256469727, 19.425098419189453, 21.102333068847656, 21.321147918701172, 23.025981903076172, 21.053565979003906, 21.95743179321289, 21.684494018554688]
+Per-frame MSE: [0.0035506151616573334, 0.005797383841127157, 0.006001925095915794, 0.005396105814725161, 0.0040892185643315315, 0.00599720049649477, 0.006016954779624939, 0.007132581900805235, 0.005865707993507385, 0.011415375396609306, 0.007758304942399263, 0.007377093657851219, 0.00498197739943862, 0.007845907472074032, 0.006371723022311926, 0.0067850141786038876]
+Per-frame SSIM: [0.9770643711090088, 0.9623730778694153, 0.9605475068092346, 0.9618802070617676, 0.9713600277900696, 0.9565339088439941, 0.9568989872932434, 0.9560506939888, 0.9673117399215698, 0.9364117383956909, 0.9567262530326843, 0.9589394927024841, 0.9706904888153076, 0.9546973705291748, 0.9623250365257263, 0.9610125422477722]
diff --git a/Meissonic/cosmos_test_output/metrics_video_3.txt b/Meissonic/cosmos_test_output/metrics_video_3.txt
new file mode 100644
index 0000000000000000000000000000000000000000..541d12165559e95bc4d39145fcb59be4c5b948d3
--- /dev/null
+++ b/Meissonic/cosmos_test_output/metrics_video_3.txt
@@ -0,0 +1,12 @@
+Video Index: 3
+Video Path: 000/000/003/3.mp4
+Caption: The video features a man in a pink shirt and a black bucket hat, wearing glasses and a necklace. He is holding a spoon and making a playful face, as if he is about to eat something. The background shows a lush garden with trees and a wooden structure. The man's expression and the spoon suggest that he is about to taste something, possibly food. The overall style of the video is casual and fun, with a focus on the man's reaction to the food.
+
+=== Metrics ===
+Average PSNR: 26.22 dB
+Average MSE: 0.002459
+Average SSIM: 0.9856
+
+Per-frame PSNR: [27.509328842163086, 26.409242630004883, 25.4619140625, 25.407241821289062, 26.446935653686523, 23.73136329650879, 25.60137176513672, 26.993793487548828, 28.306987762451172, 25.729787826538086, 25.27326774597168, 26.266807556152344, 27.462078094482422, 25.950550079345703, 26.63888168334961, 26.327953338623047]
+Per-frame MSE: [0.001774463220499456, 0.0022859980817884207, 0.002843207446858287, 0.0028792270459234715, 0.0022662426345050335, 0.004235099535435438, 0.002753359731286764, 0.0019981153309345245, 0.0014767315005883574, 0.002673137467354536, 0.00296943006105721, 0.0023622140288352966, 0.0017938758246600628, 0.0025406500790268183, 0.0021682626102119684, 0.002329188399016857]
+Per-frame SSIM: [0.9894547462463379, 0.9864593744277954, 0.983065664768219, 0.9832437634468079, 0.9867878556251526, 0.9748696088790894, 0.9840085506439209, 0.9884393215179443, 0.991378664970398, 0.9843630194664001, 0.9830930829048157, 0.986225962638855, 0.989523708820343, 0.9850807189941406, 0.9871661067008972, 0.9861734509468079]
diff --git a/Meissonic/inference.py b/Meissonic/inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..e67cdb72c941f77ff27675cac53ff2f53a0cf03b
--- /dev/null
+++ b/Meissonic/inference.py
@@ -0,0 +1,65 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from torchvision import transforms
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+
+device = 'cuda'
+
+model_path = "MeissonFlow/Meissonic"
+model = Transformer2DModel.from_pretrained(model_path,subfolder="transformer",)
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", )
+# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder",)
+text_encoder = CLIPTextModelWithProjection.from_pretrained( #using original text enc for stable sampling
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ )
+tokenizer = CLIPTokenizer.from_pretrained(model_path,subfolder="tokenizer",)
+scheduler = Scheduler.from_pretrained(model_path,subfolder="scheduler",)
+pipe=Pipeline(vq_model, tokenizer=tokenizer,text_encoder=text_encoder,transformer=model,scheduler=scheduler)
+
+pipe = pipe.to(device)
+
+steps = 64
+CFG = 9
+resolution = 1024
+negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+prompts = [
+ "Two actors are posing for a pictur with one wearing a black and white face paint.",
+ "A large body of water with a rock in the middle and mountains in the background.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "A statue of a man with a crown on his head.",
+ "A man in a yellow wet suit is holding a big black dog in the water.",
+ "A white table with a vase of flowers and a cup of coffee on top of it.",
+ "A woman stands on a dock in the fog.",
+ "A woman is standing next to a picture of another woman."
+]
+
+batched_generation = False
+num_images = len(prompts) if batched_generation else 1
+
+images = pipe(
+ prompt=prompts[:num_images],
+ negative_prompt=[negative_prompt] * num_images,
+ height=resolution,
+ width=resolution,
+ guidance_scale=CFG,
+ num_inference_steps=steps
+ ).images
+
+output_dir = "./output"
+os.makedirs(output_dir, exist_ok=True)
+for i, prompt in enumerate(prompts[:num_images]):
+ sanitized_prompt = prompt.replace(" ", "_")
+ file_path = os.path.join(output_dir, f"{sanitized_prompt}_{resolution}_{steps}_{CFG}.png")
+ images[i].save(file_path)
+ print(f"The {i+1}/{num_images} image is saved to {file_path}")
diff --git a/Meissonic/inference_fp16.py b/Meissonic/inference_fp16.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d7af22ff083b4606b95bd928e0a674ed83ace29
--- /dev/null
+++ b/Meissonic/inference_fp16.py
@@ -0,0 +1,64 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from torchvision import transforms
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+
+device = 'cuda'
+dtype = torch.bfloat16
+model_path = "MeissonFlow/Meissonic"
+model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+# text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder", torch_dtype=dtype)
+text_encoder = CLIPTextModelWithProjection.from_pretrained( #using original text enc for stable sampling
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",torch_dtype=dtype)
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", torch_dtype=dtype)
+scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
+pipe=Pipeline(vq_model, tokenizer=tokenizer,text_encoder=text_encoder,transformer=model,scheduler=scheduler)
+
+pipe = pipe.to(device)
+
+steps = 64
+CFG = 9
+resolution = 1024
+negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+prompts = [
+ "Two actors are posing for a pictur with one wearing a black and white face paint.",
+ "A large body of water with a rock in the middle and mountains in the background.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "A statue of a man with a crown on his head.",
+ "A man in a yellow wet suit is holding a big black dog in the water.",
+ "A white table with a vase of flowers and a cup of coffee on top of it.",
+ "A woman stands on a dock in the fog.",
+ "A woman is standing next to a picture of another woman."
+]
+
+batched_generation = False
+num_images = len(prompts) if batched_generation else 1
+
+images = pipe(
+ prompt=prompts[:num_images],
+ negative_prompt=[negative_prompt] * num_images,
+ height=resolution,
+ width=resolution,
+ guidance_scale=CFG,
+ num_inference_steps=steps
+ ).images
+
+output_dir = "./output"
+os.makedirs(output_dir, exist_ok=True)
+for i, prompt in enumerate(prompts[:num_images]):
+ sanitized_prompt = prompt.replace(" ", "_")
+ file_path = os.path.join(output_dir, f"{sanitized_prompt}_{resolution}_{steps}_{CFG}.png")
+ images[i].save(file_path)
+ print(f"The {i+1}/{num_images} image is saved to {file_path}")
diff --git a/Meissonic/inference_fp16_Monetico.py b/Meissonic/inference_fp16_Monetico.py
new file mode 100644
index 0000000000000000000000000000000000000000..0460597cc5a11813b218a95bff1201d88a2014e4
--- /dev/null
+++ b/Meissonic/inference_fp16_Monetico.py
@@ -0,0 +1,64 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from torchvision import transforms
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+
+device = 'cuda'
+dtype = torch.bfloat16
+model_path = "Collov-Labs/Monetico"
+model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder", torch_dtype=dtype) # better for Monetico
+# text_encoder = CLIPTextModelWithProjection.from_pretrained( #more stable sampling for some cases
+# "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=dtype
+# )
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", torch_dtype=dtype)
+scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler", torch_dtype=dtype)
+pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
+pipe.to(device)
+
+steps = 48
+CFG = 9
+resolution = 512
+negative_prompt = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+prompts = [
+ "Two actors are posing for a pictur with one wearing a black and white face paint.",
+ "A large body of water with a rock in the middle and mountains in the background.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "A statue of a man with a crown on his head.",
+ "A man in a yellow wet suit is holding a big black dog in the water.",
+ "A white table with a vase of flowers and a cup of coffee on top of it.",
+ "A woman stands on a dock in the fog.",
+ "A woman is standing next to a picture of another woman."
+]
+
+batched_generation = False
+num_images = len(prompts) if batched_generation else 1
+
+images = pipe(
+ prompt=prompts[:num_images],
+ negative_prompt=[negative_prompt] * num_images,
+ height=resolution,
+ width=resolution,
+ guidance_scale=CFG,
+ num_inference_steps=steps
+ ).images
+
+output_dir = "./output"
+os.makedirs(output_dir, exist_ok=True)
+for i, prompt in enumerate(prompts[:num_images]):
+ sanitized_prompt = prompt.replace(" ", "_")
+ file_path = os.path.join(output_dir, f"{sanitized_prompt}_{resolution}_{steps}_{CFG}.png")
+ images[i].save(file_path)
+ print(f"The {i+1}/{num_images} image is saved to {file_path}")
diff --git a/Meissonic/inference_fp8.py b/Meissonic/inference_fp8.py
new file mode 100644
index 0000000000000000000000000000000000000000..2804af3a06a02400fb69366d414bf419ade183b6
--- /dev/null
+++ b/Meissonic/inference_fp8.py
@@ -0,0 +1,103 @@
+import os
+import sys
+sys.path.append("./")
+
+import torch
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+import time
+import argparse
+
+from torchao.quantization.quant_api import (
+ quantize_,
+ float8_weight_only, # A8W8 FP8
+)
+
+device = 'cuda'
+
+def get_quantization_method(method):
+ quantization_methods = {
+ 'fp8': lambda: float8_weight_only(),
+ }
+ return quantization_methods.get(method, None)
+
+def load_models(quantization_method=None):
+ model_path = "MeissonFlow/Meissonic"
+ dtype = torch.float16
+ model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", torch_dtype=dtype)
+ vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", torch_dtype=dtype)
+ text_encoder = CLIPTextModelWithProjection.from_pretrained(
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+ torch_dtype=dtype
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+ scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
+
+ if quantization_method:
+ quant_method = get_quantization_method(quantization_method)
+ if quant_method:
+ quantize_(model, quant_method())
+ else:
+ print(f"Unsupported quantization method: {quantization_method}")
+
+
+ pipe = Pipeline(vq_model, tokenizer=tokenizer, text_encoder=text_encoder, transformer=model, scheduler=scheduler)
+ return pipe.to(device)
+
+def run_inference(pipe, prompt, negative_prompt, resolution, cfg, steps):
+ return pipe(prompt=prompt, negative_prompt=negative_prompt, height=resolution, width=resolution, guidance_scale=cfg, num_inference_steps=steps).images[0]
+
+def main(quantization_method):
+ steps = 64
+ CFG = 9
+ resolution = 1024
+ negative_prompts = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+ prompts = [
+ "Two actors are posing for a pictur with one wearing a black and white face paint.",
+ "A large body of water with a rock in the middle and mountains in the background.",
+ "A white and blue coffee mug with a picture of a man on it.",
+ "The sun is setting over a city skyline with a river in the foreground.",
+ "A black and white cat with blue eyes.",
+ "Three boats in the ocean with a rainbow in the sky.",
+ "A robot playing the piano.",
+ "A cat wearing a hat.",
+ "A dog in a jungle.",
+ ]
+
+ output_dir = "./output"
+ os.makedirs(output_dir, exist_ok=True)
+
+ pipe = load_models(quantization_method)
+ start_time = time.time()
+ total_memory_used = 0
+ for i, prompt in enumerate(prompts):
+ torch.cuda.reset_peak_memory_stats()
+ image_start_time = time.time()
+ image = run_inference(pipe, prompt, negative_prompts, resolution, CFG, steps)
+ image_end_time = time.time()
+ image.save(os.path.join(output_dir, f"{prompt[:10]}_{resolution}_{steps}_{CFG}_{quantization_method}.png"))
+
+ memory_used = torch.cuda.max_memory_reserved() / (1024 ** 3) # Convert to GB
+ total_memory_used += memory_used
+
+ print(f"Image {i+1} time: {image_end_time - image_start_time:.2f} seconds")
+ print(f"Image {i+1} max memory used: {memory_used:.2f} GB")
+
+ total_time = time.time() - start_time
+ avg_memory_used = total_memory_used / len(prompts)
+ print(f"Total inference time ({quantization_method}): {total_time:.2f} seconds")
+ print(f"Average memory used per image: {avg_memory_used:.2f} GB")
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(description="Run inference with specified quantization method.")
+ parser.add_argument("--quantization", type=str, choices=['fp8'],
+ help="Quantization method to use")
+ args = parser.parse_args()
+ main(args.quantization)
diff --git a/Meissonic/inpaint.py b/Meissonic/inpaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..4fa97623ac9d05d51645a3e61f6cb35a2f90327b
--- /dev/null
+++ b/Meissonic/inpaint.py
@@ -0,0 +1,55 @@
+import os
+import sys
+sys.path.append("./")
+
+import argparse
+import json
+from PIL import Image
+from src.transformer import Transformer2DModel
+from src.pipeline_inpaint import InpaintPipeline
+from src.scheduler import Scheduler
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+
+def get_parse_args():
+ parser = argparse.ArgumentParser(description="Meissonic Inpaint and Outpaint")
+ parser.add_argument("--mode", type=str,default="inpaint", choices=["inpaint", "outpaint"], help="Inpaint or Outpaint")
+ return parser.parse_args()
+
+if __name__ == "__main__":
+ args = get_parse_args()
+ device = 'cuda'
+
+ model_path = "MeissonFlow/Meissonic"
+ model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer", )
+ vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", )
+ # text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder",)
+ text_encoder = CLIPTextModelWithProjection.from_pretrained( # using original text enc for stable sampling
+ "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", )
+ scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler", )
+
+ pipe=InpaintPipeline(vq_model, tokenizer=tokenizer,text_encoder=text_encoder,transformer=model,scheduler=scheduler)
+ pipe = pipe.to(device)
+
+ with open(f"./assets/{args.mode}/cases.json", 'r', encoding='utf-8') as file:
+ cases = json.load(file)
+ item = cases[0]
+
+ steps = 64
+ CFG = 9
+ resolution = 1024
+ negative_prompts = item["negative_prompts"] if "negative_prompts" in item.keys() else "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"
+
+ image = Image.open(item["input"]).resize((resolution, resolution)).convert("RGB")
+ mask = Image.open(item["mask"]).resize((resolution, resolution)).convert("RGB")
+
+ image = pipe(prompt=item["prompt"],negative_prompt=negative_prompts,image =image, mask_image =mask, guidance_scale=CFG, num_inference_steps=steps).images[0]
+
+ output_dir = "./output"
+ os.makedirs(output_dir, exist_ok=True)
+ image.save(os.path.join(output_dir, f"{item['prompt'][:10]}_{resolution}_{steps}_{CFG}.png"))
\ No newline at end of file
diff --git a/Meissonic/predict.py b/Meissonic/predict.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d24702bbaa8ba3fd16f4588d377808e83f5e240
--- /dev/null
+++ b/Meissonic/predict.py
@@ -0,0 +1,105 @@
+# Prediction interface for Cog ⚙️
+# https://cog.run/python
+
+import os
+import subprocess
+import time
+import torch
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+from diffusers import VQModel
+from cog import BasePredictor, Input, Path
+
+from src.transformer import Transformer2DModel
+from src.pipeline import Pipeline
+from src.scheduler import Scheduler
+
+
+MODEL_CACHE = "model_cache"
+MODEL_URL = (
+ f"https://weights.replicate.delivery/default/viiika/Meissonic/{MODEL_CACHE}.tar"
+)
+
+os.environ.update(
+ {
+ "HF_DATASETS_OFFLINE": "1",
+ "TRANSFORMERS_OFFLINE": "1",
+ "HF_HOME": MODEL_CACHE,
+ "TORCH_HOME": MODEL_CACHE,
+ "HF_DATASETS_CACHE": MODEL_CACHE,
+ "TRANSFORMERS_CACHE": MODEL_CACHE,
+ "HUGGINGFACE_HUB_CACHE": MODEL_CACHE,
+ }
+)
+
+
+def download_weights(url, dest):
+ start = time.time()
+ print("downloading url: ", url)
+ print("downloading to: ", dest)
+ subprocess.check_call(["pget", "-x", url, dest], close_fds=False)
+ print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+ def setup(self) -> None:
+ """Load the model into memory to make running multiple predictions efficient"""
+
+ if not os.path.exists(MODEL_CACHE):
+ download_weights(MODEL_URL, MODEL_CACHE)
+
+ model_path = f"{MODEL_CACHE}/MeissonFlow/Meissonic"
+ model = Transformer2DModel.from_pretrained(model_path, subfolder="transformer")
+ vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae")
+ text_encoder = CLIPTextModelWithProjection.from_pretrained( # more stable sampling for some cases
+ f"{MODEL_CACHE}/laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+ scheduler = Scheduler.from_pretrained(model_path, subfolder="scheduler")
+ self.pipe = Pipeline(
+ vq_model,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=model,
+ scheduler=scheduler,
+ ).to("cuda")
+
+ def predict(
+ self,
+ prompt: str = Input(
+ description="Input prompt",
+ default="a photo of an astronaut riding a horse on mars",
+ ),
+ negative_prompt: str = Input(
+ description="Specify things to not see in the output",
+ default="worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark",
+ ),
+ num_inference_steps: int = Input(
+ description="Number of denoising steps", ge=1, le=100, default=64
+ ),
+ guidance_scale: float = Input(
+ description="Scale for classifier-free guidance", ge=0, le=20, default=9
+ ),
+ seed: int = Input(
+ description="Random seed. Leave blank to randomize the seed", default=None
+ ),
+ ) -> Path:
+ """Run a single prediction on the model"""
+ if seed is None:
+ seed = int.from_bytes(os.urandom(2), "big")
+ print(f"Using seed: {seed}")
+ torch.manual_seed(seed)
+
+ image = self.pipe(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ height=1024,
+ width=1024,
+ guidance_scale=guidance_scale,
+ num_inference_steps=num_inference_steps,
+ ).images[0]
+ output_path = f"/tmp/out.png"
+ image.save(output_path)
+ return Path(output_path)
diff --git a/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/.gitattributes b/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..14a69d3c1c50c140b42093ed67c383e51f98c237
--- /dev/null
+++ b/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/.gitattributes
@@ -0,0 +1,38 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+autoencoder.jit filter=lfs diff=lfs merge=lfs -text
+decoder.jit filter=lfs diff=lfs merge=lfs -text
+encoder.jit filter=lfs diff=lfs merge=lfs -text
diff --git a/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/README.md b/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..d7338fbf966ac341b466f5d1d10c0e62a67421b6
--- /dev/null
+++ b/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8/README.md
@@ -0,0 +1,326 @@
+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+ https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+library_name: nemo
+---
+# **Cosmos Tokenizer**: A suite of image and video tokenizers
+
+[**Website**](https://research.nvidia.com/labs/dir/cosmos-tokenizer) | [**Code**](https://github.com/NVIDIA/Cosmos-Tokenizer) | [**Video**](https://youtu.be/Soy_myOfWIU)
+
+
+# Model Overview
+
+## Description:
+**Cosmos Tokenizer** is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation.
+
+
+Our tokenizers come in two types: **Continuous** (C) and **Discrete** (D), each with **Image** (I) and **Video** (V) variants:
+* Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like [Stable Diffusion](https://github.com/CompVis/stable-diffusion). These embeddings are suitable for models that generate data by sampling from continuous distributions.
+* Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as [VideoPoet](https://sites.research.google/videopoet/). This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models.
+
+
+| | Continuous ( C ) | Discrete ( D ) |
+| ------------------|---------------------|---------------------|
+| **Images ( I )** | Cosmos-Tokenizer-CI | Cosmos-Tokenizer-DI |
+| **Videos ( V )** | Cosmos-Tokenizer-CV | Cosmos-Tokenizer-DV |
+
+
+Given an image or a video, Cosmos Tokenizer outputs either continuous latents or discrete tokens. Cosmos Tokenizer achieves spatial compression rates of 8x8 or 16x16 and temporal compression factors of 4x or 8x, resulting in a total compression factor of up to 2048x (=8x16x16). Cosmos Tokenizer delivers 8x more total compression than state-of-the-art (SOTA) methods while simultaneously maintaining higher image quality and running up to 12x faster than the best available SOTA tokenizers.
+
+**Model Developer**: NVIDIA
+
+## Model Versions
+
+The initial release (v1.0) of Cosmos Tokenizer includes the following tokenizers:
+* **Continuous Tokenizers**
+ * Continuous Image (CI) Tokenizer
+ * [Cosmos-Tokenizer-CI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI8x8) (8x8 spatial compression)
+ * [Cosmos-Tokenizer-CI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI16x16) (16x16 spatial compression)
+ * Continuous Video (CV) Tokenizer
+ * [Cosmos-Tokenizer-CV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-CV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x16x16) (8x temporal compression, 16x16 spatial compression)
+* **Discrete Tokenizers**
+ * Discrete Image (DI) Tokenizer
+ * [Cosmos-Tokenizer-DI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI8x8) (8x8 spatial compression)
+ * [Cosmos-Tokenizer-DI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI16x16) (16x16 spatial compression)
+ * Discrete Video (DV) Tokenizer
+ * [Cosmos-Tokenizer-DV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-DV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-DV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x16x16) (8x temporal compression, 16x16 spatial compression)
+
+
+### License/Terms of Use:
+[NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+
+Under the NVIDIA Open Model License, NVIDIA confirms:
+
+* Models are commercially usable.
+* You are free to create and distribute Derivative Models.
+* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
+
+## Model Architecture:
+
+We designed Cosmos Tokenizer using a lightweight and computationally efficient architecture, featuring a temporally causal design. Specifically, we employ causal temporal convolution and causal temporal attention layers to preserve the natural temporal order of video frames, ensuring seamless tokenization of images and videos using a single unified network architecture. The encoder and decoder form a symmetrical pair, which are mirrors of each other. The encoder starts with a 2-level [Haar wavelet](https://link.springer.com/book/10.1007/978-3-319-04295-4) transform layer, which down-samples inputs by a factor of 4 in both spatial and temporal dimensions. Likewise, the decoder ends with an inverse wavelet transform. We employ the vanilla autoencoder (AE) formulation to model the latent space for continuous tokenizers. For discrete tokenizers, we adopt the [Finite-Scalar-Quantization](https://openreview.net/forum?id=8ishA3LxN8) (FSQ) as the latent space quantizer.
+
+
+
+
+
+## Input/Output Specifications
+
+### Encoder
+* **Input**
+ * **Types:** Images or Videos
+ * **Format:** RGB (Red, Green, Blue)
+ * **Resolution:**
+ * Minimum: 256px (shorter side)
+ * Maximum: Up to 4K
+ * **Video Length:** Up to 8 seconds for 1080p videos (bounded by A100 80G GPU memory; higher resolutions will have shorter supported durations)
+
+* **Output**
+ * **Types:** Tokens
+ * Continuous Image/Video Tokenizers: Continuous value feature vectors
+ * Discrete Image/Video Tokenizers: Integer indices
+
+### Decoder
+* **Input**
+ * **Types:** Tokens from encoder
+
+* **Output**
+ * **Types:** Images or Videos (matching input type)
+ * **Format:** RGB (Red, Green, Blue)
+ * **Resolution:** Same as input resolution
+ * **Video Length:** Same as input video length
+
+## Software Integration (Required For NVIDIA Models Only):
+**Runtime Engine(s):**
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer)
+* [NeMo](https://github.com/NVIDIA/NeMo) (please install the latest version from the GitHub main branch)
+
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Ampere (e.g., A100)
+* NVIDIA Hopper (e.g., H100)
+
+Note: We have only tested Cosmos Tokenizer with BF16 precision on Ampere and Hopper GPUs. If you are using older versions of NVIDIA GPUs (e.g., NVIDIA Volta GPUs), you may need to switch to FP32 precision.
+
+
+**Operating System(s):**
+* Linux (We have not tested on other operating systems.)
+
+# Usage
+Inference Engines:
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) (PyTorch)
+* [NeMo](https://github.com/NVIDIA/NeMo)
+
+## Inference with `Cosmos-Tokenizer` (PyTorch)
+### Step-1: Installation of `Cosmos-Tokenizer`
+Note: Currently, the `Cosmos-Tokenizer` code is only supported on Linux.
+
+- Please clone the `Cosmos-Tokenizer` from GitHub repo [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+ ```bash
+ git clone https://github.com/NVIDIA/Cosmos-Tokenizer.git
+ cd Cosmos-Tokenizer
+ ```
+- Install dependencies
+
+ ```bash
+ pip3 install -r requirements.txt
+ apt-get install -y ffmpeg
+ ```
+
+- Preferably, you could build a docker image using our provided Dockerfile.
+ ```bash
+ docker build -t cosmos-docker -f Dockerfile.
+ # You can run the container as:
+ docker run --gpus all -it --rm -v /home/${USER}:/home/${USER} \
+ --workdir ${PWD} cosmos-docker /bin/bash
+ ```
+
+### Step-2: Download Pre-trained Checkpoints
+- Create a local directory for the pre-trained checkpoints and download the
+pre-trained checkpoints from HuggingFace.
+
+ ```python
+ from huggingface_hub import login, snapshot_download
+ import os
+ # You could get your Hugging Face token from https://huggingface.co/settings/tokens
+ login(token=, add_to_git_credential=True)
+ # You could specify the tokenizers you want to download.
+ model_names = [
+ "Cosmos-Tokenizer-CI8x8",
+ "Cosmos-Tokenizer-CI16x16",
+ "Cosmos-Tokenizer-CV4x8x8",
+ "Cosmos-Tokenizer-CV8x8x8",
+ "Cosmos-Tokenizer-CV8x16x16",
+ "Cosmos-Tokenizer-DI8x8",
+ "Cosmos-Tokenizer-DI16x16",
+ "Cosmos-Tokenizer-DV4x8x8",
+ "Cosmos-Tokenizer-DV8x8x8",
+ "Cosmos-Tokenizer-DV8x16x16",
+ ]
+ for model_name in model_names:
+ hf_repo = "nvidia/" + model_name
+ local_dir = "pretrained_ckpts/" + model_name
+ os.makedirs(local_dir, exist_ok=True)
+ print(f"downloading {model_name} to {local_dir}...")
+ snapshot_download(repo_id=hf_repo, local_dir=local_dir)
+ ```
+
+- Under the ech checkpoint directory `pretrained_ckpts/`, we provide the encoder,
+decoder and the full autoencoder JIT models.
+
+ ```bash
+ ├── pretrained_ckpts/
+ │ ├── Cosmos-Tokenizer-DV8x8x8/
+ │ │ ├── encoder.jit
+ │ │ ├── decoder.jit
+ │ │ ├── autoencoder.jit
+ │ ...
+ ```
+
+### Step-3: Run Inference
+You can use the following example commands to encode and decode images or videos. For each, the same command works for both continuous and discrete tokenization. Simply provide the proper JIT-compiled ckpt to `checkpoint_enc`, `checkpoint_dec`, or the full autoencoder ckpt to `checkpoint`.
+
+```python
+import torch
+from cosmos_tokenizer.video_lib import CausalVideoTokenizer
+model_name = "Cosmos-Tokenizer-DV4x8x8"
+input_tensor = torch.randn(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16)
+encoder = CausalVideoTokenizer(checkpoint_enc=f'pretrained_ckpts/{model_name}/encoder.jit')
+(indices, codes) = encoder.encode(input_tensor)
+torch.testing.assert_close(indices.shape, (1, 3, 64, 64))
+torch.testing.assert_close(codes.shape, (1, 6, 3, 64, 64))
+
+# The input tensor can be reconstructed by the decoder as:
+decoder = CausalVideoTokenizer(checkpoint_dec=f'pretrained_ckpts/{model_name}/decoder.jit')
+reconstructed_tensor = decoder.decode(indices)
+torch.testing.assert_close(reconstructed_tensor.shape, input_tensor.shape)
+```
+
+The `indices` will have the shape `(1, 3, 64, 64)` and contain integral values in the range `[1..64K]`, where the first of the three integral maps represents the first frame.
+The `codes` will contain the pre-quantization continuous latent with shape `(1, 6, 3, 64, 64)`, where C=6 represents the number of FSQ levels.
+
+**Note**: More inference usage commands, including both TorchScript (JIT) and PyTorch Inference APIs on real images and videos, can be found on our GitHub repository [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+
+## Inference with NeMo
+
+### Step-1: Install NeMo
+Please install NeMo from the GitHub `main` branch following the instructions [here](https://github.com/NVIDIA/NeMo?tab=readme-ov-file#pip-from-a-source-branch).
+
+### Step-2: Run Inference
+Run the following code to tokenize the video:
+
+```python
+import torch
+from nemo.collections.common.video_tokenizers.cosmos_vision_tokenizer import CausalVideoTokenizer
+model_name = "Cosmos-Tokenizer-DV4x8x8"
+model = CausalVideoTokenizer.from_pretrained(model_name)
+input_tensor = torch.randn(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16)
+(indices, codes) = model.encode(input_tensor)
+```
+Please see the [Cosmos Tokenizer README within the NeMo repository](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/common/video_tokenizers) for additional examples to create training datasets with the Cosmos Tokenizer.
+
+
+# Evaluation
+
+## TokenizationPerformance Comparison
+We have extensively evaluated the **Cosmos Tokenizer** suite on various image and video benchmark datasets. In addition to commonly used datasets such as [MS-COCO](https://cocodataset.org/#home) and [DAVIS](https://davischallenge.org/), in order to cover a wide variety of visual data and standardize the evaluation, we created a benchmark called [TokenBench](https://github.com/NVlabs/Token-Bench), which is a mixed sampling of video data from diverse domains.
+
+| Tokenizer | Compression Ratio | Quantization | PSNR (DAVIS) | SSIM (DAVIS) | rFVD (DAVIS) | PSNR (TokenBench) | SSIM (TokenBench) | rFVD (TokenBench) |
+|-----------|------------------|--------------|--------------|--------------|--------------|------------------|------------------|------------------|
+| VideoGPT | 4×4×4 | VQ | 32.23 | **0.850** | 72.33 | 35.11 | **0.914** | **13.85** |
+| Omnitokenizer | 4×8×8 | VQ | 28.44 | 0.712 | 188.60 | 30.15 | 0.827 | 53.55 |
+| Cosmos-Tokenizer-DV | 4×8×8 | FSQ | **32.98** | 0.818 | **37.36** | **35.13** | 0.887 | 19.67 |
+| Cosmos-Tokenizer-DV | 8×8×8 | FSQ | 32.11 | 0.775 | 100.15 | 34.74 | 0.872 | 43.86 |
+| Cosmos-Tokenizer-DV | 8×16×16 | FSQ | 31.42 | 0.716 | 241.52 | 33.71 | 0.828 | 113.48 |
+
+* We compare with the state-of-the-art discrete video tokenizer, [OmniTokenizer](https://github.com/FoundationVision/OmniTokenizer).
+* Evaluation metrics:
+ * Peak Signal-to-Noise Ratio (PSNR)
+ * Structural Similarity (SSIM)
+ * Reconstruction Fréchet Video Distance (rFVD)
+
+## Runtime Comparison
+
+The following table shows the number of parameters and the averaged encoding and decoding times per image or video frame, measured on a single A100 80GB GPU. For comparison, we also list the parameters and average speeds of prior state-of-the-art tokenizer(s) with the same compression ratio.
+
+| Tokenizer | Resolution | Compression Ratio | Parameters | Time (ms) |
+|----------------|------------|-------------------|------------|-----------|
+| OmniTokenizer | 720x1280 | 4×8×8 | 54M | 53.2 |
+| Cosmos-DV | 720x1280 | 4×8×8 | 105M | 51.5 |
+
+Note: We benchmarked the runtime for images under the 8x8 compression and videos under the 4×8×8 compression. Tokenizers with different compression ratios are not included in this comparison.
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+### Bias
+
+Field | Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None
+Measures taken to mitigate against unwanted bias: | None
+
+
+### Explainability
+
+Field | Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain: | Tokenization of images and videos
+Model Type: | Auto-Encoder
+Intended Users: | Generative AI developers for image and video generation models
+Output: | Images/Videos and Latent Tokens
+Describe how the model works: | Compresses and decompresses visual input (image/video).
+Technical Limitations: | Due to tokenizer compression limitations, some visual information (such as small text and other structured fine details) may not be reconstructed accurately.
+Verified to have met prescribed NVIDIA quality standards: | Yes
+Performance Metrics: | Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Reconstruction Fréchet Video Distance (rFVD), Reconstruction Fréchet Inception Distance (rFID), Latency
+Potential Known Risks: | Tokenizer's output can parse all forms of input, including what may be considered toxic, offensive, or indecent.
+Licensing: | [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+
+
+### Privacy
+Field | Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal information? | No
+Protected class data used to create this model? | None Known
+Was consent obtained for any personal data used? | None Known
+How often is dataset reviewed? | Before Release
+Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable
+If personal collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable
+If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable
+If personal collected for the development of this AI model, was it minimized to only what was required? | Not Applicable
+Is there provenance for all datasets used in training? | Yes
+Does data labeling (annotation, metadata) comply with privacy laws? | Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable
+
+### Safety
+
+Field | Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s): | Tokenization of images and videos
+Describe the life critical impact (if present). | None Known
+Use Case Restrictions: | See [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.
+
+
+### Plus Plus (++) Promise
+
+We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
+* Verified to comply with current applicable disclosure laws, regulations, and industry standards.
+* Verified to comply with applicable privacy labeling requirements.
+* Annotated to describe the collector/source (NVIDIA or a third-party).
+* Characterized for technical limitations.
+* Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
+* Reviewed before release.
+* Tagged for known restrictions and potential safety implications.
+
+
+# Core Contributors
+Fitsum Reda, Jinwei Gu, Xian Liu, Songwei Ge, Ting-Chun Wang, Haoxiang Wang, Ming-Yu Liu
\ No newline at end of file
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+ "architectures": [
+ "CosmosTokenizer"
+ ],
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+
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diff --git a/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV8x8x8/README.md b/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV8x8x8/README.md
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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+ https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
+library_name: nemo
+---
+# **Cosmos Tokenizer**: A suite of image and video tokenizers
+
+[**Website**](https://research.nvidia.com/labs/dir/cosmos-tokenizer) | [**Code**](https://github.com/NVIDIA/Cosmos-Tokenizer) | **Video**
+
+
+# Model Overview
+
+## Description:
+**Cosmos Tokenizer** is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation.
+
+
+Our tokenizers come in two types: **Continuous** (C) and **Discrete** (D), each with **Image** (I) and **Video** (V) variants:
+* Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like [Stable Diffusion](https://github.com/CompVis/stable-diffusion). These embeddings are suitable for models that generate data by sampling from continuous distributions.
+* Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as [VideoPoet](https://sites.research.google/videopoet/). This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models.
+
+
+| | Continuous ( C ) | Discrete ( D ) |
+| ------------------|---------------------|---------------------|
+| **Images ( I )** | Cosmos-Tokenizer-CI | Cosmos-Tokenizer-DI |
+| **Videos ( V )** | Cosmos-Tokenizer-CV | Cosmos-Tokenizer-DV |
+
+
+Given an image or a video, Cosmos Tokenizer outputs either continuous latents or discrete tokens. Cosmos Tokenizer achieves spatial compression rates of 8x8 or 16x16 and temporal compression factors of 4x or 8x, resulting in a total compression factor of up to 2048x (=8x16x16). Cosmos Tokenizer delivers 8x more total compression than state-of-the-art (SOTA) methods while simultaneously maintaining higher image quality and running up to 12x faster than the best available SOTA tokenizers.
+
+**Model Developer**: NVIDIA
+
+## Model Versions
+
+The initial release (v1.0) of Cosmos Tokenizer includes the following tokenizers:
+* **Continuous Tokenizers**
+ * Continuous Image (CI) Tokenizer
+ * [Cosmos-Tokenizer-CI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI8x8) (8x8 spatial compression)
+ * [Cosmos-Tokenizer-CI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI16x16) (16x16 spatial compression)
+ * Continuous Video (CV) Tokenizer
+ * [Cosmos-Tokenizer-CV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-CV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x16x16) (8x temporal compression, 16x16 spatial compression)
+* **Discrete Tokenizers**
+ * Discrete Image (DI) Tokenizer
+ * [Cosmos-Tokenizer-DI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI8x8) (8x8 spatial compression)
+ * [Cosmos-Tokenizer-DI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI16x16) (16x16 spatial compression)
+ * Discrete Video (DV) Tokenizer
+ * [Cosmos-Tokenizer-DV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-DV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-Tokenizer-DV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x16x16) (8x temporal compression, 16x16 spatial compression)
+
+
+### License/Terms of Use:
+[NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+
+Under the NVIDIA Open Model License, NVIDIA confirms:
+
+* Models are commercially usable.
+* You are free to create and distribute Derivative Models.
+* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
+
+## Model Architecture:
+
+We designed Cosmos Tokenizer using a lightweight and computationally efficient architecture, featuring a temporally causal design. Specifically, we employ causal temporal convolution and causal temporal attention layers to preserve the natural temporal order of video frames, ensuring seamless tokenization of images and videos using a single unified network architecture. The encoder and decoder form a symmetrical pair, which are mirrors of each other. The encoder starts with a 2-level [Haar wavelet](https://link.springer.com/book/10.1007/978-3-319-04295-4) transform layer, which down-samples inputs by a factor of 4 in both spatial and temporal dimensions. Likewise, the decoder ends with an inverse wavelet transform. We employ the vanilla autoencoder (AE) formulation to model the latent space for continuous tokenizers. For discrete tokenizers, we adopt the [Finite-Scalar-Quantization](https://openreview.net/forum?id=8ishA3LxN8) (FSQ) as the latent space quantizer.
+
+
+
+
+
+## Input/Output Specifications
+
+### Encoder
+* **Input**
+ * **Types:** Images or Videos
+ * **Format:** RGB (Red, Green, Blue)
+ * **Resolution:**
+ * Minimum: 256px (shorter side)
+ * Maximum: Up to 4K
+ * **Video Length:** Up to 8 seconds for 1080p videos (bounded by A100 80G GPU memory; higher resolutions will have shorter supported durations)
+
+* **Output**
+ * **Types:** Tokens
+ * Continuous Image/Video Tokenizers: Continuous value feature vectors
+ * Discrete Image/Video Tokenizers: Integer indices
+
+### Decoder
+* **Input**
+ * **Types:** Tokens from encoder
+
+* **Output**
+ * **Types:** Images or Videos (matching input type)
+ * **Format:** RGB (Red, Green, Blue)
+ * **Resolution:** Same as input resolution
+ * **Video Length:** Same as input video length
+
+## Software Integration (Required For NVIDIA Models Only):
+**Runtime Engine(s):**
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer)
+* [NeMo](https://github.com/NVIDIA/NeMo) (please install the latest version from the GitHub main branch)
+
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Ampere (e.g., A100)
+* NVIDIA Hopper (e.g., H100)
+
+Note: We have only tested Cosmos Tokenizer with BF16 precision on Ampere and Hopper GPUs. If you are using older versions of NVIDIA GPUs (e.g., NVIDIA Volta GPUs), you may need to switch to FP32 precision.
+
+
+**Operating System(s):**
+* Linux (We have not tested on other operating systems.)
+
+# Usage
+Inference Engines:
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) (PyTorch)
+* [NeMo](https://github.com/NVIDIA/NeMo)
+
+## Inference with `Cosmos-Tokenizer` (PyTorch)
+### Step-1: Installation of `Cosmos-Tokenizer`
+Note: Currently, the `Cosmos-Tokenizer` code is only supported on Linux.
+
+- Please clone the `Cosmos-Tokenizer` from GitHub repo [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+ ```bash
+ git clone https://github.com/NVIDIA/Cosmos-Tokenizer.git
+ cd Cosmos-Tokenizer
+ ```
+- Install dependencies
+
+ ```bash
+ pip3 install -r requirements.txt
+ apt-get install -y ffmpeg
+ ```
+
+- Preferably, you could build a docker image using our provided Dockerfile.
+ ```bash
+ docker build -t cosmos-docker -f Dockerfile.
+ # You can run the container as:
+ docker run --gpus all -it --rm -v /home/${USER}:/home/${USER} \
+ --workdir ${PWD} cosmos-docker /bin/bash
+ ```
+
+### Step-2: Download Pre-trained Checkpoints
+- Create a local directory for the pre-trained checkpoints and download the
+pre-trained checkpoints from HuggingFace.
+
+ ```python
+ from huggingface_hub import login, snapshot_download
+ import os
+ # You could get your Hugging Face token from https://huggingface.co/settings/tokens
+ login(token=, add_to_git_credential=True)
+ # You could specify the tokenizers you want to download.
+ model_names = [
+ "Cosmos-Tokenizer-CI8x8",
+ "Cosmos-Tokenizer-CI16x16",
+ "Cosmos-Tokenizer-CV4x8x8",
+ "Cosmos-Tokenizer-CV8x8x8",
+ "Cosmos-Tokenizer-CV8x16x16",
+ "Cosmos-Tokenizer-DI8x8",
+ "Cosmos-Tokenizer-DI16x16",
+ "Cosmos-Tokenizer-DV4x8x8",
+ "Cosmos-Tokenizer-DV8x8x8",
+ "Cosmos-Tokenizer-DV8x16x16",
+ ]
+ for model_name in model_names:
+ hf_repo = "nvidia/" + model_name
+ local_dir = "pretrained_ckpts/" + model_name
+ os.makedirs(local_dir, exist_ok=True)
+ print(f"downloading {model_name} to {local_dir}...")
+ snapshot_download(repo_id=hf_repo, local_dir=local_dir)
+ ```
+
+- Under the ech checkpoint directory `pretrained_ckpts/`, we provide the encoder,
+decoder and the full autoencoder JIT models.
+
+ ```bash
+ ├── pretrained_ckpts/
+ │ ├── Cosmos-Tokenizer-DV8x8x8/
+ │ │ ├── encoder.jit
+ │ │ ├── decoder.jit
+ │ │ ├── autoencoder.jit
+ │ ...
+ ```
+
+### Step-3: Run Inference
+You can use the following example commands to encode and decode images or videos. For each, the same command works for both continuous and discrete tokenization. Simply provide the proper JIT-compiled ckpt to `checkpoint_enc`, `checkpoint_dec`, or the full autoencoder ckpt to `checkpoint`.
+
+```python
+import torch
+from cosmos_tokenizer.video_lib import CausalVideoTokenizer
+model_name = "Cosmos-Tokenizer-DV4x8x8"
+input_tensor = torch.randn(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16)
+encoder = CausalVideoTokenizer(checkpoint_enc=f'pretrained_ckpts/{model_name}/encoder.jit')
+(indices, codes) = encoder.encode(input_tensor)
+torch.testing.assert_close(indices.shape, (1, 3, 64, 64))
+torch.testing.assert_close(codes.shape, (1, 6, 3, 64, 64))
+
+# The input tensor can be reconstructed by the decoder as:
+decoder = CausalVideoTokenizer(checkpoint_dec=f'pretrained_ckpts/{model_name}/decoder.jit')
+reconstructed_tensor = decoder.decode(indices)
+torch.testing.assert_close(reconstructed_tensor.shape, input_tensor.shape)
+```
+
+The `indices` will have the shape `(1, 3, 64, 64)` and contain integral values in the range `[1..64K]`, where the first of the three integral maps represents the first frame.
+The `codes` will contain the pre-quantization continuous latent with shape `(1, 6, 3, 64, 64)`, where C=6 represents the number of FSQ levels.
+
+**Note**: More inference usage commands, including both TorchScript (JIT) and PyTorch Inference APIs on real images and videos, can be found on our GitHub repository [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+
+## Inference with NeMo
+
+### Step-1: Install NeMo
+Please install NeMo from the GitHub `main` branch following the instructions [here](https://github.com/NVIDIA/NeMo?tab=readme-ov-file#pip-from-a-source-branch).
+
+### Step-2: Run Inference
+Run the following code to tokenize the video:
+
+```python
+import torch
+from nemo.collections.common.video_tokenizers.cosmos_vision_tokenizer import CausalVideoTokenizer
+model_name = "Cosmos-Tokenizer-DV4x8x8"
+model = CausalVideoTokenizer.from_pretrained(model_name)
+input_tensor = torch.randn(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16)
+(indices, codes) = model.encode(input_tensor)
+```
+Please see the [Cosmos Tokenizer README within the NeMo repository](https://github.com/NVIDIA/NeMo/tree/main/nemo/collections/common/video_tokenizers) for additional examples to create training datasets with the Cosmos Tokenizer.
+
+# Evaluation
+
+## TokenizationPerformance Comparison
+We have extensively evaluated the **Cosmos Tokenizer** suite on various image and video benchmark datasets. In addition to commonly used datasets such as [MS-COCO](https://cocodataset.org/#home) and [DAVIS](https://davischallenge.org/), in order to cover a wide variety of visual data and standardize the evaluation, we created a benchmark called [TokenBench](https://github.com/NVlabs/Token-Bench), which is a mixed sampling of video data from diverse domains.
+
+| Tokenizer | Compression Ratio | Quantization | PSNR (DAVIS) | SSIM (DAVIS) | rFVD (DAVIS) | PSNR (TokenBench) | SSIM (TokenBench) | rFVD (TokenBench) |
+|-----------|------------------|--------------|--------------|--------------|--------------|------------------|------------------|------------------|
+| VideoGPT | 4×4×4 | VQ | 32.23 | **0.850** | 72.33 | 35.11 | **0.914** | **13.85** |
+| Omnitokenizer | 4×8×8 | VQ | 28.44 | 0.712 | 188.60 | 30.15 | 0.827 | 53.55 |
+| Cosmos-Tokenizer-DV | 4×8×8 | FSQ | **32.98** | 0.818 | **37.36** | **35.13** | 0.887 | 19.67 |
+| Cosmos-Tokenizer-DV | 8×8×8 | FSQ | 32.11 | 0.775 | 100.15 | 34.74 | 0.872 | 43.86 |
+| Cosmos-Tokenizer-DV | 8×16×16 | FSQ | 31.42 | 0.716 | 241.52 | 33.71 | 0.828 | 113.48 |
+
+* We compare with the state-of-the-art discrete video tokenizer, [OmniTokenizer](https://github.com/FoundationVision/OmniTokenizer).
+* Evaluation metrics:
+ * Peak Signal-to-Noise Ratio (PSNR)
+ * Structural Similarity (SSIM)
+ * Reconstruction Fréchet Video Distance (rFVD)
+
+## Runtime Comparison
+
+The following table shows the number of parameters and the averaged encoding and decoding times per image or video frame, measured on a single A100 80GB GPU. For comparison, we also list the parameters and average speeds of prior state-of-the-art tokenizer(s) with the same compression ratio.
+
+| Tokenizer | Resolution | Compression Ratio | Parameters | Time (ms) |
+|----------------|------------|-------------------|------------|-----------|
+| OmniTokenizer | 720x1280 | 4×8×8 | 54M | 53.2 |
+| Cosmos-DV | 720x1280 | 4×8×8 | 105M | 51.5 |
+
+Note: We benchmarked the runtime for images under the 8x8 compression and videos under the 4×8×8 compression. Tokenizers with different compression ratios are not included in this comparison.
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+### Bias
+
+Field | Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None
+Measures taken to mitigate against unwanted bias: | None
+
+
+### Explainability
+
+Field | Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain: | Tokenization of images and videos
+Model Type: | Auto-Encoder
+Intended Users: | Generative AI developers for image and video generation models
+Output: | Images/Videos and Latent Tokens
+Describe how the model works: | Compresses and decompresses visual input (image/video).
+Technical Limitations: | Due to tokenizer compression limitations, some visual information (such as small text and other structured fine details) may not be reconstructed accurately.
+Verified to have met prescribed NVIDIA quality standards: | Yes
+Performance Metrics: | Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Reconstruction Fréchet Video Distance (rFVD), Reconstruction Fréchet Inception Distance (rFID), Latency
+Potential Known Risks: | Tokenizer's output can parse all forms of input, including what may be considered toxic, offensive, or indecent.
+Licensing: | [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+
+
+### Privacy
+Field | Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal information? | No
+Protected class data used to create this model? | None Known
+Was consent obtained for any personal data used? | None Known
+How often is dataset reviewed? | Before Release
+Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable
+If personal collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable
+If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable
+If personal collected for the development of this AI model, was it minimized to only what was required? | Not Applicable
+Is there provenance for all datasets used in training? | Yes
+Does data labeling (annotation, metadata) comply with privacy laws? | Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable
+
+### Safety
+
+Field | Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s): | Tokenization of images and videos
+Describe the life critical impact (if present). | None Known
+Use Case Restrictions: | See [NVIDIA Open Model License](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf)
+Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.
+
+
+### Plus Plus (++) Promise
+
+We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
+* Verified to comply with current applicable disclosure laws, regulations, and industry standards.
+* Verified to comply with applicable privacy labeling requirements.
+* Annotated to describe the collector/source (NVIDIA or a third-party).
+* Characterized for technical limitations.
+* Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
+* Reviewed before release.
+* Tagged for known restrictions and potential safety implications.
+
+
+# Core Contributors
+Fitsum Reda, Jinwei Gu, Xian Liu, Songwei Ge, Ting-Chun Wang, Haoxiang Wang, Ming-Yu Liu
\ No newline at end of file
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+{
+ "architectures": [
+ "CosmosTokenizer"
+ ],
+}
+
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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diff --git a/Meissonic/pretrained_ckpts/Cosmos-1.0-Tokenizer-DV8x16x16/README.md b/Meissonic/pretrained_ckpts/Cosmos-1.0-Tokenizer-DV8x16x16/README.md
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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+ https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
+library_name: cosmos
+tags:
+- nvidia
+- nemo
+- cosmos
+extra_gated_prompt: >-
+ # NVIDIA Open Model License Agreement
+
+ Version Release Date: January 6, 2025
+
+ This NVIDIA Open Model License Agreement (the "Agreement") is a legal agreement between the Legal Entity You represent, or if no entity is identified, You and NVIDIA Corporation and its Affiliates ("NVIDIA") and governs Your use of the Models that NVIDIA provides to You under this Agreement. NVIDIA and You are each a "party" and collectively the "parties."
+
+ NVIDIA models released under this Agreement are intended to be used permissively and enable the further development of AI technologies. Subject to the terms of this Agreement, NVIDIA confirms that:
+
+ * Models are commercially usable.
+
+ * You are free to create and distribute Derivative Models.
+
+ * NVIDIA does not claim ownership to any outputs generated using the Models or Model Derivatives.
+
+ By using, reproducing, modifying, distributing, performing or displaying any portion or element of the Model or Derivative Model, or otherwise accepting the terms of this Agreement, you agree to be bound by this Agreement.
+
+ ## 1. Definitions
+
+ The following definitions apply to this Agreement:
+
+ 1.1. "NVIDIA Cosmos Model" means a multimodal Model shared under this Agreement.
+
+ 1.2. "Derivative Model" means all (a) modifications to the Model, (b) works based on the Model, and (c) any other derivative works of the Model. An output is not a Derivative Model.
+
+ 1.3. "Legal Entity" means the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (a) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (b) ownership of fifty percent (50%) or more of the outstanding shares, or (c) beneficial ownership of such entity.
+
+ 1.4. "Model" means the machine learning model, software, checkpoints, learnt weights, algorithms, parameters, configuration files and documentation shared under this Agreement.
+
+ 1.5. "You" or "Your" means an individual or Legal Entity exercising permissions granted by this Agreement.
+
+ ## 2. Conditions for Use, License Grant, AI Ethics and IP Ownership
+
+ 2.1. Conditions for Use. The Model and any Derivative Model are subject to additional terms as described in Section 2 and Section 3 of this Agreement and govern Your use. If You institute copyright or patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model or a Derivative Model constitutes direct or contributory copyright or patent infringement, then any licenses granted to You under this Agreement for that Model or Derivative Model will terminate as of the date such litigation is filed. If You bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained in the Model, your rights under this Agreement will automatically terminate. NVIDIA may update this Agreement to comply with legal and regulatory requirements at any time and You agree to either comply with any updated license or cease Your copying, use, and distribution of the Model and any Derivative Model.
+
+ 2.2. License Grant. The rights granted herein are explicitly conditioned on Your full compliance with the terms of this Agreement. Subject to the terms and conditions of this Agreement, NVIDIA hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, revocable (as stated in Section 2.1) license to publicly perform, publicly display, reproduce, use, create derivative works of, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution) and import the Model.
+
+ 2.3. AI Ethics. Use of the Models under the Agreement must be consistent with NVIDIA's Trustworthy AI terms found at https://www.nvidia.com/en-us/agreements/trustworthy-ai/terms/.
+
+ 2.4. NVIDIA owns the Model and any Model Derivatives created by NVIDIA. Subject to NVIDIA's underlying ownership rights in the Model or its Model Derivatives, You are and will be the owner of Your Model Derivatives. NVIDIA claims no ownership rights in outputs. You are responsible for outputs and their subsequent uses. Except as expressly granted in this Agreement, (a) NVIDIA reserves all rights, interests and remedies in connection with the Model and (b) no other license or right is granted to you by implication, estoppel or otherwise.
+
+ ## 3. Redistribution
+
+ You may reproduce and distribute copies of the Model or Derivative Models thereof in any medium, with or without modifications, provided that You meet the following conditions:
+
+ 3.1. If you distribute the Model, You must give any other recipients of the Model a copy of this Agreement and include the following attribution notice within a "Notice" text file with such copies: "Licensed by NVIDIA Corporation under the NVIDIA Open Model License";
+
+ 3.2. If you distribute or make available a NVIDIA Cosmos Model, or a product or service (including an AI model) that contains or uses a NVIDIA Cosmos Model, use a NVIDIA Cosmos Model to create a Derivative Model, or use a NVIDIA Cosmos Model or its outputs to create, train, fine tune, or otherwise improve an AI model, you will include "Built on NVIDIA Cosmos" on a related website, user interface, blogpost, about page, or product documentation; and
+
+ 3.3. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Models as a whole, provided Your use, reproduction, and distribution of the Model otherwise complies with the conditions stated in this Agreement.
+
+ ## 4. Trademarks
+
+ This Agreement does not grant permission to use the trade names, trademarks, service marks, or product names of NVIDIA, except as required for reasonable and customary use in describing the origin of the Model and reproducing the content of the "Notice" text file.
+
+ ## **5. Disclaimer of Warranty**
+
+ **Unless required by applicable law or agreed to in writing, NVIDIA provides the Model on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Model, Derivative Models and outputs and assume any risks associated with Your exercise of permissions under this Agreement.**
+
+ ## **6. Limitation of Liability**
+
+ **In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, will NVIDIA be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this Agreement or out of the use or inability to use the Model, Derivative Models or outputs (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if NVIDIA has been advised of the possibility of such damages.**
+
+ ## 7. Indemnity
+
+ You will indemnify and hold harmless NVIDIA from and against any claim by any third party arising out of or related to your use or distribution of the Model, Model Derivatives or outputs.
+
+ ## 8. Feedback
+
+ NVIDIA appreciates your feedback, and You agree that NVIDIA may use it without restriction or compensation to You.
+
+ ## 9. Governing Law
+
+ This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods. The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts; except that, either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.
+
+ ## 10. Trade and Compliance
+
+ You agree to comply with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations. These laws include restrictions on destinations, end-users and end-use.
+extra_gated_fields:
+ By clicking Submit below, I accept the terms of the NVIDIA Open Model License Agreement and acknowledge that I am an adult of legal age of majority in the country in which the Cosmos Models will be used and have authority to accept this Agreement: checkbox
+extra_gated_description: >-
+ The information you provide will be collected, stored, processed and shared in accordance with the [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/).
+extra_gated_button_content: Submit
+---
+# **Cosmos Tokenizer**: A suite of image and video tokenizers
+
+[**Website**](https://research.nvidia.com/labs/dir/cosmos-tokenizer) | [**GitHub**](https://github.com/NVIDIA/Cosmos-Tokenizer) | [**NVIDIA News**](https://blogs.nvidia.com/blog/robot-learning-humanoid-development/) | [**NVIDIA Blog**](https://developer.nvidia.com/blog/state-of-the-art-multimodal-generative-ai-model-development-with-nvidia-nemo/) | [**Hugging Face**](https://huggingface.co/collections/nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6) | [**YouTube**](https://youtu.be/Soy_myOfWIU) | [**Paper**](https://arxiv.org/abs/2501.03575)
+
+# Model Overview
+
+## Description:
+**Cosmos Tokenizer** is a suite of visual tokenizers for images and videos that delivers various compression rates while maintaining high reconstruction quality. Cosmos Tokenizer can serve as an effective and efficient building block in both diffusion-based and autoregressive models for image and video generation. This model is ready for commercial use.
+
+
+Our tokenizers come in two types: **Continuous** (C) and **Discrete** (D), each with **Image** (I) and **Video** (V) variants:
+* Continuous tokenizers encode visual data into continuous latent embeddings, as shown in latent diffusion models like [Stable Diffusion](https://github.com/CompVis/stable-diffusion). These embeddings are suitable for models that generate data by sampling from continuous distributions.
+* Discrete tokenizers encode visual data into discrete latent codes, mapping them into quantized indices, as seen in autoregressive transformers such as [VideoPoet](https://sites.research.google/videopoet/). This discretization is required for models that generate data by optimizing the cross-entropy loss, such as the GPT models.
+
+
+| | Continuous ( C ) | Discrete ( D ) |
+| ------------------|---------------------|---------------------|
+| **Images ( I )** | Cosmos-Tokenizer-CI | Cosmos-Tokenizer-DI |
+| **Videos ( V )** | Cosmos-Tokenizer-CV | Cosmos-Tokenizer-DV |
+
+
+Given an image or a video, Cosmos Tokenizer outputs either continuous latents or discrete tokens. Cosmos Tokenizer achieves spatial compression rates of 8x8 or 16x16 and temporal compression factors of 4x or 8x, resulting in a total compression factor of up to 2048x (=8x16x16). Cosmos Tokenizer delivers 8x more total compression than state-of-the-art (SOTA) methods while simultaneously maintaining higher image quality and running up to 12x faster than the best available SOTA tokenizers.
+
+**Model Developer**: NVIDIA
+
+## Model Versions
+
+This release (v1.0) of Cosmos Tokenizer includes the following tokenizers:
+* **Continuous Tokenizers**
+ * [Cosmos-1.0-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-CV8x8x8) (8x temporal compression, 8x8 spatial compression, 720 short spatial size, 121 frames context)
+* **Discrete Tokenizers**
+ * [Cosmos-1.0-Tokenizer-DV8x16x16](https://huggingface.co/nvidia/Cosmos-1.0-Tokenizer-DV8x16x16) (8x temporal compression, 16x16 spatial compression, 720 short spatial size, 49 frames context)
+
+The previous release (v0.1) of Cosmos Tokenizer included the following tokenizers:
+* **Continuous Tokenizers**
+ * Continuous Image (CI) Tokenizer
+ * [Cosmos-0.1-Tokenizer-CI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI8x8) (8x8 spatial compression)
+ * [Cosmos-0.1-Tokenizer-CI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CI16x16) (16x16 spatial compression)
+ * Continuous Video (CV) Tokenizer
+ * [Cosmos-0.1-Tokenizer-CV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-0.1-Tokenizer-CV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-0.1-Tokenizer-CV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x16x16) (8x temporal compression, 16x16 spatial compression)
+* **Discrete Tokenizers**
+ * Discrete Image (DI) Tokenizer
+ * [Cosmos-0.1-Tokenizer-DI8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI8x8) (8x8 spatial compression)
+ * [Cosmos-0.1-Tokenizer-DI16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DI16x16) (16x16 spatial compression)
+ * Discrete Video (DV) Tokenizer
+ * [Cosmos-0.1-Tokenizer-DV4x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV4x8x8) (4x temporal compression, 8x8 spatial compression)
+ * [Cosmos-1.0-Tokenizer-DV8x8x8](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x8x8) (8x temporal compression, 8x8 spatial compression)
+ * [Cosmos-1.0-Tokenizer-DV8x16x16](https://huggingface.co/nvidia/Cosmos-Tokenizer-DV8x16x16) (8x temporal compression, 16x16 spatial compression)
+
+
+
+### License:
+This model is released under the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).
+
+Under the NVIDIA Open Model License, NVIDIA confirms:
+
+* Models are commercially usable.
+* You are free to create and distribute Derivative Models.
+* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
+
+**Important Note**: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or
+associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained
+in the Model, your rights under [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.
+
+## Model Architecture:
+
+We designed Cosmos Tokenizer using a lightweight and computationally efficient architecture, featuring a temporally causal design. Specifically, we employ causal temporal convolution and causal temporal attention layers to preserve the natural temporal order of video frames, ensuring seamless tokenization of images and videos using a single unified network architecture. The encoder and decoder form a symmetrical pair, which are mirrors of each other. The encoder starts with a 2-level [Haar wavelet](https://link.springer.com/book/10.1007/978-3-319-04295-4) transform layer, which down-samples inputs by a factor of 4 in both spatial and temporal dimensions. Likewise, the decoder ends with an inverse wavelet transform. We employ the vanilla autoencoder (AE) formulation to model the latent space for continuous tokenizers. For discrete tokenizers, we adopt the [Finite-Scalar-Quantization](https://openreview.net/forum?id=8ishA3LxN8) (FSQ) as the latent space quantizer.
+
+
+
+
+
+## Input/Output Specifications
+
+### Encoder
+* **Input**
+ * **Type:** Images or Videos
+ * **Format:** RGB (Red, Green, Blue)
+ * **Properties:**
+ * **Resolution:** Minimum: 256px (shorter side). Maximum: Up to 4K
+ * **Video Length:** Up to 8 seconds for 1080p videos (bounded by A100 80G GPU memory; higher resolutions will have shorter supported durations)
+
+* **Output**
+ * **Type:** Tokens
+ * **Properties:**
+ * Integer indices ranging from 0 to 63,999
+
+### Decoder
+* **Input**
+ * **Type:** Tokens
+ * **Properties:**
+ * Integer indices ranging from 0 to 63,999
+
+* **Output**
+ * **Type** Images or Videos (matching input type)
+ * **Format:** RGB (Red, Green, Blue)
+ * **Properties:**
+ * **Resolution:** Same as input resolution
+ * **Video Length:** Same as input video length
+
+## Software Integration (Required For NVIDIA Models Only):
+**Runtime Engine(s):**
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer)
+
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Ampere (e.g., A100)
+* NVIDIA Hopper (e.g., H100)
+
+Note: We have only tested Cosmos Tokenizer with BF16 precision on Ampere and Hopper GPUs. If you are using older versions of NVIDIA GPUs (e.g., NVIDIA Volta GPUs), you may need to switch to FP32 precision.
+
+
+**Operating System(s):**
+* Linux (We have not tested on other operating systems.)
+
+# Usage
+Inference Engines:
+* [Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer) (PyTorch)
+
+## Inference with `Cosmos-Tokenizer` (PyTorch)
+### Step-1: Installation of `Cosmos-Tokenizer`
+Note: Currently, the `Cosmos-Tokenizer` code is only supported on Linux.
+
+- Please clone the `Cosmos-Tokenizer` from GitHub repo [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+ ```bash
+ git clone https://github.com/NVIDIA/Cosmos-Tokenizer.git
+ cd Cosmos-Tokenizer
+ ```
+- Install dependencies
+
+ ```bash
+ pip3 install -r requirements.txt
+ apt-get install -y ffmpeg
+ ```
+
+- Preferably, you could build a docker image using our provided Dockerfile.
+ ```bash
+ docker build -t cosmos-docker -f Dockerfile.
+ # You can run the container as:
+ docker run --gpus all -it --rm -v /home/${USER}:/home/${USER} \
+ --workdir ${PWD} cosmos-docker /bin/bash
+ ```
+
+### Step-2: Download Pre-trained Checkpoints
+- Create a local directory for the pre-trained checkpoints and download the
+pre-trained checkpoints from HuggingFace.
+
+ ```python
+ from huggingface_hub import login, snapshot_download
+ import os
+ # You could get your Hugging Face token from https://huggingface.co/settings/tokens
+ login(token=, add_to_git_credential=True)
+ # You could specify the tokenizers you want to download.
+ model_names = [
+ "Cosmos-1.0-Tokenizer-DV8x16x16",
+ ]
+ for model_name in model_names:
+ hf_repo = "nvidia/" + model_name
+ local_dir = "pretrained_ckpts/" + model_name
+ os.makedirs(local_dir, exist_ok=True)
+ print(f"downloading {model_name} to {local_dir}...")
+ snapshot_download(repo_id=hf_repo, local_dir=local_dir)
+ ```
+
+- Under the ech checkpoint directory `pretrained_ckpts/`, we provide the encoder,
+decoder and the full autoencoder JIT models.
+
+ ```bash
+ ├── pretrained_ckpts/
+ │ ├── Cosmos-1.0-Tokenizer-DV8x16x16/
+ │ │ ├── encoder.jit
+ │ │ ├── decoder.jit
+ │ │ ├── autoencoder.jit
+ │ ...
+ ```
+
+### Step-3: Run Inference
+You can use the following example commands to encode and decode images or videos. For each, the same command works for both continuous and discrete tokenization. Simply provide the proper JIT-compiled ckpt to `checkpoint_enc`, `checkpoint_dec`, or the full autoencoder ckpt to `checkpoint`.
+
+```python
+import torch
+from cosmos_tokenizer.video_lib import CausalVideoTokenizer
+model_name = "Cosmos-1.0-Tokenizer-DV8x16x16"
+input_tensor = torch.randn(1, 3, 9, 512, 512).to('cuda').to(torch.bfloat16)
+encoder = CausalVideoTokenizer(checkpoint_enc=f'pretrained_ckpts/{model_name}/encoder.jit')
+(indices, codes) = encoder.encode(input_tensor)
+torch.testing.assert_close(indices.shape, (1, 2, 32, 32))
+torch.testing.assert_close(codes.shape, (1, 6, 2, 32, 32))
+
+# The input tensor can be reconstructed by the decoder as:
+decoder = CausalVideoTokenizer(checkpoint_dec=f'pretrained_ckpts/{model_name}/decoder.jit')
+reconstructed_tensor = decoder.decode(indices)
+torch.testing.assert_close(reconstructed_tensor.shape, input_tensor.shape)
+```
+
+The `indices` will have the shape `(1, 2, 32, 32)` and contain integral values in the range `[1..64K]`, where the first of the three integral maps represents the first frame.
+The `codes` will contain the pre-quantization continuous latent with shape `(1, 6, 2, 32, 32)`, where C=6 represents the number of FSQ levels.
+
+**Note**: More inference usage commands, including both TorchScript (JIT) and PyTorch Inference APIs on real images and videos, can be found on our GitHub repository [github.com/NVIDIA/Cosmos-Tokenizer](https://github.com/NVIDIA/Cosmos-Tokenizer).
+
+
+# Evaluation
+
+## TokenizationPerformance Comparison
+We have extensively evaluated the additional **Cosmos Tokenizer** models on [DAVIS](https://davischallenge.org/) video benchmark dataset.
+
+| Tokenizer | Compression Ratio | Height | Num. of Frames | Quantization | PSNR (DAVIS) | SSIM (DAVIS) | rFVD (DAVIS) |
+|-----------|------------------|--------------|--------------|--------------|--------------|--------------|--------------|
+| VideoGPT | 4×4×4 | - | - | VQ | 32.23 | **0.850** | 72.33 |
+| OmniTokenizer | 4×8×8 | - | - | VQ | 28.44 | 0.712 | 188.60 |
+| Cosmos-Tokenizer-DV | 4×8×8 | 720 | 17 | FSQ | **32.98** | 0.818 | **37.36** |
+| Cosmos-Tokenizer-DV | 8×8×8 | 720 | 17 | FSQ | 32.11 | 0.775 | 100.15 |
+| Cosmos-Tokenizer-DV | 8×16×16 | 720 | 17 | FSQ | 31.42 | 0.716 | 241.52 |
+| Cosmos-Tokenizer-DV | 8×16×16 | 720 | 49 | FSQ | 31.59 | 0.719 | 259.33 |
+
+
+* We compare with the state-of-the-art discrete video tokenizer, [OmniTokenizer](https://github.com/FoundationVision/OmniTokenizer).
+* Evaluation metrics:
+ * Peak Signal-to-Noise Ratio (PSNR)
+ * Structural Similarity (SSIM)
+ * Reconstruction Fréchet Video Distance (rFVD)
+
+## Runtime Comparison
+
+The following table shows the number of parameters and the averaged encoding and decoding times per image or video frame, measured on a single A100 80GB GPU. For comparison, we also list the parameters and average speeds of prior state-of-the-art tokenizer(s) with the same compression ratio.
+
+| Tokenizer | Resolution | Compression Ratio | Parameters | Time (ms) |
+|----------------|------------|-------------------|------------|-----------|
+| OmniTokenizer | 720x1280 | 4×8×8 | 54M | 53.2 |
+| Cosmos-DV | 720x1280 | 4×8×8 | 105M | 51.5 |
+
+Note: We benchmarked the runtime for images under the 8x8 compression and videos under the 4×8×8 compression. Tokenizers with different compression ratios are not included in this comparison.
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+### Plus Plus (++) Promise
+
+We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
+* Verified to comply with current applicable disclosure laws, regulations, and industry standards.
+* Verified to comply with applicable privacy labeling requirements.
+* Annotated to describe the collector/source (NVIDIA or a third-party).
+* Characterized for technical limitations.
+* Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
+* Reviewed before release.
+* Tagged for known restrictions and potential safety implications.
+
+For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+### Bias
+
+Field | Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None
+Measures taken to mitigate against unwanted bias: | None
+
+
+### Explainability
+
+Field | Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain: | Tokenization of images and videos
+Model Type: | Auto-Encoder
+Intended Users: | Generative AI developers for image and video generation models
+Output: | Images/Videos and Latent Tokens
+Describe how the model works: | Compresses and decompresses visual input (image/video).
+Technical Limitations: | Due to tokenizer compression limitations, some visual information (such as small text and other structured fine details) may not be reconstructed accurately. The tokenizers may not produce as high of a reconstruction results for videos with low resolution, e.g. less than 320p.
+Verified to have met prescribed NVIDIA quality standards: | Yes
+Performance Metrics: | Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Reconstruction Fréchet Video Distance (rFVD), Reconstruction Fréchet Inception Distance (rFID), Latency
+Potential Known Risks: | Tokenizer's output can parse all forms of input, including what may be considered toxic, offensive, or indecent.
+Licensing: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
+
+
+### Privacy
+Field | Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal information? | No
+Protected class data used to create this model? | None Known
+Was consent obtained for any personal data used? | None Known
+How often is dataset reviewed? | Before Release
+Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable
+If personal collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable
+If personal collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable
+If personal collected for the development of this AI model, was it minimized to only what was required? | Not Applicable
+Is there provenance for all datasets used in training? | Yes
+Does data labeling (annotation, metadata) comply with privacy laws? | Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable
+
+### Safety
+
+Field | Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s): | Tokenization of images and videos
+Describe the life critical impact (if present). | None Known
+Use Case Restrictions: | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
+Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.
+
+
+# Core Contributors
+Fitsum Reda, Jinwei Gu, Xian Liu, Songwei Ge, Ting-Chun Wang, Haoxiang Wang, Ming-Yu Liu
\ No newline at end of file
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+ "CosmosTokenizer"
+ ],
+}
+
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index 0000000000000000000000000000000000000000..5be0900a5551d62cb295248f76186f2f665c51d0
--- /dev/null
+++ b/Meissonic/pretrained_ckpts/Cosmos-1.0-Tokenizer-DV8x16x16/model_config.yaml
@@ -0,0 +1 @@
+nemo_version: https://github.com/NVIDIA/NeMo/commit/6a5d4b5d19e05262a4182a83613753d424153a8f
\ No newline at end of file
diff --git a/Meissonic/requirements.txt b/Meissonic/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..30e0da1834eb9c1d95d70c9f269f3d5225443582
--- /dev/null
+++ b/Meissonic/requirements.txt
@@ -0,0 +1,21 @@
+--extra-index-url https://download.pytorch.org/whl/cu124
+accelerate
+pytorch-lightning
+torch
+torchvision
+tqdm
+transformers
+numpy
+gradio
+diffusers
+bitsandbytes
+open_clip_torch
+datasets
+peft
+pillow
+wandb
+dask
+pyarrow
+huggingface_hub
+peft
+sentencepiece
\ No newline at end of file
diff --git a/Meissonic/src/attention.py b/Meissonic/src/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..4dbbe03fc79e1eb1509dfd98720b60196144878d
--- /dev/null
+++ b/Meissonic/src/attention.py
@@ -0,0 +1,179 @@
+# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import torch
+
+try:
+ import flash_attn_interface
+ FLASH_ATTN_3_AVAILABLE = True
+except ModuleNotFoundError:
+ FLASH_ATTN_3_AVAILABLE = False
+
+try:
+ import flash_attn
+ FLASH_ATTN_2_AVAILABLE = True
+except ModuleNotFoundError:
+ FLASH_ATTN_2_AVAILABLE = False
+
+import warnings
+
+__all__ = [
+ 'flash_attention',
+ 'attention',
+]
+
+
+def flash_attention(
+ q,
+ k,
+ v,
+ q_lens=None,
+ k_lens=None,
+ dropout_p=0.,
+ softmax_scale=None,
+ q_scale=None,
+ causal=False,
+ window_size=(-1, -1),
+ deterministic=False,
+ dtype=torch.bfloat16,
+ version=None,
+):
+ """
+ q: [B, Lq, Nq, C1].
+ k: [B, Lk, Nk, C1].
+ v: [B, Lk, Nk, C2]. Nq must be divisible by Nk.
+ q_lens: [B].
+ k_lens: [B].
+ dropout_p: float. Dropout probability.
+ softmax_scale: float. The scaling of QK^T before applying softmax.
+ causal: bool. Whether to apply causal attention mask.
+ window_size: (left right). If not (-1, -1), apply sliding window local attention.
+ deterministic: bool. If True, slightly slower and uses more memory.
+ dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
+ """
+ half_dtypes = (torch.float16, torch.bfloat16)
+ assert dtype in half_dtypes
+ assert q.device.type == 'cuda' and q.size(-1) <= 256
+
+ # params
+ b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
+
+ def half(x):
+ return x if x.dtype in half_dtypes else x.to(dtype)
+
+ # preprocess query
+ if q_lens is None:
+ q = half(q.flatten(0, 1))
+ q_lens = torch.tensor(
+ [lq] * b, dtype=torch.int32).to(
+ device=q.device, non_blocking=True)
+ else:
+ q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
+
+ # preprocess key, value
+ if k_lens is None:
+ k = half(k.flatten(0, 1))
+ v = half(v.flatten(0, 1))
+ k_lens = torch.tensor(
+ [lk] * b, dtype=torch.int32).to(
+ device=k.device, non_blocking=True)
+ else:
+ k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
+ v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
+
+ q = q.to(v.dtype)
+ k = k.to(v.dtype)
+
+ if q_scale is not None:
+ q = q * q_scale
+
+ if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
+ warnings.warn(
+ 'Flash attention 3 is not available, use flash attention 2 instead.'
+ )
+
+ # apply attention
+ if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
+ # Note: dropout_p, window_size are not supported in FA3 now.
+ x = flash_attn_interface.flash_attn_varlen_func(
+ q=q,
+ k=k,
+ v=v,
+ cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+ 0, dtype=torch.int32).to(q.device, non_blocking=True),
+ cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+ 0, dtype=torch.int32).to(q.device, non_blocking=True),
+ seqused_q=None,
+ seqused_k=None,
+ max_seqlen_q=lq,
+ max_seqlen_k=lk,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ deterministic=deterministic)[0].unflatten(0, (b, lq))
+ else:
+ assert FLASH_ATTN_2_AVAILABLE
+ x = flash_attn.flash_attn_varlen_func(
+ q=q,
+ k=k,
+ v=v,
+ cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+ 0, dtype=torch.int32).to(q.device, non_blocking=True),
+ cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+ 0, dtype=torch.int32).to(q.device, non_blocking=True),
+ max_seqlen_q=lq,
+ max_seqlen_k=lk,
+ dropout_p=dropout_p,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=window_size,
+ deterministic=deterministic).unflatten(0, (b, lq))
+
+ # output
+ return x.type(out_dtype)
+
+
+def attention(
+ q,
+ k,
+ v,
+ q_lens=None,
+ k_lens=None,
+ dropout_p=0.,
+ softmax_scale=None,
+ q_scale=None,
+ causal=False,
+ window_size=(-1, -1),
+ deterministic=False,
+ dtype=torch.bfloat16,
+ fa_version=None,
+):
+ if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
+ return flash_attention(
+ q=q,
+ k=k,
+ v=v,
+ q_lens=q_lens,
+ k_lens=k_lens,
+ dropout_p=dropout_p,
+ softmax_scale=softmax_scale,
+ q_scale=q_scale,
+ causal=causal,
+ window_size=window_size,
+ deterministic=deterministic,
+ dtype=dtype,
+ version=fa_version,
+ )
+ else:
+ if q_lens is not None or k_lens is not None:
+ warnings.warn(
+ 'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.'
+ )
+ attn_mask = None
+
+ q = q.transpose(1, 2).to(dtype)
+ k = k.transpose(1, 2).to(dtype)
+ v = v.transpose(1, 2).to(dtype)
+
+ out = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
+
+ out = out.transpose(1, 2).contiguous()
+ return out
diff --git a/Meissonic/src/pipeline.py b/Meissonic/src/pipeline.py
new file mode 100644
index 0000000000000000000000000000000000000000..f82e41130368e7232d8d164e5560355993cc711f
--- /dev/null
+++ b/Meissonic/src/pipeline.py
@@ -0,0 +1,370 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.models import VQModel
+from diffusers.utils import replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+from src.scheduler import Scheduler
+from src.transformer import Transformer2DModel
+
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+
+def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+ latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+ latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+ latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+ latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+ latent_image_ids = latent_image_ids.reshape(
+ latent_image_id_height * latent_image_id_width, latent_image_id_channels
+ )
+
+ return latent_image_ids.to(device=device, dtype=dtype)
+
+
+class Pipeline(DiffusionPipeline):
+ image_processor: VaeImageProcessor
+ vqvae: VQModel
+ tokenizer: CLIPTokenizer
+ text_encoder: CLIPTextModelWithProjection
+ transformer: Transformer2DModel
+ scheduler: Scheduler
+ # tokenizer_t5: T5Tokenizer
+ # text_encoder_t5: T5ForConditionalGeneration
+
+ model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+ def __init__(
+ self,
+ vqvae: VQModel,
+ tokenizer: CLIPTokenizer,
+ text_encoder: CLIPTextModelWithProjection,
+ transformer: Transformer2DModel,
+ scheduler: Scheduler,
+ # tokenizer_t5: T5Tokenizer,
+ # text_encoder_t5: T5ForConditionalGeneration,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vqvae=vqvae,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=transformer,
+ scheduler=scheduler,
+ # tokenizer_t5=tokenizer_t5,
+ # text_encoder_t5=text_encoder_t5,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Optional[Union[List[str], str]] = None,
+ height: Optional[int] = 1024,
+ width: Optional[int] = 1024,
+ num_inference_steps: int = 48,
+ guidance_scale: float = 9.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ generator: Optional[torch.Generator] = None,
+ latents: Optional[torch.IntTensor] = None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+ output_type="pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ micro_conditioning_aesthetic_score: int = 6,
+ micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ ):
+ """
+ The call function to the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+ height (`int`, *optional*, defaults to `self.transformer.config.sample_size * self.vae_scale_factor`):
+ The height in pixels of the generated image.
+ width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
+ The width in pixels of the generated image.
+ num_inference_steps (`int`, *optional*, defaults to 16):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*, defaults to 10.0):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+ pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ generator (`torch.Generator`, *optional*):
+ A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+ generation deterministic.
+ latents (`torch.IntTensor`, *optional*):
+ Pre-generated tokens representing latent vectors in `self.vqvae`, to be used as inputs for image
+ gneration. If not provided, the starting latents will be completely masked.
+ prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+ provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+ pooled and projected final hidden states.
+ encoder_hidden_states (`torch.Tensor`, *optional*):
+ Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+ negative_prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+ not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+ negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+ Analogous to `encoder_hidden_states` for the positive prompt.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+ plain tuple.
+ callback (`Callable`, *optional*):
+ A function that calls every `callback_steps` steps during inference. The function is called with the
+ following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+ callback_steps (`int`, *optional*, defaults to 1):
+ The frequency at which the `callback` function is called. If not specified, the callback is called at
+ every step.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+ [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+ The targeted aesthetic score according to the laion aesthetic classifier. See
+ https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ The targeted height, width crop coordinates. See the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+ Configures the temperature scheduler on `self.scheduler` see `Scheduler#set_timesteps`.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+ If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+ `tuple` is returned where the first element is a list with the generated images.
+ """
+ if (prompt_embeds is not None and encoder_hidden_states is None) or (
+ prompt_embeds is None and encoder_hidden_states is not None
+ ):
+ raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+ if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+ negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+ ):
+ raise ValueError(
+ "pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+ )
+
+ if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+ raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+ if isinstance(prompt, str):
+ prompt = [prompt]
+
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ batch_size = batch_size * num_images_per_prompt
+
+ if height is None:
+ height = self.transformer.config.sample_size * self.vae_scale_factor
+
+ if width is None:
+ width = self.transformer.config.sample_size * self.vae_scale_factor
+
+ if prompt_embeds is None:
+ input_ids = self.tokenizer(
+ prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+ # input_ids_t5 = self.tokenizer_t5(
+ # prompt,
+ # return_tensors="pt",
+ # padding="max_length",
+ # truncation=True,
+ # max_length=512,
+ # ).input_ids.to(self._execution_device)
+
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ # outputs_t5 = self.text_encoder_t5(input_ids_t5, decoder_input_ids = input_ids_t5 ,return_dict=True, output_hidden_states=True)
+ prompt_embeds = outputs.text_embeds
+ encoder_hidden_states = outputs.hidden_states[-2]
+ # encoder_hidden_states = outputs_t5.encoder_hidden_states[-2]
+
+ prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+ encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ if guidance_scale > 1.0:
+ if negative_prompt_embeds is None:
+ if negative_prompt is None:
+ negative_prompt = [""] * len(prompt)
+
+ if isinstance(negative_prompt, str):
+ negative_prompt = [negative_prompt]
+
+ input_ids = self.tokenizer(
+ negative_prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+ # input_ids_t5 = self.tokenizer_t5(
+ # prompt,
+ # return_tensors="pt",
+ # padding="max_length",
+ # truncation=True,
+ # max_length=512,
+ # ).input_ids.to(self._execution_device)
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ # outputs_t5 = self.text_encoder_t5(input_ids_t5, decoder_input_ids = input_ids_t5 ,return_dict=True, output_hidden_states=True)
+ negative_prompt_embeds = outputs.text_embeds
+ negative_encoder_hidden_states = outputs.hidden_states[-2]
+ # negative_encoder_hidden_states = outputs_t5.encoder_hidden_states[-2]
+
+
+
+ negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+ negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+ encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+ # Note that the micro conditionings _do_ flip the order of width, height for the original size
+ # and the crop coordinates. This is how it was done in the original code base
+ micro_conds = torch.tensor(
+ [
+ width,
+ height,
+ micro_conditioning_crop_coord[0],
+ micro_conditioning_crop_coord[1],
+ micro_conditioning_aesthetic_score,
+ ],
+ device=self._execution_device,
+ dtype=encoder_hidden_states.dtype,
+ )
+ micro_conds = micro_conds.unsqueeze(0)
+ micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+ shape = (batch_size, height // self.vae_scale_factor, width // self.vae_scale_factor)
+
+ if latents is None:
+ latents = torch.full(
+ shape, self.scheduler.config.mask_token_id, dtype=torch.long, device=self._execution_device
+ )
+
+ self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+
+ num_warmup_steps = len(self.scheduler.timesteps) - num_inference_steps * self.scheduler.order
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, timestep in enumerate(self.scheduler.timesteps):
+ if guidance_scale > 1.0:
+ model_input = torch.cat([latents] * 2)
+ else:
+ model_input = latents
+ if height == 1024: #args.resolution == 1024:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0], model_input.shape[-2],model_input.shape[-1],model_input.device,model_input.dtype)
+ else:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0],2*model_input.shape[-2],2*model_input.shape[-1],model_input.device,model_input.dtype)
+ txt_ids = torch.zeros(encoder_hidden_states.shape[1],3).to(device = encoder_hidden_states.device, dtype = encoder_hidden_states.dtype)
+ model_output = self.transformer(
+ hidden_states = model_input,
+ micro_conds=micro_conds,
+ pooled_projections=prompt_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ img_ids = img_ids,
+ txt_ids = txt_ids,
+ timestep = torch.tensor([timestep], device=model_input.device, dtype=torch.long),
+ # guidance = 7,
+ # cross_attention_kwargs=cross_attention_kwargs,
+ )
+
+ if guidance_scale > 1.0:
+ uncond_logits, cond_logits = model_output.chunk(2)
+ model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+ latents = self.scheduler.step(
+ model_output=model_output,
+ timestep=timestep,
+ sample=latents,
+ generator=generator,
+ ).prev_sample
+
+ if i == len(self.scheduler.timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, timestep, latents)
+
+ if output_type == "latent":
+ output = latents
+ else:
+ needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+ if needs_upcasting:
+ self.vqvae.float()
+
+ output = self.vqvae.decode(
+ latents,
+ force_not_quantize=True,
+ shape=(
+ batch_size,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ self.vqvae.config.latent_channels,
+ ),
+ ).sample.clip(0, 1)
+ output = self.image_processor.postprocess(output, output_type)
+
+ if needs_upcasting:
+ self.vqvae.half()
+
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (output,)
+
+ return ImagePipelineOutput(output)
\ No newline at end of file
diff --git a/Meissonic/src/pipeline_img2img.py b/Meissonic/src/pipeline_img2img.py
new file mode 100644
index 0000000000000000000000000000000000000000..acb67d6eb6d376fd22719f9401ff49d4a9354196
--- /dev/null
+++ b/Meissonic/src/pipeline_img2img.py
@@ -0,0 +1,337 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import UVit2DModel, VQModel
+from diffusers.utils import replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+
+from src.scheduler import Scheduler
+from src.transformer import Transformer2DModel
+from src.pipeline import _prepare_latent_image_ids
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> image = pipe(prompt, input_image).images[0]
+ ```
+"""
+
+class Img2ImgPipeline(DiffusionPipeline):
+ image_processor: VaeImageProcessor
+ vqvae: VQModel
+ tokenizer: CLIPTokenizer
+ text_encoder: CLIPTextModelWithProjection
+ transformer: Transformer2DModel #UVit2DModel
+ scheduler: Scheduler
+
+ model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+ # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before
+ # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter
+ # off the meta device. There should be a way to fix this instead of just not offloading it
+ _exclude_from_cpu_offload = ["vqvae"]
+
+ def __init__(
+ self,
+ vqvae: VQModel,
+ tokenizer: CLIPTokenizer,
+ text_encoder: CLIPTextModelWithProjection,
+ transformer: Transformer2DModel, #UVit2DModel,
+ scheduler: Scheduler,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vqvae=vqvae,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=transformer,
+ scheduler=scheduler,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Optional[Union[List[str], str]] = None,
+ image: PipelineImageInput = None,
+ strength: float = 0.5,
+ num_inference_steps: int = 12,
+ guidance_scale: float = 10.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ generator: Optional[torch.Generator] = None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+ output_type="pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ micro_conditioning_aesthetic_score: int = 6,
+ micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ ):
+ """
+ The call function to the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+ image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+ `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+ numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+ or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+ list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+ latents as `image`, but if passing latents directly it is not encoded again.
+ strength (`float`, *optional*, defaults to 0.5):
+ Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+ starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+ on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+ process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+ essentially ignores `image`.
+ num_inference_steps (`int`, *optional*, defaults to 12):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*, defaults to 10.0):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+ pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ generator (`torch.Generator`, *optional*):
+ A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+ generation deterministic.
+ prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+ provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+ pooled and projected final hidden states.
+ encoder_hidden_states (`torch.Tensor`, *optional*):
+ Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+ negative_prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+ not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+ negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+ Analogous to `encoder_hidden_states` for the positive prompt.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+ plain tuple.
+ callback (`Callable`, *optional*):
+ A function that calls every `callback_steps` steps during inference. The function is called with the
+ following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+ callback_steps (`int`, *optional*, defaults to 1):
+ The frequency at which the `callback` function is called. If not specified, the callback is called at
+ every step.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+ [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+ The targeted aesthetic score according to the laion aesthetic classifier. See
+ https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ The targeted height, width crop coordinates. See the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+ Configures the temperature scheduler on `self.scheduler` see `Scheduler#set_timesteps`.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+ If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+ `tuple` is returned where the first element is a list with the generated images.
+ """
+
+ if (prompt_embeds is not None and encoder_hidden_states is None) or (
+ prompt_embeds is None and encoder_hidden_states is not None
+ ):
+ raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+ if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+ negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+ ):
+ raise ValueError(
+ "pass either both `negative_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+ )
+
+ if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+ raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+ if isinstance(prompt, str):
+ prompt = [prompt]
+
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ batch_size = batch_size * num_images_per_prompt
+
+ if prompt_embeds is None:
+ input_ids = self.tokenizer(
+ prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ prompt_embeds = outputs.text_embeds
+ encoder_hidden_states = outputs.hidden_states[-2]
+
+ prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+ encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ if guidance_scale > 1.0:
+ if negative_prompt_embeds is None:
+ if negative_prompt is None:
+ negative_prompt = [""] * len(prompt)
+
+ if isinstance(negative_prompt, str):
+ negative_prompt = [negative_prompt]
+
+ input_ids = self.tokenizer(
+ negative_prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ negative_prompt_embeds = outputs.text_embeds
+ negative_encoder_hidden_states = outputs.hidden_states[-2]
+
+ negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+ negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+ encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+ image = self.image_processor.preprocess(image)
+
+ height, width = image.shape[-2:]
+
+ # Note that the micro conditionings _do_ flip the order of width, height for the original size
+ # and the crop coordinates. This is how it was done in the original code base
+ micro_conds = torch.tensor(
+ [
+ width,
+ height,
+ micro_conditioning_crop_coord[0],
+ micro_conditioning_crop_coord[1],
+ micro_conditioning_aesthetic_score,
+ ],
+ device=self._execution_device,
+ dtype=encoder_hidden_states.dtype,
+ )
+
+ micro_conds = micro_conds.unsqueeze(0)
+ micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+ self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+ num_inference_steps = int(len(self.scheduler.timesteps) * strength)
+ start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps
+
+ needs_upcasting = False # = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+ if needs_upcasting:
+ self.vqvae.float()
+
+ latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
+ latents_bsz, channels, latents_height, latents_width = latents.shape
+ latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)
+ latents = self.scheduler.add_noise(
+ latents, self.scheduler.timesteps[start_timestep_idx - 1], generator=generator
+ )
+ latents = latents.repeat(num_images_per_prompt, 1, 1)
+
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i in range(start_timestep_idx, len(self.scheduler.timesteps)):
+ timestep = self.scheduler.timesteps[i]
+
+ if guidance_scale > 1.0:
+ model_input = torch.cat([latents] * 2)
+ else:
+ model_input = latents
+ if height == 1024: #args.resolution == 1024:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0], model_input.shape[-2],model_input.shape[-1],model_input.device,model_input.dtype)
+ else:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0],2*model_input.shape[-2],2*model_input.shape[-1],model_input.device,model_input.dtype)
+ txt_ids = torch.zeros(encoder_hidden_states.shape[1],3).to(device = encoder_hidden_states.device, dtype = encoder_hidden_states.dtype)
+ model_output = self.transformer(
+ model_input,
+ micro_conds=micro_conds,
+ pooled_projections=prompt_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ # cross_attention_kwargs=cross_attention_kwargs,
+ img_ids = img_ids,
+ txt_ids = txt_ids,
+ timestep = torch.tensor([timestep], device=model_input.device, dtype=torch.long),
+ )
+
+ if guidance_scale > 1.0:
+ uncond_logits, cond_logits = model_output.chunk(2)
+ model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+ latents = self.scheduler.step(
+ model_output=model_output,
+ timestep=timestep,
+ sample=latents,
+ generator=generator,
+ ).prev_sample
+
+ if i == len(self.scheduler.timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, timestep, latents)
+
+ if output_type == "latent":
+ output = latents
+ else:
+ output = self.vqvae.decode(
+ latents,
+ force_not_quantize=True,
+ shape=(
+ batch_size,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ self.vqvae.config.latent_channels,
+ ),
+ ).sample.clip(0, 1)
+ output = self.image_processor.postprocess(output, output_type)
+
+ if needs_upcasting:
+ self.vqvae.half()
+
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (output,)
+
+ return ImagePipelineOutput(output)
diff --git a/Meissonic/src/pipeline_inpaint.py b/Meissonic/src/pipeline_inpaint.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e8a08b0dcf357660ac9038e59b05a95eefb94a4
--- /dev/null
+++ b/Meissonic/src/pipeline_inpaint.py
@@ -0,0 +1,361 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import torch
+from transformers import CLIPTextModelWithProjection, CLIPTokenizer
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.models import VQModel
+from diffusers.utils import replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from src.scheduler import Scheduler
+from src.transformer import Transformer2DModel
+from src.pipeline import _prepare_latent_image_ids
+
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> pipe(prompt, input_image, mask).images[0].save("out.png")
+ ```
+"""
+
+class InpaintPipeline(DiffusionPipeline):
+ image_processor: VaeImageProcessor
+ vqvae: VQModel
+ tokenizer: CLIPTokenizer
+ text_encoder: CLIPTextModelWithProjection
+ transformer: Transformer2DModel #UVit2DModel
+ scheduler: Scheduler
+
+ model_cpu_offload_seq = "text_encoder->transformer->vqvae"
+
+ # TODO - when calling self.vqvae.quantize, it uses self.vqvae.quantize.embedding.weight before
+ # the forward method of self.vqvae.quantize, so the hook doesn't get called to move the parameter
+ # off the meta device. There should be a way to fix this instead of just not offloading it
+ _exclude_from_cpu_offload = ["vqvae"]
+
+ def __init__(
+ self,
+ vqvae: VQModel,
+ tokenizer: CLIPTokenizer,
+ text_encoder: CLIPTextModelWithProjection,
+ transformer: Transformer2DModel, #UVit2DModel,
+ scheduler: Scheduler,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vqvae=vqvae,
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=transformer,
+ scheduler=scheduler,
+ )
+ self.vae_scale_factor = 2 ** (len(self.vqvae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_normalize=False)
+ self.mask_processor = VaeImageProcessor(
+ vae_scale_factor=self.vae_scale_factor,
+ do_normalize=False,
+ do_binarize=True,
+ do_convert_grayscale=True,
+ do_resize=True,
+ )
+ self.scheduler.register_to_config(masking_schedule="linear")
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Optional[Union[List[str], str]] = None,
+ image: PipelineImageInput = None,
+ mask_image: PipelineImageInput = None,
+ strength: float = 1.0,
+ num_inference_steps: int = 12,
+ guidance_scale: float = 10.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ generator: Optional[torch.Generator] = None,
+ prompt_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ negative_prompt_embeds: Optional[torch.Tensor] = None,
+ negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+ output_type="pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ micro_conditioning_aesthetic_score: int = 6,
+ micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ ):
+ """
+ The call function to the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
+ image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+ `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both
+ numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list
+ or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a
+ list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
+ latents as `image`, but if passing latents directly it is not encoded again.
+ mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
+ `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
+ are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a
+ single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one
+ color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B,
+ H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W,
+ 1)`, or `(H, W)`.
+ strength (`float`, *optional*, defaults to 1.0):
+ Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
+ starting point and more noise is added the higher the `strength`. The number of denoising steps depends
+ on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
+ process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
+ essentially ignores `image`.
+ num_inference_steps (`int`, *optional*, defaults to 16):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*, defaults to 10.0):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+ pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ generator (`torch.Generator`, *optional*):
+ A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+ generation deterministic.
+ prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+ provided, text embeddings are generated from the `prompt` input argument. A single vector from the
+ pooled and projected final hidden states.
+ encoder_hidden_states (`torch.Tensor`, *optional*):
+ Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
+ negative_prompt_embeds (`torch.Tensor`, *optional*):
+ Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
+ not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
+ negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+ Analogous to `encoder_hidden_states` for the positive prompt.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+ plain tuple.
+ callback (`Callable`, *optional*):
+ A function that calls every `callback_steps` steps during inference. The function is called with the
+ following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+ callback_steps (`int`, *optional*, defaults to 1):
+ The frequency at which the `callback` function is called. If not specified, the callback is called at
+ every step.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+ [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+ The targeted aesthetic score according to the laion aesthetic classifier. See
+ https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ The targeted height, width crop coordinates. See the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+ Configures the temperature scheduler on `self.scheduler` see `Scheduler#set_timesteps`.
+
+ Examples:
+
+ Returns:
+ [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
+ If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
+ `tuple` is returned where the first element is a list with the generated images.
+ """
+
+ if (prompt_embeds is not None and encoder_hidden_states is None) or (
+ prompt_embeds is None and encoder_hidden_states is not None
+ ):
+ raise ValueError("pass either both `prompt_embeds` and `encoder_hidden_states` or neither")
+
+ if (negative_prompt_embeds is not None and negative_encoder_hidden_states is None) or (
+ negative_prompt_embeds is None and negative_encoder_hidden_states is not None
+ ):
+ raise ValueError(
+ "pass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neither"
+ )
+
+ if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
+ raise ValueError("pass only one of `prompt` or `prompt_embeds`")
+
+ if isinstance(prompt, str):
+ prompt = [prompt]
+
+ if prompt is not None:
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ batch_size = batch_size * num_images_per_prompt
+
+ if prompt_embeds is None:
+ input_ids = self.tokenizer(
+ prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ prompt_embeds = outputs.text_embeds
+ encoder_hidden_states = outputs.hidden_states[-2]
+
+ prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
+ encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ if guidance_scale > 1.0:
+ if negative_prompt_embeds is None:
+ if negative_prompt is None:
+ negative_prompt = [""] * len(prompt)
+
+ if isinstance(negative_prompt, str):
+ negative_prompt = [negative_prompt]
+
+ input_ids = self.tokenizer(
+ negative_prompt,
+ return_tensors="pt",
+ padding="max_length",
+ truncation=True,
+ max_length=77, #self.tokenizer.model_max_length,
+ ).input_ids.to(self._execution_device)
+
+ outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
+ negative_prompt_embeds = outputs.text_embeds
+ negative_encoder_hidden_states = outputs.hidden_states[-2]
+
+ negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
+ negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
+ encoder_hidden_states = torch.concat([negative_encoder_hidden_states, encoder_hidden_states])
+
+ image = self.image_processor.preprocess(image)
+
+ height, width = image.shape[-2:]
+
+ # Note that the micro conditionings _do_ flip the order of width, height for the original size
+ # and the crop coordinates. This is how it was done in the original code base
+ micro_conds = torch.tensor(
+ [
+ width,
+ height,
+ micro_conditioning_crop_coord[0],
+ micro_conditioning_crop_coord[1],
+ micro_conditioning_aesthetic_score,
+ ],
+ device=self._execution_device,
+ dtype=encoder_hidden_states.dtype,
+ )
+
+ micro_conds = micro_conds.unsqueeze(0)
+ micro_conds = micro_conds.expand(2 * batch_size if guidance_scale > 1.0 else batch_size, -1)
+
+ self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+ num_inference_steps = int(len(self.scheduler.timesteps) * strength)
+ start_timestep_idx = len(self.scheduler.timesteps) - num_inference_steps
+
+ needs_upcasting = False #self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
+
+ if needs_upcasting:
+ self.vqvae.float()
+
+ latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
+ latents_bsz, channels, latents_height, latents_width = latents.shape
+ latents = self.vqvae.quantize(latents)[2][2].reshape(latents_bsz, latents_height, latents_width)
+
+ mask = self.mask_processor.preprocess(
+ mask_image, height // self.vae_scale_factor, width // self.vae_scale_factor
+ )
+ mask = mask.reshape(mask.shape[0], latents_height, latents_width).bool().to(latents.device)
+ latents[mask] = self.scheduler.config.mask_token_id
+
+ starting_mask_ratio = mask.sum() / latents.numel()
+
+ latents = latents.repeat(num_images_per_prompt, 1, 1)
+
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i in range(start_timestep_idx, len(self.scheduler.timesteps)):
+ timestep = self.scheduler.timesteps[i]
+
+ if guidance_scale > 1.0:
+ model_input = torch.cat([latents] * 2)
+ else:
+ model_input = latents
+
+ if height == 1024: #args.resolution == 1024:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0], model_input.shape[-2],model_input.shape[-1],model_input.device,model_input.dtype)
+ else:
+ img_ids = _prepare_latent_image_ids(model_input.shape[0],2*model_input.shape[-2],2*model_input.shape[-1],model_input.device,model_input.dtype)
+ txt_ids = torch.zeros(encoder_hidden_states.shape[1],3).to(device = encoder_hidden_states.device, dtype = encoder_hidden_states.dtype)
+ model_output = self.transformer(
+ model_input,
+ micro_conds=micro_conds,
+ pooled_projections=prompt_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ # cross_attention_kwargs=cross_attention_kwargs,
+ img_ids = img_ids,
+ txt_ids = txt_ids,
+ timestep = torch.tensor([timestep], device=model_input.device, dtype=torch.long),
+ )
+
+ if guidance_scale > 1.0:
+ uncond_logits, cond_logits = model_output.chunk(2)
+ model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+
+ latents = self.scheduler.step(
+ model_output=model_output,
+ timestep=timestep,
+ sample=latents,
+ generator=generator,
+ starting_mask_ratio=starting_mask_ratio,
+ ).prev_sample
+
+ if i == len(self.scheduler.timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, timestep, latents)
+
+ if output_type == "latent":
+ output = latents
+ else:
+ output = self.vqvae.decode(
+ latents,
+ force_not_quantize=True,
+ shape=(
+ batch_size,
+ height // self.vae_scale_factor,
+ width // self.vae_scale_factor,
+ self.vqvae.config.latent_channels,
+ ),
+ ).sample.clip(0, 1)
+ output = self.image_processor.postprocess(output, output_type)
+
+ if needs_upcasting:
+ self.vqvae.half()
+
+ self.maybe_free_model_hooks()
+
+ if not return_dict:
+ return (output,)
+
+ return ImagePipelineOutput(output)
diff --git a/Meissonic/src/pipeline_video.py b/Meissonic/src/pipeline_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c81182f25ce5d192a06cffc736bc1f519ff1a8f
--- /dev/null
+++ b/Meissonic/src/pipeline_video.py
@@ -0,0 +1,1139 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import sys
+import os
+# Add project root to path to allow imports when running as script
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from transformers import T5Tokenizer, T5EncoderModel
+from diffusers.utils import replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
+from src.scheduler_video import Scheduler
+from src.transformer_video import WanDiscreteVideoTransformer
+
+# Global debug flag - set to False to disable debug prints
+DEBUG_PIPELINE = False
+
+
+@dataclass
+class VideoPipelineOutput:
+ """
+ Output class for video generation pipelines.
+
+ Args:
+ videos: Generated videos. Can be:
+ - torch.Tensor of shape [B, C, F, H, W] when output_type="pt"
+ - List of tensors (one per batch) when output_type="latent"
+ - numpy.ndarray when output_type="np"
+ - List of PIL Images (frames) when output_type="pil"
+ """
+ videos: Union[torch.Tensor, List[torch.Tensor], List, Any]
+
+
+class CosmosVideoTokenizer(nn.Module):
+ """
+ Wrapper around a Cosmos DV (Discrete Video) tokenizer for video encoding/decoding.
+
+ This class provides a clean interface to encode videos into discrete codes and decode
+ them back to video tensors. It wraps the Cosmos DV tokenizer models loaded from HuggingFace.
+
+ Attributes:
+ t_downsample (int): Temporal compression factor (frames downsampled by this factor).
+ h_downsample (int): Height compression factor (height downsampled by this factor).
+ w_downsample (int): Width compression factor (width downsampled by this factor).
+ codebook_size (int): Number of unique discrete codes in the codebook.
+ mask_token_id (int): Token ID used for masking during diffusion. Set to codebook_size,
+ meaning valid token indices are [0, codebook_size-1], and codebook_size
+ is reserved for masking.
+ """
+
+ def __init__(self, model_id: str, device: torch.device, dtype: torch.dtype):
+ """
+ Initialize the Cosmos DV video tokenizer.
+
+ Args:
+ model_id (str): HuggingFace model identifier (e.g., "Cosmos-Tokenizer-DV8x16x16-720p").
+ Can be a full repo_id like "nvidia/Cosmos-0.1-Tokenizer-DV8x16x16".
+ device (torch.device): Device to load the tokenizer on (e.g., "cuda" or "cpu").
+ dtype (torch.dtype): Data type for the tokenizer (e.g., torch.float32, torch.bfloat16).
+ """
+ super().__init__()
+ self.device = device
+ self.dtype = dtype
+ self.model_id = model_id
+
+ # Try to load from HuggingFace Hub
+ try:
+ # from huggingface_hub import snapshot_download
+ # import os
+
+ # # Handle both full repo_id and just model name
+ # if "/" not in model_id:
+ # repo_id = f"nvidia/{model_id}"
+ # else:
+ # repo_id = model_id
+
+ # # Download the model
+ # local_dir = f"pretrained_ckpts/{model_id.replace('/', '_')}"
+ # os.makedirs(local_dir, exist_ok=True)
+ # snapshot_download(repo_id=repo_id, local_dir=local_dir)
+ local_dir = "/mnt/Meissonic/pretrained_ckpts/Cosmos-0.1-Tokenizer-DV4x8x8"
+
+ # Try loading as torch.jit models
+ encoder_path = f"{local_dir}/encoder.jit"
+ decoder_path = f"{local_dir}/decoder.jit"
+
+ if os.path.exists(encoder_path) and os.path.exists(decoder_path):
+ # Load models in float32 (TorchScript models often don't support dtype conversion well)
+ # We'll convert inputs to float32 when needed
+ self.encoder = torch.jit.load(encoder_path).to(device).eval()
+ self.decoder = torch.jit.load(decoder_path).to(device).eval()
+ # Store the model dtype (typically float32 for TorchScript)
+ self.model_dtype = torch.float32
+ else:
+ # Try alternative loading methods (e.g., from diffusers or transformers)
+ raise FileNotFoundError(f"Could not find encoder.jit or decoder.jit in {local_dir}")
+
+ except Exception as e:
+ # Fallback: try loading via diffusers or other methods
+ try:
+ # Alternative: try AutoModel or other loading methods
+ # This is a placeholder - adjust based on actual Cosmos tokenizer API
+ raise NotImplementedError(
+ f"Failed to load Cosmos tokenizer from {model_id}. "
+ f"Error: {e}. Please ensure the model is available on HuggingFace Hub."
+ )
+ except Exception as e2:
+ raise RuntimeError(
+ f"Could not load Cosmos tokenizer: {e}. "
+ f"Please check that the model_id '{model_id}' is correct and accessible."
+ ) from e2
+
+ # Compression factors for DV8x16x16 model
+ # These values depend on the specific model architecture
+ # For Cosmos-Tokenizer-DV8x16x16-720p:
+ # - Temporal: 8x downsampling (16 frames -> 2 frames)
+ # - Spatial: 16x16 downsampling (480x848 -> 30x53)
+ self.t_downsample = 4 #8 # Temporal compression factor
+ self.h_downsample = 8 #16 # Height compression factor
+ self.w_downsample = 8 #16 # Width compression factor
+ self.codebook_size = 64000 #65536 # Number of unique codes (2^16)
+
+ # Mask token ID: codebook_size is reserved for masking during diffusion
+ # This ensures all Cosmos codes [0, codebook_size-1] remain valid
+ # Extended vocab: [0, codebook_size-1] = valid codes, codebook_size = mask_token_id
+ self.mask_token_id = self.codebook_size
+
+ def encode(self, video: torch.Tensor) -> torch.LongTensor:
+ """
+ Encode a video tensor into discrete code indices.
+
+ Args:
+ video (torch.Tensor): Input video tensor of shape [B, C, F, H, W].
+ Values should be in [0, 1] or [0, 255].
+ If values are > 1.0, they will be normalized to [0, 1].
+
+ Returns:
+ torch.LongTensor: Encoded discrete codes of shape [B, F', H', W'] where:
+ - F' ≈ F // t_downsample (temporal dimension after compression, may vary slightly due to padding)
+ - H' = H // h_downsample (height after compression)
+ - W' = W // w_downsample (width after compression)
+
+ Note:
+ The actual output shape may differ slightly from the theoretical compression
+ due to model-specific padding or overlap behavior.
+ """
+ # Normalize video to [0, 1] if necessary
+ if video.max() > 1.0:
+ video = video / 255.0
+
+ # Ensure video is on correct device and convert to model dtype (typically float32)
+ # TorchScript models often require float32 inputs
+ video = video.to(self.device).to(self.model_dtype)
+
+ # Encode the video
+ with torch.no_grad():
+ # The encoder typically returns (indices, ...) or just indices
+ # Adjust based on actual Cosmos encoder API
+ if hasattr(self.encoder, 'encode'):
+ result = self.encoder.encode(video)
+ if isinstance(result, tuple):
+ indices = result[0]
+ else:
+ indices = result
+ else:
+ # Direct call if encoder is a callable model
+ result = self.encoder(video)
+ if isinstance(result, tuple):
+ indices = result[0]
+ else:
+ indices = result
+
+ # Ensure indices are LongTensor
+ if not isinstance(indices, torch.LongTensor):
+ indices = indices.long()
+
+ return indices
+
+ def decode(self, codes: torch.LongTensor) -> torch.Tensor:
+ """
+ Decode discrete code indices back into a video tensor.
+
+ Args:
+ codes (torch.LongTensor): Encoded discrete codes of shape [B, F', H', W'].
+
+ Returns:
+ torch.Tensor: Reconstructed video tensor of shape [B, C, F, H, W] where:
+ - F ≈ F' * t_downsample (may vary slightly due to model-specific behavior)
+ - H = H' * h_downsample
+ - W = W' * w_downsample
+ Values are in [0, 1] range.
+
+ Note:
+ The output frame count may differ slightly from the original input due to
+ model-specific temporal interpolation or padding behavior.
+ """
+ # Ensure codes are on correct device
+ codes = codes.to(self.device)
+
+ # Decode the codes
+ with torch.no_grad():
+ if hasattr(self.decoder, 'decode'):
+ reconstructed_video = self.decoder.decode(codes)
+ else:
+ # Direct call if decoder is a callable model
+ reconstructed_video = self.decoder(codes)
+
+ # Ensure output is in [0, 1] range and convert to desired dtype
+ reconstructed_video = torch.clamp(reconstructed_video, 0.0, 1.0)
+
+ # Convert to the tokenizer's dtype if different from model dtype
+ if reconstructed_video.dtype != self.dtype:
+ reconstructed_video = reconstructed_video.to(self.dtype)
+
+ return reconstructed_video
+
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+
+class Pipeline(DiffusionPipeline):
+ tokenizer: T5Tokenizer
+ text_encoder: T5EncoderModel
+ transformer: WanDiscreteVideoTransformer
+ scheduler: Scheduler
+ video_tokenizer: CosmosVideoTokenizer
+
+ model_cpu_offload_seq = "text_encoder->transformer->video_tokenizer"
+
+ def __init__(
+ self,
+ tokenizer: T5Tokenizer,
+ text_encoder: T5EncoderModel,
+ transformer: WanDiscreteVideoTransformer,
+ scheduler: Scheduler,
+ video_tokenizer: CosmosVideoTokenizer,
+ text_len: int = 512,
+ num_frames: int = 16,
+ height: int = 480,
+ width: int = 848,
+ ):
+ """
+ Initialize the video diffusion pipeline.
+
+ Args:
+ tokenizer (T5Tokenizer): Wan-style T5 tokenizer (UMT5) for text encoding.
+ text_encoder (T5EncoderModel): Wan-style T5 encoder (UMT5-base outputs 768, UMT5-large outputs 4096).
+ transformer (WanDiscreteVideoTransformer): The discrete video transformer model
+ that handles token embedding and logits prediction. Supports dynamic input dimensions.
+ scheduler (Scheduler): The diffusion scheduler.
+ video_tokenizer (CosmosVideoTokenizer): Cosmos DV tokenizer
+ for video encoding/decoding. Required for video generation.
+ text_len (int): Maximum text sequence length (default: 512).
+ num_frames (int): Default number of frames in the video (default: 16).
+ Can be overridden in __call__. Must be divisible by tokenizer's t_downsample (8).
+ height (int): Default height of the video in pixels (default: 480).
+ Can be overridden in __call__. Must be divisible by tokenizer's h_downsample (16).
+ width (int): Default width of the video in pixels (default: 848).
+ Can be overridden in __call__. Must be divisible by tokenizer's w_downsample (16).
+
+ Note:
+ The transformer now supports dynamic input dimensions, so users can generate videos
+ with different frame counts and resolutions by specifying them in __call__().
+ """
+ super().__init__()
+
+ self.register_modules(
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=transformer,
+ scheduler=scheduler,
+ video_tokenizer=video_tokenizer,
+ )
+ self.text_len = text_len
+ # Store default video dimensions (can be overridden in __call__)
+ self.num_frames = num_frames
+ self.height = height
+ self.width = width
+
+ # Get codebook size from video tokenizer
+ self.codebook_size = video_tokenizer.codebook_size
+
+ # IMPORTANT: Index mapping semantics for discrete diffusion
+ # ============================================================
+ # Cosmos tokenizer outputs indices in [0, codebook_size-1] (all valid codes, no mask)
+ # We extend the vocab by adding mask_token_id = codebook_size
+ #
+ # Model vocab: [0, codebook_size] where:
+ # - [0, codebook_size-1] = actual Cosmos codes (direct mapping, no shift needed)
+ # - codebook_size = mask_token_id (reserved for masking during diffusion)
+ #
+ # This design ensures:
+ # - All Cosmos codes remain valid (no information loss)
+ # - mask_token_id is outside the codebook range, safe for masking
+ # - No need for +1/-1 mapping, Cosmos codes map directly to [0, codebook_size-1]
+ #
+ # When decoding back to Cosmos:
+ # - Model outputs in [0, codebook_size] (may contain codebook_size=mask)
+ # - Filter out mask tokens: clamp to [0, codebook_size-1]
+ # - This ensures Cosmos only sees valid codes [0, codebook_size-1]
+ # ============================================================
+
+ # Set mask_token_id to codebook_size (outside valid code range)
+ # This will be used by scheduler
+ self.mask_token_id = self.codebook_size
+
+ # Calculate default compressed dimensions
+ # These are used as defaults in __call__ and can be overridden
+ self.F_prime = num_frames // video_tokenizer.t_downsample
+ self.H_prime = height // video_tokenizer.h_downsample
+ self.W_prime = width // video_tokenizer.w_downsample
+
+ def _encode_prompt_wan(
+ self,
+ prompt: Union[str, List[str]],
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+ """
+ Encode prompt(s) using Wan's T5 text encoder.
+
+ Args:
+ prompt (Union[str, List[str]]): The prompt or prompts to encode.
+ negative_prompt (Optional[Union[str, List[str]]]): The negative prompt(s) for CFG.
+ num_images_per_prompt (int): Number of images to generate per prompt.
+ do_classifier_free_guidance (bool): Whether to use classifier-free guidance.
+
+ Returns:
+ Tuple[torch.Tensor, Optional[torch.Tensor]]:
+ - encoder_hidden_states: [B_total, L_text, D_text] where B_total = B * num_images_per_prompt
+ - encoder_hidden_states_neg: [B_total, L_text, D_text] for CFG, or None if not using CFG
+ """
+ if isinstance(prompt, str):
+ prompt = [prompt]
+
+ # Tokenize prompts
+ input_ids = self.tokenizer(
+ prompt,
+ padding="max_length",
+ truncation=True,
+ max_length=self.text_len,
+ return_tensors="pt"
+ )["input_ids"].to(self._execution_device)
+
+ # Encode prompts
+ with torch.no_grad():
+ outputs = self.text_encoder(input_ids, return_dict=True)
+ encoder_hidden_states = outputs.last_hidden_state # [B, L_text, D_text]
+
+ # Repeat for num_images_per_prompt
+ encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+
+ # Handle negative prompts for CFG
+ encoder_hidden_states_neg = None
+ if do_classifier_free_guidance:
+ if negative_prompt is None:
+ negative_prompt = [""] * len(prompt)
+
+ if isinstance(negative_prompt, str):
+ negative_prompt = [negative_prompt]
+
+ # Tokenize negative prompts
+ negative_input_ids = self.tokenizer(
+ negative_prompt,
+ padding="max_length",
+ truncation=True,
+ max_length=self.text_len,
+ return_tensors="pt"
+ )["input_ids"].to(self._execution_device)
+
+ # Encode negative prompts
+ with torch.no_grad():
+ negative_outputs = self.text_encoder(negative_input_ids, return_dict=True)
+ encoder_hidden_states_neg = negative_outputs.last_hidden_state # [B, L_text, D_text]
+
+ # Repeat for num_images_per_prompt
+ encoder_hidden_states_neg = encoder_hidden_states_neg.repeat(num_images_per_prompt, 1, 1)
+
+ # Assertions for shape verification
+ B_total = len(prompt) * num_images_per_prompt
+ assert encoder_hidden_states.shape == (B_total, self.text_len, encoder_hidden_states.shape[-1]), (
+ f"Expected encoder_hidden_states shape ({B_total}, {self.text_len}, D_text), "
+ f"got {encoder_hidden_states.shape}"
+ )
+ if encoder_hidden_states_neg is not None:
+ assert encoder_hidden_states_neg.shape == (B_total, self.text_len, encoder_hidden_states_neg.shape[-1]), (
+ f"Expected encoder_hidden_states_neg shape ({B_total}, {self.text_len}, D_text), "
+ f"got {encoder_hidden_states_neg.shape}"
+ )
+
+ return encoder_hidden_states, encoder_hidden_states_neg
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Optional[Union[List[str], str]] = None,
+ num_frames: Optional[int] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 48,
+ guidance_scale: float = 9.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ generator: Optional[torch.Generator] = None,
+ latents: Optional[torch.IntTensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ negative_encoder_hidden_states: Optional[torch.Tensor] = None,
+ output_type="pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ micro_conditioning_aesthetic_score: int = 6,
+ micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ ):
+ """
+ The call function to the pipeline for generation.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide image generation. If not defined, you need to pass `encoder_hidden_states`.
+ num_frames (`int`, *optional*, defaults to `self.num_frames`):
+ Number of frames in the generated video.
+ height (`int`, *optional*, defaults to `self.height`):
+ The height in pixels of the generated video.
+ width (`int`, *optional*, defaults to `self.width`):
+ The width in pixels of the generated video.
+ num_inference_steps (`int`, *optional*, defaults to 16):
+ The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+ expense of slower inference.
+ guidance_scale (`float`, *optional*, defaults to 10.0):
+ A higher guidance scale value encourages the model to generate images closely linked to the text
+ `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
+ negative_prompt (`str` or `List[str]`, *optional*):
+ The prompt or prompts to guide what to not include in image generation. If not defined, you need to
+ pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
+ num_images_per_prompt (`int`, *optional*, defaults to 1):
+ The number of images to generate per prompt.
+ generator (`torch.Generator`, *optional*):
+ A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+ generation deterministic.
+ latents (`torch.LongTensor`, *optional*):
+ Pre-generated 3D video codes of shape `[B, F', H', W']` where F', H', W' are the compressed
+ dimensions after Cosmos tokenization. If not provided, the starting codes will be completely
+ masked (filled with mask_token_id).
+ encoder_hidden_states (`torch.Tensor`, *optional*):
+ Pre-generated encoder hidden states from the T5 text encoder. If not provided, will be generated
+ from the `prompt` input argument using Wan's T5 encoder.
+ negative_encoder_hidden_states (`torch.Tensor`, *optional*):
+ Pre-generated negative encoder hidden states for classifier-free guidance. If not provided, will be
+ generated from the `negative_prompt` input argument.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+ plain tuple.
+ callback (`Callable`, *optional*):
+ A function that calls every `callback_steps` steps during inference. The function is called with the
+ following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
+ callback_steps (`int`, *optional*, defaults to 1):
+ The frequency at which the `callback` function is called. If not specified, the callback is called at
+ every step.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
+ [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
+ The targeted aesthetic score according to the laion aesthetic classifier. See
+ https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ The targeted height, width crop coordinates. See the micro-conditioning section of
+ https://arxiv.org/abs/2307.01952.
+ temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)):
+ Configures the temperature scheduler on `self.scheduler` see `Scheduler#set_timesteps`.
+
+ Examples:
+
+ Returns:
+ [`VideoPipelineOutput`] or `tuple`:
+ If `return_dict` is `True`, [`VideoPipelineOutput`] is returned, otherwise a
+ `tuple` is returned where the first element contains the generated videos.
+ The output format depends on `output_type`:
+ - `"latent"`: Discrete code tensor of shape `[B, F', H', W']`
+ - `"pt"`: Float tensor of shape `[B, C, F, H, W]` with values in [0, 1]
+ - `"np"`: Numpy array of shape `[B, C, F, H, W]`
+ - `"pil"`: List of lists of PIL Images (one list per batch, each containing frames)
+ """
+ # Validate inputs
+ if prompt is None and encoder_hidden_states is None:
+ raise ValueError("Either `prompt` or `encoder_hidden_states` must be provided")
+
+ if prompt is not None and encoder_hidden_states is not None:
+ raise ValueError("Cannot pass both `prompt` and `encoder_hidden_states`")
+
+ # Determine batch size
+ if prompt is not None:
+ if isinstance(prompt, str):
+ prompt = [prompt]
+ batch_size = len(prompt)
+ else:
+ batch_size = encoder_hidden_states.shape[0]
+
+ batch_size = batch_size * num_images_per_prompt
+
+ # Use provided dimensions or fall back to defaults
+ if num_frames is None:
+ num_frames = self.num_frames
+ if height is None:
+ height = self.height
+ if width is None:
+ width = self.width
+
+ # Validate dimensions are divisible by tokenizer's downsampling factors
+ t_ds = self.video_tokenizer.t_downsample
+ h_ds = self.video_tokenizer.h_downsample
+ w_ds = self.video_tokenizer.w_downsample
+
+ # if num_frames % t_ds != 0:
+ # raise ValueError(
+ # f"num_frames ({num_frames}) must be divisible by temporal downsampling factor ({t_ds})"
+ # )
+ # if height % h_ds != 0:
+ # raise ValueError(
+ # f"height ({height}) must be divisible by height downsampling factor ({h_ds})"
+ # )
+ # if width % w_ds != 0:
+ # raise ValueError(
+ # f"width ({width}) must be divisible by width downsampling factor ({w_ds})"
+ # )
+
+ # Calculate compressed dimensions for this generation
+ F_prime = num_frames // t_ds
+ H_prime = height // h_ds
+ W_prime = width // w_ds
+
+ # Encode prompts using Wan's T5 encoder
+ do_classifier_free_guidance = guidance_scale > 1.0
+
+ if encoder_hidden_states is None:
+ encoder_hidden_states, encoder_hidden_states_neg = self._encode_prompt_wan(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ num_images_per_prompt=num_images_per_prompt,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ )
+ else:
+ # Use pre-computed encoder_hidden_states
+ encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+ if do_classifier_free_guidance:
+ if negative_encoder_hidden_states is None:
+ raise ValueError("`negative_encoder_hidden_states` must be provided when using guidance_scale > 1.0")
+ encoder_hidden_states_neg = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
+ else:
+ encoder_hidden_states_neg = None
+
+ # Stack negative and positive for classifier-free guidance
+ if do_classifier_free_guidance:
+ # Stack [negative, positive] along batch dimension
+ encoder_hidden_states = torch.cat([encoder_hidden_states_neg, encoder_hidden_states], dim=0)
+ # Verify shape: should be (2 * batch_size, text_len, text_dim)
+ assert encoder_hidden_states.shape[0] == 2 * batch_size, (
+ f"Expected batch size {2 * batch_size} after CFG stacking, got {encoder_hidden_states.shape[0]}"
+ )
+
+ # Initialize 3D video codes: [B, F', H', W']
+ # Note: latents_codes use extended vocab [0, codebook_size] where:
+ # - [0, codebook_size-1] = valid Cosmos codes (direct mapping)
+ # - codebook_size = mask_token_id
+ # If provided latents are from Cosmos (range [0, codebook_size-1]), they're already correct
+ if latents is None:
+ # Start with all mask tokens (codebook_size) in extended vocab space
+ latents_codes = torch.full(
+ (batch_size, F_prime, H_prime, W_prime),
+ self.mask_token_id, # codebook_size = mask_token_id
+ dtype=torch.long,
+ device=self._execution_device
+ )
+ else:
+ # If latents are provided, assume they are already in extended vocab format [0, codebook_size]
+ # Cosmos codes [0, codebook_size-1] map directly, no shift needed
+ latents_codes = latents
+ assert latents_codes.shape[1:] == (F_prime, H_prime, W_prime), (
+ f"Expected latents shape [B, {F_prime}, {H_prime}, {W_prime}], "
+ f"got {latents_codes.shape}"
+ )
+ # Verify values are in extended vocab range [0, codebook_size]
+ assert latents_codes.min() >= 0 and latents_codes.max() <= self.codebook_size, (
+ f"Latents values should be in [0, {self.codebook_size}] (extended vocab), "
+ f"got range [{latents_codes.min()}, {latents_codes.max()}]"
+ )
+
+ # Print initial latents shape for debugging
+ if DEBUG_PIPELINE:
+ print(f"Initial latents_codes shape: {latents_codes.shape} [B, F', H', W']")
+
+ self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
+
+ num_warmup_steps = len(self.scheduler.timesteps) - num_inference_steps * self.scheduler.order
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, timestep in enumerate(self.scheduler.timesteps):
+ # Handle classifier-free guidance: duplicate codes if needed
+ # IMPORTANT: Always build latents_codes_input from latents_codes (not from previous iteration's latents_codes_input)
+ if guidance_scale > 1.0:
+ latents_codes_input = torch.cat([latents_codes] * 2, dim=0)
+ batch_size_total = 2 * batch_size
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] CFG: latents_codes.shape={latents_codes.shape}, latents_codes_input.shape={latents_codes_input.shape}, encoder_hidden_states.shape={encoder_hidden_states.shape}")
+ else:
+ latents_codes_input = latents_codes
+ batch_size_total = batch_size
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] latents_codes.shape={latents_codes.shape}, latents_codes_input.shape={latents_codes_input.shape}, encoder_hidden_states.shape={encoder_hidden_states.shape}")
+
+ # Verify shapes before transformer call
+ assert latents_codes_input.shape[0] == batch_size_total, (
+ f"latents_codes_input batch mismatch: {latents_codes_input.shape[0]} != {batch_size_total}"
+ )
+ assert encoder_hidden_states.shape[0] == batch_size_total, (
+ f"encoder_hidden_states batch mismatch: {encoder_hidden_states.shape[0]} != {batch_size_total}"
+ )
+
+ # Prepare timestep tensor: [B_total]
+ timestep_tensor = torch.full(
+ (batch_size_total,),
+ timestep,
+ dtype=torch.long,
+ device=self._execution_device
+ )
+
+ # Call transformer
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] Before transformer: tokens.shape={latents_codes_input.shape}, timesteps.shape={timestep_tensor.shape}, encoder_hidden_states.shape={encoder_hidden_states.shape}")
+ logits = self.transformer(
+ tokens=latents_codes_input,
+ timesteps=timestep_tensor,
+ encoder_hidden_states=encoder_hidden_states,
+ y=None,
+ )
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] After transformer: logits.shape={logits.shape}, expected batch={batch_size_total}")
+
+ # Verify logits shape matches expected token count
+ # logits: [B_total, vocab_size, F_out, H_out, W_out] where vocab_size = codebook_size + 1
+ # latents_codes: [B_total, F', H', W'] with values in [0, vocab_size-1]
+ vocab_size = self.codebook_size + 1
+ assert logits.shape[0] == batch_size_total, (
+ f"Logits batch size mismatch: {logits.shape[0]} != {batch_size_total}"
+ )
+ assert logits.shape[1] == vocab_size, (
+ f"Logits vocab size mismatch: {logits.shape[1]} != {vocab_size} (expected codebook_size+1)"
+ )
+
+ # Apply classifier-free guidance if needed
+ # logits shape: [B_total, vocab_size, F_out, H_out, W_out]
+ if guidance_scale > 1.0:
+ uncond_logits, cond_logits = logits.chunk(2, dim=0)
+ logits = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
+ # After CFG, logits batch becomes batch_size (not 2*batch_size)
+ # We'll use latents_codes (not latents_codes_input) for flattening, so no need to update it here
+
+ # Flatten video tokens for scheduler: [B, F, H, W] -> [B, N] where N = F*H*W
+ # Scheduler expects 1D token sequences, so we flatten the spatial-temporal dimensions
+ # Use logits.shape[0] as the batch size (after CFG, this is batch_size, not batch_size_total)
+ B_flat, vocab_size, F_flat, H_flat, W_flat = logits.shape
+ N = F_flat * H_flat * W_flat
+
+ # After CFG, logits batch is batch_size (not 2*batch_size)
+ # Use latents_codes (not latents_codes_input) for flattening, since latents_codes is always [B, F, H, W]
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] After CFG: logits.shape={logits.shape}, latents_codes.shape={latents_codes.shape}")
+
+ # Handle shape mismatch: transformer output may have different spatial dimensions due to patch_size
+ # Crop or pad latents_codes to match logits dimensions
+ if latents_codes.shape[1:] != (F_flat, H_flat, W_flat):
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] Shape mismatch detected: latents_codes {latents_codes.shape[1:]} != logits {logits.shape[2:]}, adjusting...")
+ B_lat, F_lat, H_lat, W_lat = latents_codes.shape
+
+ # Create a new tensor with the correct shape, filled with mask_token_id
+ old_shape = latents_codes.shape
+ new_latents_codes = torch.full(
+ (B_flat, F_flat, H_flat, W_flat),
+ self.mask_token_id,
+ dtype=latents_codes.dtype,
+ device=latents_codes.device
+ )
+
+ # Copy overlapping region from latents_codes
+ F_copy = min(F_lat, F_flat)
+ H_copy = min(H_lat, H_flat)
+ W_copy = min(W_lat, W_flat)
+ new_latents_codes[:, :F_copy, :H_copy, :W_copy] = latents_codes[:, :F_copy, :H_copy, :W_copy]
+
+ latents_codes = new_latents_codes
+ if DEBUG_PIPELINE:
+ print(f"[DEBUG] Adjusted latents_codes from {old_shape} to {latents_codes.shape} (copied {F_copy}x{H_copy}x{W_copy} region)")
+
+ # Verify shapes match after adjustment
+ assert latents_codes.shape == (B_flat, F_flat, H_flat, W_flat), (
+ f"Shape mismatch after adjustment: logits [B, vocab, F, H, W]={logits.shape}, "
+ f"latents_codes [B, F, H, W]={latents_codes.shape}"
+ )
+ assert logits.numel() == B_flat * vocab_size * N, (
+ f"logits.numel: {logits.numel()}, reshape target: {B_flat*vocab_size*N}"
+ )
+
+ tokens_flat = latents_codes.view(B_flat, N) # [B, N]
+ logits_flat = logits.permute(0, 2, 3, 4, 1).reshape(B_flat, N, vocab_size) # [B, N, vocab_size]
+ assert (tokens_flat >= 0).all() and (tokens_flat < vocab_size).all(), (
+ f"[DEBUG] Out-of-range token: min={tokens_flat.min().item()}, max={tokens_flat.max().item()}, vocab_size={vocab_size}"
+ )
+
+ # Scheduler step: update discrete codes based on logits
+ # Scheduler works on 1D token sequences [B_total, N] with logits [B_total, N, vocab]
+ scheduler_output = self.scheduler.step(
+ model_output=logits_flat, # [B_total, N, vocab]
+ timestep=timestep,
+ sample=tokens_flat, # [B_total, N]
+ generator=generator,
+ )
+
+ # Unflatten back to video grid: [B, N] -> [B, F, H, W]
+ # Note: After CFG, B_flat = batch_size (not 2*batch_size), so latents_codes_updated is already [B, F, H, W]
+ tokens_flat = scheduler_output.prev_sample # [B, N]
+ latents_codes = tokens_flat.view(B_flat, F_flat, H_flat, W_flat) # [B, F, H, W]
+
+ # No need to slice after CFG, because B_flat is already batch_size (not 2*batch_size)
+ # The CFG merging was done on logits before flattening, so latents_codes is already the correct size
+
+ if i == len(self.scheduler.timesteps) - 1 or (
+ (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0
+ ):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ step_idx = i // getattr(self.scheduler, "order", 1)
+ callback(step_idx, timestep, latents_codes)
+
+ # Final codes after denoising: [B, F', H', W']
+ final_codes = latents_codes
+
+ self.maybe_free_model_hooks()
+
+ # Handle output based on output_type
+ if output_type == "latent":
+ # Return discrete code tensor [B, F', H', W']
+ output = final_codes
+ # Verify output dtype and shape
+ assert output.dtype in (torch.long, torch.int64), \
+ f"Expected latent output dtype torch.long or torch.int64, got {output.dtype}"
+ assert output.shape == (batch_size, F_prime, H_prime, W_prime), (
+ f"Expected latent output shape {(batch_size, F_prime, H_prime, W_prime)}, "
+ f"got {output.shape}"
+ )
+ else:
+ # Decode codes back to RGB video using Cosmos tokenizer
+ # IMPORTANT: Model uses extended vocab [0, codebook_size] where:
+ # - [0, codebook_size-1] = valid Cosmos codes (direct mapping)
+ # - codebook_size = mask_token_id
+ # Cosmos expects [0, codebook_size-1], so we filter out mask tokens
+ with torch.no_grad():
+ # Map from model vocab [0, codebook_size] to Cosmos vocab [0, codebook_size-1]
+ # Clamp to [0, codebook_size-1] to filter out mask_token_id (codebook_size)
+ cosmos_codes = torch.clamp(final_codes, min=0, max=self.codebook_size - 1)
+ videos = self.video_tokenizer.decode(cosmos_codes)
+
+ # Postprocess to standard video output format
+ # videos is in [0, 1] range as float tensor
+ if output_type == "np":
+ # Convert to numpy array (convert to float32 first, numpy doesn't support bfloat16)
+ videos_cpu = videos.cpu()
+ if videos_cpu.dtype != torch.float32:
+ videos_cpu = videos_cpu.to(torch.float32)
+ output = videos_cpu.numpy()
+ elif output_type == "pil":
+ # Convert to list of PIL Images (one list per batch, each containing frames)
+ import numpy as np
+ from PIL import Image
+ output = []
+ videos_cpu = videos.cpu()
+ if videos_cpu.dtype != torch.float32:
+ videos_cpu = videos_cpu.to(torch.float32)
+ for b in range(batch_size):
+ video_frames = []
+ for f in range(videos_cpu.shape[2]): # Loop over frames
+ frame = videos_cpu[b, :, f, :, :].numpy() # [C, H, W]
+ frame = np.transpose(frame, (1, 2, 0)) # [H, W, C]
+ frame = (frame * 255).astype(np.uint8)
+ video_frames.append(Image.fromarray(frame))
+ output.append(video_frames)
+ else:
+ # output_type == "pt", keep as float tensor in [0, 1]
+ output = videos
+
+ if not return_dict:
+ return (output,)
+
+ return VideoPipelineOutput(videos=output)
+
+
+def test_cosmos_tokenizer_shapes():
+ """
+ Independent test for CosmosVideoTokenizer encode/decode shape verification.
+
+ Tests:
+ - Encode video tensor [B, C, F, H, W] -> codes [B, F', H', W']
+ - Decode codes [B, F', H', W'] -> video tensor [B, C, F, H, W]
+ - Verify shape consistency and compression factors
+ """
+ import torch
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ dtype = torch.float32
+
+ print("=" * 80)
+ print("[Test] CosmosVideoTokenizer encode/decode shape test")
+ print("=" * 80)
+
+ try:
+ model_id = "Cosmos-1.0-Tokenizer-DV8x16x16"
+ print(f"Loading tokenizer: {model_id}")
+
+ video_tokenizer = CosmosVideoTokenizer(model_id=model_id, device=device, dtype=dtype)
+ print(f"✓ Tokenizer loaded")
+ print(f" Codebook size: {video_tokenizer.codebook_size}")
+ print(f" Mask token ID: {video_tokenizer.mask_token_id}")
+ print(f" Compression: {video_tokenizer.t_downsample}x{video_tokenizer.h_downsample}x{video_tokenizer.w_downsample}")
+
+ # Test encode/decode with video shape that aligns well with compression factors
+ # Use frame count that's a multiple of t_downsample to minimize rounding issues
+ # t_downsample=8, so use 16 frames (16/8=2 compressed frames)
+ # h_downsample=16, w_downsample=16, so use dimensions divisible by 16
+ B, C, F, H, W = 1, 3, 16, 480, 848
+ test_video = torch.rand(B, C, F, H, W, device=device, dtype=dtype)
+ print(f"\nInput video shape: {test_video.shape} [B, C, F, H, W]")
+
+ # Encode
+ codes = video_tokenizer.encode(test_video)
+ assert codes.ndim == 4, f"Expected codes to be 4D [B, F', H', W'], got {codes.ndim}D"
+ assert codes.shape[0] == B, f"Batch size mismatch: {codes.shape[0]} != {B}"
+
+ F_prime = codes.shape[1]
+ H_prime = codes.shape[2]
+ W_prime = codes.shape[3]
+ print(f"Encoded codes shape: {codes.shape} [B, F', H', W']")
+
+ # Verify compression factors (allow small rounding errors for temporal dimension)
+ expected_F_prime = F // video_tokenizer.t_downsample
+ assert abs(F_prime - expected_F_prime) <= 1, \
+ f"Frame compression mismatch: {F_prime} vs expected ~{expected_F_prime} (from {F} // {video_tokenizer.t_downsample})"
+
+ expected_H_prime = H // video_tokenizer.h_downsample
+ assert H_prime == expected_H_prime, \
+ f"Height compression mismatch: {H_prime} vs {expected_H_prime} (from {H} // {video_tokenizer.h_downsample})"
+
+ expected_W_prime = W // video_tokenizer.w_downsample
+ assert W_prime == expected_W_prime, \
+ f"Width compression mismatch: {W_prime} vs {expected_W_prime} (from {W} // {video_tokenizer.w_downsample})"
+
+ # Decode
+ decoded = video_tokenizer.decode(codes)
+ decoded_B, decoded_C, decoded_F, decoded_H, decoded_W = decoded.shape
+
+ # Verify decoded shape (allow small rounding errors for temporal dimension)
+ assert decoded_B == B, f"Decoded batch size mismatch: {decoded_B} != {B}"
+ assert decoded_C == C, f"Decoded channel mismatch: {decoded_C} != {C}"
+ assert decoded_H == H, f"Decoded height mismatch: {decoded_H} != {H}"
+ assert decoded_W == W, f"Decoded width mismatch: {decoded_W} != {W}"
+
+ # Frame count may differ slightly due to tokenizer's temporal interpolation/padding
+ # Allow ±1 frame tolerance
+ assert abs(decoded_F - F) <= 1, \
+ f"Decoded frame count mismatch: {decoded_F} vs {F} (allowed ±1 frame tolerance)"
+
+ print(f"Decoded video shape: {decoded.shape} [B, C, F, H, W]")
+ if decoded_F != F:
+ print(f" Note: Frame count differs by {decoded_F - F} (expected {F}, got {decoded_F})")
+ print(f" This is acceptable due to tokenizer's temporal interpolation behavior")
+
+ print(f"\n✓ All shape checks passed!")
+ print(f" Compression: {F}x{H}x{W} -> {F_prime}x{H_prime}x{W_prime} -> {decoded_F}x{H}x{W}")
+ print(f" Compression ratios: temporal={F_prime/F:.3f}, spatial={H_prime*W_prime/(H*W):.3f}")
+ return True
+
+ except Exception as e:
+ print(f"\n✗ CosmosVideoTokenizer shape test failed: {e}")
+ import traceback
+ traceback.print_exc()
+ return False
+
+
+def test_pipeline_forward_latent_only(pipe, device):
+ """
+ Test pipeline forward pass with latent-only output (no decoding).
+
+ This test verifies:
+ - Pipeline initialization and forward pass
+ - Shape consistency through the denoising loop
+ - Latent output format [B, F', H', W']
+ - Token value ranges [0, codebook_size]
+ """
+ print("\n" + "=" * 80)
+ print("[Test] Pipeline forward pass (latent-only output)")
+ print("=" * 80)
+
+ try:
+ prompt = ["a test prompt"]
+ num_frames = 8
+ height = 256
+ width = 448
+ num_inference_steps = 2
+
+ print(f"Test parameters:")
+ print(f" prompt: {prompt}")
+ print(f" num_frames: {num_frames}")
+ print(f" height: {height}, width: {width}")
+ print(f" num_inference_steps: {num_inference_steps}")
+ print(f" output_type: 'latent'")
+
+ # Run pipeline with latent output
+ result = pipe(
+ prompt=prompt,
+ num_frames=num_frames,
+ height=height,
+ width=width,
+ num_inference_steps=num_inference_steps,
+ output_type="latent",
+ return_dict=True,
+ )
+
+ # Verify output shape and type
+ output = result.videos
+ assert isinstance(output, torch.Tensor), f"Expected torch.Tensor, got {type(output)}"
+ assert output.dtype in (torch.long, torch.int64), \
+ f"Expected dtype torch.long or torch.int64, got {output.dtype}"
+
+ # Calculate expected compressed dimensions
+ F_prime = num_frames // pipe.video_tokenizer.t_downsample
+ H_prime = height // pipe.video_tokenizer.h_downsample
+ W_prime = width // pipe.video_tokenizer.w_downsample
+
+ expected_shape = (1, F_prime, H_prime, W_prime)
+ assert output.shape == expected_shape, \
+ f"Expected output shape {expected_shape}, got {output.shape}"
+
+ print(f"\n✓ Output shape verified: {output.shape} [B, F', H', W']")
+
+ # Check token value ranges
+ min_val = output.min().item()
+ max_val = output.max().item()
+ codebook_size = pipe.video_tokenizer.codebook_size
+
+ print(f"Token value range: [{min_val}, {max_val}]")
+ print(f"Codebook size: {codebook_size}")
+
+ # Tokens should be in [0, codebook_size] (codebook_size is mask_token_id)
+ assert min_val >= 0, f"Token values should be >= 0, got min={min_val}"
+ assert max_val <= codebook_size, \
+ f"Token values should be <= {codebook_size}, got max={max_val}"
+
+ # Print sample tokens
+ sample_tokens = output[0, 0, :5, :5].cpu().numpy()
+ print(f"\nSample tokens (first 5x5 of first frame):")
+ print(sample_tokens)
+
+ print(f"\n✓ All latent-only tests passed!")
+ return True
+
+ except Exception as e:
+ print(f"\n✗ Pipeline forward test failed: {e}")
+ import traceback
+ traceback.print_exc()
+ return False
+
+
+# if __name__ == "__main__":
+# """
+# Comprehensive test for the video diffusion pipeline.
+
+# Test sequence:
+# 1. test_cosmos_tokenizer_shapes() - Verify CosmosVideoTokenizer encode/decode
+# 2. Build all pipeline components
+# 3. test_pipeline_forward_latent_only() - Test pipeline forward pass with latent output
+# 4. Full pipeline test with PIL output (optional, after latent test passes)
+# """
+# import torch
+
+# # Set device and dtype
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# dtype = torch.float32
+
+# print("=" * 80)
+# print("Testing Video Diffusion Pipeline")
+# print("=" * 80)
+
+# # Step 1: Test CosmosVideoTokenizer shapes
+# if not test_cosmos_tokenizer_shapes():
+# print("\n✗ CosmosVideoTokenizer test failed. Exiting.")
+# exit(1)
+
+# # Step 2: Build pipeline components
+# print("\n" + "=" * 80)
+# print("Building Pipeline Components")
+# print("=" * 80)
+
+# try:
+# model_id = "Cosmos-1.0-Tokenizer-DV8x16x16"
+# print(f"\nLoading video_tokenizer: {model_id}")
+# video_tokenizer = CosmosVideoTokenizer(model_id=model_id, device=device, dtype=dtype)
+# print(f"✓ Video tokenizer loaded")
+
+# print(f"\nLoading T5 tokenizer and encoder...")
+# from transformers import T5Tokenizer, T5EncoderModel
+# tokenizer = T5Tokenizer.from_pretrained('google/umt5-base')
+# text_encoder = T5EncoderModel.from_pretrained('google/umt5-base').to(device, dtype=dtype)
+# print(f"✓ T5 components loaded")
+
+# print(f"\nInitializing Scheduler...")
+# from src.scheduler_video import Scheduler
+# # mask_token_id = codebook_size (outside valid code range [0, codebook_size-1])
+# scheduler = Scheduler(
+# mask_token_id=video_tokenizer.mask_token_id, # = codebook_size
+# masking_schedule="cosine"
+# )
+# print(f"✓ Scheduler initialized")
+# print(f" Scheduler mask_token_id: {scheduler.config.mask_token_id}")
+# print(f" Video tokenizer codebook_size: {video_tokenizer.codebook_size}")
+
+# print(f"\nLoading transformer...")
+# from src.transformer_video import WanDiscreteVideoTransformer
+
+# # Calculate compressed dimensions for transformer
+# num_frames = 8
+# height = 256
+# width = 448
+# F_prime = num_frames // video_tokenizer.t_downsample
+# H_prime = height // video_tokenizer.h_downsample
+# W_prime = width // video_tokenizer.w_downsample
+
+# # Get actual text encoder output dimension
+# # UMT5-base outputs 768 dimensions, not 4096
+# text_dim_actual = text_encoder.config.d_model
+# print(f" Text encoder output dimension: {text_dim_actual}")
+
+# transformer = WanDiscreteVideoTransformer(
+# codebook_size=video_tokenizer.codebook_size,
+# vocab_size=video_tokenizer.codebook_size + 1,
+# num_frames=F_prime,
+# height=H_prime,
+# width=W_prime,
+# model_type='t2v',
+# patch_size=(1, 2, 2),
+# text_len=512,
+# in_dim=16,
+# dim=2048,
+# ffn_dim=8192,
+# freq_dim=256,
+# text_dim=text_dim_actual, # Use actual text encoder dimension (768 for UMT5-base)
+# out_dim=16,
+# num_heads=16,
+# num_layers=32,
+# window_size=(-1, -1),
+# qk_norm=True,
+# cross_attn_norm=True,
+# eps=1e-6
+# ).to(device, dtype=dtype)
+# print(f"✓ Transformer initialized")
+
+# print(f"\nInitializing Pipeline...")
+# pipe = Pipeline(
+# tokenizer=tokenizer,
+# text_encoder=text_encoder,
+# transformer=transformer,
+# scheduler=scheduler,
+# video_tokenizer=video_tokenizer,
+# text_len=512,
+# num_frames=num_frames,
+# height=height,
+# width=width,
+# ).to(device)
+# print(f"✓ Pipeline initialized")
+
+# except Exception as e:
+# print(f"\n✗ Failed to build pipeline components: {e}")
+# import traceback
+# traceback.print_exc()
+# exit(1)
+
+# # Step 3: Test pipeline forward pass with latent-only output
+# if not test_pipeline_forward_latent_only(pipe, device):
+# print("\n✗ Pipeline forward test failed. Exiting.")
+# exit(1)
+
+# # Step 4: Optional - Full pipeline test with PIL output
+# # (Uncomment when latent-only test passes)
+# # print("\n" + "=" * 80)
+# # print("[Test] Full pipeline test with PIL output")
+# # print("=" * 80)
+# # try:
+# # result = pipe(
+# # prompt="a test video",
+# # num_frames=num_frames,
+# # height=height,
+# # width=width,
+# # num_inference_steps=2,
+# # output_type="pil",
+# # return_dict=True,
+# # )
+# # print(f"✓ Full pipeline test passed!")
+# # except Exception as e:
+# # print(f"✗ Full pipeline test failed: {e}")
+# # import traceback
+# # traceback.print_exc()
+
+# print("\n" + "=" * 80)
+# print("All tests passed successfully!")
+# print("=" * 80)
\ No newline at end of file
diff --git a/Meissonic/src/scheduler.py b/Meissonic/src/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..3d2fe4276351ffb0ec5883c59c4985a5ca9bd859
--- /dev/null
+++ b/Meissonic/src/scheduler.py
@@ -0,0 +1,175 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import BaseOutput
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+
+def gumbel_noise(t, generator=None):
+ device = generator.device if generator is not None else t.device
+ noise = torch.zeros_like(t, device=device).uniform_(0, 1, generator=generator).to(t.device)
+ return -torch.log((-torch.log(noise.clamp(1e-20))).clamp(1e-20))
+
+
+def mask_by_random_topk(mask_len, probs, temperature=1.0, generator=None):
+ confidence = torch.log(probs.clamp(1e-20)) + temperature * gumbel_noise(probs, generator=generator)
+ sorted_confidence = torch.sort(confidence, dim=-1).values
+ cut_off = torch.gather(sorted_confidence, 1, mask_len.long())
+ masking = confidence < cut_off
+ return masking
+
+
+@dataclass
+class SchedulerOutput(BaseOutput):
+ """
+ Output class for the scheduler's `step` function output.
+
+ Args:
+ prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
+ Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+ denoising loop.
+ pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
+ The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+ `pred_original_sample` can be used to preview progress or for guidance.
+ """
+
+ prev_sample: torch.Tensor
+ pred_original_sample: torch.Tensor = None
+
+
+class Scheduler(SchedulerMixin, ConfigMixin):
+ order = 1
+
+ temperatures: torch.Tensor
+
+ @register_to_config
+ def __init__(
+ self,
+ mask_token_id: int,
+ masking_schedule: str = "cosine",
+ ):
+ self.temperatures = None
+ self.timesteps = None
+
+ def set_timesteps(
+ self,
+ num_inference_steps: int,
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ device: Union[str, torch.device] = None,
+ ):
+ self.timesteps = torch.arange(num_inference_steps, device=device).flip(0)
+
+ if isinstance(temperature, (tuple, list)):
+ self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=device)
+ else:
+ self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=device)
+
+ def step(
+ self,
+ model_output: torch.Tensor,
+ timestep: torch.long,
+ sample: torch.LongTensor,
+ starting_mask_ratio: int = 1,
+ generator: Optional[torch.Generator] = None,
+ return_dict: bool = True,
+ ) -> Union[SchedulerOutput, Tuple]:
+ two_dim_input = sample.ndim == 3 and model_output.ndim == 4
+
+ if two_dim_input:
+ batch_size, codebook_size, height, width = model_output.shape
+ sample = sample.reshape(batch_size, height * width)
+ model_output = model_output.reshape(batch_size, codebook_size, height * width).permute(0, 2, 1)
+
+ unknown_map = sample == self.config.mask_token_id
+
+ probs = model_output.softmax(dim=-1)
+
+ device = probs.device
+ probs_ = probs.to(generator.device) if generator is not None else probs # handles when generator is on CPU
+ if probs_.device.type == "cpu" and probs_.dtype != torch.float32:
+ probs_ = probs_.float() # multinomial is not implemented for cpu half precision
+ probs_ = probs_.reshape(-1, probs.size(-1))
+ pred_original_sample = torch.multinomial(probs_, 1, generator=generator).to(device=device)
+ pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])
+ pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)
+
+ if timestep == 0:
+ prev_sample = pred_original_sample
+ else:
+ seq_len = sample.shape[1]
+ step_idx = (self.timesteps == timestep).nonzero()
+ ratio = (step_idx + 1) / len(self.timesteps)
+
+ if self.config.masking_schedule == "cosine":
+ mask_ratio = torch.cos(ratio * math.pi / 2)
+ elif self.config.masking_schedule == "linear":
+ mask_ratio = 1 - ratio
+ else:
+ raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
+
+ mask_ratio = starting_mask_ratio * mask_ratio
+
+ mask_len = (seq_len * mask_ratio).floor()
+ # do not mask more than amount previously masked
+ mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)
+ # mask at least one
+ mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)
+
+ selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]
+ # Ignores the tokens given in the input by overwriting their confidence.
+ selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max)
+
+ masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx], generator)
+
+ # Masks tokens with lower confidence.
+ prev_sample = torch.where(masking, self.config.mask_token_id, pred_original_sample)
+
+ if two_dim_input:
+ prev_sample = prev_sample.reshape(batch_size, height, width)
+ pred_original_sample = pred_original_sample.reshape(batch_size, height, width)
+
+ if not return_dict:
+ return (prev_sample, pred_original_sample)
+
+ return SchedulerOutput(prev_sample, pred_original_sample)
+
+ def add_noise(self, sample, timesteps, generator=None):
+ step_idx = (self.timesteps == timesteps).nonzero()
+ ratio = (step_idx + 1) / len(self.timesteps)
+
+ if self.config.masking_schedule == "cosine":
+ mask_ratio = torch.cos(ratio * math.pi / 2)
+ elif self.config.masking_schedule == "linear":
+ mask_ratio = 1 - ratio
+ else:
+ raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
+
+ mask_indices = (
+ torch.rand(
+ sample.shape, device=generator.device if generator is not None else sample.device, generator=generator
+ ).to(sample.device)
+ < mask_ratio
+ )
+
+ masked_sample = sample.clone()
+
+ masked_sample[mask_indices] = self.config.mask_token_id
+
+ return masked_sample
diff --git a/Meissonic/src/scheduler_video.py b/Meissonic/src/scheduler_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3e02c7d9fe23349b1c1deca7d85018a9467713f
--- /dev/null
+++ b/Meissonic/src/scheduler_video.py
@@ -0,0 +1,188 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import BaseOutput
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
+
+
+def gumbel_noise(t, generator=None):
+ device = generator.device if generator is not None else t.device
+ noise = torch.zeros_like(t, device=device).uniform_(0, 1, generator=generator).to(t.device)
+ return -torch.log((-torch.log(noise.clamp(1e-20))).clamp(1e-20))
+
+
+def mask_by_random_topk(mask_len, probs, temperature=1.0, generator=None):
+ confidence = torch.log(probs.clamp(1e-20)) + temperature * gumbel_noise(probs, generator=generator)
+ sorted_confidence = torch.sort(confidence, dim=-1).values
+ cut_off = torch.gather(sorted_confidence, 1, mask_len.long())
+ masking = confidence < cut_off
+ return masking
+
+
+@dataclass
+class SchedulerOutput(BaseOutput):
+ """
+ Output class for the scheduler's `step` function output.
+
+ Args:
+ prev_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
+ Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+ denoising loop.
+ pred_original_sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images):
+ The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+ `pred_original_sample` can be used to preview progress or for guidance.
+ """
+
+ prev_sample: torch.Tensor
+ pred_original_sample: torch.Tensor = None
+
+
+class Scheduler(SchedulerMixin, ConfigMixin):
+ order = 1
+
+ temperatures: torch.Tensor
+
+ @register_to_config
+ def __init__(
+ self,
+ mask_token_id: int,
+ masking_schedule: str = "cosine",
+ ):
+ self.temperatures = None
+ self.timesteps = None
+
+ def set_timesteps(
+ self,
+ num_inference_steps: int,
+ temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
+ device: Union[str, torch.device] = None,
+ ):
+ self.timesteps = torch.arange(num_inference_steps, device=device).flip(0)
+
+ if isinstance(temperature, (tuple, list)):
+ self.temperatures = torch.linspace(temperature[0], temperature[1], num_inference_steps, device=device)
+ else:
+ self.temperatures = torch.linspace(temperature, 0.01, num_inference_steps, device=device)
+
+ def step(
+ self,
+ model_output: torch.Tensor,
+ timestep: torch.long,
+ sample: torch.LongTensor,
+ starting_mask_ratio: int = 1,
+ generator: Optional[torch.Generator] = None,
+ return_dict: bool = True,
+ ) -> Union[SchedulerOutput, Tuple]:
+ # Handle different input shapes: 1D, 2D (image), or 3D (video)
+ # All are flattened to 1D token sequences [B, N] for unified processing
+ two_dim_input = sample.ndim == 3 and model_output.ndim == 4 # [B, H, W] & [B, vocab, H, W]
+ three_dim_input = sample.ndim == 4 and model_output.ndim == 5 # [B, F, H, W] & [B, vocab, F, H, W]
+
+ if two_dim_input:
+ # Image case: [B, H, W] -> [B, H*W]
+ batch_size, vocab_size, height, width = model_output.shape
+ sample = sample.reshape(batch_size, height * width)
+ model_output = model_output.reshape(batch_size, vocab_size, height * width).permute(0, 2, 1)
+ elif three_dim_input:
+ # Video case: [B, F, H, W] -> [B, F*H*W]
+ batch_size, vocab_size, num_frames, height, width = model_output.shape
+ sample = sample.reshape(batch_size, num_frames * height * width)
+ model_output = model_output.reshape(batch_size, vocab_size, num_frames * height * width).permute(0, 2, 1)
+
+ unknown_map = sample == self.config.mask_token_id
+
+ probs = model_output.softmax(dim=-1)
+
+ device = probs.device
+ probs_ = probs.to(generator.device) if generator is not None else probs # handles when generator is on CPU
+ if probs_.device.type == "cpu" and probs_.dtype != torch.float32:
+ probs_ = probs_.float() # multinomial is not implemented for cpu half precision
+ probs_ = probs_.reshape(-1, probs.size(-1))
+ pred_original_sample = torch.multinomial(probs_, 1, generator=generator).to(device=device)
+ pred_original_sample = pred_original_sample[:, 0].view(*probs.shape[:-1])
+ pred_original_sample = torch.where(unknown_map, pred_original_sample, sample)
+
+ if timestep == 0:
+ prev_sample = pred_original_sample
+ else:
+ seq_len = sample.shape[1]
+ step_idx = (self.timesteps == timestep).nonzero()
+ ratio = (step_idx + 1) / len(self.timesteps)
+
+ if self.config.masking_schedule == "cosine":
+ mask_ratio = torch.cos(ratio * math.pi / 2)
+ elif self.config.masking_schedule == "linear":
+ mask_ratio = 1 - ratio
+ else:
+ raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
+
+ mask_ratio = starting_mask_ratio * mask_ratio
+
+ mask_len = (seq_len * mask_ratio).floor()
+ # do not mask more than amount previously masked
+ mask_len = torch.min(unknown_map.sum(dim=-1, keepdim=True) - 1, mask_len)
+ # mask at least one
+ mask_len = torch.max(torch.tensor([1], device=model_output.device), mask_len)
+
+ selected_probs = torch.gather(probs, -1, pred_original_sample[:, :, None])[:, :, 0]
+ # Ignores the tokens given in the input by overwriting their confidence.
+ selected_probs = torch.where(unknown_map, selected_probs, torch.finfo(selected_probs.dtype).max)
+
+ masking = mask_by_random_topk(mask_len, selected_probs, self.temperatures[step_idx], generator)
+
+ # Masks tokens with lower confidence.
+ prev_sample = torch.where(masking, self.config.mask_token_id, pred_original_sample)
+
+ # Reshape back to original dimensions
+ if two_dim_input:
+ prev_sample = prev_sample.reshape(batch_size, height, width)
+ pred_original_sample = pred_original_sample.reshape(batch_size, height, width)
+ elif three_dim_input:
+ prev_sample = prev_sample.reshape(batch_size, num_frames, height, width)
+ pred_original_sample = pred_original_sample.reshape(batch_size, num_frames, height, width)
+
+ if not return_dict:
+ return (prev_sample, pred_original_sample)
+
+ return SchedulerOutput(prev_sample, pred_original_sample)
+
+ def add_noise(self, sample, timesteps, generator=None):
+ step_idx = (self.timesteps == timesteps).nonzero()
+ ratio = (step_idx + 1) / len(self.timesteps)
+
+ if self.config.masking_schedule == "cosine":
+ mask_ratio = torch.cos(ratio * math.pi / 2)
+ elif self.config.masking_schedule == "linear":
+ mask_ratio = 1 - ratio
+ else:
+ raise ValueError(f"unknown masking schedule {self.config.masking_schedule}")
+
+ mask_indices = (
+ torch.rand(
+ sample.shape, device=generator.device if generator is not None else sample.device, generator=generator
+ ).to(sample.device)
+ < mask_ratio
+ )
+
+ masked_sample = sample.clone()
+
+ masked_sample[mask_indices] = self.config.mask_token_id
+
+ return masked_sample
diff --git a/Meissonic/src/transformer.py b/Meissonic/src/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbce4e73ae35ea5f3e21084456bef097b3f76c83
--- /dev/null
+++ b/Meissonic/src/transformer.py
@@ -0,0 +1,1116 @@
+# Copyright 2024 Black Forest Labs, The HuggingFace Team, The InstantX Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward, BasicTransformerBlock, SkipFFTransformerBlock
+from diffusers.models.attention_processor import (
+ Attention,
+ AttentionProcessor,
+ FluxAttnProcessor2_0,
+ # FusedFluxAttnProcessor2_0,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle, GlobalResponseNorm, RMSNorm
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings,TimestepEmbedding, get_timestep_embedding #,FluxPosEmbed
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers.models.resnet import Downsample2D, Upsample2D
+
+from typing import List
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+
+
+def get_3d_rotary_pos_embed(
+ embed_dim, crops_coords, grid_size, temporal_size, theta: int = 10000, use_real: bool = True
+) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+ """
+ RoPE for video tokens with 3D structure.
+
+ Args:
+ embed_dim: (`int`):
+ The embedding dimension size, corresponding to hidden_size_head.
+ crops_coords (`Tuple[int]`):
+ The top-left and bottom-right coordinates of the crop.
+ grid_size (`Tuple[int]`):
+ The grid size of the spatial positional embedding (height, width).
+ temporal_size (`int`):
+ The size of the temporal dimension.
+ theta (`float`):
+ Scaling factor for frequency computation.
+ use_real (`bool`):
+ If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+ Returns:
+ `torch.Tensor`: positional embedding with shape `(temporal_size * grid_size[0] * grid_size[1], embed_dim/2)`.
+ """
+ start, stop = crops_coords
+ grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+ grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+ grid_t = np.linspace(0, temporal_size, temporal_size, endpoint=False, dtype=np.float32)
+
+ # Compute dimensions for each axis
+ dim_t = embed_dim // 4
+ dim_h = embed_dim // 8 * 3
+ dim_w = embed_dim // 8 * 3
+
+ # Temporal frequencies
+ freqs_t = 1.0 / (theta ** (torch.arange(0, dim_t, 2).float() / dim_t))
+ grid_t = torch.from_numpy(grid_t).float()
+ freqs_t = torch.einsum("n , f -> n f", grid_t, freqs_t)
+ freqs_t = freqs_t.repeat_interleave(2, dim=-1)
+
+ # Spatial frequencies for height and width
+ freqs_h = 1.0 / (theta ** (torch.arange(0, dim_h, 2).float() / dim_h))
+ freqs_w = 1.0 / (theta ** (torch.arange(0, dim_w, 2).float() / dim_w))
+ grid_h = torch.from_numpy(grid_h).float()
+ grid_w = torch.from_numpy(grid_w).float()
+ freqs_h = torch.einsum("n , f -> n f", grid_h, freqs_h)
+ freqs_w = torch.einsum("n , f -> n f", grid_w, freqs_w)
+ freqs_h = freqs_h.repeat_interleave(2, dim=-1)
+ freqs_w = freqs_w.repeat_interleave(2, dim=-1)
+
+ # Broadcast and concatenate tensors along specified dimension
+ def broadcast(tensors, dim=-1):
+ num_tensors = len(tensors)
+ shape_lens = {len(t.shape) for t in tensors}
+ assert len(shape_lens) == 1, "tensors must all have the same number of dimensions"
+ shape_len = list(shape_lens)[0]
+ dim = (dim + shape_len) if dim < 0 else dim
+ dims = list(zip(*(list(t.shape) for t in tensors)))
+ expandable_dims = [(i, val) for i, val in enumerate(dims) if i != dim]
+ assert all(
+ [*(len(set(t[1])) <= 2 for t in expandable_dims)]
+ ), "invalid dimensions for broadcastable concatenation"
+ max_dims = [(t[0], max(t[1])) for t in expandable_dims]
+ expanded_dims = [(t[0], (t[1],) * num_tensors) for t in max_dims]
+ expanded_dims.insert(dim, (dim, dims[dim]))
+ expandable_shapes = list(zip(*(t[1] for t in expanded_dims)))
+ tensors = [t[0].expand(*t[1]) for t in zip(tensors, expandable_shapes)]
+ return torch.cat(tensors, dim=dim)
+
+ freqs = broadcast((freqs_t[:, None, None, :], freqs_h[None, :, None, :], freqs_w[None, None, :, :]), dim=-1)
+
+ t, h, w, d = freqs.shape
+ freqs = freqs.view(t * h * w, d)
+
+ # Generate sine and cosine components
+ sin = freqs.sin()
+ cos = freqs.cos()
+
+ if use_real:
+ return cos, sin
+ else:
+ freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+ return freqs_cis
+
+
+def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True):
+ """
+ RoPE for image tokens with 2d structure.
+
+ Args:
+ embed_dim: (`int`):
+ The embedding dimension size
+ crops_coords (`Tuple[int]`)
+ The top-left and bottom-right coordinates of the crop.
+ grid_size (`Tuple[int]`):
+ The grid size of the positional embedding.
+ use_real (`bool`):
+ If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+ Returns:
+ `torch.Tensor`: positional embedding with shape `( grid_size * grid_size, embed_dim/2)`.
+ """
+ start, stop = crops_coords
+ grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32)
+ grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32)
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ grid = np.stack(grid, axis=0) # [2, W, H]
+
+ grid = grid.reshape([2, 1, *grid.shape[1:]])
+ pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+ return pos_embed
+
+
+def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
+ assert embed_dim % 4 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_rotary_pos_embed(
+ embed_dim // 2, grid[0].reshape(-1), use_real=use_real
+ ) # (H*W, D/2) if use_real else (H*W, D/4)
+ emb_w = get_1d_rotary_pos_embed(
+ embed_dim // 2, grid[1].reshape(-1), use_real=use_real
+ ) # (H*W, D/2) if use_real else (H*W, D/4)
+
+ if use_real:
+ cos = torch.cat([emb_h[0], emb_w[0]], dim=1) # (H*W, D)
+ sin = torch.cat([emb_h[1], emb_w[1]], dim=1) # (H*W, D)
+ return cos, sin
+ else:
+ emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2)
+ return emb
+
+
+def get_2d_rotary_pos_embed_lumina(embed_dim, len_h, len_w, linear_factor=1.0, ntk_factor=1.0):
+ assert embed_dim % 4 == 0
+
+ emb_h = get_1d_rotary_pos_embed(
+ embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor
+ ) # (H, D/4)
+ emb_w = get_1d_rotary_pos_embed(
+ embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor
+ ) # (W, D/4)
+ emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1) # (H, W, D/4, 1)
+ emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1) # (H, W, D/4, 1)
+
+ emb = torch.cat([emb_h, emb_w], dim=-1).flatten(2) # (H, W, D/2)
+ return emb
+
+
+def get_1d_rotary_pos_embed(
+ dim: int,
+ pos: Union[np.ndarray, int],
+ theta: float = 10000.0,
+ use_real=False,
+ linear_factor=1.0,
+ ntk_factor=1.0,
+ repeat_interleave_real=True,
+ freqs_dtype=torch.float32, # torch.float32 (hunyuan, stable audio), torch.float64 (flux)
+):
+ """
+ Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+ This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the end
+ index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in complex64
+ data type.
+
+ Args:
+ dim (`int`): Dimension of the frequency tensor.
+ pos (`np.ndarray` or `int`): Position indices for the frequency tensor. [S] or scalar
+ theta (`float`, *optional*, defaults to 10000.0):
+ Scaling factor for frequency computation. Defaults to 10000.0.
+ use_real (`bool`, *optional*):
+ If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+ linear_factor (`float`, *optional*, defaults to 1.0):
+ Scaling factor for the context extrapolation. Defaults to 1.0.
+ ntk_factor (`float`, *optional*, defaults to 1.0):
+ Scaling factor for the NTK-Aware RoPE. Defaults to 1.0.
+ repeat_interleave_real (`bool`, *optional*, defaults to `True`):
+ If `True` and `use_real`, real part and imaginary part are each interleaved with themselves to reach `dim`.
+ Otherwise, they are concateanted with themselves.
+ freqs_dtype (`torch.float32` or `torch.float64`, *optional*, defaults to `torch.float32`):
+ the dtype of the frequency tensor.
+ Returns:
+ `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2]
+ """
+ assert dim % 2 == 0
+
+ if isinstance(pos, int):
+ pos = np.arange(pos)
+ theta = theta * ntk_factor
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=freqs_dtype)[: (dim // 2)] / dim)) / linear_factor # [D/2]
+ t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S]
+ freqs = torch.outer(t, freqs) # type: ignore # [S, D/2]
+ if use_real and repeat_interleave_real:
+ freqs_cos = freqs.cos().repeat_interleave(2, dim=1).float() # [S, D]
+ freqs_sin = freqs.sin().repeat_interleave(2, dim=1).float() # [S, D]
+ return freqs_cos, freqs_sin
+ elif use_real:
+ freqs_cos = torch.cat([freqs.cos(), freqs.cos()], dim=-1).float() # [S, D]
+ freqs_sin = torch.cat([freqs.sin(), freqs.sin()], dim=-1).float() # [S, D]
+ return freqs_cos, freqs_sin
+ else:
+ freqs_cis = torch.polar(torch.ones_like(freqs), freqs).float() # complex64 # [S, D/2]
+ return freqs_cis
+
+
+class FluxPosEmbed(nn.Module):
+ # modified from https://github.com/black-forest-labs/flux/blob/c00d7c60b085fce8058b9df845e036090873f2ce/src/flux/modules/layers.py#L11
+ def __init__(self, theta: int, axes_dim: List[int]):
+ super().__init__()
+ self.theta = theta
+ self.axes_dim = axes_dim
+
+ def forward(self, ids: torch.Tensor) -> torch.Tensor:
+ n_axes = ids.shape[-1]
+ cos_out = []
+ sin_out = []
+ pos = ids.squeeze().float().cpu().numpy()
+ is_mps = ids.device.type == "mps"
+ freqs_dtype = torch.float32 if is_mps else torch.float64
+ for i in range(n_axes):
+ cos, sin = get_1d_rotary_pos_embed(
+ self.axes_dim[i], pos[:, i], repeat_interleave_real=True, use_real=True, freqs_dtype=freqs_dtype
+ )
+ cos_out.append(cos)
+ sin_out.append(sin)
+ freqs_cos = torch.cat(cos_out, dim=-1).to(ids.device)
+ freqs_sin = torch.cat(sin_out, dim=-1).to(ids.device)
+ return freqs_cos, freqs_sin
+
+
+
+class FusedFluxAttnProcessor2_0:
+ """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+ def __init__(self):
+ if not hasattr(F, "scaled_dot_product_attention"):
+ raise ImportError(
+ "FusedFluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+ )
+
+ def __call__(
+ self,
+ attn: Attention,
+ hidden_states: torch.FloatTensor,
+ encoder_hidden_states: torch.FloatTensor = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ image_rotary_emb: Optional[torch.Tensor] = None,
+ ) -> torch.FloatTensor:
+ batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+ # `sample` projections.
+ qkv = attn.to_qkv(hidden_states)
+ split_size = qkv.shape[-1] // 3
+ query, key, value = torch.split(qkv, split_size, dim=-1)
+
+ inner_dim = key.shape[-1]
+ head_dim = inner_dim // attn.heads
+
+ query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+ key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+ value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+ if attn.norm_q is not None:
+ query = attn.norm_q(query)
+ if attn.norm_k is not None:
+ key = attn.norm_k(key)
+
+ # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+ # `context` projections.
+ if encoder_hidden_states is not None:
+ encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
+ split_size = encoder_qkv.shape[-1] // 3
+ (
+ encoder_hidden_states_query_proj,
+ encoder_hidden_states_key_proj,
+ encoder_hidden_states_value_proj,
+ ) = torch.split(encoder_qkv, split_size, dim=-1)
+
+ encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+ batch_size, -1, attn.heads, head_dim
+ ).transpose(1, 2)
+ encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+ batch_size, -1, attn.heads, head_dim
+ ).transpose(1, 2)
+ encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+ batch_size, -1, attn.heads, head_dim
+ ).transpose(1, 2)
+
+ if attn.norm_added_q is not None:
+ encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+ if attn.norm_added_k is not None:
+ encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+ # attention
+ query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+ key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+ value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+ if image_rotary_emb is not None:
+ from diffusers.models.embeddings import apply_rotary_emb
+
+ query = apply_rotary_emb(query, image_rotary_emb)
+ key = apply_rotary_emb(key, image_rotary_emb)
+
+ hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+ hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+ hidden_states = hidden_states.to(query.dtype)
+
+ if encoder_hidden_states is not None:
+ encoder_hidden_states, hidden_states = (
+ hidden_states[:, : encoder_hidden_states.shape[1]],
+ hidden_states[:, encoder_hidden_states.shape[1] :],
+ )
+
+ # linear proj
+ hidden_states = attn.to_out[0](hidden_states)
+ # dropout
+ hidden_states = attn.to_out[1](hidden_states)
+ encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+ return hidden_states, encoder_hidden_states
+ else:
+ return hidden_states
+
+
+
+@maybe_allow_in_graph
+class SingleTransformerBlock(nn.Module):
+ r"""
+ A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+ Reference: https://arxiv.org/abs/2403.03206
+
+ Parameters:
+ dim (`int`): The number of channels in the input and output.
+ num_attention_heads (`int`): The number of heads to use for multi-head attention.
+ attention_head_dim (`int`): The number of channels in each head.
+ context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+ processing of `context` conditions.
+ """
+
+ def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
+ super().__init__()
+ self.mlp_hidden_dim = int(dim * mlp_ratio)
+
+ self.norm = AdaLayerNormZeroSingle(dim)
+ self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
+ self.act_mlp = nn.GELU(approximate="tanh")
+ self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
+
+ processor = FluxAttnProcessor2_0()
+ self.attn = Attention(
+ query_dim=dim,
+ cross_attention_dim=None,
+ dim_head=attention_head_dim,
+ heads=num_attention_heads,
+ out_dim=dim,
+ bias=True,
+ processor=processor,
+ qk_norm="rms_norm",
+ eps=1e-6,
+ pre_only=True,
+ )
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ temb: torch.FloatTensor,
+ image_rotary_emb=None,
+ ):
+ residual = hidden_states
+ norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+ mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+ attn_output = self.attn(
+ hidden_states=norm_hidden_states,
+ image_rotary_emb=image_rotary_emb,
+ )
+
+ hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+ gate = gate.unsqueeze(1)
+ hidden_states = gate * self.proj_out(hidden_states)
+ hidden_states = residual + hidden_states
+ if hidden_states.dtype == torch.float16:
+ hidden_states = hidden_states.clip(-65504, 65504)
+
+ return hidden_states
+
+@maybe_allow_in_graph
+class TransformerBlock(nn.Module):
+ r"""
+ A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+ Reference: https://arxiv.org/abs/2403.03206
+
+ Parameters:
+ dim (`int`): The number of channels in the input and output.
+ num_attention_heads (`int`): The number of heads to use for multi-head attention.
+ attention_head_dim (`int`): The number of channels in each head.
+ context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+ processing of `context` conditions.
+ """
+
+ def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6):
+ super().__init__()
+
+ self.norm1 = AdaLayerNormZero(dim)
+
+ self.norm1_context = AdaLayerNormZero(dim)
+
+ if hasattr(F, "scaled_dot_product_attention"):
+ processor = FluxAttnProcessor2_0()
+ else:
+ raise ValueError(
+ "The current PyTorch version does not support the `scaled_dot_product_attention` function."
+ )
+ self.attn = Attention(
+ query_dim=dim,
+ cross_attention_dim=None,
+ added_kv_proj_dim=dim,
+ dim_head=attention_head_dim,
+ heads=num_attention_heads,
+ out_dim=dim,
+ context_pre_only=False,
+ bias=True,
+ processor=processor,
+ qk_norm=qk_norm,
+ eps=eps,
+ )
+
+ self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+ self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+ # self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="swiglu")
+
+ self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+ self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+ # self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="swiglu")
+
+ # let chunk size default to None
+ self._chunk_size = None
+ self._chunk_dim = 0
+
+ def forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ encoder_hidden_states: torch.FloatTensor,
+ temb: torch.FloatTensor,
+ image_rotary_emb=None,
+ ):
+ norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
+
+ norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
+ encoder_hidden_states, emb=temb
+ )
+ # Attention.
+ attn_output, context_attn_output = self.attn(
+ hidden_states=norm_hidden_states,
+ encoder_hidden_states=norm_encoder_hidden_states,
+ image_rotary_emb=image_rotary_emb,
+ )
+
+ # Process attention outputs for the `hidden_states`.
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ hidden_states = hidden_states + attn_output
+
+ norm_hidden_states = self.norm2(hidden_states)
+ norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+ ff_output = self.ff(norm_hidden_states)
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+ hidden_states = hidden_states + ff_output
+
+ # Process attention outputs for the `encoder_hidden_states`.
+
+ context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
+ encoder_hidden_states = encoder_hidden_states + context_attn_output
+
+ norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+ norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+
+ context_ff_output = self.ff_context(norm_encoder_hidden_states)
+ encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
+ if encoder_hidden_states.dtype == torch.float16:
+ encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
+
+ return encoder_hidden_states, hidden_states
+
+
+class UVit2DConvEmbed(nn.Module):
+ def __init__(self, in_channels, block_out_channels, vocab_size, elementwise_affine, eps, bias):
+ super().__init__()
+ self.embeddings = nn.Embedding(vocab_size, in_channels)
+ self.layer_norm = RMSNorm(in_channels, eps, elementwise_affine)
+ self.conv = nn.Conv2d(in_channels, block_out_channels, kernel_size=1, bias=bias)
+
+ def forward(self, input_ids):
+ embeddings = self.embeddings(input_ids)
+ embeddings = self.layer_norm(embeddings)
+ embeddings = embeddings.permute(0, 3, 1, 2)
+ embeddings = self.conv(embeddings)
+ return embeddings
+
+class ConvMlmLayer(nn.Module):
+ def __init__(
+ self,
+ block_out_channels: int,
+ in_channels: int,
+ use_bias: bool,
+ ln_elementwise_affine: bool,
+ layer_norm_eps: float,
+ codebook_size: int,
+ ):
+ super().__init__()
+ self.conv1 = nn.Conv2d(block_out_channels, in_channels, kernel_size=1, bias=use_bias)
+ self.layer_norm = RMSNorm(in_channels, layer_norm_eps, ln_elementwise_affine)
+ self.conv2 = nn.Conv2d(in_channels, codebook_size, kernel_size=1, bias=use_bias)
+
+ def forward(self, hidden_states):
+ hidden_states = self.conv1(hidden_states)
+ hidden_states = self.layer_norm(hidden_states.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+ logits = self.conv2(hidden_states)
+ return logits
+
+class SwiGLU(nn.Module):
+ r"""
+ A [variant](https://arxiv.org/abs/2002.05202) of the gated linear unit activation function. It's similar to `GEGLU`
+ but uses SiLU / Swish instead of GeLU.
+
+ Parameters:
+ dim_in (`int`): The number of channels in the input.
+ dim_out (`int`): The number of channels in the output.
+ bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
+ """
+
+ def __init__(self, dim_in: int, dim_out: int, bias: bool = True):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2, bias=bias)
+ self.activation = nn.SiLU()
+
+ def forward(self, hidden_states):
+ hidden_states = self.proj(hidden_states)
+ hidden_states, gate = hidden_states.chunk(2, dim=-1)
+ return hidden_states * self.activation(gate)
+
+class ConvNextBlock(nn.Module):
+ def __init__(
+ self, channels, layer_norm_eps, ln_elementwise_affine, use_bias, hidden_dropout, hidden_size, res_ffn_factor=4
+ ):
+ super().__init__()
+ self.depthwise = nn.Conv2d(
+ channels,
+ channels,
+ kernel_size=3,
+ padding=1,
+ groups=channels,
+ bias=use_bias,
+ )
+ self.norm = RMSNorm(channels, layer_norm_eps, ln_elementwise_affine)
+ self.channelwise_linear_1 = nn.Linear(channels, int(channels * res_ffn_factor), bias=use_bias)
+ self.channelwise_act = nn.GELU()
+ self.channelwise_norm = GlobalResponseNorm(int(channels * res_ffn_factor))
+ self.channelwise_linear_2 = nn.Linear(int(channels * res_ffn_factor), channels, bias=use_bias)
+ self.channelwise_dropout = nn.Dropout(hidden_dropout)
+ self.cond_embeds_mapper = nn.Linear(hidden_size, channels * 2, use_bias)
+
+ def forward(self, x, cond_embeds):
+ x_res = x
+
+ x = self.depthwise(x)
+
+ x = x.permute(0, 2, 3, 1)
+ x = self.norm(x)
+
+ x = self.channelwise_linear_1(x)
+ x = self.channelwise_act(x)
+ x = self.channelwise_norm(x)
+ x = self.channelwise_linear_2(x)
+ x = self.channelwise_dropout(x)
+
+ x = x.permute(0, 3, 1, 2)
+
+ x = x + x_res
+
+ scale, shift = self.cond_embeds_mapper(F.silu(cond_embeds)).chunk(2, dim=1)
+ x = x * (1 + scale[:, :, None, None]) + shift[:, :, None, None]
+
+ return x
+
+class Simple_UVitBlock(nn.Module):
+ def __init__(
+ self,
+ channels,
+ ln_elementwise_affine,
+ layer_norm_eps,
+ use_bias,
+ downsample: bool,
+ upsample: bool,
+ ):
+ super().__init__()
+
+ if downsample:
+ self.downsample = Downsample2D(
+ channels,
+ use_conv=True,
+ padding=0,
+ name="Conv2d_0",
+ kernel_size=2,
+ norm_type="rms_norm",
+ eps=layer_norm_eps,
+ elementwise_affine=ln_elementwise_affine,
+ bias=use_bias,
+ )
+ else:
+ self.downsample = None
+
+ if upsample:
+ self.upsample = Upsample2D(
+ channels,
+ use_conv_transpose=True,
+ kernel_size=2,
+ padding=0,
+ name="conv",
+ norm_type="rms_norm",
+ eps=layer_norm_eps,
+ elementwise_affine=ln_elementwise_affine,
+ bias=use_bias,
+ interpolate=False,
+ )
+ else:
+ self.upsample = None
+
+ def forward(self, x):
+ # print("before,", x.shape)
+ if self.downsample is not None:
+ # print('downsample')
+ x = self.downsample(x)
+
+ if self.upsample is not None:
+ # print('upsample')
+ x = self.upsample(x)
+ # print("after,", x.shape)
+ return x
+
+class Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+ """
+ The Transformer model introduced in Flux.
+
+ Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+ Parameters:
+ patch_size (`int`): Patch size to turn the input data into small patches.
+ in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
+ num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
+ num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
+ attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
+ num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
+ joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+ pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
+ guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+ """
+
+ _supports_gradient_checkpointing = False #True
+ # Due to NotImplementedError: DDPOptimizer backend: Found a higher order op in the graph. This is not supported. Please turn off DDP optimizer using torch._dynamo.config.optimize_ddp=False. Note that this can cause performance degradation because there will be one bucket for the entire Dynamo graph.
+ # Please refer to this issue - https://github.com/pytorch/pytorch/issues/104674.
+ _no_split_modules = ["TransformerBlock", "SingleTransformerBlock"]
+
+ @register_to_config
+ def __init__(
+ self,
+ patch_size: int = 1,
+ in_channels: int = 64,
+ num_layers: int = 19,
+ num_single_layers: int = 38,
+ attention_head_dim: int = 128,
+ num_attention_heads: int = 24,
+ joint_attention_dim: int = 4096,
+ pooled_projection_dim: int = 768,
+ guidance_embeds: bool = False, # unused in our implementation
+ axes_dims_rope: Tuple[int] = (16, 56, 56),
+ vocab_size: int = 8256,
+ codebook_size: int = 8192,
+ downsample: bool = False,
+ upsample: bool = False,
+ ):
+ super().__init__()
+ self.out_channels = in_channels
+ self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+
+ self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+ text_time_guidance_cls = (
+ CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+ )
+ self.time_text_embed = text_time_guidance_cls(
+ embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
+ )
+
+ self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [
+ TransformerBlock(
+ dim=self.inner_dim,
+ num_attention_heads=self.config.num_attention_heads,
+ attention_head_dim=self.config.attention_head_dim,
+ )
+ for i in range(self.config.num_layers)
+ ]
+ )
+
+ self.single_transformer_blocks = nn.ModuleList(
+ [
+ SingleTransformerBlock(
+ dim=self.inner_dim,
+ num_attention_heads=self.config.num_attention_heads,
+ attention_head_dim=self.config.attention_head_dim,
+ )
+ for i in range(self.config.num_single_layers)
+ ]
+ )
+
+
+ self.gradient_checkpointing = False
+
+ in_channels_embed = self.inner_dim
+ ln_elementwise_affine = True
+ layer_norm_eps = 1e-06
+ use_bias = False
+ micro_cond_embed_dim = 1280
+ self.embed = UVit2DConvEmbed(
+ in_channels_embed, self.inner_dim, self.config.vocab_size, ln_elementwise_affine, layer_norm_eps, use_bias
+ )
+ self.mlm_layer = ConvMlmLayer(
+ self.inner_dim, in_channels_embed, use_bias, ln_elementwise_affine, layer_norm_eps, self.config.codebook_size
+ )
+ self.cond_embed = TimestepEmbedding(
+ micro_cond_embed_dim + self.config.pooled_projection_dim, self.inner_dim, sample_proj_bias=use_bias
+ )
+ self.encoder_proj_layer_norm = RMSNorm(self.inner_dim, layer_norm_eps, ln_elementwise_affine)
+ self.project_to_hidden_norm = RMSNorm(in_channels_embed, layer_norm_eps, ln_elementwise_affine)
+ self.project_to_hidden = nn.Linear(in_channels_embed, self.inner_dim, bias=use_bias)
+ self.project_from_hidden_norm = RMSNorm(self.inner_dim, layer_norm_eps, ln_elementwise_affine)
+ self.project_from_hidden = nn.Linear(self.inner_dim, in_channels_embed, bias=use_bias)
+
+ self.down_block = Simple_UVitBlock(
+ self.inner_dim,
+ ln_elementwise_affine,
+ layer_norm_eps,
+ use_bias,
+ downsample,
+ False,
+ )
+ self.up_block = Simple_UVitBlock(
+ self.inner_dim, #block_out_channels,
+ ln_elementwise_affine,
+ layer_norm_eps,
+ use_bias,
+ False,
+ upsample=upsample,
+ )
+
+ # self.fuse_qkv_projections()
+
+ @property
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+ def attn_processors(self) -> Dict[str, AttentionProcessor]:
+ r"""
+ Returns:
+ `dict` of attention processors: A dictionary containing all attention processors used in the model with
+ indexed by its weight name.
+ """
+ # set recursively
+ processors = {}
+
+ def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+ if hasattr(module, "get_processor"):
+ processors[f"{name}.processor"] = module.get_processor()
+
+ for sub_name, child in module.named_children():
+ fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+ return processors
+
+ for name, module in self.named_children():
+ fn_recursive_add_processors(name, module, processors)
+
+ return processors
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+ def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+ r"""
+ Sets the attention processor to use to compute attention.
+
+ Parameters:
+ processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+ The instantiated processor class or a dictionary of processor classes that will be set as the processor
+ for **all** `Attention` layers.
+
+ If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+ processor. This is strongly recommended when setting trainable attention processors.
+
+ """
+ count = len(self.attn_processors.keys())
+
+ if isinstance(processor, dict) and len(processor) != count:
+ raise ValueError(
+ f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+ f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+ )
+
+ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+ if hasattr(module, "set_processor"):
+ if not isinstance(processor, dict):
+ module.set_processor(processor)
+ else:
+ module.set_processor(processor.pop(f"{name}.processor"))
+
+ for sub_name, child in module.named_children():
+ fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+ for name, module in self.named_children():
+ fn_recursive_attn_processor(name, module, processor)
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
+ def fuse_qkv_projections(self):
+ """
+ Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
+ are fused. For cross-attention modules, key and value projection matrices are fused.
+
+
+
+ This API is 🧪 experimental.
+
+
+ """
+ self.original_attn_processors = None
+
+ for _, attn_processor in self.attn_processors.items():
+ if "Added" in str(attn_processor.__class__.__name__):
+ raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+ self.original_attn_processors = self.attn_processors
+
+ for module in self.modules():
+ if isinstance(module, Attention):
+ module.fuse_projections(fuse=True)
+
+ self.set_attn_processor(FusedFluxAttnProcessor2_0())
+
+ # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+ def unfuse_qkv_projections(self):
+ """Disables the fused QKV projection if enabled.
+
+
+
+ This API is 🧪 experimental.
+
+
+
+ """
+ if self.original_attn_processors is not None:
+ self.set_attn_processor(self.original_attn_processors)
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if hasattr(module, "gradient_checkpointing"):
+ module.gradient_checkpointing = value
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ encoder_hidden_states: torch.Tensor = None,
+ pooled_projections: torch.Tensor = None,
+ timestep: torch.LongTensor = None,
+ img_ids: torch.Tensor = None,
+ txt_ids: torch.Tensor = None,
+ guidance: torch.Tensor = None,
+ joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+ controlnet_block_samples= None,
+ controlnet_single_block_samples=None,
+ return_dict: bool = True,
+ micro_conds: torch.Tensor = None,
+ ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+ """
+ The [`FluxTransformer2DModel`] forward method.
+
+ Args:
+ hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+ Input `hidden_states`.
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+ Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+ pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+ from the embeddings of input conditions.
+ timestep ( `torch.LongTensor`):
+ Used to indicate denoising step.
+ block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+ A list of tensors that if specified are added to the residuals of transformer blocks.
+ joint_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+ tuple.
+
+ Returns:
+ If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+ `tuple` where the first element is the sample tensor.
+ """
+ micro_cond_encode_dim = 256 # same as self.config.micro_cond_encode_dim = 256 from amused
+ micro_cond_embeds = get_timestep_embedding(
+ micro_conds.flatten(), micro_cond_encode_dim, flip_sin_to_cos=True, downscale_freq_shift=0
+ )
+ micro_cond_embeds = micro_cond_embeds.reshape((hidden_states.shape[0], -1))
+
+ pooled_projections = torch.cat([pooled_projections, micro_cond_embeds], dim=1)
+ pooled_projections = pooled_projections.to(dtype=self.dtype)
+ pooled_projections = self.cond_embed(pooled_projections).to(encoder_hidden_states.dtype)
+
+
+ hidden_states = self.embed(hidden_states)
+
+ encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+ encoder_hidden_states = self.encoder_proj_layer_norm(encoder_hidden_states)
+ hidden_states = self.down_block(hidden_states)
+
+ batch_size, channels, height, width = hidden_states.shape
+ hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels)
+ hidden_states = self.project_to_hidden_norm(hidden_states)
+ hidden_states = self.project_to_hidden(hidden_states)
+
+
+ if joint_attention_kwargs is not None:
+ joint_attention_kwargs = joint_attention_kwargs.copy()
+ lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+ else:
+ lora_scale = 1.0
+
+ if USE_PEFT_BACKEND:
+ # weight the lora layers by setting `lora_scale` for each PEFT layer
+ scale_lora_layers(self, lora_scale)
+ else:
+ if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+ logger.warning(
+ "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+ )
+
+ timestep = timestep.to(hidden_states.dtype) * 1000
+ if guidance is not None:
+ guidance = guidance.to(hidden_states.dtype) * 1000
+ else:
+ guidance = None
+ temb = (
+ self.time_text_embed(timestep, pooled_projections)
+ if guidance is None
+ else self.time_text_embed(timestep, guidance, pooled_projections)
+ )
+
+ if txt_ids.ndim == 3:
+ logger.warning(
+ "Passing `txt_ids` 3d torch.Tensor is deprecated."
+ "Please remove the batch dimension and pass it as a 2d torch Tensor"
+ )
+ txt_ids = txt_ids[0]
+ if img_ids.ndim == 3:
+ logger.warning(
+ "Passing `img_ids` 3d torch.Tensor is deprecated."
+ "Please remove the batch dimension and pass it as a 2d torch Tensor"
+ )
+ img_ids = img_ids[0]
+ ids = torch.cat((txt_ids, img_ids), dim=0)
+
+ image_rotary_emb = self.pos_embed(ids)
+
+ for index_block, block in enumerate(self.transformer_blocks):
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(block),
+ hidden_states,
+ encoder_hidden_states,
+ temb,
+ image_rotary_emb,
+ **ckpt_kwargs,
+ )
+
+ else:
+ encoder_hidden_states, hidden_states = block(
+ hidden_states=hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ temb=temb,
+ image_rotary_emb=image_rotary_emb,
+ )
+
+
+ # controlnet residual
+ if controlnet_block_samples is not None:
+ interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+ interval_control = int(np.ceil(interval_control))
+ hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+ hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+ for index_block, block in enumerate(self.single_transformer_blocks):
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(block),
+ hidden_states,
+ temb,
+ image_rotary_emb,
+ **ckpt_kwargs,
+ )
+
+ else:
+ hidden_states = block(
+ hidden_states=hidden_states,
+ temb=temb,
+ image_rotary_emb=image_rotary_emb,
+ )
+
+ # controlnet residual
+ if controlnet_single_block_samples is not None:
+ interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+ interval_control = int(np.ceil(interval_control))
+ hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+ hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+ + controlnet_single_block_samples[index_block // interval_control]
+ )
+
+ hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+
+ hidden_states = self.project_from_hidden_norm(hidden_states)
+ hidden_states = self.project_from_hidden(hidden_states)
+
+
+ hidden_states = hidden_states.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2)
+
+ hidden_states = self.up_block(hidden_states)
+
+ if USE_PEFT_BACKEND:
+ # remove `lora_scale` from each PEFT layer
+ unscale_lora_layers(self, lora_scale)
+
+ output = self.mlm_layer(hidden_states)
+ # self.unfuse_qkv_projections()
+ if not return_dict:
+ return (output,)
+
+
+ return output
\ No newline at end of file
diff --git a/Meissonic/src/transformer_video.py b/Meissonic/src/transformer_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..d16c9629c95c3f9dca6a949d991c9a973481336d
--- /dev/null
+++ b/Meissonic/src/transformer_video.py
@@ -0,0 +1,1379 @@
+
+import math
+from typing import Optional
+import os
+DISABLE_FLASH_ATTN = os.environ.get("WAN_DISABLE_FLASH_ATTN", "0") == "1"
+
+import torch
+import torch.nn as nn
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+
+# Global debug flag - set to False to disable debug prints
+DEBUG_TRANSFORMER = False
+
+# from .attention import flash_attention
+import torch
+
+try:
+ import flash_attn_interface
+ FLASH_ATTN_3_AVAILABLE = True
+except ModuleNotFoundError:
+ FLASH_ATTN_3_AVAILABLE = False
+
+try:
+ import flash_attn
+ FLASH_ATTN_2_AVAILABLE = True
+except ModuleNotFoundError:
+ FLASH_ATTN_2_AVAILABLE = False
+
+import warnings
+
+__all__ = [
+ 'flash_attention',
+ 'attention',
+]
+
+def _sdpa_attention(
+ q, k, v,
+ q_lens=None,
+ k_lens=None,
+ dropout_p=0.0,
+ softmax_scale=None,
+ q_scale=None,
+ causal=False,
+ dtype=torch.bfloat16,
+):
+ # q/k/v: [B, L, N, D]
+ B, Lq, N, D = q.shape
+ Lk = k.shape[1]
+ out_dtype = q.dtype
+
+ # cast
+ q = q.to(dtype)
+ k = k.to(dtype)
+ v = v.to(dtype)
+
+ # emulate flash-attn softmax_scale override (SDPA默认是 1/sqrt(D))
+ # 若 softmax_scale = 1/sqrt(D)（常见默认），下面乘子会变成 1，不影响
+ if softmax_scale is not None:
+ q = q * (softmax_scale * math.sqrt(D))
+ if q_scale is not None:
+ q = q * q_scale
+
+ # [B, N, L, D]
+ q = q.transpose(1, 2)
+ k = k.transpose(1, 2)
+ v = v.transpose(1, 2)
+
+ attn_mask = None
+
+ # key padding mask: shape [B, 1, 1, Lk] additive -inf
+ if k_lens is not None:
+ # ensure on same device/dtype
+ k_lens = k_lens.to(device=q.device, dtype=torch.long)
+
+ kpos = torch.arange(Lk, device=q.device).view(1, 1, 1, Lk)
+ invalid = kpos >= k_lens.view(B, 1, 1, 1)
+
+ attn_mask = torch.zeros((B, 1, 1, Lk), device=q.device, dtype=q.dtype)
+ attn_mask.masked_fill_(invalid, float("-inf"))
+
+ if q_lens is not None:
+ q_lens = q_lens.to(device=q.device, dtype=torch.long)
+
+ # 目前不额外处理 q_lens（很多实现也不处理，通常 loss 会忽略 pad query）
+ out = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v,
+ attn_mask=attn_mask,
+ is_causal=causal,
+ dropout_p=dropout_p,
+ )
+
+ out = out.transpose(1, 2).contiguous() # [B, Lq, N, D]
+ return out.to(out_dtype)
+def flash_attention(
+ q,
+ k,
+ v,
+ q_lens=None,
+ k_lens=None,
+ dropout_p=0.,
+ softmax_scale=None,
+ q_scale=None,
+ causal=False,
+ window_size=(-1, -1),
+ deterministic=False,
+ dtype=torch.bfloat16,
+ version=None,
+):
+ half_dtypes = (torch.float16, torch.bfloat16)
+ assert dtype in half_dtypes
+
+ # 如果禁用 flash-attn 或者根本没装：fallback 到 SDPA（不需要外部依赖）
+ use_fa3 = (not DISABLE_FLASH_ATTN) and FLASH_ATTN_3_AVAILABLE and (version is None or version == 3)
+ use_fa2 = (not DISABLE_FLASH_ATTN) and (not use_fa3) and FLASH_ATTN_2_AVAILABLE
+
+ if not (use_fa3 or use_fa2):
+ if window_size != (-1, -1):
+ warnings.warn(
+ "flash-attn not available: window_size local attention will be ignored and fall back to global SDPA."
+ )
+ return _sdpa_attention(
+ q=q, k=k, v=v,
+ q_lens=q_lens, k_lens=k_lens,
+ dropout_p=dropout_p,
+ softmax_scale=softmax_scale,
+ q_scale=q_scale,
+ causal=causal,
+ dtype=dtype,
+ )
+
+ # ===== 原来的 flash-attn 路径（基本照搬）=====
+ assert q.device.type == 'cuda' and q.size(-1) <= 256
+
+ b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
+
+ def half(x):
+ return x if x.dtype in half_dtypes else x.to(dtype)
+
+ # preprocess query
+ if q_lens is None:
+ q = half(q.flatten(0, 1))
+ q_lens = torch.tensor([lq] * b, dtype=torch.int32, device=q.device)
+ else:
+ # 你原实现这里会导致 unflatten 形状风险；一般别用 q_lens 变长路径
+ q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
+
+ # preprocess key, value
+ if k_lens is None:
+ k = half(k.flatten(0, 1))
+ v = half(v.flatten(0, 1))
+ k_lens = torch.tensor([lk] * b, dtype=torch.int32, device=k.device)
+ else:
+ k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
+ v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
+
+ q = q.to(v.dtype)
+ k = k.to(v.dtype)
+
+ if q_scale is not None:
+ q = q * q_scale
+
+ if use_fa3:
+ x = flash_attn_interface.flash_attn_varlen_func(
+ q=q,
+ k=k,
+ v=v,
+ cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32),
+ cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32),
+ seqused_q=None,
+ seqused_k=None,
+ max_seqlen_q=lq,
+ max_seqlen_k=lk,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ deterministic=deterministic
+ )[0].unflatten(0, (b, lq))
+ else:
+ x = flash_attn.flash_attn_varlen_func(
+ q=q,
+ k=k,
+ v=v,
+ cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(0, dtype=torch.int32),
+ cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(0, dtype=torch.int32),
+ max_seqlen_q=lq,
+ max_seqlen_k=lk,
+ dropout_p=dropout_p,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=window_size,
+ deterministic=deterministic
+ ).unflatten(0, (b, lq))
+
+ return x.type(out_dtype)
+
+
+# def flash_attention(
+# q,
+# k,
+# v,
+# q_lens=None,
+# k_lens=None,
+# dropout_p=0.,
+# softmax_scale=None,
+# q_scale=None,
+# causal=False,
+# window_size=(-1, -1),
+# deterministic=False,
+# dtype=torch.bfloat16,
+# version=None,
+# ):
+# """
+# q: [B, Lq, Nq, C1].
+# k: [B, Lk, Nk, C1].
+# v: [B, Lk, Nk, C2]. Nq must be divisible by Nk.
+# q_lens: [B].
+# k_lens: [B].
+# dropout_p: float. Dropout probability.
+# softmax_scale: float. The scaling of QK^T before applying softmax.
+# causal: bool. Whether to apply causal attention mask.
+# window_size: (left right). If not (-1, -1), apply sliding window local attention.
+# deterministic: bool. If True, slightly slower and uses more memory.
+# dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
+# """
+# half_dtypes = (torch.float16, torch.bfloat16)
+# assert dtype in half_dtypes
+# assert q.device.type == 'cuda' and q.size(-1) <= 256
+
+# # params
+# b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
+
+# def half(x):
+# return x if x.dtype in half_dtypes else x.to(dtype)
+
+# # preprocess query
+# if q_lens is None:
+# q = half(q.flatten(0, 1))
+# q_lens = torch.tensor(
+# [lq] * b, dtype=torch.int32).to(
+# device=q.device, non_blocking=True)
+# else:
+# q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
+
+# # preprocess key, value
+# if k_lens is None:
+# k = half(k.flatten(0, 1))
+# v = half(v.flatten(0, 1))
+# k_lens = torch.tensor(
+# [lk] * b, dtype=torch.int32).to(
+# device=k.device, non_blocking=True)
+# else:
+# k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
+# v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
+
+# q = q.to(v.dtype)
+# k = k.to(v.dtype)
+
+# if q_scale is not None:
+# q = q * q_scale
+
+# if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
+# warnings.warn(
+# 'Flash attention 3 is not available, use flash attention 2 instead.'
+# )
+
+# # apply attention
+# if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
+# # Note: dropout_p, window_size are not supported in FA3 now.
+# x = flash_attn_interface.flash_attn_varlen_func(
+# q=q,
+# k=k,
+# v=v,
+# cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+# 0, dtype=torch.int32).to(q.device, non_blocking=True),
+# cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+# 0, dtype=torch.int32).to(q.device, non_blocking=True),
+# seqused_q=None,
+# seqused_k=None,
+# max_seqlen_q=lq,
+# max_seqlen_k=lk,
+# softmax_scale=softmax_scale,
+# causal=causal,
+# deterministic=deterministic)[0].unflatten(0, (b, lq))
+# else:
+# assert FLASH_ATTN_2_AVAILABLE
+# x = flash_attn.flash_attn_varlen_func(
+# q=q,
+# k=k,
+# v=v,
+# cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+# 0, dtype=torch.int32).to(q.device, non_blocking=True),
+# cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+# 0, dtype=torch.int32).to(q.device, non_blocking=True),
+# max_seqlen_q=lq,
+# max_seqlen_k=lk,
+# dropout_p=dropout_p,
+# softmax_scale=softmax_scale,
+# causal=causal,
+# window_size=window_size,
+# deterministic=deterministic).unflatten(0, (b, lq))
+
+# # output
+# return x.type(out_dtype)
+
+
+def attention(
+ q,
+ k,
+ v,
+ q_lens=None,
+ k_lens=None,
+ dropout_p=0.,
+ softmax_scale=None,
+ q_scale=None,
+ causal=False,
+ window_size=(-1, -1),
+ deterministic=False,
+ dtype=torch.bfloat16,
+ fa_version=None,
+):
+ if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
+ return flash_attention(
+ q=q,
+ k=k,
+ v=v,
+ q_lens=q_lens,
+ k_lens=k_lens,
+ dropout_p=dropout_p,
+ softmax_scale=softmax_scale,
+ q_scale=q_scale,
+ causal=causal,
+ window_size=window_size,
+ deterministic=deterministic,
+ dtype=dtype,
+ version=fa_version,
+ )
+ else:
+ if q_lens is not None or k_lens is not None:
+ warnings.warn(
+ 'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.'
+ )
+ attn_mask = None
+
+ q = q.transpose(1, 2).to(dtype)
+ k = k.transpose(1, 2).to(dtype)
+ v = v.transpose(1, 2).to(dtype)
+
+ out = torch.nn.functional.scaled_dot_product_attention(
+ q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
+
+ out = out.transpose(1, 2).contiguous()
+ return out
+
+
+__all__ = ['WanModel']
+
+
+def sinusoidal_embedding_1d(dim, position):
+ # preprocess
+ assert dim % 2 == 0
+ half = dim // 2
+ # Ensure position is on CPU for float64 computation to avoid CUDA issues
+ # Convert to float64 for precision, then move back to original device
+ device = position.device
+ position = position.to(torch.float64)
+
+ # calculation
+ # Create range tensor on same device as position
+ arange_tensor = torch.arange(half, dtype=torch.float64, device=device)
+ sinusoid = torch.outer(
+ position, torch.pow(10000, -arange_tensor.div(half)))
+ x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
+ return x
+
+
+@torch.amp.autocast('cuda', enabled=False)
+def rope_params(max_seq_len, dim, theta=10000):
+ assert dim % 2 == 0
+ freqs = torch.outer(
+ torch.arange(max_seq_len),
+ 1.0 / torch.pow(theta,
+ torch.arange(0, dim, 2).to(torch.float64).div(dim)))
+ freqs = torch.polar(torch.ones_like(freqs), freqs)
+ return freqs
+
+
+@torch.amp.autocast('cuda', enabled=False)
+def rope_apply(x, grid_sizes, freqs):
+ n, c = x.size(2), x.size(3) // 2
+ # Save original dtype to restore it later
+ original_dtype = x.dtype
+
+ # split freqs
+ freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
+
+ # loop over samples
+ output = []
+ for i, (f, h, w) in enumerate(grid_sizes.tolist()):
+ seq_len = f * h * w
+
+ # precompute multipliers
+ x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
+ seq_len, n, -1, 2))
+ freqs_i = torch.cat([
+ freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+ freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+ freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+ ],
+ dim=-1).reshape(seq_len, 1, -1)
+
+ # apply rotary embedding
+ x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
+ # Convert back to original dtype before concatenating
+ x_i = x_i.to(dtype=original_dtype)
+ # Handle the remaining part of the sequence
+ x_remaining = x[i, seq_len:]
+ if x_remaining.numel() > 0:
+ x_i = torch.cat([x_i, x_remaining])
+ else:
+ x_i = x_i
+
+ # append to collection
+ output.append(x_i)
+ # Stack and ensure dtype matches original input
+ return torch.stack(output).to(dtype=original_dtype)
+
+
+class WanRMSNorm(nn.Module):
+
+ def __init__(self, dim, eps=1e-5):
+ super().__init__()
+ self.dim = dim
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(dim))
+
+ def forward(self, x):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L, C]
+ """
+ # Ensure weight dtype matches input dtype
+ return self._norm(x.float()).type_as(x) * self.weight.type_as(x)
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
+
+
+class WanLayerNorm(nn.LayerNorm):
+
+ def __init__(self, dim, eps=1e-6, elementwise_affine=False):
+ super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
+
+ def forward(self, x):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L, C]
+ """
+ # Convert to float32 for numerical stability, ensuring weights match input dtype
+ original_dtype = x.dtype
+ x_float = x.float()
+ if self.elementwise_affine:
+ weight_float = self.weight.float() if self.weight is not None else None
+ bias_float = self.bias.float() if self.bias is not None else None
+ # Use torch.nn.functional.layer_norm directly with converted weights
+ result = torch.nn.functional.layer_norm(x_float, self.normalized_shape, weight_float, bias_float, self.eps)
+ else:
+ result = super().forward(x_float)
+ return result.to(dtype=original_dtype)
+
+
+class WanSelfAttention(nn.Module):
+
+ def __init__(self,
+ dim,
+ num_heads,
+ window_size=(-1, -1),
+ qk_norm=True,
+ eps=1e-6):
+ assert dim % num_heads == 0
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ self.head_dim = dim // num_heads
+ self.window_size = window_size
+ self.qk_norm = qk_norm
+ self.eps = eps
+
+ # layers
+ self.q = nn.Linear(dim, dim)
+ self.k = nn.Linear(dim, dim)
+ self.v = nn.Linear(dim, dim)
+ self.o = nn.Linear(dim, dim)
+ self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
+ self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
+
+ def forward(self, x, seq_lens, grid_sizes, freqs):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L, num_heads, C / num_heads]
+ seq_lens(Tensor): Shape [B]
+ grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+ freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+ """
+ b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
+
+ # query, key, value function
+ def qkv_fn(x):
+ q = self.norm_q(self.q(x)).view(b, s, n, d)
+ k = self.norm_k(self.k(x)).view(b, s, n, d)
+ v = self.v(x).view(b, s, n, d)
+ return q, k, v
+
+ q, k, v = qkv_fn(x)
+
+ # Save input dtype to ensure output matches
+ input_dtype = x.dtype
+
+ x = flash_attention(
+ q=rope_apply(q, grid_sizes, freqs),
+ k=rope_apply(k, grid_sizes, freqs),
+ v=v,
+ k_lens=seq_lens,
+ window_size=self.window_size)
+
+ # Ensure output dtype matches input dtype (in case rope_apply or flash_attention changed it)
+ x = x.to(dtype=input_dtype)
+
+ # output
+ x = x.flatten(2)
+ x = self.o(x)
+ return x
+
+
+class WanCrossAttention(WanSelfAttention):
+
+ def forward(self, x, context, context_lens):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L1, C]
+ context(Tensor): Shape [B, L2, C]
+ context_lens(Tensor): Shape [B]
+ """
+ b, n, d = x.size(0), self.num_heads, self.head_dim
+
+ # Save input dtype to ensure output matches
+ input_dtype = x.dtype
+
+ # compute query, key, value
+ q = self.norm_q(self.q(x)).view(b, -1, n, d)
+ k = self.norm_k(self.k(context)).view(b, -1, n, d)
+ v = self.v(context).view(b, -1, n, d)
+
+ # compute attention
+ x = flash_attention(q, k, v, k_lens=context_lens)
+
+ # Ensure output dtype matches input dtype
+ x = x.to(dtype=input_dtype)
+
+ # output
+ x = x.flatten(2)
+ x = self.o(x)
+ return x
+
+
+class WanAttentionBlock(nn.Module):
+
+ def __init__(self,
+ dim,
+ ffn_dim,
+ num_heads,
+ window_size=(-1, -1),
+ qk_norm=True,
+ cross_attn_norm=False,
+ eps=1e-6):
+ super().__init__()
+ self.dim = dim
+ self.ffn_dim = ffn_dim
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.qk_norm = qk_norm
+ self.cross_attn_norm = cross_attn_norm
+ self.eps = eps
+
+ # layers
+ self.norm1 = WanLayerNorm(dim, eps)
+ self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
+ eps)
+ self.norm3 = WanLayerNorm(
+ dim, eps,
+ elementwise_affine=True) if cross_attn_norm else nn.Identity()
+ self.cross_attn = WanCrossAttention(dim, num_heads, (-1, -1), qk_norm,
+ eps)
+ self.norm2 = WanLayerNorm(dim, eps)
+ self.ffn = nn.Sequential(
+ nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
+ nn.Linear(ffn_dim, dim))
+
+ # modulation
+ self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+ def forward(
+ self,
+ x,
+ e,
+ seq_lens,
+ grid_sizes,
+ freqs,
+ context,
+ context_lens,
+ ):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L, C]
+ e(Tensor): Shape [B, L1, 6, C]
+ seq_lens(Tensor): Shape [B], length of each sequence in batch
+ grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+ freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+ """
+ # Convert e to float32 for modulation computation (modulation expects float32)
+ e_float32 = e.to(dtype=torch.float32) if e.dtype != torch.float32 else e
+ with torch.amp.autocast('cuda', dtype=torch.float32):
+ e = (self.modulation.unsqueeze(0) + e_float32).chunk(6, dim=2)
+ assert e[0].dtype == torch.float32
+
+ # self-attention
+ # Ensure input dtype matches model weights (convert e to match x's dtype)
+ x_dtype = x.dtype
+ e_0 = e[0].squeeze(2).to(dtype=x_dtype)
+ e_1 = e[1].squeeze(2).to(dtype=x_dtype)
+ e_2 = e[2].squeeze(2).to(dtype=x_dtype)
+ attn_input = self.norm1(x) * (1 + e_1) + e_0
+ y = self.self_attn(attn_input, seq_lens, grid_sizes, freqs)
+ # Ensure dtype consistency: y and e_2 should match x's dtype
+ x = x + (y * e_2).to(dtype=x_dtype)
+
+ # cross-attention & ffn function
+ def cross_attn_ffn(x, context, context_lens, e):
+ x = x + self.cross_attn(self.norm3(x), context, context_lens)
+ # Ensure dtype consistency for FFN input
+ x_dtype = x.dtype
+ e_3 = e[3].squeeze(2).to(dtype=x_dtype)
+ e_4 = e[4].squeeze(2).to(dtype=x_dtype)
+ e_5 = e[5].squeeze(2).to(dtype=x_dtype)
+ ffn_input = self.norm2(x) * (1 + e_4) + e_3
+ y = self.ffn(ffn_input)
+ # Ensure dtype consistency: y and e_5 should match x's dtype
+ x = x + (y * e_5).to(dtype=x_dtype)
+ return x
+
+ x = cross_attn_ffn(x, context, context_lens, e)
+ return x
+
+
+class Head(nn.Module):
+
+ def __init__(self, dim, out_dim, patch_size, eps=1e-6):
+ super().__init__()
+ self.dim = dim
+ self.out_dim = out_dim
+ self.patch_size = patch_size
+ self.eps = eps
+
+ # layers
+ out_dim = math.prod(patch_size) * out_dim
+ self.norm = WanLayerNorm(dim, eps)
+ self.head = nn.Linear(dim, out_dim)
+
+ # modulation
+ self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
+
+ def forward(self, x, e):
+ r"""
+ Args:
+ x(Tensor): Shape [B, L1, C]
+ e(Tensor): Shape [B, L1, C]
+ """
+ # Convert e to float32 for modulation computation (modulation expects float32)
+ e_float32 = e.to(dtype=torch.float32) if e.dtype != torch.float32 else e
+ with torch.amp.autocast('cuda', dtype=torch.float32):
+ e = (self.modulation.unsqueeze(0) + e_float32.unsqueeze(2)).chunk(2, dim=2)
+ # Ensure dtype consistency: convert e to match x's dtype
+ x_dtype = x.dtype
+ e_0 = e[0].squeeze(2).to(dtype=x_dtype)
+ e_1 = e[1].squeeze(2).to(dtype=x_dtype)
+ head_input = self.norm(x) * (1 + e_1) + e_0
+ x = self.head(head_input)
+ return x
+
+
+class WanModel(ModelMixin, ConfigMixin):
+ r"""
+ Wan diffusion backbone supporting both text-to-video and image-to-video.
+ """
+
+ ignore_for_config = [
+ 'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim', 'window_size'
+ ]
+ _no_split_modules = ['WanAttentionBlock']
+
+ @register_to_config
+ def __init__(self,
+ model_type='t2v',
+ patch_size=(1, 2, 2),
+ text_len=512,
+ in_dim=16,
+ dim=2048,
+ ffn_dim=8192,
+ freq_dim=256,
+ text_dim=4096,
+ out_dim=16,
+ num_heads=16,
+ num_layers=32,
+ window_size=(-1, -1),
+ qk_norm=True,
+ cross_attn_norm=True,
+ eps=1e-6):
+ r"""
+ Initialize the diffusion model backbone.
+
+ Args:
+ model_type (`str`, *optional*, defaults to 't2v'):
+ Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
+ patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
+ 3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
+ text_len (`int`, *optional*, defaults to 512):
+ Fixed length for text embeddings
+ in_dim (`int`, *optional*, defaults to 16):
+ Input video channels (C_in)
+ dim (`int`, *optional*, defaults to 2048):
+ Hidden dimension of the transformer
+ ffn_dim (`int`, *optional*, defaults to 8192):
+ Intermediate dimension in feed-forward network
+ freq_dim (`int`, *optional*, defaults to 256):
+ Dimension for sinusoidal time embeddings
+ text_dim (`int`, *optional*, defaults to 4096):
+ Input dimension for text embeddings
+ out_dim (`int`, *optional*, defaults to 16):
+ Output video channels (C_out)
+ num_heads (`int`, *optional*, defaults to 16):
+ Number of attention heads
+ num_layers (`int`, *optional*, defaults to 32):
+ Number of transformer blocks
+ window_size (`tuple`, *optional*, defaults to (-1, -1)):
+ Window size for local attention (-1 indicates global attention)
+ qk_norm (`bool`, *optional*, defaults to True):
+ Enable query/key normalization
+ cross_attn_norm (`bool`, *optional*, defaults to False):
+ Enable cross-attention normalization
+ eps (`float`, *optional*, defaults to 1e-6):
+ Epsilon value for normalization layers
+ """
+
+ super().__init__()
+
+ assert model_type in ['t2v', 'i2v', 'ti2v', 's2v']
+ self.model_type = model_type
+
+ self.patch_size = patch_size
+ self.text_len = text_len
+ self.in_dim = in_dim
+ self.dim = dim
+ self.ffn_dim = ffn_dim
+ self.freq_dim = freq_dim
+ self.text_dim = text_dim
+ self.out_dim = out_dim
+ self.num_heads = num_heads
+ self.num_layers = num_layers
+ self.window_size = window_size
+ self.qk_norm = qk_norm
+ self.cross_attn_norm = cross_attn_norm
+ self.eps = eps
+
+ # embeddings
+ self.patch_embedding = nn.Conv3d(
+ in_dim, dim, kernel_size=patch_size, stride=patch_size)
+ self.text_embedding = nn.Sequential(
+ nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
+ nn.Linear(dim, dim))
+
+ self.time_embedding = nn.Sequential(
+ nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
+ self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
+
+ # blocks
+ self.blocks = nn.ModuleList([
+ WanAttentionBlock(dim, ffn_dim, num_heads, window_size, qk_norm,
+ cross_attn_norm, eps) for _ in range(num_layers)
+ ])
+
+ # head
+ self.head = Head(dim, out_dim, patch_size, eps)
+
+ # buffers (don't use register_buffer otherwise dtype will be changed in to())
+ assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
+ d = dim // num_heads
+ self.freqs = torch.cat([
+ rope_params(1024, d - 4 * (d // 6)),
+ rope_params(1024, 2 * (d // 6)),
+ rope_params(1024, 2 * (d // 6))
+ ],
+ dim=1)
+
+ # initialize weights
+ self.init_weights()
+
+ def forward(
+ self,
+ x,
+ t,
+ context,
+ seq_len,
+ y=None,
+ context_lens=None,
+ ):
+ r"""
+ Forward pass through the diffusion model
+
+ Args:
+ x (List[Tensor]):
+ List of input video tensors, each with shape [C_in, F, H, W]
+ t (Tensor):
+ Diffusion timesteps tensor of shape [B]
+ context (List[Tensor]):
+ List of text embeddings each with shape [L, C]
+ seq_len (`int`):
+ Maximum sequence length for positional encoding
+ y (List[Tensor], *optional*):
+ Conditional video inputs for image-to-video mode, same shape as x
+
+ Returns:
+ List[Tensor]:
+ List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
+ """
+ if self.model_type == 'i2v':
+ assert y is not None
+ # params
+ device = self.patch_embedding.weight.device
+ if self.freqs.device != device:
+ self.freqs = self.freqs.to(device)
+
+ if y is not None:
+ x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
+
+ # embeddings
+ # Ensure input dtype matches patch_embedding weight dtype
+ patch_weight_dtype = self.patch_embedding.weight.dtype
+ x = [self.patch_embedding(u.unsqueeze(0).to(dtype=patch_weight_dtype)) for u in x]
+ grid_sizes = torch.stack(
+ [torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
+ x = [u.flatten(2).transpose(1, 2) for u in x]
+ seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
+ # seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long, device=u.device)
+
+ assert seq_lens.max() <= seq_len
+ x = torch.cat([
+ torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
+ dim=1) for u in x
+ ])
+
+ # time embeddings
+ if t.dim() == 1:
+ t = t.expand(t.size(0), seq_len)
+ with torch.amp.autocast('cuda', dtype=torch.float32):
+ bt = t.size(0)
+ t = t.flatten()
+ e = self.time_embedding(
+ sinusoidal_embedding_1d(self.freq_dim,
+ t).unflatten(0, (bt, seq_len)).float())
+ e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+ assert e.dtype == torch.float32 and e0.dtype == torch.float32
+
+ # Keep e and e0 as float32 for modulation computation
+ # They will be converted to x.dtype inside WanAttentionBlock.forward and Head.forward when needed
+
+ # context
+ # Use provided context_lens or compute from context if not provided
+ if context_lens is None:
+ # Fallback: assume all contexts have full length
+ context_lens = torch.full((len(context),), self.text_len, dtype=torch.long, device=x[0].device)
+ else:
+ # Ensure context_lens is on correct device
+ context_lens = context_lens.to(device=x[0].device, dtype=torch.long)
+
+ # Ensure context input dtype matches text_embedding weight dtype
+ text_weight_dtype = self.text_embedding[0].weight.dtype
+ context = self.text_embedding(
+ torch.stack([
+ torch.cat(
+ [u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
+ for u in context
+ ]).to(dtype=text_weight_dtype))
+
+ # arguments
+ kwargs = dict(
+ e=e0,
+ seq_lens=seq_lens,
+ grid_sizes=grid_sizes,
+ freqs=self.freqs,
+ context=context,
+ context_lens=context_lens)
+
+ for block in self.blocks:
+ x = block(x, **kwargs)
+
+ # head
+ x = self.head(x, e)
+
+ # unpatchify
+ x = self.unpatchify(x, grid_sizes)
+ return [u.float() for u in x]
+
+ def unpatchify(self, x, grid_sizes):
+ r"""
+ Reconstruct video tensors from patch embeddings.
+
+ Args:
+ x (List[Tensor]):
+ List of patchified features, each with shape [L, C_out * prod(patch_size)]
+ grid_sizes (Tensor):
+ Original spatial-temporal grid dimensions before patching,
+ shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
+
+ Returns:
+ List[Tensor]:
+ Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
+ """
+
+ c = self.out_dim
+ out = []
+ for u, v in zip(x, grid_sizes.tolist()):
+ u = u[:math.prod(v)].view(*v, *self.patch_size, c)
+ u = torch.einsum('fhwpqrc->cfphqwr', u)
+ u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
+ out.append(u)
+ return out
+
+ def init_weights(self):
+ r"""
+ Initialize model parameters using Xavier initialization.
+ """
+
+ # basic init
+ for m in self.modules():
+ if isinstance(m, nn.Linear):
+ nn.init.xavier_uniform_(m.weight)
+ if m.bias is not None:
+ nn.init.zeros_(m.bias)
+
+ # init embeddings
+ nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
+ for m in self.text_embedding.modules():
+ if isinstance(m, nn.Linear):
+ nn.init.normal_(m.weight, std=.02)
+ for m in self.time_embedding.modules():
+ if isinstance(m, nn.Linear):
+ nn.init.normal_(m.weight, std=.02)
+
+ # init output layer
+ nn.init.zeros_(self.head.head.weight)
+
+
+
+
+class WanDiscreteVideoTransformer(ModelMixin, ConfigMixin):
+ r"""
+ Wrapper around :class:`WanModel` that makes it usable as a **discrete video diffusion backbone**.
+
+ The goals of this wrapper are:
+
+ - keep the inner :class:`WanModel` architecture and parameter names intact so that Wan-1.3B
+ weights can later be loaded directly into ``self.backbone``;
+ - expose a simpler interface that takes **discrete codebook indices** (from a 2D VQ-VAE on
+ pseudo-video) and returns **logits over the codebook** for each spatio‑temporal position.
+
+ Notes
+ -----
+ - This class does **not** try to be drop‑in compatible with Meissonic's 2D ``Transformer2DModel``.
+ It is a parallel, video‑oriented path that still follows the same *discrete diffusion* principle:
+ predict per‑token logits given masked tokens + text.
+ - Pseudo‑video is represented as a 4D integer tensor ``[B, F, H, W]`` of codebook indices.
+ How to get these tokens from the current 2D VQ-VAE (e.g. per‑frame encoding & stacking)
+ is left to the higher‑level training / pipeline code.
+ """
+
+ _supports_gradient_checkpointing = True
+
+ @register_to_config
+ def __init__(
+ self,
+ # discrete codebook settings
+ codebook_size: int,
+ vocab_size: int,
+ # video layout
+ num_frames: int,
+ height: int,
+ width: int,
+ # Wan backbone hyper‑parameters (mirrors WanModel.__init__)
+ model_type: str = 't2v',
+ patch_size: tuple = (1, 2, 2),
+ text_len: int = 512,
+ in_dim: int = 16,
+ dim: int = 2048,
+ ffn_dim: int = 8192,
+ freq_dim: int = 256,
+ text_dim: int = 4096,
+ out_dim: int = 16,
+ num_heads: int = 16,
+ num_layers: int = 32,
+ window_size: tuple = (-1, -1),
+ qk_norm: bool = True,
+ cross_attn_norm: bool = True,
+ eps: float = 1e-6,
+ ):
+ super().__init__()
+
+ # save a minimal set of attributes useful for downstream tooling
+ self.codebook_size = codebook_size
+ self.vocab_size = vocab_size
+ self.num_frames = num_frames
+ self.height = height
+ self.width = width
+
+ # 1) backbone: keep WanModel intact for future weight loading
+ self.backbone = WanModel(
+ model_type=model_type,
+ patch_size=patch_size,
+ text_len=text_len,
+ in_dim=in_dim,
+ dim=dim,
+ ffn_dim=ffn_dim,
+ freq_dim=freq_dim,
+ text_dim=text_dim,
+ out_dim=out_dim,
+ num_heads=num_heads,
+ num_layers=num_layers,
+ window_size=window_size,
+ qk_norm=qk_norm,
+ cross_attn_norm=cross_attn_norm,
+ eps=eps,
+ )
+
+ # 2) discrete token embedding -> continuous video volume
+ #
+ # Input: tokens [B, F, H, W] with values in [0, vocab_size) where:
+ # - [0, codebook_size-1] = actual Cosmos codes (direct mapping, no shift)
+ # - codebook_size = mask_token_id (reserved for masking)
+ # Output: list of length B with tensors [in_dim, F, H, W]
+ #
+ # We keep this outside the backbone so that loading official Wan 1.3B weights
+ # into self.backbone will still work without clashes.
+ # Note: vocab_size = codebook_size + 1 to accommodate mask_token_id = codebook_size
+ self.token_embedding = nn.Embedding(vocab_size, in_dim)
+
+ # 3) projection from continuous video output -> logits over codebook
+ #
+ # Backbone output: list of B tensors [out_dim, F, H', W']
+ # We map it with a 3D 1x1x1 conv to [vocab_size, F, H', W'].
+ # Note: vocab_size = codebook_size + 1, where codebook_size is reserved for mask_token_id
+ self.logits_head = nn.Conv3d(out_dim, vocab_size, kernel_size=1)
+
+ # Gradient checkpointing support
+ self.gradient_checkpointing = False
+
+ def _tokens_to_video(self, tokens: torch.LongTensor) -> list:
+ r"""
+ Convert discrete tokens ``[B, F, H, W]`` into a list of length ``B`` where each element
+ is a dense video tensor ``[in_dim, F, H, W]`` suitable for :class:`WanModel`.
+
+ Note:
+ This method now supports dynamic input dimensions. The num_frames, height, width
+ stored in config are used as defaults/for seq_len calculation, but inputs can
+ have different dimensions as long as they're valid.
+ """
+ assert tokens.dim() == 4, f"expected [B, F, H, W] tokens, got {tokens.shape}"
+ # Dynamic dimensions - no strict dimension checks, WanModel handles variable sizes
+
+ # [B, F, H, W, in_dim]
+ # Ensure output dtype matches token_embedding weight dtype
+ x = self.token_embedding(tokens)
+ # Ensure dtype matches model's expected dtype (usually bfloat16 for mixed precision)
+ token_embedding_dtype = self.token_embedding.weight.dtype
+ x = x.to(dtype=token_embedding_dtype)
+ # [B, in_dim, F, H, W]
+ x = x.permute(0, 4, 1, 2, 3).contiguous()
+
+ # WanModel expects a list of [C_in, F, H, W]
+ return [x_i for x_i in x]
+
+ def _text_to_list(self, encoder_hidden_states: torch.Tensor) -> list:
+ r"""
+ Convert batched text embeddings ``[B, L, C]`` into the list-of-tensors format
+ expected by :class:`WanModel`.
+ """
+ assert encoder_hidden_states.dim() == 3, (
+ f"expected encoder_hidden_states [B, L, C], got {encoder_hidden_states.shape}")
+ return [e for e in encoder_hidden_states]
+
+ def _set_gradient_checkpointing(self, enable=True, gradient_checkpointing_func=None):
+ """Set gradient checkpointing for the module."""
+ self.gradient_checkpointing = enable
+
+ def forward(
+ self,
+ tokens: torch.LongTensor,
+ timesteps: torch.LongTensor,
+ encoder_hidden_states: torch.FloatTensor,
+ y: Optional[list] = None,
+ context_lens: Optional[torch.LongTensor] = None,
+ ) -> torch.FloatTensor:
+ r"""
+ Forward pass of the **discrete video transformer**.
+
+ Args:
+ tokens (`torch.LongTensor` of shape `[B, F, H, W]`):
+ Discrete codebook indices (e.g. from a 2D VQ-VAE applied frame‑wise).
+ timesteps (`torch.LongTensor` of shape `[B]` or `[B, F * H * W]`):
+ Diffusion timestep(s), following the same semantics as Meissonic's scalar timesteps.
+ encoder_hidden_states (`torch.FloatTensor` of shape `[B, L, C_text]`):
+ Text embeddings (e.g. from CLIP). Each sample corresponds to one video.
+ y (`Optional[list]`):
+ Optional conditional video list passed to the underlying :class:`WanModel`
+ for i2v / ti2v / s2v variants. For now this is surfaced as a raw passthrough
+ and can be left as ``None`` for pure text‑to‑video.
+
+ Returns:
+ `torch.FloatTensor`:
+ Logits over the codebook of shape `[B, codebook_size, F, H_out, W_out]`, where
+ `(H_out, W_out)` depend on the Wan patch configuration. For the default
+ `patch_size=(1, 2, 2)` and input ``H=W=height``, we have
+ ``H_out = height // 2`` and ``W_out = width // 2``.
+ """
+ device = tokens.device
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] Input: tokens.shape={tokens.shape}, encoder_hidden_states.shape={encoder_hidden_states.shape}, timesteps.shape={timesteps.shape}")
+ x_list = self._tokens_to_video(tokens)
+ context_list = self._text_to_list(encoder_hidden_states)
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] After conversion: len(x_list)={len(x_list)}, len(context_list)={len(context_list)}")
+ if len(x_list) > 0:
+ print(f"[DEBUG-transformer] x_list[0].shape={x_list[0].shape}")
+ if len(context_list) > 0:
+ print(f"[DEBUG-transformer] context_list[0].shape={context_list[0].shape}")
+
+ # Calculate seq_len from actual input dimensions (supports dynamic sizes)
+ # tokens: [B, F, H, W] -> after patchification: seq_len = F * (H/p_h) * (W/p_w)
+ _, f_in, h_in, w_in = tokens.shape
+ h_patch = h_in // self.backbone.patch_size[1]
+ w_patch = w_in // self.backbone.patch_size[2]
+ seq_len = f_in * h_patch * w_patch
+
+ # Prepare timesteps in the exact shape WanModel.forward expects.
+ # Its current implementation assumes `t` is either [B, seq_len] or will be
+ # expanded from 1D; the 1D branch is slightly buggy for non-singleton dims,
+ # so we always give it a [B, seq_len] tensor here.
+ if timesteps.dim() == 1:
+ # [B] -> [B, 1] -> [B, seq_len] (broadcast along sequence)
+ t_model = timesteps.to(device).unsqueeze(1).expand(-1, seq_len)
+ elif timesteps.dim() == 2:
+ assert timesteps.size(1) == seq_len, (
+ f"Expected timesteps second dim == seq_len ({seq_len}), "
+ f"but got {timesteps.size(1)}"
+ )
+ t_model = timesteps.to(device)
+ else:
+ raise ValueError(
+ f"Unsupported timesteps shape {timesteps.shape}; "
+ "expected [B] or [B, seq_len]"
+ )
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] t_model.shape={t_model.shape}")
+
+ # WanModel.forward expects:
+ # x: List[Tensor [C_in, F, H, W]]
+ # t: Tensor [B] or [B, seq_len]
+ # context: List[Tensor [L, C_text]]
+ # seq_len: int
+ # y: Optional[List[Tensor]]
+ # context_lens: Optional[Tensor [B]]
+ if self.training and self.gradient_checkpointing:
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ # Unpack inputs: x_list, t, context_list, seq_len, y, context_lens
+ x_in, t_in, context_in, seq_len_in, y_in, context_lens_in = inputs
+ return module(x=x_in, t=t_in, context=context_in, seq_len=seq_len_in, y=y_in, context_lens=context_lens_in)
+ return custom_forward
+
+ # Use gradient checkpointing for the backbone
+ ckpt_kwargs = {"use_reentrant": False}
+ out_list = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(self.backbone),
+ x_list,
+ t_model,
+ context_list,
+ seq_len,
+ y,
+ context_lens,
+ **ckpt_kwargs,
+ )
+ else:
+ out_list = self.backbone(
+ x=x_list,
+ t=t_model,
+ context=context_list,
+ seq_len=seq_len,
+ y=y,
+ context_lens=context_lens,
+ )
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] After backbone: len(out_list)={len(out_list)}")
+ if len(out_list) > 0:
+ print(f"[DEBUG-transformer] out_list[0].shape={out_list[0].shape}")
+
+ # out_list: length B, each [C_out, F, H_out, W_out]
+ vids = torch.stack(out_list, dim=0) # [B, C_out, F, H_out, W_out]
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] After stack: vids.shape={vids.shape}")
+ # Ensure vids dtype matches logits_head weight dtype
+ vids = vids.to(dtype=self.logits_head.weight.dtype)
+ logits = self.logits_head(vids) # [B, vocab_size, F, H_out, W_out] where vocab_size = codebook_size + 1
+ if DEBUG_TRANSFORMER:
+ print(f"[DEBUG-transformer] Final logits.shape={logits.shape}")
+ return logits
+
+# def _available_device():
+# return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+# def test_wan_discrete_video_transformer_forward_and_shapes():
+# """
+# Basic smoke test:
+# - build a tiny WanDiscreteVideoTransformer
+# - run a forward pass with random pseudo-video tokens + random text
+# - check output shapes, parameter count and (if CUDA present) memory usage
+# """
+
+# device = _available_device()
+
+# # small config to keep the test lightweight
+# codebook_size = 128
+# vocab_size = codebook_size + 1 # reserve one for mask if needed later
+# num_frames = 2
+# height = 16
+# width = 16
+
+# model = WanDiscreteVideoTransformer(
+# codebook_size=codebook_size,
+# vocab_size=vocab_size,
+# num_frames=num_frames,
+# height=height,
+# width=width,
+# # shrink Wan backbone for the unit test
+# in_dim=32,
+# dim=64,
+# ffn_dim=128,
+# freq_dim=32,
+# text_dim=64,
+# out_dim=32,
+# num_heads=4,
+# num_layers=2,
+# ).to(device)
+# model.eval()
+
+# batch_size = 2
+
+# # pseudo-video tokens from 2D VQ-VAE on frames: [B, F, H, W]
+# tokens = torch.randint(
+# low=0,
+# high=codebook_size,
+# size=(batch_size, num_frames, height, width),
+# dtype=torch.long,
+# device=device,
+# )
+
+# # text: [B, L, C_text]
+# text_seq_len = 8
+# encoder_hidden_states = torch.randn(
+# batch_size, text_seq_len, model.backbone.text_dim, device=device
+# )
+
+# # timesteps: [B]
+# timesteps = torch.randint(
+# low=0, high=1000, size=(batch_size,), dtype=torch.long, device=device
+# )
+
+# # track memory if CUDA is available
+# if device == "cuda":
+# torch.cuda.reset_peak_memory_stats()
+# mem_before = torch.cuda.memory_allocated()
+# else:
+# mem_before = 0
+
+# with torch.no_grad():
+# logits = model(
+# tokens=tokens,
+# timesteps=timesteps,
+# encoder_hidden_states=encoder_hidden_states,
+# y=None,
+# )
+
+# if device == "cuda":
+# mem_after = torch.cuda.memory_allocated()
+# peak_mem = torch.cuda.max_memory_allocated()
+# else:
+# mem_after = mem_before
+# peak_mem = mem_before
+
+# # logits: [B, codebook_size, F, H_out, W_out]
+# assert logits.shape[0] == batch_size
+# assert logits.shape[1] == codebook_size
+# assert logits.shape[2] == num_frames
+
+# # WanModel returns unpatchified videos, so spatial size matches the input grid.
+# h_out = height
+# w_out = width
+# assert logits.shape[3] == h_out
+# assert logits.shape[4] == w_out
+
+# # parameter count sanity check (just ensure it's > 0 and finite)
+# num_params = sum(p.numel() for p in model.parameters())
+# assert num_params > 0
+# assert math.isfinite(float(num_params))
+
+# # memory sanity check (on CUDA the forward pass should allocate > 0 bytes)
+# if device == "cuda":
+# assert peak_mem >= mem_after >= mem_before
+
+
+
+# import torch
+# from safetensors import safe_open
+# # from src.transformer_video import WanDiscreteVideoTransformer
+
+# ckpt_path = "/mnt/Meissonic/model/diffusion_pytorch_model.safetensors"
+
+# # 1) 按你想匹配 wan2.1 的超参实例化（这里写一份常用配置，务必与 ckpt 对齐）
+# model = WanDiscreteVideoTransformer(
+# codebook_size=128, # 离散侧自定义
+# vocab_size=129,
+# num_frames=2,
+# height=16,
+# width=16,
+# # Wan backbone 超参需与 ckpt 完全一致
+# model_type="t2v",
+# patch_size=(1, 2, 2),
+# in_dim=16,
+# dim=1536,
+# ffn_dim=8960,
+# freq_dim=256,
+# text_dim=4096,
+# out_dim=16,
+# num_heads=12,
+# num_layers=30,
+# window_size=(-1, -1),
+# qk_norm=True,
+# cross_attn_norm=True,
+# eps=1e-6,
+# )
+
+# # 2) 读取 safetensors
+# state_dict = {}
+# with safe_open(ckpt_path, framework="pt", device="cpu") as f:
+# for k in f.keys():
+# state_dict[k] = f.get_tensor(k)
+
+# # 3) 尝试加载到 backbone（不碰 token_embedding/logits_head）
+# missing, unexpected = model.backbone.load_state_dict(state_dict, strict=False)
+
+# print("Missing keys:", missing[:50], "... total", len(missing))
+# print("Unexpected keys:", unexpected[:50], "... total", len(unexpected))
+# print("Backbone params (M):", sum(p.numel() for p in model.backbone.parameters()) / 1e6)
+# print("Params (M):", sum(p.numel() for p in model.parameters()) / 1e6)
+
+# # if __name__ == '__main__':
+# # # test_wan_discrete_video_transformer_forward_and_shapes()
+# # print('WanDiscreteVideoTransformer forward pass test: PASSED')
+
+
+
+
\ No newline at end of file
diff --git a/Meissonic/tests/test.py b/Meissonic/tests/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..a93eee1f28259d6ad78c0ee8ba9157dfabdb5d54
--- /dev/null
+++ b/Meissonic/tests/test.py
@@ -0,0 +1,111 @@
+import math
+
+import torch
+
+from src.transformer_video import WanDiscreteVideoTransformer
+
+
+def _available_device():
+ return "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def test_wan_discrete_video_transformer_forward_and_shapes():
+ """
+ Basic smoke test:
+ - build a tiny WanDiscreteVideoTransformer
+ - run a forward pass with random pseudo-video tokens + random text
+ - check output shapes, parameter count and (if CUDA present) memory usage
+ """
+
+ device = _available_device()
+
+ # small config to keep the test lightweight
+ codebook_size = 128
+ vocab_size = codebook_size + 1 # reserve one for mask if needed later
+ num_frames = 2
+ height = 16
+ width = 16
+
+ model = WanDiscreteVideoTransformer(
+ codebook_size=codebook_size,
+ vocab_size=vocab_size,
+ num_frames=num_frames,
+ height=height,
+ width=width,
+ # shrink Wan backbone for the unit test
+ in_dim=32,
+ dim=64,
+ ffn_dim=128,
+ freq_dim=32,
+ text_dim=64,
+ out_dim=32,
+ num_heads=4,
+ num_layers=2,
+ ).to(device)
+ model.eval()
+
+ batch_size = 2
+
+ # pseudo-video tokens from 2D VQ-VAE on frames: [B, F, H, W]
+ tokens = torch.randint(
+ low=0,
+ high=codebook_size,
+ size=(batch_size, num_frames, height, width),
+ dtype=torch.long,
+ device=device,
+ )
+
+ # text: [B, L, C_text]
+ text_seq_len = 8
+ encoder_hidden_states = torch.randn(
+ batch_size, text_seq_len, model.backbone.text_dim, device=device
+ )
+
+ # timesteps: [B]
+ timesteps = torch.randint(
+ low=0, high=1000, size=(batch_size,), dtype=torch.long, device=device
+ )
+
+ # track memory if CUDA is available
+ if device == "cuda":
+ torch.cuda.reset_peak_memory_stats()
+ mem_before = torch.cuda.memory_allocated()
+ else:
+ mem_before = 0
+
+ with torch.no_grad():
+ logits = model(
+ tokens=tokens,
+ timesteps=timesteps,
+ encoder_hidden_states=encoder_hidden_states,
+ y=None,
+ )
+
+ if device == "cuda":
+ mem_after = torch.cuda.memory_allocated()
+ peak_mem = torch.cuda.max_memory_allocated()
+ else:
+ mem_after = mem_before
+ peak_mem = mem_before
+
+ # logits: [B, codebook_size, F, H_out, W_out]
+ assert logits.shape[0] == batch_size
+ assert logits.shape[1] == codebook_size
+ assert logits.shape[2] == num_frames
+
+ # spatial size after Wan patch embedding with default patch_size (1, 2, 2)
+ h_out = height // model.backbone.patch_size[1]
+ w_out = width // model.backbone.patch_size[2]
+ assert logits.shape[3] == h_out
+ assert logits.shape[4] == w_out
+
+ # parameter count sanity check (just ensure it's > 0 and finite)
+ num_params = sum(p.numel() for p in model.parameters())
+ assert num_params > 0
+ assert math.isfinite(float(num_params))
+
+ # memory sanity check (on CUDA the forward pass should allocate > 0 bytes)
+ if device == "cuda":
+ assert peak_mem >= mem_after >= mem_before
+
+
diff --git a/Meissonic/train/FEATURE_EXTRACTION_README.md b/Meissonic/train/FEATURE_EXTRACTION_README.md
new file mode 100644
index 0000000000000000000000000000000000000000..064843a220cf2442413f8d30ed72363e735a9f07
--- /dev/null
+++ b/Meissonic/train/FEATURE_EXTRACTION_README.md
@@ -0,0 +1,142 @@
+# 特征提取和预计算特征训练指南
+
+本文档说明如何使用预提取的特征来加速视频训练。
+
+## 概述
+
+为了提升训练效率，我们可以预先提取：
+1. **视频特征（codes）**：使用 CosmosVideoTokenizer 将视频编码为离散token
+2. **文本特征（embeddings）**：使用 T5/UMT5 将文本编码为embedding
+
+训练时直接加载这些预提取的特征，避免每次训练都重新编码。
+
+## 步骤1：提取特征
+
+使用 `extract_features.py` 脚本提取特征：
+
+```bash
+python train/extract_features.py \
+ --csv_path /path/to/OpenVid1M_reorganized.csv \
+ --video_root_dir /path/to/video_reorg \
+ --output_dir /path/to/extracted_features \
+ --text_encoder_architecture umt5-base \
+ --video_tokenizer_model_id Cosmos-1.0-Tokenizer-DV8x16x16 \
+ --num_frames 16 \
+ --video_height 480 \
+ --video_width 848 \
+ --batch_size 4 \
+ --num_workers 4
+```
+
+### 参数说明
+
+- `--csv_path`: OpenVid1M CSV文件路径
+- `--video_root_dir`: 视频文件根目录（可选，会自动检测）
+- `--output_dir`: 特征保存目录
+- `--text_encoder_architecture`: 文本编码器架构（umt5-base/umt5-xxl/t5）
+- `--video_tokenizer_model_id`: Cosmos视频tokenizer模型ID
+- `--num_frames`, `--video_height`, `--video_width`: 视频参数
+- `--batch_size`: 批处理大小（根据GPU内存调整）
+- `--num_workers`: 数据加载器工作进程数
+- `--max_samples`: 最大处理样本数（用于测试，默认处理全部）
+- `--resume_from_index`: 从指定索引恢复提取（用于中断后恢复）
+
+### 输出结构
+
+提取的特征会保存在 `output_dir` 下，采用三层目录结构以避免单个文件夹下文件过多：
+
+```
+extracted_features/
+├── video_codes/ # 视频codes（三层目录结构）
+│ ├── 000/ # 第一层：index // 1000000
+│ │ ├── 000/ # 第二层：(index // 1000) % 1000
+│ │ │ ├── 000/ # 第三层：index % 1000
+│ │ │ │ ├── 00000000.npy
+│ │ │ │ └── ...
+│ │ │ └── ...
+│ │ └── ...
+│ └── ...
+├── text_embeddings/ # 文本embeddings（三层目录结构）
+│ ├── 000/
+│ │ ├── 000/
+│ │ │ ├── 000/
+│ │ │ │ ├── 00000000.npy
+│ │ │ │ └── ...
+│ │ │ └── ...
+│ │ └── ...
+│ └── ...
+└── metadata.json # 元数据（包含样本信息）
+```
+
+**目录结构说明**：
+- 对于索引 `index`，文件路径为：`level1/level2/level3/index.npy`
+ - `level1 = index // 1000000` (0-999)
+ - `level2 = (index // 1000) % 1000` (0-999)
+ - `level3 = index % 1000` (0-999)
+- 例如：索引 `1234567` 的文件路径为 `001/234/567/1234567.npy`
+
+这种结构可以支持最多1,000,000,000个样本，每层最多1000个文件夹，避免单个文件夹下文件过多的问题。
+
+## 步骤2：使用预提取特征训练
+
+在训练脚本中使用 `--use_precomputed_features` 和 `--features_dir` 参数：
+
+```bash
+python train/train_mei_video.py \
+ --use_precomputed_features \
+ --features_dir /path/to/extracted_features \
+ --text_encoder_architecture umt5-base \
+ --video_tokenizer_model_id Cosmos-1.0-Tokenizer-DV8x16x16 \
+ --num_frames 16 \
+ --video_height 480 \
+ --video_width 848 \
+ --train_batch_size 8 \
+ --learning_rate 3e-4 \
+ --max_train_steps 10000 \
+ --output_dir ./output \
+ --mixed_precision bf16 \
+ --gradient_checkpointing \
+ --wan_pretrained_path /path/to/wan/weights \
+ --wan_backbone_lr_ratio 0.1 \
+ --freeze_wan_backbone # 可选：冻结backbone
+```
+
+### 关键参数
+
+- `--use_precomputed_features`: 启用预提取特征模式
+- `--features_dir`: 预提取特征的目录路径
+- 其他训练参数保持不变
+
+## 优势
+
+1. **训练速度提升**：避免每次训练都重新编码视频和文本
+2. **GPU利用率提升**：减少CPU-GPU数据传输
+3. **内存效率**：特征文件比原始视频小得多
+4. **可重复性**：使用相同的特征确保训练一致性
+
+## 注意事项
+
+1. **特征一致性**：确保提取特征时使用的参数（num_frames, height, width, text_encoder）与训练时一致
+2. **存储空间**：1M样本的特征大约需要几十GB存储空间
+3. **恢复提取**：如果提取中断，可以使用 `--resume_from_index` 参数恢复
+4. **验证**：训练时仍需要text_encoder进行验证，但不会用于训练数据编码
+
+## 故障排除
+
+### 特征提取失败
+
+- 检查视频文件路径是否正确
+- 检查GPU内存是否足够（可以减小batch_size）
+- 查看日志中的错误信息
+
+### 训练时找不到特征
+
+- 确认 `--features_dir` 路径正确
+- 确认特征文件存在（检查 `video_codes/` 和 `text_embeddings/` 目录）
+- 检查 `metadata.json` 文件是否存在
+
+### 维度不匹配
+
+- 确保提取和训练时使用相同的视频参数（num_frames, height, width）
+- 确保使用相同的text_encoder架构
+
diff --git a/Meissonic/train/check_codebook_range.py b/Meissonic/train/check_codebook_range.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f015b106b650c0af6fe21e1e3e2819e5e954c19
--- /dev/null
+++ b/Meissonic/train/check_codebook_range.py
@@ -0,0 +1,295 @@
+#!/usr/bin/env python3
+"""
+Check codebook range by iterating through videos and extracting codes.
+
+This script loads videos from the dataset, encodes them to get video codes,
+and tracks the min/max values to determine the codebook range.
+"""
+
+import argparse
+import os
+import sys
+import logging
+from tqdm import tqdm
+import torch
+import numpy as np
+
+sys.path.append(os.getcwd())
+
+from train.dataset_utils import OpenVid1MDataset, PrecomputedFeatureDataset
+from src.pipeline_video import CosmosVideoTokenizer
+from transformers import T5Tokenizer
+from torch.utils.data import DataLoader
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Check codebook range from video dataset")
+
+ parser.add_argument(
+ "--csv_path",
+ type=str,
+ default=None,
+ help="Path to OpenVid1M CSV file (if using raw videos)",
+ )
+ parser.add_argument(
+ "--video_root_dir",
+ type=str,
+ default=None,
+ help="Root directory containing video files",
+ )
+ parser.add_argument(
+ "--features_dir",
+ type=str,
+ default=None,
+ help="Directory containing pre-extracted features (if using precomputed features)",
+ )
+ parser.add_argument(
+ "--video_tokenizer_model_id",
+ type=str,
+ default="Cosmos-1.0-Tokenizer-DV8x16x16",
+ help="HuggingFace model ID for Cosmos video tokenizer",
+ )
+ parser.add_argument(
+ "--num_frames",
+ type=int,
+ default=16,
+ help="Number of frames per video",
+ )
+ parser.add_argument(
+ "--video_height",
+ type=int,
+ default=480,
+ help="Video height",
+ )
+ parser.add_argument(
+ "--video_width",
+ type=int,
+ default=848,
+ help="Video width",
+ )
+ parser.add_argument(
+ "--text_encoder_architecture",
+ type=str,
+ default="umt5-base",
+ choices=["umt5-base", "umt5-xxl", "t5"],
+ help="Text encoder architecture",
+ )
+ parser.add_argument(
+ "--batch_size",
+ type=int,
+ default=1,
+ help="Batch size (use 1 for detailed per-sample tracking)",
+ )
+ parser.add_argument(
+ "--max_samples",
+ type=int,
+ default=None,
+ help="Maximum number of samples to check. If None, check all.",
+ )
+ parser.add_argument(
+ "--check_interval",
+ type=int,
+ default=10,
+ help="Print statistics every N samples",
+ )
+
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ dtype = torch.float32
+
+ logger.info(f"Using device: {device}")
+
+ # Initialize video tokenizer (only needed if not using precomputed features)
+ video_tokenizer = None
+ use_precomputed = args.features_dir is not None
+
+ if not use_precomputed:
+ if args.csv_path is None:
+ raise ValueError("Either --csv_path or --features_dir must be provided")
+
+ logger.info(f"Loading video tokenizer: {args.video_tokenizer_model_id}")
+ video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=device,
+ dtype=dtype
+ )
+ video_tokenizer.requires_grad_(False)
+ video_tokenizer.eval()
+
+ # Get tokenizer info
+ logger.info(f"Video tokenizer codebook_size: {video_tokenizer.codebook_size}")
+ logger.info(f"Video tokenizer mask_token_id: {video_tokenizer.mask_token_id}")
+
+ # Create dataset
+ if use_precomputed:
+ logger.info(f"Using precomputed features from: {args.features_dir}")
+ dataset = PrecomputedFeatureDataset(
+ features_dir=args.features_dir,
+ num_samples=args.max_samples,
+ )
+ else:
+ # Auto-detect video_root_dir if not provided
+ if args.video_root_dir is None:
+ csv_dir = os.path.dirname(args.csv_path)
+ if os.path.exists(os.path.join(csv_dir, 'video_reorg')):
+ video_root_dir = os.path.join(csv_dir, 'video_reorg')
+ elif os.path.exists(os.path.join(os.path.dirname(csv_dir), 'video_reorg')):
+ video_root_dir = os.path.join(os.path.dirname(csv_dir), 'video_reorg')
+ else:
+ video_root_dir = csv_dir
+ logger.warning(f"Video directory not found, using CSV directory: {video_root_dir}")
+ else:
+ video_root_dir = args.video_root_dir
+
+ # Create tokenizer for dataset
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base"
+ else:
+ raise ValueError(f"Unknown text encoder: {args.text_encoder_architecture}")
+
+ tokenizer = T5Tokenizer.from_pretrained(model_id)
+
+ dataset = OpenVid1MDataset(
+ csv_path=args.csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ text_encoder_architecture=args.text_encoder_architecture,
+ use_random_temporal_crop=False, # Fixed sampling for consistency
+ use_random_crop=False, # Center crop for consistency
+ )
+
+ if args.max_samples is not None:
+ dataset.data = dataset.data[:args.max_samples]
+ logger.info(f"Limited dataset to {len(dataset)} samples")
+
+ logger.info(f"Dataset size: {len(dataset)}")
+
+ # Create dataloader
+ dataloader = DataLoader(
+ dataset,
+ batch_size=args.batch_size,
+ shuffle=False,
+ num_workers=0, # Use 0 to avoid multiprocessing issues
+ pin_memory=False,
+ )
+
+ # Initialize statistics
+ global_min = None
+ global_max = None
+ total_samples = 0
+ failed_samples = 0
+
+ logger.info("Starting to check codebook range...")
+ logger.info("=" * 80)
+
+ with torch.no_grad():
+ for batch_idx, batch in enumerate(tqdm(dataloader, desc="Checking codes")):
+ try:
+ if use_precomputed:
+ # Use pre-extracted video codes
+ video_codes = batch["video_codes"] # [B, F', H', W']
+ if isinstance(video_codes, torch.Tensor):
+ video_codes = video_codes.long()
+ else:
+ video_codes = torch.from_numpy(video_codes).long()
+ else:
+ # Encode videos to get codes
+ videos = batch["video"].to(device, non_blocking=True) # [B, C, F, H, W]
+ video_codes = video_tokenizer.encode(videos) # [B, F', H', W']
+ video_codes = video_codes.cpu().long()
+
+ # Update statistics
+ batch_min = video_codes.min().item()
+ batch_max = video_codes.max().item()
+
+ if global_min is None:
+ global_min = batch_min
+ global_max = batch_max
+ else:
+ global_min = min(global_min, batch_min)
+ global_max = max(global_max, batch_max)
+
+ total_samples += video_codes.shape[0]
+
+ # Print statistics periodically
+ if (batch_idx + 1) % args.check_interval == 0 or batch_idx == 0:
+ print(f"\n[Sample {total_samples}]")
+ print(f" Current batch range: [{batch_min}, {batch_max}]")
+ print(f" Global range so far: [{global_min}, {global_max}]")
+ print(f" Codebook size (expected): {video_tokenizer.codebook_size if video_tokenizer else 'N/A'}")
+ if video_tokenizer:
+ expected_max = video_tokenizer.codebook_size - 1
+ print(f" Expected max (codebook_size - 1): {expected_max}")
+ if global_max > expected_max:
+ print(f" ⚠️ WARNING: Found code {global_max} > expected max {expected_max}!")
+ if global_min < 0:
+ print(f" ⚠️ WARNING: Found code {global_min} < 0!")
+
+ # Print unique values count for current batch
+ unique_values = torch.unique(video_codes).tolist()
+ print(f" Unique values in batch: {len(unique_values)}")
+ if len(unique_values) <= 20:
+ print(f" Values: {sorted(unique_values)}")
+ else:
+ print(f" Min unique: {min(unique_values)}, Max unique: {max(unique_values)}")
+ print("-" * 80)
+
+ except Exception as e:
+ failed_samples += args.batch_size
+ logger.error(f"Failed to process batch {batch_idx}: {e}")
+ continue
+
+ # Final summary
+ logger.info("=" * 80)
+ logger.info("FINAL STATISTICS:")
+ logger.info(f" Total samples processed: {total_samples}")
+ logger.info(f" Failed samples: {failed_samples}")
+ logger.info(f" Global min code: {global_min}")
+ logger.info(f" Global max code: {global_max}")
+ logger.info(f" Code range: [{global_min}, {global_max}]")
+
+ if video_tokenizer:
+ expected_max = video_tokenizer.codebook_size - 1
+ logger.info(f" Expected max (codebook_size - 1): {expected_max}")
+ logger.info(f" Codebook size: {video_tokenizer.codebook_size}")
+ logger.info(f" Mask token ID: {video_tokenizer.mask_token_id}")
+
+ if global_max > expected_max:
+ logger.warning(f" ⚠️ WARNING: Found code {global_max} > expected max {expected_max}!")
+ elif global_max == expected_max:
+ logger.info(f" ✓ Max code matches expected max")
+ else:
+ logger.info(f" Note: Max code {global_max} < expected max {expected_max} (some codes may not be used)")
+
+ if global_min < 0:
+ logger.warning(f" ⚠️ WARNING: Found code {global_min} < 0!")
+ elif global_min == 0:
+ logger.info(f" ✓ Min code is 0 (as expected)")
+ else:
+ logger.info(f" Note: Min code {global_min} > 0 (some codes may not be used)")
+
+ logger.info("=" * 80)
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/Meissonic/train/dataset_utils.py b/Meissonic/train/dataset_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..350968ac56c1fdc16408b045aefb40cb7327509a
--- /dev/null
+++ b/Meissonic/train/dataset_utils.py
@@ -0,0 +1,1240 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import torch
+from torch.utils.data import Dataset
+from torchvision import transforms
+from PIL.ImageOps import exif_transpose
+from PIL import Image
+import io
+import pyarrow.parquet as pq
+import random
+import bisect
+import pyarrow.fs as fs
+import csv
+import numpy as np
+import logging
+
+logger = logging.getLogger(__name__)
+
+@torch.no_grad()
+def tokenize_prompt(tokenizer, prompt, text_encoder_architecture='open_clip'): # support open_clip, CLIP, T5/UMT5
+ if text_encoder_architecture == 'CLIP' or text_encoder_architecture == 'open_clip':
+ tokenizer_output = tokenizer(
+ prompt,
+ truncation=True,
+ padding="max_length",
+ max_length=77,
+ return_tensors="pt",
+ )
+ return tokenizer_output.input_ids, tokenizer_output.attention_mask
+ elif text_encoder_architecture in ['umt5-base', 'umt5-xxl', 't5']:
+ # T5/UMT5 tokenizer - return both input_ids and attention_mask
+ tokenizer_output = tokenizer(
+ prompt,
+ truncation=True,
+ padding="max_length",
+ max_length=512,
+ return_tensors="pt",
+ )
+ return tokenizer_output.input_ids, tokenizer_output.attention_mask
+ elif text_encoder_architecture == 'CLIP_T5_base': # we have two tokenizers, 1st for CLIP, 2nd for T5
+ input_ids = []
+ input_ids.append(tokenizer[0](
+ prompt,
+ truncation=True,
+ padding="max_length",
+ max_length=77,
+ return_tensors="pt",
+ ).input_ids)
+ input_ids.append(tokenizer[1](
+ prompt,
+ truncation=True,
+ padding="max_length",
+ max_length=512,
+ return_tensors="pt",
+ ).input_ids)
+ return input_ids
+ else:
+ raise ValueError(f"Unknown text_encoder_architecture: {text_encoder_architecture}")
+
+def encode_prompt(text_encoder, input_ids, text_encoder_architecture='open_clip', attention_mask=None): # support open_clip, CLIP, T5/UMT5
+ if text_encoder_architecture == 'CLIP' or text_encoder_architecture == 'open_clip':
+ outputs = text_encoder(input_ids=input_ids, return_dict=True, output_hidden_states=True)
+ encoder_hidden_states = outputs.hidden_states[-2]
+ cond_embeds = outputs[0]
+ return encoder_hidden_states, cond_embeds, None # CLIP不需要context_lens
+ elif text_encoder_architecture in ['umt5-base', 'umt5-xxl', 't5']:
+ # T5/UMT5 encoder - only returns encoder_hidden_states, no pooled projection
+ outputs = text_encoder(input_ids=input_ids, return_dict=True)
+ encoder_hidden_states = outputs.last_hidden_state
+ # For T5, we don't have a pooled projection, so return None or a dummy tensor
+ # The video pipeline doesn't use cond_embeds, so we can return None
+ cond_embeds = None
+
+ # Calculate actual context lengths from input_ids
+ # Find the actual length by looking for padding tokens
+ batch_size = input_ids.shape[0]
+ context_lens_list = []
+ for i in range(batch_size):
+ input_ids_cpu = input_ids[i].cpu()
+ # Find first padding token or use full length
+ # For T5, padding token is typically 0, but let's be safe
+ actual_len = 512
+ for j in range(512):
+ # Check for common padding tokens (0, 1 for some models)
+ if input_ids_cpu[j] in [0, 1] or input_ids_cpu[j] == text_encoder.config.pad_token_id:
+ actual_len = j
+ break
+ # Ensure minimum length of 1
+ actual_len = max(actual_len, 1)
+ context_lens_list.append(actual_len)
+ context_lens = torch.tensor(context_lens_list, dtype=torch.long, device=input_ids.device)
+
+ return encoder_hidden_states, cond_embeds, context_lens
+ elif text_encoder_architecture == 'CLIP_T5_base':
+ outputs_clip = text_encoder[0](input_ids=input_ids[0], return_dict=True, output_hidden_states=True)
+ outputs_t5 = text_encoder[1](input_ids=input_ids[1], decoder_input_ids=torch.zeros_like(input_ids[1]),
+ return_dict=True, output_hidden_states=True)
+ encoder_hidden_states = outputs_t5.encoder_hidden_states[-2]
+ cond_embeds = outputs_clip[0]
+
+ # For CLIP_T5, context_lens is None (not supported yet)
+ return encoder_hidden_states, cond_embeds, None
+ else:
+ raise ValueError(f"Unknown text_encoder_architecture: {text_encoder_architecture}")
+
+
+def process_image(image, size, Norm=False, hps_score = 6.0):
+ image = exif_transpose(image)
+
+ if not image.mode == "RGB":
+ image = image.convert("RGB")
+
+ orig_height = image.height
+ orig_width = image.width
+
+ image = transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR)(image)
+
+ c_top, c_left, _, _ = transforms.RandomCrop.get_params(image, output_size=(size, size))
+ image = transforms.functional.crop(image, c_top, c_left, size, size)
+ image = transforms.ToTensor()(image)
+
+ if Norm:
+ image = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)(image)
+
+ micro_conds = torch.tensor(
+ [orig_width, orig_height, c_top, c_left, hps_score],
+ )
+
+ return {"image": image, "micro_conds": micro_conds}
+
+
+class MyParquetDataset(Dataset):
+ def __init__(self, root_dir, tokenizer=None, size=512,
+ text_encoder_architecture='CLIP', norm=False):
+ random.seed(23)
+
+ self.root_dir = root_dir
+ self.dataset_receipt = {'MSCOCO_part1': {'total_num': 6212, 'ratio':1}, 'MSCOCO_part2': {'total_num': 6212, 'ratio':1}}
+
+ self.tokenizer = tokenizer
+ self.size = size
+ self.text_encoder_architecture = text_encoder_architecture
+ self.norm = norm
+
+ self.hdfs = fs.HadoopFileSystem(host="", port=0000) # TODO: change to your own HDFS host and port
+ self._init_mixed_parquet_dir_list()
+
+ self.file_metadata = []
+ self.cumulative_sizes = [0]
+ total = 0
+ for path in self.parquet_files:
+ try:
+ with pq.ParquetFile(path, filesystem=self.hdfs) as pf:
+ num_rows = pf.metadata.num_rows
+ self.file_metadata.append({
+ 'path': path,
+ 'num_rows': num_rows,
+ 'global_offset': total
+ })
+ total += num_rows
+ self.cumulative_sizes.append(total)
+ except Exception as e:
+ print(f"Error processing {path}: {str(e)}")
+ continue
+
+ # init cache
+ self.current_file = None
+ self.cached_data = None
+ self.cached_file_index = -1
+
+ def _init_mixed_parquet_dir_list(self):
+ print('Loading parquet files, please be patient...')
+ self.parquet_files = []
+
+ for key, value in self.dataset_receipt.items():
+ # Generate a list of standard Parquet file paths, lazy load
+ hdfs_path = os.path.join(self.root_dir, key)
+
+ num = value['total_num']
+ sampled_list = random.sample(
+ [f"{hdfs_path}/train-{idx:05d}-of-{num:05d}.parquet" for idx in range(num)],
+ k=int(num * value['ratio'])
+ )
+ self.parquet_files += sampled_list
+
+ def __len__(self):
+ return self.cumulative_sizes[-1]
+
+ def _locate_file(self, global_idx):
+ # Use binary search to quickly locate files
+ file_index = bisect.bisect_right(self.cumulative_sizes, global_idx) - 1
+ if file_index < 0 or file_index >= len(self.file_metadata):
+ raise IndexError(f"Index {global_idx} out of range")
+
+ file_info = self.file_metadata[file_index]
+ local_idx = global_idx - file_info['global_offset']
+ return file_index, local_idx
+
+ def _load_file(self, file_index):
+ """Load Parquet files into cache on demand"""
+ if self.cached_file_index != file_index:
+ file_info = self.file_metadata[file_index]
+ try:
+ table = pq.read_table(file_info['path'], filesystem=self.hdfs)
+ self.cached_data = table.to_pydict()
+ self.cached_file_index = file_index
+ except Exception as e:
+ print(f"Error loading {file_info['path']}: {str(e)}")
+ raise
+
+ def __getitem__(self, idx):
+ file_index, local_idx = self._locate_file(idx)
+ self._load_file(file_index)
+ sample = {k: v[local_idx] for k, v in self.cached_data.items()}
+
+ # cprint(sample.keys(), 'red')
+ generated_caption, image_path = sample['task2'], sample['image'] # only suitable for my data
+ instance_image = Image.open(io.BytesIO(image_path['bytes']))
+
+ # if instance_image.width < self.size or instance_image.height < self.size:
+ # raise ValueError(f"Image at {image_path} is too small")
+
+ rv = process_image(instance_image, self.size, self.norm)
+
+ if isinstance(self.tokenizer, list):
+ _tmp_ = tokenize_prompt(self.tokenizer, generated_caption, self.text_encoder_architecture)
+ rv["prompt_input_ids"] = [_tmp_[0][0], _tmp_[1][0]]
+ rv["prompt_attention_mask"] = [_tmp_[0][1], _tmp_[1][1]]
+ else:
+ input_ids, attention_mask = tokenize_prompt(self.tokenizer, generated_caption, self.text_encoder_architecture)
+ rv["prompt_input_ids"] = input_ids
+ rv["prompt_attention_mask"] = attention_mask
+
+ return rv
+
+class HuggingFaceDataset(Dataset):
+ def __init__(
+ self,
+ hf_dataset,
+ tokenizer,
+ image_key,
+ prompt_key,
+ prompt_prefix=None,
+ size=512,
+ text_encoder_architecture='CLIP',
+ ):
+ self.size = size
+ self.image_key = image_key
+ self.prompt_key = prompt_key
+ self.tokenizer = tokenizer
+ self.hf_dataset = hf_dataset
+ self.prompt_prefix = prompt_prefix
+ self.text_encoder_architecture = text_encoder_architecture
+
+ def __len__(self):
+ return len(self.hf_dataset)
+
+ def __getitem__(self, index):
+ item = self.hf_dataset[index]
+
+ rv = process_image(item[self.image_key], self.size)
+
+ prompt = item[self.prompt_key]
+
+ if self.prompt_prefix is not None:
+ prompt = self.prompt_prefix + prompt
+
+ if isinstance(self.tokenizer, list):
+ _tmp_ = tokenize_prompt(self.tokenizer, prompt, self.text_encoder_architecture)
+ rv["prompt_input_ids"] = [_tmp_[0][0],_tmp_[1][0]]
+ rv["prompt_attention_mask"] = [_tmp_[0][1],_tmp_[1][1]]
+ else:
+ input_ids, attention_mask = tokenize_prompt(self.tokenizer, prompt, self.text_encoder_architecture)
+ rv["prompt_input_ids"] = input_ids
+ rv["prompt_attention_mask"] = attention_mask
+
+ return rv
+
+
+def process_video(video_tensor, num_frames, height, width, use_random_crop=True):
+ """
+ Process video tensor for training.
+
+ Uses aspect-ratio preserving resize + crop to avoid distortion.
+
+ Args:
+ video_tensor: Video tensor of shape [C, F, H, W] or [F, H, W, C]
+ num_frames: Target number of frames
+ height: Target height
+ width: Target width
+ use_random_crop: If True, use random crop (for training). If False, use center crop (for validation/feature extraction)
+
+ Returns:
+ Processed video tensor of shape [C, F, H, W] in [0, 1] range
+ """
+ # Ensure video is in [C, F, H, W] format
+ if video_tensor.dim() == 4:
+ if video_tensor.shape[0] == 3 or video_tensor.shape[0] == 1:
+ # Already in [C, F, H, W] format
+ pass
+ elif video_tensor.shape[-1] == 3 or video_tensor.shape[-1] == 1:
+ # [F, H, W, C] -> [C, F, H, W]
+ video_tensor = video_tensor.permute(3, 0, 1, 2)
+ else:
+ raise ValueError(f"Unexpected video tensor shape: {video_tensor.shape}")
+
+ # Normalize to [0, 1] if needed
+ if video_tensor.max() > 1.0:
+ video_tensor = video_tensor / 255.0
+
+ C, F, H, W = video_tensor.shape
+
+ # Temporal resampling: ensure exactly num_frames frames
+ if F != num_frames:
+ if F < num_frames:
+ # If video is shorter, pad by repeating the last frame
+ num_pad = num_frames - F
+ last_frame = video_tensor[:, -1:, :, :] # [C, 1, H, W]
+ padding = last_frame.repeat(1, num_pad, 1, 1) # [C, num_pad, H, W]
+ video_tensor = torch.cat([video_tensor, padding], dim=1) # [C, num_frames, H, W]
+ F = num_frames
+ else:
+ # If video is longer, randomly select a continuous segment of num_frames
+ max_start = F - num_frames
+ start_idx = random.randint(0, max_start)
+ indices = torch.arange(start_idx, start_idx + num_frames)
+ video_tensor = video_tensor[:, indices, :, :]
+ F = num_frames # Update F after temporal resampling
+
+ # Spatial resizing: aspect-ratio preserving resize + crop
+ if H != height or W != width:
+ # Step 1: Aspect-ratio preserving resize
+ # Calculate scale factors for both dimensions
+ scale_h = height / H
+ scale_w = width / W
+
+ # Use the larger scale to ensure both dimensions are at least as large as target
+ # This way, after resize, we can crop to exact target size
+ scale = max(scale_h, scale_w)
+
+ # Calculate new dimensions maintaining aspect ratio
+ new_H = int(H * scale)
+ new_W = int(W * scale)
+
+ # Ensure we have at least the target size (handle rounding)
+ if new_H < height:
+ new_H = height
+ if new_W < width:
+ new_W = width
+
+ # Resize maintaining aspect ratio
+ # Process each frame: [C, F, H, W] -> reshape to [C*F, 1, H, W] for interpolation
+ video_tensor = torch.nn.functional.interpolate(
+ video_tensor.reshape(C * F, 1, H, W),
+ size=(new_H, new_W),
+ mode='bilinear',
+ align_corners=False
+ ).reshape(C, F, new_H, new_W)
+
+ # Step 2: Crop to target size (height, width)
+ # Calculate crop coordinates
+ if use_random_crop:
+ # Random crop for training (data augmentation)
+ max_h = new_H - height
+ max_w = new_W - width
+ if max_h < 0 or max_w < 0:
+ # If resized image is smaller than target, pad instead
+ pad_h = max(0, height - new_H)
+ pad_w = max(0, width - new_W)
+ video_tensor = torch.nn.functional.pad(
+ video_tensor,
+ (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2),
+ mode='constant',
+ value=0
+ )
+ # If still not exact size, crop or pad
+ if video_tensor.shape[2] != height or video_tensor.shape[3] != width:
+ video_tensor = torch.nn.functional.interpolate(
+ video_tensor.reshape(C * F, 1, video_tensor.shape[2], video_tensor.shape[3]),
+ size=(height, width),
+ mode='bilinear',
+ align_corners=False
+ ).reshape(C, F, height, width)
+ else:
+ crop_h = random.randint(0, max_h)
+ crop_w = random.randint(0, max_w)
+ video_tensor = video_tensor[:, :, crop_h:crop_h + height, crop_w:crop_w + width]
+ else:
+ # Center crop for validation/feature extraction (deterministic)
+ crop_h = (new_H - height) // 2
+ crop_w = (new_W - width) // 2
+ if crop_h < 0 or crop_w < 0:
+ # If resized image is smaller than target, pad instead
+ pad_h = max(0, height - new_H)
+ pad_w = max(0, width - new_W)
+ video_tensor = torch.nn.functional.pad(
+ video_tensor,
+ (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2),
+ mode='constant',
+ value=0
+ )
+ # If still not exact size, crop or pad
+ if video_tensor.shape[2] != height or video_tensor.shape[3] != width:
+ video_tensor = torch.nn.functional.interpolate(
+ video_tensor.reshape(C * F, 1, video_tensor.shape[2], video_tensor.shape[3]),
+ size=(height, width),
+ mode='bilinear',
+ align_corners=False
+ ).reshape(C, F, height, width)
+ else:
+ video_tensor = video_tensor[:, :, crop_h:crop_h + height, crop_w:crop_w + width]
+
+ # Final verification: ensure output has exactly the expected shape
+ C, F, H, W = video_tensor.shape
+ assert F == num_frames, f"Frame count mismatch: expected {num_frames}, got {F}"
+ assert H == height, f"Height mismatch: expected {height}, got {H}"
+ assert W == width, f"Width mismatch: expected {width}, got {W}"
+
+ return video_tensor
+
+
+class VideoDataset(Dataset):
+ """
+ Dataset for video training, compatible with HuggingFace datasets format.
+ Supports OpenVid1M and similar video-text datasets.
+ """
+ def __init__(
+ self,
+ hf_dataset,
+ tokenizer,
+ video_key="video",
+ prompt_key="caption",
+ prompt_prefix=None,
+ num_frames=16,
+ height=480,
+ width=848,
+ text_encoder_architecture='umt5-base',
+ use_random_crop=True, # Random crop for training, center crop for validation
+ ):
+ self.hf_dataset = hf_dataset
+ self.tokenizer = tokenizer
+ self.video_key = video_key
+ self.prompt_key = prompt_key
+ self.prompt_prefix = prompt_prefix
+ self.num_frames = num_frames
+ self.height = height
+ self.width = width
+ self.text_encoder_architecture = text_encoder_architecture
+ self.use_random_crop = use_random_crop
+
+ def __len__(self):
+ return len(self.hf_dataset)
+
+ def __getitem__(self, index):
+ item = self.hf_dataset[index]
+
+ # Load video
+ video = item[self.video_key]
+
+ # Convert to tensor if needed (handle different formats)
+ if isinstance(video, list):
+ # List of PIL Images or tensors
+ frames = []
+ for frame in video:
+ if isinstance(frame, Image.Image):
+ frame = transforms.ToTensor()(frame)
+ frames.append(frame)
+ video_tensor = torch.stack(frames, dim=1) # [C, F, H, W]
+ elif isinstance(video, torch.Tensor):
+ video_tensor = video
+ else:
+ raise ValueError(f"Unsupported video type: {type(video)}")
+
+ # Process video
+ video_tensor = process_video(video_tensor, self.num_frames, self.height, self.width)
+
+ # Ensure video tensor has exactly the expected shape
+ C, F, H, W = video_tensor.shape
+ if F != self.num_frames or H != self.height or W != self.width:
+ # If shape doesn't match, create a properly sized tensor
+ video_tensor = torch.nn.functional.interpolate(
+ video_tensor.reshape(C * F, 1, H, W),
+ size=(self.height, self.width),
+ mode='bilinear',
+ align_corners=False
+ ).reshape(C, F, self.height, self.width)
+ # Ensure exactly num_frames
+ if F < self.num_frames:
+ # Pad by repeating last frame
+ num_pad = self.num_frames - F
+ last_frame = video_tensor[:, -1:, :, :]
+ padding = last_frame.repeat(1, num_pad, 1, 1)
+ video_tensor = torch.cat([video_tensor, padding], dim=1)
+ elif F > self.num_frames:
+ # Crop to num_frames
+ video_tensor = video_tensor[:, :self.num_frames, :, :]
+
+ # Clone to ensure storage is resizable (required for DataLoader collate)
+ video_tensor = video_tensor.contiguous().clone()
+
+ # Process prompt
+ prompt = item[self.prompt_key]
+ if self.prompt_prefix is not None:
+ prompt = self.prompt_prefix + prompt
+
+ prompt_input_ids, prompt_attention_mask = tokenize_prompt(self.tokenizer, prompt, self.text_encoder_architecture)
+ # Clone to ensure storage is resizable
+ prompt_input_ids = prompt_input_ids.clone()
+ prompt_attention_mask = prompt_attention_mask.clone()
+
+ rv = {
+ "video": video_tensor, # [C, num_frames, height, width], guaranteed shape
+ "prompt_input_ids": prompt_input_ids,
+ "prompt_attention_mask": prompt_attention_mask
+ }
+
+ return rv
+
+
+class OpenVid1MDataset(Dataset):
+ """
+ Dataset for OpenVid1M video-text pairs from CSV file.
+
+ CSV format:
+ video,caption,aesthetic score,motion score,temporal consistency score,camera motion,frame,fps,seconds,new_id
+
+ Returns:
+ dict with keys:
+ - "video": torch.Tensor of shape [C, F, H, W] in [0, 1] range
+ - "prompt_input_ids": torch.Tensor of tokenized prompt
+ """
+ def __init__(
+ self,
+ csv_path,
+ video_root_dir,
+ tokenizer,
+ num_frames=16,
+ height=480,
+ width=848,
+ text_encoder_architecture='umt5-base',
+ prompt_prefix=None,
+ use_random_temporal_crop=True, # If False, always sample from the beginning
+ use_random_crop=True, # Random crop for training, center crop for validation/feature extraction
+ ):
+ """
+ Args:
+ csv_path: Path to the CSV file containing video metadata
+ video_root_dir: Root directory where video files are stored
+ tokenizer: Text tokenizer
+ num_frames: Target number of frames to extract
+ height: Target height
+ width: Target width
+ text_encoder_architecture: Architecture of text encoder
+ prompt_prefix: Optional prefix to add to prompts
+ """
+ self.csv_path = csv_path
+ self.video_root_dir = video_root_dir
+ self.tokenizer = tokenizer
+ self.num_frames = num_frames
+ self.height = height
+ self.width = width
+ self.text_encoder_architecture = text_encoder_architecture
+ self.prompt_prefix = prompt_prefix
+ self.use_random_temporal_crop = use_random_temporal_crop
+ self.use_random_crop = use_random_crop
+
+ # Load CSV data
+ self.data = []
+ with open(csv_path, 'r', encoding='utf-8') as f:
+ reader = csv.DictReader(f)
+ for row in reader:
+ self.data.append(row)
+
+ logger.info(f"Loaded {len(self.data)} video entries from {csv_path}")
+
+ # Try to import video loading library
+ self.video_loader = None
+ try:
+ import decord
+ decord.bridge.set_bridge('torch')
+ self.video_loader = 'decord'
+ logger.info("Using decord for video loading")
+ except ImportError:
+ try:
+ import av
+ self.video_loader = 'av'
+ logger.info("Using PyAV for video loading")
+ except ImportError:
+ try:
+ import cv2
+ self.video_loader = 'cv2'
+ logger.info("Using OpenCV for video loading")
+ except ImportError:
+ raise ImportError(
+ "No video loading library found. Please install one of: "
+ "decord (pip install decord), PyAV (pip install av), or opencv-python (pip install opencv-python)"
+ )
+
+ def __len__(self):
+ return len(self.data)
+
+ def _load_video_decord(self, video_path):
+ """Load video using decord"""
+ import decord
+ vr = decord.VideoReader(video_path, ctx=decord.cpu(0))
+ total_frames = len(vr)
+
+ # Sample frames: random temporal crop (continuous segment) for better temporal coherence
+ if total_frames <= self.num_frames:
+ indices = list(range(total_frames))
+ else:
+ if self.use_random_temporal_crop:
+ # Randomly select a continuous segment of num_frames
+ max_start = total_frames - self.num_frames
+ start_idx = random.randint(0, max_start)
+ else:
+ # Fixed sampling: always start from the beginning
+ start_idx = 0
+ indices = list(range(start_idx, start_idx + self.num_frames))
+
+ frames = vr.get_batch(indices) # [F, H, W, C] in uint8
+ # If using torch bridge, frames is already a torch Tensor
+ if isinstance(frames, torch.Tensor):
+ frames = frames.float() # [F, H, W, C]
+ else:
+ # Use torch.tensor() instead of torch.from_numpy() to ensure a complete copy
+ # This avoids "Trying to resize storage that is not resizable" errors in DataLoader collate
+ frames = torch.tensor(frames, dtype=torch.float32) # [F, H, W, C], fully copied
+ frames = frames.permute(3, 0, 1, 2) # [C, F, H, W]
+ frames = frames / 255.0 # Normalize to [0, 1]
+
+ return frames
+
+ def _load_video_av(self, video_path):
+ """Load video using PyAV"""
+ import av
+ container = av.open(video_path)
+ frames = []
+
+ # Get video stream
+ video_stream = container.streams.video[0]
+ total_frames = video_stream.frames if video_stream.frames > 0 else None
+
+ # Sample frames: random temporal crop (continuous segment) for better temporal coherence
+ if total_frames is None:
+ # If we can't get frame count, decode all frames and sample
+ frame_list = []
+ for frame in container.decode(video_stream):
+ frame_list.append(frame)
+ total_frames = len(frame_list)
+ if total_frames <= self.num_frames:
+ frame_indices = list(range(total_frames))
+ else:
+ if self.use_random_temporal_crop:
+ # Randomly select a continuous segment of num_frames
+ max_start = total_frames - self.num_frames
+ start_idx = random.randint(0, max_start)
+ else:
+ # Fixed sampling: always start from the beginning
+ start_idx = 0
+ frame_indices = list(range(start_idx, start_idx + self.num_frames))
+ frames = [transforms.ToTensor()(frame_list[i].to_image()) for i in frame_indices]
+ else:
+ if total_frames <= self.num_frames:
+ frame_indices = list(range(total_frames))
+ else:
+ if self.use_random_temporal_crop:
+ # Randomly select a continuous segment of num_frames
+ max_start = total_frames - self.num_frames
+ start_idx = random.randint(0, max_start)
+ else:
+ # Fixed sampling: always start from the beginning
+ start_idx = 0
+ frame_indices = list(range(start_idx, start_idx + self.num_frames))
+
+ frame_idx = 0
+ for frame in container.decode(video_stream):
+ if frame_idx in frame_indices:
+ img = frame.to_image() # PIL Image
+ img_tensor = transforms.ToTensor()(img) # [C, H, W]
+ frames.append(img_tensor)
+ if len(frames) >= self.num_frames:
+ break
+ frame_idx += 1
+
+ container.close()
+
+ if len(frames) == 0:
+ raise ValueError(f"No frames extracted from {video_path}")
+
+ # Stack frames: [C, F, H, W]
+ video_tensor = torch.stack(frames, dim=1)
+
+ # Pad if needed
+ if video_tensor.shape[1] < self.num_frames:
+ padding = torch.zeros(
+ video_tensor.shape[0],
+ self.num_frames - video_tensor.shape[1],
+ video_tensor.shape[2],
+ video_tensor.shape[3]
+ )
+ video_tensor = torch.cat([video_tensor, padding], dim=1)
+
+ return video_tensor
+
+ def _load_video_cv2(self, video_path):
+ """Load video using OpenCV"""
+ import cv2
+ cap = cv2.VideoCapture(video_path)
+ frames = []
+
+ total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+ # Sample frames: random temporal crop (continuous segment) for better temporal coherence
+ if total_frames <= self.num_frames:
+ frame_indices = list(range(total_frames))
+ else:
+ if self.use_random_temporal_crop:
+ # Randomly select a continuous segment of num_frames
+ max_start = total_frames - self.num_frames
+ start_idx = random.randint(0, max_start)
+ else:
+ # Fixed sampling: always start from the beginning
+ start_idx = 0
+ frame_indices = list(range(start_idx, start_idx + self.num_frames))
+
+ frame_idx = 0
+ while True:
+ ret, frame = cap.read()
+ if not ret:
+ break
+ if frame_idx in frame_indices:
+ # Convert BGR to RGB
+ frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+ # Convert to tensor [C, H, W] and normalize to [0, 1]
+ # Use torch.tensor() instead of torch.from_numpy() to ensure a complete copy
+ # This avoids "Trying to resize storage that is not resizable" errors in DataLoader collate
+ frame_tensor = torch.tensor(frame_rgb, dtype=torch.float32).permute(2, 0, 1) / 255.0
+ frames.append(frame_tensor)
+ if len(frames) >= self.num_frames:
+ break
+ frame_idx += 1
+
+ cap.release()
+
+ if len(frames) == 0:
+ raise ValueError(f"No frames extracted from {video_path}")
+
+ # Stack frames: [C, F, H, W]
+ video_tensor = torch.stack(frames, dim=1)
+
+ # Pad if needed
+ if video_tensor.shape[1] < self.num_frames:
+ padding = torch.zeros(
+ video_tensor.shape[0],
+ self.num_frames - video_tensor.shape[1],
+ video_tensor.shape[2],
+ video_tensor.shape[3]
+ )
+ video_tensor = torch.cat([video_tensor, padding], dim=1)
+
+ return video_tensor
+
+ def _load_video(self, video_path):
+ """Load video from path using the available video loader"""
+ full_path = os.path.join(self.video_root_dir, video_path)
+
+ if not os.path.exists(full_path):
+ raise FileNotFoundError(f"Video file not found: {full_path}")
+
+ if self.video_loader == 'decord':
+ return self._load_video_decord(full_path)
+ elif self.video_loader == 'av':
+ return self._load_video_av(full_path)
+ elif self.video_loader == 'cv2':
+ return self._load_video_cv2(full_path)
+ else:
+ raise ValueError(f"Unknown video loader: {self.video_loader}")
+
+ def __getitem__(self, index):
+ row = self.data[index]
+
+ # Load video
+ video_path = row['video']
+ try:
+ video_tensor = self._load_video(video_path)
+ except Exception as e:
+ # If video loading fails, return a zero tensor and log error
+ logger.warning(f"Failed to load video {video_path}: {e}")
+ video_tensor = torch.zeros(3, self.num_frames, self.height, self.width)
+
+ # Process video: aspect-ratio preserving resize + crop to target dimensions
+ video_tensor = process_video(video_tensor, self.num_frames, self.height, self.width, use_random_crop=self.use_random_crop)
+
+ # Ensure video tensor has exactly the expected shape
+ C, F, H, W = video_tensor.shape
+ if F != self.num_frames or H != self.height or W != self.width:
+ # If shape doesn't match, create a properly sized tensor
+ video_tensor = torch.nn.functional.interpolate(
+ video_tensor.reshape(C * F, 1, H, W),
+ size=(self.height, self.width),
+ mode='bilinear',
+ align_corners=False
+ ).reshape(C, F, self.height, self.width)
+ # Ensure exactly num_frames
+ if F < self.num_frames:
+ # Pad by repeating last frame
+ num_pad = self.num_frames - F
+ last_frame = video_tensor[:, -1:, :, :]
+ padding = last_frame.repeat(1, num_pad, 1, 1)
+ video_tensor = torch.cat([video_tensor, padding], dim=1)
+ elif F > self.num_frames:
+ # Crop to num_frames
+ video_tensor = video_tensor[:, :self.num_frames, :, :]
+
+ # Clone to ensure storage is resizable (required for DataLoader collate)
+ video_tensor = video_tensor.contiguous().clone()
+
+ # Process prompt
+ prompt = row['caption']
+ if self.prompt_prefix is not None:
+ prompt = self.prompt_prefix + prompt
+
+ prompt_input_ids, prompt_attention_mask = tokenize_prompt(self.tokenizer, prompt, self.text_encoder_architecture)
+ # Clone to ensure storage is resizable
+ prompt_input_ids = prompt_input_ids.clone()
+ prompt_attention_mask = prompt_attention_mask.clone()
+
+ return {
+ "video": video_tensor, # [C, num_frames, height, width], guaranteed shape
+ "prompt_input_ids": prompt_input_ids,
+ "prompt_attention_mask": prompt_attention_mask
+ }
+
+
+class TinyOpenVid1MDataset(OpenVid1MDataset):
+ """
+ A tiny subset of OpenVid1MDataset for overfitting experiments.
+ Only takes the first N samples from the full dataset.
+ """
+ def __init__(
+ self,
+ csv_path,
+ video_root_dir=None,
+ tokenizer=None,
+ num_frames=16,
+ height=480,
+ width=848,
+ text_encoder_architecture='umt5-base',
+ prompt_prefix=None,
+ max_samples=256, # Only use first N samples
+ seed=42, # Fixed seed for reproducibility
+ ):
+ """
+ Args:
+ max_samples: Maximum number of samples to use (default: 256)
+ seed: Random seed for reproducibility (default: 42)
+ """
+ # Initialize parent class
+ super().__init__(
+ csv_path=csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=num_frames,
+ height=height,
+ width=width,
+ text_encoder_architecture=text_encoder_architecture,
+ prompt_prefix=prompt_prefix,
+ )
+
+ # Limit to first max_samples
+ original_len = len(self.data)
+ if original_len > max_samples:
+ # Use fixed seed to ensure reproducibility
+ import random
+ random.seed(seed)
+ # Shuffle with fixed seed, then take first max_samples
+ indices = list(range(original_len))
+ random.shuffle(indices)
+ self.data = [self.data[i] for i in indices[:max_samples]]
+ logger.info(f"Limited dataset to {max_samples} samples (from {original_len} total) for overfitting experiment")
+ else:
+ logger.info(f"Using all {len(self.data)} samples (less than max_samples={max_samples})")
+
+
+def get_hierarchical_path(base_dir, index):
+ """
+ Get hierarchical path for loading features from 3-level directory structure.
+
+ Structure: base_dir/level1/level2/level3/filename.npy
+ - level1: index // 1000000 (0-999)
+ - level2: (index // 1000) % 1000 (0-999)
+ - level3: index % 1000 (0-999)
+
+ Args:
+ base_dir: Base directory for features
+ index: Sample index
+
+ Returns:
+ Full path to the file
+ """
+ level1 = index // 1000000
+ level2 = (index // 1000) % 1000
+ level3 = index % 1000
+
+ file_path = os.path.join(
+ base_dir,
+ f"{level1:03d}",
+ f"{level2:03d}",
+ f"{level3:03d}",
+ f"{index:08d}.npy"
+ )
+
+ return file_path
+
+
+class PrecomputedVideoOnlyDataset(Dataset):
+ """
+ Dataset for loading pre-extracted video codes only, with text processed at runtime.
+
+ This dataset loads video codes that were pre-extracted by extract_features.py,
+ but requires a tokenizer for text processing during training.
+
+ Features are stored in a 3-level hierarchical directory structure:
+ - video_codes/level1/level2/level3/index.npy
+ - text data comes from metadata (captions)
+ """
+
+ def __init__(
+ self,
+ features_dir,
+ tokenizer=None,
+ text_encoder_architecture='umt5-xxl',
+ prompt_prefix=None,
+ num_samples=None,
+ start_index=0,
+ ):
+ """
+ Args:
+ features_dir: Directory containing extracted features (should have video_codes/ subdir and metadata.json)
+ tokenizer: Text tokenizer for processing prompts at runtime
+ text_encoder_architecture: Text encoder architecture
+ prompt_prefix: Optional prefix to add to prompts
+ num_samples: Number of samples to use. If None, use all available samples.
+ start_index: Starting index for samples (for resuming or subset selection)
+ """
+ self.features_dir = features_dir
+ self.video_codes_dir = os.path.join(features_dir, "video_codes")
+ self.tokenizer = tokenizer
+ self.text_encoder_architecture = text_encoder_architecture
+ self.prompt_prefix = prompt_prefix
+ self.metadata_file = os.path.join(features_dir, "metadata.json")
+
+ # Load metadata
+ if os.path.exists(self.metadata_file):
+ import json
+ with open(self.metadata_file, 'r') as f:
+ self.metadata = json.load(f)
+ logger.info(f"Loaded metadata from {self.metadata_file}")
+ logger.info(f" Total samples in metadata: {self.metadata.get('num_samples', 'unknown')}")
+
+ # Get available indices from metadata
+ if 'samples' in self.metadata and len(self.metadata['samples']) > 0:
+ self.samples_data = {s['index']: s for s in self.metadata['samples']}
+ available_indices = sorted(self.samples_data.keys())
+ else:
+ # Fallback: infer from directory structure
+ available_indices = self._scan_hierarchical_directory(self.video_codes_dir)
+ else:
+ raise FileNotFoundError(f"Metadata file not found: {self.metadata_file}. Required for PrecomputedVideoOnlyDataset.")
+
+ # Filter by start_index and num_samples
+ available_indices = [idx for idx in available_indices if idx >= start_index]
+ if num_samples is not None:
+ available_indices = available_indices[:num_samples]
+
+ self.indices = available_indices
+ logger.info(f"PrecomputedVideoOnlyDataset: {len(self.indices)} samples available")
+ if len(self.indices) > 0:
+ logger.info(f" Index range: {min(self.indices)} to {max(self.indices)}")
+
+ def _scan_hierarchical_directory(self, base_dir):
+ """
+ Scan hierarchical directory structure to find all available indices.
+
+ Args:
+ base_dir: Base directory to scan
+
+ Returns:
+ List of available indices
+ """
+ available_indices = []
+
+ if not os.path.exists(base_dir):
+ raise FileNotFoundError(f"Directory not found: {base_dir}")
+
+ # Scan level1 directories (000-999)
+ for level1 in range(1000):
+ level1_dir = os.path.join(base_dir, f"{level1:03d}")
+ if not os.path.exists(level1_dir):
+ continue
+
+ # Scan level2 directories (000-999)
+ for level2 in range(1000):
+ level2_dir = os.path.join(level1_dir, f"{level2:03d}")
+ if not os.path.exists(level2_dir):
+ continue
+
+ # Scan level3 directories (000-999)
+ for level3 in range(1000):
+ level3_dir = os.path.join(level2_dir, f"{level3:03d}")
+ if not os.path.exists(level3_dir):
+ continue
+
+ # List all .npy files in level3 directory
+ for filename in os.listdir(level3_dir):
+ if filename.endswith('.npy'):
+ try:
+ index = int(filename.replace('.npy', ''))
+ available_indices.append(index)
+ except ValueError:
+ continue
+
+ return sorted(available_indices)
+
+ def __len__(self):
+ return len(self.indices)
+
+ def __getitem__(self, idx):
+ sample_idx = self.indices[idx]
+
+ # Get hierarchical paths for video codes
+ video_code_path = get_hierarchical_path(self.video_codes_dir, sample_idx)
+
+ # Load video codes
+ if not os.path.exists(video_code_path):
+ raise FileNotFoundError(f"Video code not found: {video_code_path}")
+ video_codes_np = np.load(video_code_path) # [F', H', W']
+ # Use torch.tensor() instead of torch.from_numpy() to ensure a complete copy
+ video_codes = torch.tensor(video_codes_np, dtype=torch.int32) # CPU tensor, int32, fully copied
+ del video_codes_np # Release numpy array reference
+
+ # Get text data from metadata
+ if sample_idx not in self.samples_data:
+ raise ValueError(f"Sample {sample_idx} not found in metadata")
+
+ sample_meta = self.samples_data[sample_idx]
+ caption = sample_meta.get('caption', '')
+ if not caption:
+ # Try alternative field names
+ caption = sample_meta.get('text', '') or sample_meta.get('prompt', '')
+
+ if not caption:
+ raise ValueError(f"No caption found for sample {sample_idx}")
+
+ # Apply prompt prefix if specified
+ if self.prompt_prefix is not None:
+ caption = self.prompt_prefix + caption
+
+ # Tokenize text
+ prompt_input_ids, prompt_attention_mask = tokenize_prompt(self.tokenizer, caption, self.text_encoder_architecture)
+ # Clone to ensure storage is resizable
+ if isinstance(prompt_input_ids, torch.Tensor) and prompt_input_ids.ndim == 2 and prompt_input_ids.shape[0] == 1:
+ prompt_input_ids = prompt_input_ids[0]
+ if isinstance(prompt_attention_mask, torch.Tensor) and prompt_attention_mask.ndim == 2 and prompt_attention_mask.shape[0] == 1:
+ prompt_attention_mask = prompt_attention_mask[0]
+
+ prompt_input_ids = prompt_input_ids.contiguous().clone()
+ prompt_attention_mask = prompt_attention_mask.contiguous().clone()
+
+ return {
+ "video_codes": video_codes, # [F', H', W'], CPU tensor, int32
+ "prompt_input_ids": prompt_input_ids, # Tokenized text
+ "prompt_attention_mask": prompt_attention_mask, # Attention mask
+ "sample_index": sample_idx,
+ }
+
+
+class PrecomputedFeatureDataset(Dataset):
+ """
+ Dataset for loading pre-extracted video codes and text embeddings.
+
+ This dataset loads features that were pre-extracted by extract_features.py,
+ avoiding the need to encode videos and text during training.
+
+ Features are stored in a 3-level hierarchical directory structure:
+ - video_codes/level1/level2/level3/index.npy
+ - text_embeddings/level1/level2/level3/index.npy
+ """
+
+ def __init__(
+ self,
+ features_dir,
+ num_samples=None,
+ start_index=0,
+ ):
+ """
+ Args:
+ features_dir: Directory containing extracted features (should have video_codes/ and text_embeddings/ subdirs)
+ num_samples: Number of samples to use. If None, use all available samples.
+ start_index: Starting index for samples (for resuming or subset selection)
+ """
+ self.features_dir = features_dir
+ self.video_codes_dir = os.path.join(features_dir, "video_codes")
+ self.text_embeddings_dir = os.path.join(features_dir, "text_embeddings")
+ self.metadata_file = os.path.join(features_dir, "metadata.json")
+
+ # Load metadata
+ if os.path.exists(self.metadata_file):
+ import json
+ with open(self.metadata_file, 'r') as f:
+ self.metadata = json.load(f)
+ logger.info(f"Loaded metadata from {self.metadata_file}")
+ logger.info(f" Total samples in metadata: {self.metadata.get('num_samples', 'unknown')}")
+
+ # Get available indices from metadata
+ if 'samples' in self.metadata and len(self.metadata['samples']) > 0:
+ available_indices = sorted([s['index'] for s in self.metadata['samples']])
+ else:
+ # Fallback: infer from directory structure
+ available_indices = self._scan_hierarchical_directory(self.video_codes_dir)
+ else:
+ # If no metadata, scan directory structure
+ logger.warning(f"Metadata file not found: {self.metadata_file}, scanning directory structure")
+ self.metadata = {}
+ available_indices = self._scan_hierarchical_directory(self.video_codes_dir)
+
+ # Filter by start_index and num_samples
+ available_indices = [idx for idx in available_indices if idx >= start_index]
+ if num_samples is not None:
+ available_indices = available_indices[:num_samples]
+
+ self.indices = available_indices
+ logger.info(f"PrecomputedFeatureDataset: {len(self.indices)} samples available")
+ if len(self.indices) > 0:
+ logger.info(f" Index range: {min(self.indices)} to {max(self.indices)}")
+
+ def _scan_hierarchical_directory(self, base_dir):
+ """
+ Scan hierarchical directory structure to find all available indices.
+
+ Args:
+ base_dir: Base directory to scan
+
+ Returns:
+ List of available indices
+ """
+ available_indices = []
+
+ if not os.path.exists(base_dir):
+ raise FileNotFoundError(f"Directory not found: {base_dir}")
+
+ # Scan level1 directories (000-999)
+ for level1 in range(1000):
+ level1_dir = os.path.join(base_dir, f"{level1:03d}")
+ if not os.path.exists(level1_dir):
+ continue
+
+ # Scan level2 directories (000-999)
+ for level2 in range(1000):
+ level2_dir = os.path.join(level1_dir, f"{level2:03d}")
+ if not os.path.exists(level2_dir):
+ continue
+
+ # Scan level3 directories (000-999)
+ for level3 in range(1000):
+ level3_dir = os.path.join(level2_dir, f"{level3:03d}")
+ if not os.path.exists(level3_dir):
+ continue
+
+ # List all .npy files in level3 directory
+ for filename in os.listdir(level3_dir):
+ if filename.endswith('.npy'):
+ try:
+ index = int(filename.replace('.npy', ''))
+ available_indices.append(index)
+ except ValueError:
+ continue
+
+ return sorted(available_indices)
+
+ def __len__(self):
+ return len(self.indices)
+
+ def __getitem__(self, idx):
+ sample_idx = self.indices[idx]
+
+ # Get hierarchical paths
+ video_code_path = get_hierarchical_path(self.video_codes_dir, sample_idx)
+ text_embedding_path = get_hierarchical_path(self.text_embeddings_dir, sample_idx)
+
+ # Load video codes
+ # Note: We load directly (not mmap) to avoid storage sharing issues with torch
+ # The files are small enough (video codes are int32, typically < 1MB per sample)
+ if not os.path.exists(video_code_path):
+ raise FileNotFoundError(f"Video code not found: {video_code_path}")
+ video_codes_np = np.load(video_code_path) # [F', H', W']
+ # Use torch.tensor() instead of torch.from_numpy() to ensure a complete copy
+ # This avoids "Trying to resize storage that is not resizable" errors in DataLoader collate
+ video_codes = torch.tensor(video_codes_np, dtype=torch.int32) # CPU tensor, int32, fully copied
+ del video_codes_np # Release numpy array reference
+
+ # Load text embedding
+ # Note: We load directly (not mmap) to avoid storage sharing issues with torch
+ if not os.path.exists(text_embedding_path):
+ raise FileNotFoundError(f"Text embedding not found: {text_embedding_path}")
+ text_embedding_np = np.load(text_embedding_path) # [L, D]
+ # Use torch.tensor() instead of torch.from_numpy() to ensure a complete copy
+ # Preserve original dtype (should be float16 from extraction)
+ text_embedding_dtype = torch.float16 if text_embedding_np.dtype == np.float16 else torch.float32
+ text_embedding = torch.tensor(text_embedding_np, dtype=text_embedding_dtype) # CPU tensor, fully copied
+ del text_embedding_np # Release numpy array reference
+
+ # Get context length from metadata if available
+ context_len = None
+ if 'samples' in self.metadata:
+ # Find the sample metadata
+ sample_meta = next((s for s in self.metadata['samples'] if s['index'] == sample_idx), None)
+ if sample_meta and 'context_len' in sample_meta:
+ context_len = sample_meta['context_len']
+
+ return {
+ "video_codes": video_codes, # [F', H', W'], CPU tensor, int32
+ "text_embedding": text_embedding, # [L, D], CPU tensor, float16/bfloat16
+ "sample_index": sample_idx,
+ "context_len": context_len, # Effective text length (int or None)
+ }
\ No newline at end of file
diff --git a/Meissonic/train/extrach_check_missing.sh b/Meissonic/train/extrach_check_missing.sh
new file mode 100644
index 0000000000000000000000000000000000000000..8dbfdc6ea5d2a2513754447253d1f635f510faad
--- /dev/null
+++ b/Meissonic/train/extrach_check_missing.sh
@@ -0,0 +1,4 @@
+python train/extract_check_missing.py \
+ --csv /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv \
+ --root /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set \
+ --world_size 8 --processes 8
diff --git a/Meissonic/train/extract.sh b/Meissonic/train/extract.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0c43948d5634466d02a3a96ce74b1a019f2c2f1b
--- /dev/null
+++ b/Meissonic/train/extract.sh
@@ -0,0 +1,67 @@
+# mv /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128_full_set /opt/dlami/nvme
+
+
+# accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 \
+# train/extract_features.py \
+# --csv_path /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv \
+# --output_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set \
+# --text_encoder_architecture umt5-xxl \
+# --video_tokenizer_model_id Cosmos-0.1-Tokenizer-DV4x8x8 \
+# --num_frames 17 \
+# --video_height 256 \
+# --video_width 256 \
+# --batch_size 64 \
+# --num_workers 8 \
+# --extract_text \
+# --extract_video \
+# --save_attention_mask \
+# --skip_existing
+
+accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25012 --num_processes 8 \
+ train/extract_features.py \
+ --csv_path /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv \
+ --output_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set \
+ --text_encoder_architecture umt5-xxl \
+ --video_tokenizer_model_id Cosmos-0.1-Tokenizer-DV4x8x8 \
+ --num_frames 17 \
+ --video_height 256 \
+ --video_width 256 \
+ --batch_size 64 \
+ --num_workers 8 \
+ --extract_text --extract_video --save_attention_mask \
+ --skip_existing \
+ --index_file /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set/missing_process_{rank}.txt \
+ --run_tag resume \
+ --merge_world_size 8
+
+
+
+# python train/extract_empty_embeds.py \
+# --text_encoder_architecture umt5-base \
+# --output_path /path/to/empty_embeds.pt \
+# --dtype float16
+
+
+# python train/train_mei_video.py \
+# --use_precomputed_features \
+# --features_dir /path/to/extracted_features \
+# --text_encoder_architecture umt5-base \
+# --video_tokenizer_model_id Cosmos-1.0-Tokenizer-DV8x16x16 \
+# --num_frames 16 \
+# --video_height 480 \
+# --video_width 848 \
+# --train_batch_size 8 \
+# --learning_rate 3e-4 \
+# --max_train_steps 10000 \
+# --output_dir ./output \
+# --mixed_precision bf16
+
+
+# python train/check_codebook_range.py \
+# --csv_path /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv \
+# --video_tokenizer_model_id Cosmos-0.1-Tokenizer-DV4x8x8 \
+# --num_frames 16 \
+# --video_height 480 \
+# --video_width 848 \
+# --check_interval 10 \
+# --max_samples 1000 # 可选：限制检查的样本数
\ No newline at end of file
diff --git a/Meissonic/train/extract_check_missing.py b/Meissonic/train/extract_check_missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..a94a35cf8443280e50d3cc63013c12544534ed6b
--- /dev/null
+++ b/Meissonic/train/extract_check_missing.py
@@ -0,0 +1,206 @@
+
+import argparse
+import json
+from pathlib import Path
+
+def count_csv_rows(csv_path: Path) -> int:
+ # 只数行数（减去表头），不读入内存
+ n = 0
+ with csv_path.open("rb") as f:
+ for _ in f:
+ n += 1
+ return max(0, n - 1)
+
+def iter_samples_from_pos(f, initial_buf: str, stop_on_trunc=True):
+ """
+ 从 '"samples": [' 后面开始，做 brace-matching 流式提取每个 { ... } 对象。
+ 即使文件末尾截断，也会尽量返回已完整解析的对象；末尾不完整对象会被丢弃。
+ """
+ in_string = False
+ escape = False
+ depth = 0
+ collecting = False
+ obj_chars = []
+
+ def feed(ch: str):
+ nonlocal in_string, escape, depth, collecting, obj_chars
+ if in_string:
+ if collecting:
+ obj_chars.append(ch)
+ if escape:
+ escape = False
+ else:
+ if ch == "\\":
+ escape = True
+ elif ch == '"':
+ in_string = False
+ return None
+
+ # not in string
+ if ch == '"':
+ in_string = True
+ if collecting:
+ obj_chars.append(ch)
+ return None
+
+ # samples array end
+ if not collecting and ch == "]":
+ return "__END__"
+
+ if ch == "{":
+ if not collecting:
+ collecting = True
+ depth = 1
+ obj_chars = ["{"]
+ else:
+ depth += 1
+ obj_chars.append("{")
+ return None
+
+ if collecting:
+ obj_chars.append(ch)
+ if ch == "{":
+ depth += 1
+ elif ch == "}":
+ depth -= 1
+ if depth == 0:
+ s = "".join(obj_chars)
+ collecting = False
+ obj_chars = []
+ try:
+ return json.loads(s)
+ except json.JSONDecodeError:
+ # 这一般只会发生在对象本身被截断/污染
+ return "__BAD_OBJECT__"
+ return None
+
+ def consume(text: str):
+ for ch in text:
+ out = feed(ch)
+ if out == "__END__":
+ return "__END__"
+ if isinstance(out, dict):
+ yield out
+ # bad object: skip
+ return None
+
+ endflag = yield from consume(initial_buf)
+ if endflag == "__END__":
+ return
+
+ while True:
+ chunk = f.read(1024 * 1024)
+ if not chunk:
+ break
+ endflag = yield from consume(chunk)
+ if endflag == "__END__":
+ return
+
+ # 文件结束：如果还在 collecting，说明截断；丢弃最后不完整对象
+ return
+
+def iter_samples_salvage(meta_path: Path):
+ """
+ 容错读取 metadata_process_i.json 中 samples 数组里的每条 sample。
+ 如果文件截断，仍尽量读出前面完整部分。
+ """
+ with meta_path.open("r", encoding="utf-8-sig") as f:
+ buf = ""
+ found = False
+
+ # 找到 '"samples"' 后的 '['
+ while True:
+ chunk = f.read(1024 * 1024)
+ if not chunk:
+ break
+ buf += chunk
+ k = buf.find('"samples"')
+ if k != -1:
+ b = buf.find("[", k)
+ if b != -1:
+ buf = buf[b + 1 :] # 从 '[' 后开始
+ found = True
+ break
+ # 控制 buf 不无限增长
+ if len(buf) > 8 * 1024 * 1024:
+ buf = buf[-4 * 1024 * 1024 :]
+
+ if not found:
+ return # 没找到 samples（文件太坏/格式不同）
+
+ yield from iter_samples_from_pos(f, buf)
+
+def main():
+ ap = argparse.ArgumentParser()
+ ap.add_argument("--csv", required=True, help="OpenVid1M_reorganized.csv 路径")
+ ap.add_argument("--root", required=True, help="extracted_features_* 根目录（含 metadata_process_*.json）")
+ ap.add_argument("--world_size", type=int, default=8)
+ ap.add_argument("--processes", type=int, default=8)
+ args = ap.parse_args()
+
+ csv_path = Path(args.csv)
+ root = Path(args.root)
+ assert csv_path.exists(), f"CSV not found: {csv_path}"
+ assert root.exists(), f"root not found: {root}"
+
+ N = count_csv_rows(csv_path)
+ print(f"CSV rows (N, 0-based indices 0..N-1): {N}")
+ print(f"world_size={args.world_size}, processes={args.processes}")
+ print("-" * 80)
+
+ all_missing = []
+
+ for r in range(args.processes):
+ meta = root / f"metadata_process_{r}.json"
+ if not meta.exists():
+ print(f"[rank {r}] metadata missing file: {meta}")
+ continue
+
+ seen = set()
+ total_samples_parsed = 0
+ bad_or_missing_index = 0
+
+ for s in iter_samples_salvage(meta):
+ total_samples_parsed += 1
+ idx = s.get("index", None)
+ if idx is None:
+ bad_or_missing_index += 1
+ continue
+ try:
+ idx = int(idx)
+ except Exception:
+ bad_or_missing_index += 1
+ continue
+ seen.add(idx)
+
+ # 理论应有 index：i, i+world_size, ...
+ exp_count = 0
+ missing = []
+ if r < N:
+ for idx in range(r, N, args.world_size):
+ exp_count += 1
+ if idx not in seen:
+ missing.append(idx)
+
+ out_txt = root / f"missing_process_{r}.txt"
+ out_txt.write_text("\n".join(map(str, missing)) + ("\n" if missing else ""), encoding="utf-8")
+
+ all_missing.extend(missing)
+
+ # 一些诊断信息：如果 parsed 样本数远小于 exp_count，基本就是 metadata 截断/没写完
+ coverage = (len(seen) / exp_count * 100.0) if exp_count > 0 else 0.0
+ print(
+ f"[rank {r}] expected={exp_count:,} parsed_samples={total_samples_parsed:,} "
+ f"unique_index={len(seen):,} idx_bad={bad_or_missing_index:,} "
+ f"missing={len(missing):,} coverage={coverage:.2f}% -> {out_txt.name}"
+ )
+
+ all_missing = sorted(set(all_missing))
+ out_all = root / "missing_all.txt"
+ out_all.write_text("\n".join(map(str, all_missing)) + ("\n" if all_missing else ""), encoding="utf-8")
+
+ print("-" * 80)
+ print(f"TOTAL missing unique indices vs CSV = {len(all_missing):,} -> {out_all}")
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
diff --git a/Meissonic/train/extract_empty_embeds.py b/Meissonic/train/extract_empty_embeds.py
new file mode 100644
index 0000000000000000000000000000000000000000..b28c7daec8f506d57ec0af32c64cf3d75dce137b
--- /dev/null
+++ b/Meissonic/train/extract_empty_embeds.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+"""
+Extract and save empty_embeds for conditional dropout.
+
+This script extracts the empty embedding (from empty string prompt)
+and saves it to a file that can be loaded during training with precomputed features.
+"""
+
+import argparse
+import os
+import json
+import torch
+from transformers import T5EncoderModel, T5Tokenizer
+from dataset_utils import tokenize_prompt, encode_prompt
+import logging
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Extract empty embeddings for conditional dropout")
+
+ parser.add_argument(
+ "--text_encoder_architecture",
+ type=str,
+ default="umt5-base",
+ choices=["umt5-base", "umt5-xxl", "t5"],
+ help="Text encoder architecture",
+ )
+ parser.add_argument(
+ "--output_path",
+ type=str,
+ required=True,
+ help="Path to save the empty_embeds (will save as .pt file and metadata as .json)",
+ )
+ parser.add_argument(
+ "--device",
+ type=str,
+ default="cuda" if torch.cuda.is_available() else "cpu",
+ help="Device to use for encoding",
+ )
+ parser.add_argument(
+ "--dtype",
+ type=str,
+ default="float16",
+ choices=["float16", "bfloat16", "float32"],
+ help="Data type for saving embeddings",
+ )
+
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+
+ # Map architecture to model ID
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base"
+ else:
+ raise ValueError(f"Unknown text encoder architecture: {args.text_encoder_architecture}")
+
+ # Map dtype
+ dtype_map = {
+ "float16": torch.float16,
+ "bfloat16": torch.bfloat16,
+ "float32": torch.float32,
+ }
+ dtype = dtype_map[args.dtype]
+
+ logger.info(f"Loading text encoder: {model_id}")
+ logger.info(f"Device: {args.device}, Dtype: {args.dtype}")
+
+ # Load text encoder and tokenizer
+ text_encoder = T5EncoderModel.from_pretrained(model_id)
+ tokenizer = T5Tokenizer.from_pretrained(model_id)
+
+ # Move to device and set dtype
+ text_encoder.to(device=args.device, dtype=dtype)
+ text_encoder.eval()
+ text_encoder.requires_grad_(False)
+
+ # Extract empty embedding
+ logger.info("Extracting empty embedding from empty string...")
+ with torch.no_grad():
+ empty_input_ids = tokenize_prompt(tokenizer, "", args.text_encoder_architecture)
+ empty_input_ids = empty_input_ids.to(args.device)
+
+ empty_embeds, cond_embeds = encode_prompt(
+ text_encoder,
+ empty_input_ids,
+ args.text_encoder_architecture
+ )
+
+ # Convert to CPU and target dtype
+ empty_embeds = empty_embeds.cpu().to(dtype)
+
+ logger.info(f"Empty embedding shape: {empty_embeds.shape}")
+ logger.info(f"Empty embedding dtype: {empty_embeds.dtype}")
+
+ # Save empty_embeds
+ output_dir = os.path.dirname(args.output_path)
+ if output_dir:
+ os.makedirs(output_dir, exist_ok=True)
+
+ # Save as .pt file
+ torch.save(empty_embeds, args.output_path)
+ logger.info(f"Saved empty_embeds to: {args.output_path}")
+
+ # Save metadata
+ metadata_path = args.output_path.replace('.pt', '.json')
+ metadata = {
+ "text_encoder_architecture": args.text_encoder_architecture,
+ "model_id": model_id,
+ "empty_embeds_shape": list(empty_embeds.shape),
+ "empty_embeds_dtype": str(empty_embeds.dtype),
+ "device": args.device,
+ "dtype": args.dtype,
+ }
+
+ with open(metadata_path, 'w') as f:
+ json.dump(metadata, f, indent=2)
+ logger.info(f"Saved metadata to: {metadata_path}")
+
+ logger.info("Done!")
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/Meissonic/train/extract_features.py b/Meissonic/train/extract_features.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2e64d81bc200e9d6d4f7633f870f4cedc0f6a21
--- /dev/null
+++ b/Meissonic/train/extract_features.py
@@ -0,0 +1,848 @@
+#!/usr/bin/env python3
+"""
+Extract video codes and text embeddings from video-text pairs for efficient training.
+
+Adds resume-by-index_file and robust merge:
+- --index_file: run only specified global indices (e.g. missing_process_0.txt)
+- --run_tag: write new per-rank metadata as metadata_process_{rank}.{tag}.json
+- Merge old possibly-truncated metadata_process_*.json + new tagged ones into metadata.json
+"""
+
+import argparse
+import os
+import sys
+import logging
+from tqdm import tqdm
+import torch
+import numpy as np
+from torch.utils.data import DataLoader, DistributedSampler, Sampler
+import json
+
+sys.path.append(os.getcwd())
+
+from train.dataset_utils import OpenVid1MDataset
+from src.pipeline_video import CosmosVideoTokenizer
+from transformers import T5Tokenizer, T5EncoderModel
+from accelerate import Accelerator
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+# -----------------------------
+# IO helpers
+# -----------------------------
+def get_hierarchical_path(base_dir, index):
+ """
+ Structure: base_dir/level1/level2/level3/filename.npy
+ - level1: index // 1000000 (0-999)
+ - level2: (index // 1000) % 1000 (0-999)
+ - level3: index % 1000 (0-999)
+ """
+ level1 = index // 1000000
+ level2 = (index // 1000) % 1000
+ level3 = index % 1000
+ dir_path = os.path.join(base_dir, f"{level1:03d}", f"{level2:03d}", f"{level3:03d}")
+ file_path = os.path.join(dir_path, f"{index:08d}.npy")
+ return dir_path, file_path
+
+
+def atomic_save_npy(path: str, arr: np.ndarray):
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ tmp = path + ".tmp"
+ with open(tmp, "wb") as f:
+ np.save(f, arr)
+ os.replace(tmp, path)
+
+
+def atomic_save_json(path: str, obj, indent=2):
+ os.makedirs(os.path.dirname(path), exist_ok=True)
+ tmp = path + ".tmp"
+ with open(tmp, "w") as f:
+ json.dump(obj, f, indent=indent)
+ os.replace(tmp, path)
+
+
+def safe_mmap_shape(npy_path: str):
+ try:
+ arr = np.load(npy_path, mmap_mode="r")
+ return list(arr.shape)
+ except Exception:
+ return None
+
+
+def normalize_input_ids(x: torch.Tensor) -> torch.Tensor:
+ if x.ndim == 3:
+ if x.shape[1] == 1:
+ x = x.squeeze(1)
+ elif x.shape[2] == 1:
+ x = x.squeeze(2)
+ else:
+ raise ValueError(f"Unexpected input_ids shape: {tuple(x.shape)}")
+ elif x.ndim != 2:
+ raise ValueError(f"Unexpected input_ids ndim: {x.ndim}, shape={tuple(x.shape)}")
+ return x
+
+
+def get_feature_paths(args, video_codes_dir, text_embeddings_dir, attention_masks_dir, sample_idx: int):
+ paths = {}
+ if args.extract_video:
+ _, vp = get_hierarchical_path(video_codes_dir, sample_idx)
+ paths["video"] = vp
+ if args.extract_text:
+ _, tp = get_hierarchical_path(text_embeddings_dir, sample_idx)
+ paths["text"] = tp
+ if args.save_attention_mask:
+ _, ap = get_hierarchical_path(attention_masks_dir, sample_idx)
+ paths["mask"] = ap
+ return paths
+
+
+# -----------------------------
+# robust salvage for truncated JSON
+# -----------------------------
+def iter_samples_salvage(meta_path: str):
+ """
+ Read possibly-truncated metadata_process_*.json and salvage complete objects in "samples":[...].
+ """
+ p = meta_path
+ if not os.path.exists(p):
+ return
+ with open(p, "r", encoding="utf-8-sig") as f:
+ buf = ""
+ found = False
+ while True:
+ chunk = f.read(1024 * 1024)
+ if not chunk:
+ break
+ buf += chunk
+ k = buf.find('"samples"')
+ if k != -1:
+ b = buf.find("[", k)
+ if b != -1:
+ buf = buf[b + 1 :]
+ found = True
+ break
+ if len(buf) > 8 * 1024 * 1024:
+ buf = buf[-4 * 1024 * 1024 :]
+ if not found:
+ return
+
+ in_string = False
+ escape = False
+ depth = 0
+ collecting = False
+ obj = []
+
+ def feed(ch):
+ nonlocal in_string, escape, depth, collecting, obj
+ if in_string:
+ if collecting:
+ obj.append(ch)
+ if escape:
+ escape = False
+ else:
+ if ch == "\\":
+ escape = True
+ elif ch == '"':
+ in_string = False
+ return None
+
+ if ch == '"':
+ in_string = True
+ if collecting:
+ obj.append(ch)
+ return None
+
+ if not collecting and ch == "]":
+ return "__END__"
+
+ if ch == "{":
+ if not collecting:
+ collecting = True
+ depth = 1
+ obj = ["{"]
+ else:
+ depth += 1
+ obj.append("{")
+ return None
+
+ if collecting:
+ obj.append(ch)
+ if ch == "{":
+ depth += 1
+ elif ch == "}":
+ depth -= 1
+ if depth == 0:
+ s = "".join(obj)
+ collecting = False
+ obj = []
+ try:
+ return json.loads(s)
+ except Exception:
+ return "__BAD__"
+ return None
+
+ def consume(text):
+ for ch in text:
+ out = feed(ch)
+ if out == "__END__":
+ return "__END__"
+ if isinstance(out, dict):
+ yield out
+
+ end = yield from consume(buf)
+ if end == "__END__":
+ return
+
+ while True:
+ chunk = f.read(1024 * 1024)
+ if not chunk:
+ break
+ end = yield from consume(chunk)
+ if end == "__END__":
+ return
+
+
+def merge_metadata(output_dir: str, merge_world_size: int, run_tag: str):
+ """
+ Merge:
+ - old metadata_process_{r}.json (may be truncated -> salvage)
+ - new metadata_process_{r}.{run_tag}.json (assumed complete)
+ into metadata.json (index-dedup, prefer new).
+ """
+ outdir = output_dir
+ by_idx = {}
+
+ # old first
+ for r in range(merge_world_size):
+ old_p = os.path.join(outdir, f"metadata_process_{r}.json")
+ for s in iter_samples_salvage(old_p) or []:
+ idx = s.get("index", None)
+ if idx is None:
+ continue
+ by_idx[int(idx)] = s
+
+ # then new (override)
+ if run_tag:
+ for r in range(merge_world_size):
+ new_p = os.path.join(outdir, f"metadata_process_{r}.{run_tag}.json")
+ if not os.path.exists(new_p):
+ continue
+ try:
+ with open(new_p, "r") as f:
+ meta = json.load(f)
+ for s in meta.get("samples", []):
+ idx = s.get("index", None)
+ if idx is None:
+ continue
+ by_idx[int(idx)] = s
+ except Exception as e:
+ logger.warning(f"Failed to load new meta {new_p}: {e}")
+
+ samples = [by_idx[k] for k in sorted(by_idx.keys())]
+
+ merged = {
+ "num_extracted_metadata": len(samples),
+ "world_size_used": merge_world_size,
+ "samples": samples,
+ }
+
+ # try pull header info from any available new meta (rank0 preferred)
+ header_src = None
+ if run_tag:
+ p0 = os.path.join(outdir, f"metadata_process_0.{run_tag}.json")
+ if os.path.exists(p0):
+ header_src = p0
+ if header_src is None:
+ p0_old = os.path.join(outdir, "metadata_process_0.json")
+ if os.path.exists(p0_old):
+ header_src = p0_old
+
+ if header_src:
+ # best-effort load; if truncated, skip
+ try:
+ with open(header_src, "r") as f:
+ m0 = json.load(f)
+ for k in [
+ "extract_video","extract_text","text_encoder_architecture","video_tokenizer_model_id",
+ "codebook_size","mask_token_id","num_frames","video_height","video_width",
+ "prompt_prefix","text_dtype","save_attention_mask","empty_embeds_shape","empty_embeds_path",
+ "num_samples_original","resume_from_index","num_samples_this_run","num_attempted",
+ "num_extracted","num_failed","num_processes","ranks_seen"
+ ]:
+ if k in m0 and m0[k] is not None:
+ merged[k] = m0[k]
+ except Exception:
+ pass
+
+ metadata_file = os.path.join(outdir, "metadata.json")
+ # backup old
+ if os.path.exists(metadata_file):
+ bak = os.path.join(outdir, "metadata.json.bak")
+ try:
+ os.replace(metadata_file, bak)
+ logger.info(f"Backed up old metadata.json -> {bak}")
+ except Exception:
+ pass
+
+ atomic_save_json(metadata_file, merged, indent=2)
+ logger.info(f"[MERGE] Wrote {metadata_file}, samples={len(samples):,}")
+
+
+# -----------------------------
+# Sampler for index list
+# -----------------------------
+class IndexListSampler(Sampler):
+ def __init__(self, indices):
+ self.indices = list(indices)
+ def __iter__(self):
+ return iter(self.indices)
+ def __len__(self):
+ return len(self.indices)
+
+
+# -----------------------------
+# Args
+# -----------------------------
+def parse_args():
+ parser = argparse.ArgumentParser(description="Extract video codes and text embeddings")
+
+ parser.add_argument("--csv_path", type=str, required=True, help="Path to OpenVid1M CSV file")
+ parser.add_argument("--video_root_dir", type=str, default=None, help="Root directory containing video files. If None, auto-detect.")
+ parser.add_argument("--output_dir", type=str, required=True, help="Output directory to save extracted features")
+
+ parser.add_argument(
+ "--text_encoder_architecture",
+ type=str,
+ default="umt5-base",
+ choices=["umt5-base", "umt5-xxl", "t5"],
+ help="Text encoder architecture",
+ )
+ parser.add_argument(
+ "--video_tokenizer_model_id",
+ type=str,
+ default="Cosmos-1.0-Tokenizer-DV8x16x16",
+ help="HuggingFace model ID for Cosmos video tokenizer",
+ )
+ parser.add_argument("--num_frames", type=int, default=16, help="Number of frames per video")
+ parser.add_argument("--video_height", type=int, default=480, help="Video height")
+ parser.add_argument("--video_width", type=int, default=848, help="Video width")
+ parser.add_argument("--batch_size", type=int, default=4, help="Batch size for feature extraction")
+ parser.add_argument("--num_workers", type=int, default=4, help="Number of dataloader workers")
+ parser.add_argument("--max_samples", type=int, default=None, help="Max samples (for testing). If None, process all.")
+ parser.add_argument("--resume_from_index", type=int, default=0, help="Resume extraction from this index")
+ parser.add_argument("--prompt_prefix", type=str, default=None, help="Prefix to add to prompts")
+
+ parser.add_argument("--extract_video", action="store_true", default=False, help="Extract video codes")
+ parser.add_argument("--extract_text", action="store_true", default=False, help="Extract text embeddings")
+
+ parser.add_argument("--text_dtype", type=str, default="bf16", choices=["fp32", "fp16", "bf16"], help="Text encoder dtype")
+
+ parser.add_argument("--skip_existing", action="store_true", help="Skip samples whose feature .npy already exist.")
+ parser.add_argument("--overwrite", action="store_true", help="Overwrite existing .npy (disables skip_existing).")
+
+ group = parser.add_mutually_exclusive_group()
+ group.add_argument("--save_attention_mask", dest="save_attention_mask", action="store_true",
+ help="Save attention mask per sample (default: on).")
+ group.add_argument("--no_save_attention_mask", dest="save_attention_mask", action="store_false",
+ help="Do NOT save attention mask per sample.")
+ parser.set_defaults(save_attention_mask=True)
+
+ # resume / merge additions
+ parser.add_argument(
+ "--index_file",
+ type=str,
+ default=None,
+ help="Text file with one global sample index per line. "
+ "Can contain '{rank}' placeholder, e.g. missing_process_{rank}.txt"
+ )
+ parser.add_argument(
+ "--run_tag",
+ type=str,
+ default=None,
+ help="Tag for new per-rank metadata file: metadata_process_{rank}.{run_tag}.json"
+ )
+ parser.add_argument(
+ "--merge_world_size",
+ type=int,
+ default=None,
+ help="How many ranks to merge for final metadata.json. "
+ "Set to 8 for your OpenVid1M run even if you resume with 1 GPU."
+ )
+
+ return parser.parse_args()
+
+
+# -----------------------------
+# Main
+# -----------------------------
+def main():
+ args = parse_args()
+ accelerator = Accelerator()
+
+ rank = accelerator.process_index
+ world_size = accelerator.num_processes
+ logger.info(f"Process {rank}/{world_size} on device {accelerator.device}")
+
+ if accelerator.is_main_process:
+ os.makedirs(args.output_dir, exist_ok=True)
+ logger.info(f"Output directory: {args.output_dir}")
+ logger.info(f"Using {accelerator.num_processes} GPUs for parallel extraction")
+ logger.info(f"Extract video codes: {args.extract_video}")
+ logger.info(f"Extract text embeddings: {args.extract_text}")
+ logger.info(f"skip_existing={args.skip_existing}, overwrite={args.overwrite}, save_attention_mask={args.save_attention_mask}")
+ logger.info(f"index_file={args.index_file}, run_tag={args.run_tag}, merge_world_size={args.merge_world_size}")
+
+ if not args.extract_video and not args.extract_text:
+ raise ValueError("At least one feature type must be enabled. Use --extract_video and/or --extract_text.")
+
+ device = accelerator.device
+ dtype = torch.float32
+
+ # ---- text encoder/tokenizer
+ text_encoder = None
+ tokenizer = None
+ if args.extract_text:
+ logger.info(f"Loading text encoder: {args.text_encoder_architecture} with dtype {args.text_dtype}")
+ text_dtype_map = {"fp32": torch.float32, "fp16": torch.float16, "bf16": torch.bfloat16}
+ text_dtype = text_dtype_map[args.text_dtype]
+
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base"
+ else:
+ raise ValueError(f"Unknown text encoder: {args.text_encoder_architecture}")
+
+ text_encoder = T5EncoderModel.from_pretrained(model_id, torch_dtype=text_dtype)
+ tokenizer = T5Tokenizer.from_pretrained(model_id)
+ text_encoder.to(device=device)
+ text_encoder.eval()
+ text_encoder.requires_grad_(False)
+
+ # empty_embeds on main process
+ if accelerator.is_main_process:
+ logger.info("Extracting empty_embeds for conditional dropout...")
+ with torch.no_grad():
+ empty = tokenizer("", return_tensors="pt", padding="max_length", max_length=512, truncation=True)
+ empty_ids = empty["input_ids"].to(device)
+ empty_mask = empty["attention_mask"].to(device)
+ outputs = text_encoder(input_ids=empty_ids, attention_mask=empty_mask)
+ empty_embeds = outputs.last_hidden_state # [1, 512, D]
+
+ empty_embeds_cpu = empty_embeds.cpu()
+ if empty_embeds_cpu.dtype == torch.bfloat16:
+ empty_embeds_cpu = empty_embeds_cpu.to(torch.float32)
+ empty_embeds_np = empty_embeds_cpu.numpy().astype(np.float16)
+ empty_embeds_path = os.path.join(args.output_dir, "empty_embeds.npy")
+ np.save(empty_embeds_path, empty_embeds_np)
+ logger.info(f"Saved empty_embeds to: {empty_embeds_path} shape={empty_embeds_np.shape} dtype={empty_embeds_np.dtype}")
+ else:
+ logger.info("Skipping text encoder loading (extract_text=False)")
+ if args.extract_video:
+ tokenizer = T5Tokenizer.from_pretrained("google/umt5-base")
+
+ # ---- video tokenizer
+ video_tokenizer = None
+ if args.extract_video:
+ logger.info(f"Loading video tokenizer: {args.video_tokenizer_model_id}")
+ video_tokenizer = CosmosVideoTokenizer(model_id=args.video_tokenizer_model_id, device=device, dtype=dtype)
+ video_tokenizer.requires_grad_(False)
+ video_tokenizer.eval()
+ else:
+ logger.info("Skipping video tokenizer loading (extract_video=False)")
+
+ # ---- auto-detect video_root_dir
+ if args.video_root_dir is None:
+ csv_dir = os.path.dirname(args.csv_path)
+ if os.path.exists(os.path.join(csv_dir, "video_reorg")):
+ video_root_dir = os.path.join(csv_dir, "video_reorg")
+ elif os.path.exists(os.path.join(os.path.dirname(csv_dir), "video_reorg")):
+ video_root_dir = os.path.join(os.path.dirname(csv_dir), "video_reorg")
+ else:
+ video_root_dir = csv_dir
+ logger.warning(f"Video directory not found, using CSV directory: {video_root_dir}")
+ else:
+ video_root_dir = args.video_root_dir
+
+ # ---- dataset
+ dataset = OpenVid1MDataset(
+ csv_path=args.csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ text_encoder_architecture=args.text_encoder_architecture,
+ prompt_prefix=args.prompt_prefix,
+ use_random_temporal_crop=False,
+ use_random_crop=False,
+ )
+
+ if args.max_samples is not None:
+ dataset.data = dataset.data[:args.max_samples]
+ logger.info(f"Limited dataset to {len(dataset)} samples")
+
+ if args.resume_from_index > 0:
+ dataset.data = dataset.data[args.resume_from_index:]
+ logger.info(f"Resuming from index {args.resume_from_index}, remaining samples: {len(dataset)}")
+
+ num_processes = accelerator.num_processes
+ process_index = accelerator.process_index
+
+ # ---- sampler: DistributedSampler OR IndexListSampler
+ if args.index_file is not None:
+ idx_path = args.index_file.format(rank=process_index)
+ with open(idx_path, "r") as f:
+ wanted_sample_idx = [int(x.strip()) for x in f if x.strip() and not x.strip().startswith("#")]
+
+ sampler_indices = []
+ for sample_idx in wanted_sample_idx:
+ global_dataset_idx = sample_idx - args.resume_from_index
+ if 0 <= global_dataset_idx < len(dataset.data):
+ sampler_indices.append(global_dataset_idx)
+
+ sampler = IndexListSampler(sampler_indices)
+ logger.info(f"[GPU {process_index}] Using index_file={idx_path}, indices={len(sampler_indices)}")
+ else:
+ sampler = DistributedSampler(
+ dataset,
+ num_replicas=num_processes,
+ rank=process_index,
+ shuffle=False,
+ drop_last=False,
+ )
+ sampler_indices = list(sampler)
+
+ dataloader = DataLoader(
+ dataset,
+ batch_size=args.batch_size,
+ sampler=sampler,
+ num_workers=args.num_workers,
+ pin_memory=True,
+ )
+
+ # ---- output dirs
+ video_codes_dir = None
+ text_embeddings_dir = None
+ attention_masks_dir = None
+ if args.extract_video:
+ video_codes_dir = os.path.join(args.output_dir, "video_codes")
+ os.makedirs(video_codes_dir, exist_ok=True)
+ if args.extract_text:
+ text_embeddings_dir = os.path.join(args.output_dir, "text_embeddings")
+ os.makedirs(text_embeddings_dir, exist_ok=True)
+ if args.save_attention_mask:
+ attention_masks_dir = os.path.join(args.output_dir, "attention_masks")
+ os.makedirs(attention_masks_dir, exist_ok=True)
+
+ # ---- load existing metadata shapes (optional)
+ metadata_file = os.path.join(args.output_dir, "metadata.json")
+ existing_shapes = {}
+ if os.path.exists(metadata_file):
+ try:
+ with open(metadata_file, "r") as f:
+ existing_meta = json.load(f)
+ for sample in existing_meta.get("samples", []):
+ idx = sample.get("index")
+ if idx is None:
+ continue
+ existing_shapes[int(idx)] = {
+ "video_code_shape": sample.get("video_code_shape"),
+ "text_embedding_shape": sample.get("text_embedding_shape"),
+ "context_len": sample.get("context_len"),
+ }
+ logger.info(f"[GPU {process_index}] Loaded existing metadata for {len(existing_shapes)} samples")
+ except Exception as e:
+ logger.warning(f"[GPU {process_index}] Failed to load existing metadata: {e}")
+
+ total_samples = len(dataset)
+ logger.info(f"[GPU {process_index}] Starting feature extraction for {total_samples} samples "
+ f"(process {process_index+1}/{num_processes}), assigned={len(sampler_indices)}")
+
+ # tokenizer info
+ codebook_size = None
+ mask_token_id = None
+ if args.extract_video and video_tokenizer is not None:
+ codebook_size = getattr(video_tokenizer, "codebook_size", None)
+ mask_token_id = getattr(video_tokenizer, "mask_token_id", None)
+ logger.info(f"[GPU {process_index}] Video tokenizer: codebook_size={codebook_size}, mask_token_id={mask_token_id}")
+
+ # empty embeds info
+ empty_embeds_shape = None
+ empty_embeds_path = os.path.join(args.output_dir, "empty_embeds.npy")
+ if args.extract_text and accelerator.is_main_process and os.path.exists(empty_embeds_path):
+ try:
+ empty_embeds_np = np.load(empty_embeds_path, mmap_mode="r")
+ empty_embeds_shape = list(empty_embeds_np.shape)
+ logger.info(f"Empty embeds shape: {empty_embeds_shape}")
+ except Exception:
+ pass
+
+ process_metadata = {
+ "process_index": process_index,
+ "num_samples_this_run": total_samples,
+ "world_size_used": world_size,
+ "rank_used": rank,
+ "extract_video": args.extract_video,
+ "extract_text": args.extract_text,
+ "text_encoder_architecture": args.text_encoder_architecture if args.extract_text else None,
+ "video_tokenizer_model_id": args.video_tokenizer_model_id if args.extract_video else None,
+ "codebook_size": codebook_size,
+ "mask_token_id": mask_token_id,
+ "num_frames": args.num_frames,
+ "video_height": args.video_height,
+ "video_width": args.video_width,
+ "prompt_prefix": args.prompt_prefix,
+ "text_dtype": args.text_dtype if args.extract_text else None,
+ "save_attention_mask": args.save_attention_mask,
+ "empty_embeds_shape": empty_embeds_shape if process_index == 0 else None,
+ "empty_embeds_path": "empty_embeds.npy" if args.extract_text else None,
+ "samples": [],
+ }
+
+ process_failed_samples = []
+ process_samples_processed = 0
+ process_attempted_samples = 0 # counts actual encoded+written attempts (post-skip)
+
+ with torch.no_grad():
+ for batch_idx, batch in enumerate(
+ tqdm(dataloader, desc=f"[GPU {process_index}] Extracting", disable=not accelerator.is_main_process)
+ ):
+ batch_size = batch["video"].shape[0] if args.extract_video else batch["prompt_input_ids"].shape[0]
+ local_start_idx = batch_idx * args.batch_size
+
+ # ---- compute sample indices for this batch (global sample_idx)
+ batch_sample_indices = []
+ for i in range(batch_size):
+ local_idx = local_start_idx + i
+ if local_idx < len(sampler_indices):
+ global_dataset_idx = sampler_indices[local_idx]
+ sample_idx = args.resume_from_index + global_dataset_idx
+ batch_sample_indices.append(sample_idx)
+ else:
+ batch_sample_indices.append(None)
+
+ # ---- compute output paths
+ batch_paths = []
+ for sidx in batch_sample_indices:
+ if sidx is None:
+ batch_paths.append(None)
+ else:
+ batch_paths.append(get_feature_paths(args, video_codes_dir, text_embeddings_dir, attention_masks_dir, sidx))
+
+ # ---- determine per-feature need flags
+ need_video = [False] * batch_size
+ need_text = [False] * batch_size
+ need_mask = [False] * batch_size
+
+ for i, (sidx, paths) in enumerate(zip(batch_sample_indices, batch_paths)):
+ if sidx is None:
+ continue
+
+ if args.overwrite:
+ need_video[i] = args.extract_video
+ need_text[i] = args.extract_text
+ need_mask[i] = args.extract_text and args.save_attention_mask
+ elif args.skip_existing:
+ if args.extract_video:
+ need_video[i] = not os.path.exists(paths["video"])
+ if args.extract_text:
+ need_text[i] = not os.path.exists(paths["text"])
+ if args.save_attention_mask:
+ need_mask[i] = not os.path.exists(paths["mask"])
+ else:
+ need_video[i] = args.extract_video
+ need_text[i] = args.extract_text
+ need_mask[i] = args.extract_text and args.save_attention_mask
+
+ need_any = [v or t or m for v, t, m in zip(need_video, need_text, need_mask)]
+
+ # IMPORTANT FIX:
+ # - normal full run: if no one needs anything, skip this batch
+ # - index_file resume: even if no extraction needed, still record metadata
+ if (not any(need_any)) and (args.index_file is None):
+ continue
+
+ process_attempted_samples += sum(need_any)
+
+ if batch_idx == 0:
+ preview = [x for x in batch_sample_indices[:5] if x is not None]
+ logger.info(f"[GPU {process_index}] First batch sample indices: {preview}")
+ logger.info(f"[GPU {process_index}] Need any in first batch: {sum(need_any)}/{len(need_any)}")
+
+ # ---- encode video for needed samples
+ video_codes = None
+ need_video_idx = [i for i, ok in enumerate(need_video) if ok]
+ map_video_pos = {i: p for p, i in enumerate(need_video_idx)}
+ if args.extract_video and len(need_video_idx) > 0:
+ videos = batch["video"].to(device, non_blocking=True)
+ videos_sel = videos[need_video_idx]
+ try:
+ vc_sel = video_tokenizer.encode(videos_sel)
+ video_codes = vc_sel.detach().cpu().numpy()
+ except Exception as e:
+ logger.error(f"[GPU {process_index}] Failed to encode video batch {batch_idx}: {e}")
+ for i in need_video_idx:
+ sidx = batch_sample_indices[i]
+ if sidx is not None:
+ process_failed_samples.append({"index": sidx, "reason": "video_encoding_failed"})
+ continue
+
+ # ---- text context lens + encode needed
+ encoder_hidden_states = None
+ attention_masks = None
+ context_lens_np_full = None
+
+ if args.extract_text:
+ prompt_input_ids = batch["prompt_input_ids"].to(device, non_blocking=True)
+ if isinstance(prompt_input_ids, (tuple, list)):
+ prompt_input_ids = prompt_input_ids[0]
+ prompt_input_ids = normalize_input_ids(prompt_input_ids).long()
+
+ pad_id = tokenizer.pad_token_id
+ attention_mask_full = (prompt_input_ids != pad_id).long()
+ context_lens_full = attention_mask_full.sum(dim=-1)
+ context_lens_np_full = context_lens_full.detach().cpu().numpy().astype(np.int32)
+
+ need_text_idx = [i for i, ok in enumerate(need_text) if ok]
+ map_text_pos = {i: p for p, i in enumerate(need_text_idx)}
+
+ if len(need_text_idx) > 0:
+ ids_sel = prompt_input_ids[need_text_idx]
+ mask_sel = attention_mask_full[need_text_idx]
+ try:
+ outputs = text_encoder(input_ids=ids_sel, attention_mask=mask_sel)
+ enc = outputs.last_hidden_state.detach().cpu()
+ if enc.dtype == torch.bfloat16:
+ enc = enc.to(torch.float32)
+ encoder_hidden_states = enc.numpy().astype(np.float16)
+ if args.save_attention_mask:
+ attention_masks = mask_sel.detach().cpu().numpy().astype(np.int32)
+ except Exception as e:
+ logger.error(f"[GPU {process_index}] Failed to encode text batch {batch_idx}: {e}")
+ for i in need_text_idx:
+ sidx = batch_sample_indices[i]
+ if sidx is not None:
+ process_failed_samples.append({"index": sidx, "reason": "text_encoding_failed"})
+ continue
+ else:
+ map_text_pos = {}
+ else:
+ need_text_idx = []
+ map_text_pos = {}
+
+ # ---- save per sample + record metadata
+ for i in range(batch_size):
+ local_idx = local_start_idx + i
+ if local_idx >= len(sampler_indices):
+ continue
+
+ global_dataset_idx = sampler_indices[local_idx]
+ sample_idx = args.resume_from_index + global_dataset_idx
+ paths = batch_paths[i]
+
+ row = dataset.data[global_dataset_idx] if global_dataset_idx < len(dataset.data) else None
+ if row is None:
+ continue
+
+ existing_info = existing_shapes.get(sample_idx, {})
+
+ video_code_shape = None
+ if args.extract_video:
+ if need_video[i]:
+ if video_codes is None:
+ process_failed_samples.append({"index": sample_idx, "reason": "video_codes_none"})
+ continue
+ pos = map_video_pos[i]
+ video_code = video_codes[pos].astype(np.int32)
+ try:
+ atomic_save_npy(paths["video"], video_code)
+ video_code_shape = list(video_code.shape)
+ except Exception as e:
+ process_failed_samples.append({"index": sample_idx, "reason": f"video_save_failed: {str(e)}"})
+ continue
+ else:
+ video_code_shape = existing_info.get("video_code_shape") or safe_mmap_shape(paths["video"])
+
+ text_embedding_shape = None
+ if args.extract_text:
+ if need_text[i]:
+ if encoder_hidden_states is None:
+ process_failed_samples.append({"index": sample_idx, "reason": "text_embeddings_none"})
+ continue
+ pos = map_text_pos[i]
+ text_emb = encoder_hidden_states[pos]
+ try:
+ atomic_save_npy(paths["text"], text_emb)
+ text_embedding_shape = list(text_emb.shape)
+ except Exception as e:
+ process_failed_samples.append({"index": sample_idx, "reason": f"text_save_failed: {str(e)}"})
+ continue
+
+ if args.save_attention_mask and need_mask[i]:
+ if attention_masks is None:
+ process_failed_samples.append({"index": sample_idx, "reason": "attention_masks_none"})
+ continue
+ try:
+ atomic_save_npy(paths["mask"], attention_masks[pos])
+ except Exception as e:
+ process_failed_samples.append({"index": sample_idx, "reason": f"mask_save_failed: {str(e)}"})
+ continue
+ else:
+ text_embedding_shape = existing_info.get("text_embedding_shape") or safe_mmap_shape(paths["text"])
+
+ sample_meta = {
+ "index": sample_idx,
+ "video_path": row.get("video", ""),
+ "caption": row.get("caption", ""),
+ }
+ if args.extract_video and video_code_shape is not None:
+ sample_meta["video_code_shape"] = video_code_shape
+ if args.extract_text and text_embedding_shape is not None:
+ sample_meta["text_embedding_shape"] = text_embedding_shape
+ if args.extract_text and context_lens_np_full is not None:
+ sample_meta["context_len"] = int(context_lens_np_full[i])
+
+ process_metadata["samples"].append(sample_meta)
+ process_samples_processed += 1
+
+ # periodic per-process metadata save (atomic, tagged)
+ if process_samples_processed > 0 and (process_samples_processed % 1000 == 0):
+ suffix = f".{args.run_tag}" if args.run_tag else ""
+ process_metadata_file = os.path.join(args.output_dir, f"metadata_process_{process_index}{suffix}.json")
+ process_metadata["num_extracted"] = process_samples_processed
+ process_metadata["failed_samples"] = process_failed_samples
+ atomic_save_json(process_metadata_file, process_metadata, indent=2)
+ logger.info(f"[GPU {process_index}] Progress: {process_samples_processed} samples recorded -> {process_metadata_file}")
+
+ accelerator.wait_for_everyone()
+
+ # final per-process metadata save
+ suffix = f".{args.run_tag}" if args.run_tag else ""
+ process_metadata_file = os.path.join(args.output_dir, f"metadata_process_{process_index}{suffix}.json")
+ process_metadata["num_attempted"] = int(process_attempted_samples)
+ process_metadata["num_extracted"] = int(process_samples_processed)
+ process_metadata["num_failed"] = int(len(process_failed_samples))
+ process_metadata["failed_samples"] = process_failed_samples
+ atomic_save_json(process_metadata_file, process_metadata, indent=2)
+
+ logger.info(f"[GPU {process_index}] Done: attempted={process_attempted_samples}, extracted(meta)={process_samples_processed}, failed={len(process_failed_samples)}")
+ accelerator.wait_for_everyone()
+
+ # merge on main process
+ if accelerator.is_main_process:
+ merge_world = args.merge_world_size if args.merge_world_size is not None else world_size
+ logger.info(f"[MERGE] merging world_size={merge_world} (run_tag={args.run_tag})")
+ merge_metadata(args.output_dir, merge_world_size=merge_world, run_tag=args.run_tag)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Meissonic/train/fix_extrach.py b/Meissonic/train/fix_extrach.py
new file mode 100644
index 0000000000000000000000000000000000000000..6138d05476d2fb9fac1ef88ebd4c25d9c75e01f2
--- /dev/null
+++ b/Meissonic/train/fix_extrach.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+import os, json, glob, csv
+from pathlib import Path
+
+OUTDIR = "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set"
+MERGED = f"{OUTDIR}/metadata.json"
+CSV_PATH = "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv"
+WORLD = 8
+
+def safe_json_load(p):
+ try:
+ with open(p, "r") as f:
+ return json.load(f)
+ except Exception:
+ return None
+
+def count_csv_rows(csv_path):
+ # robust row count excluding header
+ n = 0
+ with open(csv_path, "r", newline="") as f:
+ reader = csv.reader(f)
+ header = next(reader, None)
+ for _ in reader:
+ n += 1
+ return n
+
+def pick_header_source():
+ # prefer resume files
+ cand = []
+ for r in range(WORLD):
+ p = f"{OUTDIR}/metadata_process_{r}.resume.json"
+ if os.path.exists(p):
+ cand.append(p)
+ if cand:
+ return cand[0]
+ # fallback to old
+ for r in range(WORLD):
+ p = f"{OUTDIR}/metadata_process_{r}.json"
+ if os.path.exists(p):
+ # old may be truncated; but header is at top so json.load may still fail.
+ # try a few ranks
+ cand.append(p)
+ for p in cand:
+ m = safe_json_load(p)
+ if m is not None:
+ return p
+ return None
+
+def main():
+ merged = safe_json_load(MERGED)
+ assert merged is not None, f"Cannot load {MERGED}"
+
+ samples = merged.get("samples", [])
+ n_list = len(samples)
+ idxs = [s.get("index") for s in samples if isinstance(s, dict)]
+ idxs_int = [int(x) for x in idxs if x is not None]
+ n_unique = len(set(idxs_int))
+ n_dup = n_list - n_unique
+
+ print("---- Current merged metadata.json ----")
+ print(f"samples list length = {n_list:,}")
+ print(f"unique indices = {n_unique:,}")
+ print(f"duplicates in samples list = {n_dup:,}")
+
+ n_csv = count_csv_rows(CSV_PATH)
+ print("---- CSV ----")
+ print(f"CSV rows (expected total) = {n_csv:,}")
+
+ header_src = pick_header_source()
+ header = {}
+ if header_src is not None:
+ src = safe_json_load(header_src)
+ if src is not None:
+ header = src
+ print(f"---- Header source ----\n{header_src}")
+ else:
+ print("---- Header source ----\nNone (json.load failed), will use minimal header")
+ else:
+ print("---- Header source ----\nNone found, will use minimal header")
+
+ # build new header like your 128-version format
+ new_meta = {
+ "num_samples_original": int(header.get("num_samples_original", n_csv)),
+ "resume_from_index": int(header.get("resume_from_index", 0)),
+ "num_samples_this_run": int(header.get("num_samples_this_run", n_csv)),
+ "num_attempted": int(header.get("num_attempted", n_unique)), # fallback
+ "num_extracted": int(header.get("num_extracted", n_unique)), # fallback
+ "num_failed": int(header.get("num_failed", 0)),
+ "num_processes": int(header.get("num_processes", WORLD)),
+ "ranks_seen": header.get("ranks_seen", list(range(WORLD))),
+ "world_size_used": int(header.get("world_size_used", WORLD)),
+ "extract_video": bool(header.get("extract_video", True)),
+ "extract_text": bool(header.get("extract_text", True)),
+ "text_encoder_architecture": header.get("text_encoder_architecture", "umt5-xxl"),
+ "video_tokenizer_model_id": header.get("video_tokenizer_model_id", "Cosmos-0.1-Tokenizer-DV4x8x8"),
+ "codebook_size": header.get("codebook_size", 64000),
+ "mask_token_id": header.get("mask_token_id", 64000),
+ "num_frames": int(header.get("num_frames", 17)),
+ "video_height": int(header.get("video_height", 256)),
+ "video_width": int(header.get("video_width", 256)),
+ "prompt_prefix": header.get("prompt_prefix", None),
+ "text_dtype": header.get("text_dtype", "bf16"),
+ "save_attention_mask": bool(header.get("save_attention_mask", True)),
+ "empty_embeds_shape": header.get("empty_embeds_shape", [1, 512, 4096]),
+ "empty_embeds_path": header.get("empty_embeds_path", "empty_embeds.npy"),
+ "samples": samples,
+ }
+
+ # backup & write
+ bak = f"{MERGED}.bak"
+ if os.path.exists(MERGED) and not os.path.exists(bak):
+ os.replace(MERGED, bak)
+ print(f"Backup: {MERGED} -> {bak}")
+
+ tmp = f"{MERGED}.tmp"
+ with open(tmp, "w") as f:
+ json.dump(new_meta, f, indent=2)
+ os.replace(tmp, MERGED)
+ print(f"Wrote: {MERGED}")
+
+if __name__ == "__main__":
+ main()
diff --git a/Meissonic/train/infer_mei_video.py b/Meissonic/train/infer_mei_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3ddc0baf12aeeae00c73c7c855dec6b6a063d00
--- /dev/null
+++ b/Meissonic/train/infer_mei_video.py
@@ -0,0 +1,497 @@
+#!/usr/bin/env python3
+"""
+Batch video generation inference script using the trained video diffusion model.
+
+This script supports processing multiple prompts in batch, with each prompt
+generating videos for all specified guidance scales.
+
+Usage:
+ python train/batch_infer_video.py --model_path /path/to/trained/model --output_dir ./output
+
+Example:
+ python train/infer_video.py \
+ --pretrained_model_name_or_path ./output/checkpoint-1000 \
+ --guidance_scales 1.0 3.0 5.0 7.0 9.0 11.0 \
+ --num_inference_steps 1000 \
+ --prompts "A person walking" "mountain" \
+ --output_dir ./batch_output
+"""
+
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+# Set memory optimization environment variables
+# os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True,max_split_size_mb:512"
+# os.environ["TORCH_USE_CUDA_DSA"] = "1"
+
+import argparse
+import torch
+from torchvision import transforms
+from torchvision.utils import save_image, make_grid
+from PIL import Image
+import numpy as np
+import time
+import logging
+
+from src.transformer_video import WanDiscreteVideoTransformer
+from src.pipeline_video import Pipeline, CosmosVideoTokenizer
+from src.scheduler_video import Scheduler
+from transformers import T5Tokenizer, T5EncoderModel
+
+logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Batch video generation inference")
+ parser.add_argument(
+ "--pretrained_model_name_or_path",
+ type=str,
+ required=True,
+ help="Path to trained model checkpoint (directory containing 'transformer' subfolder)",
+ )
+ parser.add_argument(
+ "--text_encoder_path",
+ type=str,
+ default="google/umt5-xxl",
+ help="Path or HuggingFace ID for T5 text encoder",
+ )
+ parser.add_argument(
+ "--video_tokenizer_model_id",
+ type=str,
+ default="Cosmos-0.1-Tokenizer-DV4x8x8",
+ help="HuggingFace model ID for Cosmos video tokenizer",
+ )
+ parser.add_argument(
+ "--num_frames",
+ type=int,
+ default=16,
+ help="Number of frames to generate",
+ )
+ parser.add_argument(
+ "--height",
+ type=int,
+ default=128,
+ help="Height of generated video",
+ )
+ parser.add_argument(
+ "--width",
+ type=int,
+ default=128,
+ help="Width of generated video",
+ )
+ parser.add_argument(
+ "--num_inference_steps",
+ type=int,
+ default=64,
+ help="Number of denoising steps",
+ )
+ parser.add_argument(
+ "--guidance_scale",
+ type=float,
+ default=9.0,
+ help="Classifier-free guidance scale (single value, ignored if --guidance_scales is provided)",
+ )
+ parser.add_argument(
+ "--guidance_scales",
+ type=float,
+ nargs="+",
+ default=None,
+ help="List of guidance scales to test (e.g., --guidance_scales 3.0 5.0 7.0 9.0). If provided, overrides --guidance_scale",
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="./generated_videos",
+ help="Directory to save generated videos",
+ )
+ parser.add_argument(
+ "--prompts",
+ type=str,
+ nargs="+",
+ required=True,
+ help="List of prompts for video generation",
+ )
+ parser.add_argument(
+ "--negative_prompt",
+ type=str,
+ default="worst quality, low quality, blurry, distortion, watermark, logo, text, jpeg artifacts",
+ help="Negative prompt for generation",
+ )
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=42,
+ help="Random seed for reproducibility",
+ )
+ parser.add_argument(
+ "--device",
+ type=str,
+ default="cuda",
+ help="Device to run inference on",
+ )
+ parser.add_argument(
+ "--dtype",
+ type=str,
+ default="bfloat16",
+ choices=["float32", "float16", "bfloat16"],
+ help="Data type for inference (bfloat16 recommended for memory efficiency)",
+ )
+ parser.add_argument(
+ "--save_frames",
+ action="store_true",
+ help="Save individual frames as images",
+ )
+ parser.add_argument(
+ "--save_grid",
+ action="store_true",
+ default=True,
+ help="Save frames as a grid image",
+ )
+ parser.add_argument(
+ "--save_mp4",
+ action="store_true",
+ help="Save video as MP4 file (requires imageio-ffmpeg)",
+ )
+ parser.add_argument(
+ "--fps",
+ type=int,
+ default=8,
+ help="Frames per second for video output (GIF/MP4)",
+ )
+ return parser.parse_args()
+
+
+def save_video_as_gif(frames, output_path, duration=100):
+ """Save video frames as GIF"""
+ if isinstance(frames[0], torch.Tensor):
+ frames = [transforms.ToPILImage()(f.clamp(0, 1)) for f in frames]
+ frames[0].save(
+ output_path,
+ save_all=True,
+ append_images=frames[1:],
+ duration=duration,
+ loop=0
+ )
+
+
+def save_video_frames(frames, output_dir, prefix="frame"):
+ """Save individual video frames as images"""
+ os.makedirs(output_dir, exist_ok=True)
+ for i, frame in enumerate(frames):
+ if isinstance(frame, torch.Tensor):
+ frame = transforms.ToPILImage()(frame.clamp(0, 1))
+ frame.save(os.path.join(output_dir, f"{prefix}_{i:04d}.png"))
+
+
+def save_video_as_mp4(frames, output_path, fps=8):
+ """Save video frames as MP4 file"""
+ try:
+ import imageio
+ import imageio_ffmpeg
+ except ImportError:
+ raise ImportError(
+ "imageio and imageio-ffmpeg are required for MP4 output. "
+ "Install with: pip install imageio imageio-ffmpeg"
+ )
+
+ # Convert frames to numpy arrays
+ if isinstance(frames[0], Image.Image):
+ frame_arrays = [np.array(frame) for frame in frames]
+ elif isinstance(frames[0], torch.Tensor):
+ frame_arrays = [
+ (frame.clamp(0, 1).permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8)
+ for frame in frames
+ ]
+ else:
+ frame_arrays = [np.array(frame) for frame in frames]
+
+ # Write MP4
+ imageio.mimwrite(output_path, frame_arrays, fps=fps, codec='libx264', quality=8)
+
+
+def main():
+ args = parse_args()
+
+ # Determine guidance scales to use
+ if args.guidance_scales is not None:
+ guidance_scales = args.guidance_scales
+ else:
+ guidance_scales = [args.guidance_scale]
+
+ # Determine if we're doing grid search
+ is_grid_search = len(guidance_scales) > 1
+ total_combinations = len(guidance_scales) * len(args.prompts)
+
+ print("=" * 80)
+ print("Batch Video Generation Inference")
+ print("=" * 80)
+ print(f"Number of prompts: {len(args.prompts)}")
+ print(f"Guidance scales: {guidance_scales}")
+ print(f"Total combinations: {total_combinations}")
+ print(f"Output directory: {args.output_dir}")
+ print(f"Model path: {args.pretrained_model_name_or_path}")
+
+ # Set device and dtype
+ device = torch.device(args.device if torch.cuda.is_available() else "cpu")
+ if args.dtype == "float16":
+ dtype = torch.float16
+ elif args.dtype == "bfloat16":
+ dtype = torch.bfloat16
+ else:
+ dtype = torch.float32
+
+ # Set random seed
+ if args.seed is not None:
+ torch.manual_seed(args.seed)
+ if torch.cuda.is_available():
+ torch.cuda.manual_seed_all(args.seed)
+ print(f"Random seed: {args.seed}")
+
+ # Create output directory
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ print("=" * 80)
+ print("Loading Video Generation Pipeline")
+ print("=" * 80)
+
+ # 1. Load video tokenizer
+ print(f"\nLoading video tokenizer: {args.video_tokenizer_model_id}")
+ video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=device,
+ dtype=dtype
+ )
+ print("✓ Video tokenizer loaded")
+ print(f" Codebook size: {video_tokenizer.codebook_size}")
+ print(f" Compression factors: {video_tokenizer.t_downsample}x{video_tokenizer.h_downsample}x{video_tokenizer.w_downsample}")
+
+ # 2. Load T5 text encoder and tokenizer
+ print(f"\nLoading text encoder: {args.text_encoder_path}")
+ tokenizer = T5Tokenizer.from_pretrained(args.text_encoder_path)
+ # Load text encoder to specified device
+ text_encoder = T5EncoderModel.from_pretrained(
+ args.text_encoder_path,
+ torch_dtype=dtype,
+ ).to(device)
+ text_encoder.eval()
+ print(f"✓ Text encoder loaded (dim: {text_encoder.config.d_model})")
+
+ # 3. Calculate compressed dimensions
+ F_prime = args.num_frames // video_tokenizer.t_downsample
+ H_prime = args.height // video_tokenizer.h_downsample
+ W_prime = args.width // video_tokenizer.w_downsample
+ print(f"\nCompressed dimensions: F'={F_prime}, H'={H_prime}, W'={W_prime}")
+
+ # 4. Load transformer model
+ print(f"\nLoading transformer from: {args.pretrained_model_name_or_path}")
+ transformer_path = args.pretrained_model_name_or_path
+ if os.path.isdir(args.pretrained_model_name_or_path):
+ # Check for transformer subfolder
+ if os.path.exists(os.path.join(args.pretrained_model_name_or_path, "transformer")):
+ transformer_path = os.path.join(args.pretrained_model_name_or_path, "transformer")
+
+ try:
+ transformer = WanDiscreteVideoTransformer.from_pretrained(
+ transformer_path,
+ torch_dtype=dtype,
+ low_cpu_mem_usage=True,
+ ).to(device)
+ print("✓ Transformer loaded from pretrained")
+ except Exception as e:
+ print(f"Failed to load pretrained transformer: {e}")
+ print("Trying to load config and state dict separately...")
+
+ # Try loading from state dict
+ import json
+ config_path = os.path.join(transformer_path, "config.json")
+ if os.path.exists(config_path):
+ with open(config_path, 'r') as f:
+ config = json.load(f)
+
+ transformer = WanDiscreteVideoTransformer(**config)
+
+ # Load state dict
+ state_dict_path = os.path.join(transformer_path, "diffusion_pytorch_model.safetensors")
+ if not os.path.exists(state_dict_path):
+ state_dict_path = os.path.join(transformer_path, "diffusion_pytorch_model.bin")
+ if not os.path.exists(state_dict_path):
+ state_dict_path = os.path.join(transformer_path, "pytorch_model.bin")
+
+ if os.path.exists(state_dict_path):
+ if state_dict_path.endswith(".safetensors"):
+ from safetensors import safe_open
+ state_dict = {}
+ with safe_open(state_dict_path, framework="pt", device="cpu") as f:
+ for k in f.keys():
+ state_dict[k] = f.get_tensor(k)
+ else:
+ state_dict = torch.load(state_dict_path, map_location="cpu")
+ transformer.load_state_dict(state_dict, strict=False)
+ print(f"✓ Loaded state dict from {state_dict_path}")
+
+ transformer = transformer.to(device, dtype=dtype)
+ else:
+ raise FileNotFoundError(f"Could not find config.json in {transformer_path}")
+
+ transformer.eval()
+
+ # 5. Initialize scheduler
+ print("Initializing scheduler...")
+ scheduler = Scheduler(
+ mask_token_id=video_tokenizer.mask_token_id,
+ masking_schedule="cosine"
+ )
+ print(f"✓ Scheduler initialized (mask_token_id: {scheduler.config.mask_token_id})")
+
+ # 6. Create pipeline
+ print("Creating video pipeline...")
+ pipe = Pipeline(
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ transformer=transformer,
+ scheduler=scheduler,
+ video_tokenizer=video_tokenizer,
+ text_len=512,
+ num_frames=args.num_frames,
+ height=args.height,
+ width=args.width,
+ )
+ print(f"✓ Pipeline created")
+ print(f" Default video dimensions: {pipe.num_frames} frames, {pipe.height}x{pipe.width}")
+
+ # 7. Generate videos for each prompt
+ print("\n" + "=" * 80)
+ print("Generating Videos")
+ print("=" * 80)
+
+ total_start_time = time.time()
+ successful_generations = 0
+ failed_generations = 0
+ combination_idx = 0
+
+ for prompt_idx, prompt in enumerate(args.prompts):
+ for guidance_scale in guidance_scales:
+ combination_idx += 1
+
+ # Create output directory for this combination
+ if is_grid_search:
+ sanitized_prompt = prompt.replace(" ", "_")[:30].replace("/", "-").replace("\\", "-")
+ combo_dir = os.path.join(
+ args.output_dir,
+ f"prompt{prompt_idx:02d}_{sanitized_prompt}",
+ f"gs{guidance_scale:.1f}_steps{args.num_inference_steps}"
+ )
+ os.makedirs(combo_dir, exist_ok=True)
+ else:
+ combo_dir = args.output_dir
+ os.makedirs(combo_dir, exist_ok=True)
+
+ total_items = total_combinations if is_grid_search else len(args.prompts)
+ prompt_display = prompt[:60] + "..." if len(prompt) > 60 else prompt
+ print(f"\n[{combination_idx}/{total_items}] "
+ f"Prompt {prompt_idx+1}/{len(args.prompts)} | "
+ f"Guidance: {guidance_scale:.1f} | Steps: {args.num_inference_steps}")
+ print(f" Prompt: '{prompt_display}'")
+
+ try:
+ generation_start = time.time()
+
+ with torch.no_grad():
+ result = pipe(
+ prompt=prompt,
+ negative_prompt=args.negative_prompt,
+ num_frames=args.num_frames,
+ height=args.height,
+ width=args.width,
+ guidance_scale=guidance_scale,
+ num_inference_steps=args.num_inference_steps,
+ output_type="pil",
+ return_dict=True,
+ )
+
+ videos = result.videos
+
+ # Handle different output formats
+ if isinstance(videos, list) and len(videos) > 0:
+ video_frames = videos[0] if isinstance(videos[0], list) else videos
+ else:
+ video_frames = videos
+
+ generation_time = time.time() - generation_start
+ print(".2f")
+
+ # Create sanitized filename from prompt
+ sanitized_prompt = prompt.replace(" ", "_")[:50].replace("/", "-").replace("\\", "-")
+ if is_grid_search:
+ base_name = f"gs{guidance_scale:.1f}_steps{args.num_inference_steps}"
+ else:
+ base_name = f"{prompt_idx:02d}_{sanitized_prompt}"
+
+ # Save video as GIF
+ gif_path = os.path.join(combo_dir, f"{base_name}.gif")
+ save_video_as_gif(video_frames, gif_path, duration=int(1000 / args.fps))
+ print(f" ✓ Saved GIF: {gif_path}")
+
+ # Save as MP4 if requested
+ if args.save_mp4:
+ mp4_path = os.path.join(combo_dir, f"{base_name}.mp4")
+ save_video_as_mp4(video_frames, mp4_path, fps=args.fps)
+ print(f" ✓ Saved MP4: {mp4_path}")
+
+ # Save as frame grid
+ if args.save_grid:
+ if isinstance(video_frames[0], Image.Image):
+ frames_tensor = torch.stack([transforms.ToTensor()(f) for f in video_frames], dim=0)
+ else:
+ frames_tensor = torch.stack(video_frames, dim=0)
+
+ grid = make_grid(frames_tensor, nrow=min(8, len(video_frames)))
+ grid_path = os.path.join(combo_dir, f"{base_name}_grid.png")
+ save_image(grid, grid_path)
+ print(f" ✓ Saved grid: {grid_path}")
+
+ # Save individual frames
+ if args.save_frames:
+ frames_dir = os.path.join(combo_dir, f"{base_name}_frames")
+ save_video_frames(video_frames, frames_dir)
+ print(f" ✓ Saved frames: {frames_dir}")
+
+ successful_generations += 1
+
+ except Exception as e:
+ failed_generations += 1
+ print(f" ✗ Failed to generate video: {e}")
+ import traceback
+ traceback.print_exc()
+
+ total_time = time.time() - total_start_time
+
+ print("\n" + "=" * 80)
+ print("Generation Summary")
+ print("=" * 80)
+ if is_grid_search:
+ print(f"Grid search over {len(guidance_scales)} guidance scales × {len(args.prompts)} prompts")
+ print(f"Total combinations: {combination_idx}")
+ print(f"Successful: {successful_generations}")
+ print(f"Failed: {failed_generations}")
+ print(".2f")
+ if successful_generations > 0:
+ print(".2f")
+ print(f"Videos saved to: {args.output_dir}")
+ if is_grid_search:
+ print("\nDirectory structure:")
+ print(f" {args.output_dir}/")
+ print(" prompt00_/")
+ print(" gs_steps/")
+ print(" gs_steps.gif")
+ print("=" * 80)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/Meissonic/train/infer_video.sh b/Meissonic/train/infer_video.sh
new file mode 100644
index 0000000000000000000000000000000000000000..15b35bf902f7f09d6931d97b6744d4ccedd0e5bf
--- /dev/null
+++ b/Meissonic/train/infer_video.sh
@@ -0,0 +1,54 @@
+# # 网格搜索：遍历不同的guidance scale和inference steps
+# python train/infer_mei_video.py \
+# --model_path /mnt/Meissonic/output/3500steps/43.1/transformer \
+# --guidance_scales 1.0 3.0 5.0 7.0 9.0 11.0 \
+# --inference_steps_list 32 48 64 80 100 \
+# --prompts "A cat playing with a ball" \
+# --output_dir ./grid_search_results \
+# --save_mp4
+
+# 只遍历guidance scale
+python train/infer_mei_video.py \
+ --pretrained_model_name_or_path /mnt/Meissonic/output/43.1/checkpoint-24500/transformer \
+ --guidance_scales 5.0 7.0 9.0 11.0 13.0 15.0 \
+ --num_inference_steps 128 \
+ --prompts \
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution." \
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner." \
+ "The video is a news segment from a television show. The style of the video is a combination of a live shot and a graphic overlay. The live shot features a woman standing in front of a building with a lawn and a fence. She is wearing a black coat and a green scarf. The graphic overlay includes a blue banner with white text that reads \"NEW THIS MORNING WAR OF WORDS FROM TRUMP AND CRUZ DEMOCRATS GEAR UP FOR DEBATE TONIGHT\". The text is in all caps and is positioned at the bottom of the screen. The overall style of the video is professional and informative." \
+ --output_dir ./gs_search_24500
+
+
+# --prompts "A person walking" \
+# --output_dir ./gs_search
+
+# python train/infer_mei_video.py \
+# --pretrained_model_name_or_path /mnt/Meissonic/output/43.1/checkpoint-8500/transformer \
+# --guidance_scales 1.0 3.0 5.0 7.0 9.0 11.0 \
+# --num_inference_steps 1000 \
+# --prompts "mountain" \
+# --output_dir ./gs_search
+
+# python train/infer_mei_video.py \
+# --pretrained_model_name_or_path /mnt/Meissonic/output/43.1/checkpoint-8500/transformer \
+# --guidance_scales 1.0 3.0 5.0 7.0 9.0 11.0 \
+# --num_inference_steps 1000 \
+# --prompts "The video is a news segment from a television show. The style of the video is a combination of a live shot and a graphic overlay. The live shot features a woman standing in front of a building with a lawn and a fence. She is wearing a black coat and a green scarf. The graphic overlay includes a blue banner with white text that reads \"NEW THIS MORNING WAR OF WORDS FROM TRUMP AND CRUZ DEMOCRATS GEAR UP FOR DEBATE TONIGHT\". The text is in all caps and is positioned at the bottom of the screen. The overall style of the video is professional and informative." \
+# --output_dir ./gs_search
+
+
+# # 只遍历inference steps
+# python train/infer_mei_video.py \
+# --model_path /mnt/Meissonic/output/3500steps/43.1/transformer \
+# --guidance_scale 7.0 \
+# --inference_steps_list 32 48 64 80 100 \
+# --prompts "A person walking" \
+# --output_dir ./steps_search
+
+# # 单个参数（不进行网格搜索，保持原有行为）
+# python train/infer_mei_video.py \
+# --model_path /mnt/Meissonic/output/3000steps \
+# --guidance_scale 7.0 \
+# --num_inference_steps 64 \
+# --prompts "A car driving" \
+# --output_dir ./single_output
\ No newline at end of file
diff --git a/Meissonic/train/run_overfit.sh b/Meissonic/train/run_overfit.sh
new file mode 100644
index 0000000000000000000000000000000000000000..0091e75da8bf68d04fe04dbf96b18c41e7d84ee1
--- /dev/null
+++ b/Meissonic/train/run_overfit.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+# Overfitting experiment script
+# This script runs a small overfitting experiment to verify implementation correctness
+
+accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 train/train_overfit.py \
+ --text_encoder_architecture umt5-base \
+ --video_tokenizer_model_id "Cosmos-1.0-Tokenizer-DV8x16x16" \
+ --instance_data_dir "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+ --max_samples 256 \
+ --num_frames 8 \
+ --video_height 64 \
+ --video_width 112 \
+ --dataloader_num_workers 8 \
+ --train_batch_size 1 \
+ --gradient_accumulation_steps 1 \
+ --learning_rate 5e-4 \
+ --max_train_steps 3000 \
+ --lr_warmup_steps 100 \
+ --gradient_checkpointing \
+ --mixed_precision bf16 \
+ --seed 42 \
+ --output_dir "./output_overfit" \
+ --logging_steps 50 \
+ --save_steps 500 \
+ --inference_steps 500 \
+ --num_inference_samples 4 \
+ --num_inference_steps 48 \
+ --wan_pretrained_path Wan-AI/Wan2.1-T2V-1.3B # Optional: path to pretrained Wan weights
+
diff --git a/Meissonic/train/test_cosmos_vqvae.py b/Meissonic/train/test_cosmos_vqvae.py
new file mode 100644
index 0000000000000000000000000000000000000000..30a15da3870c15fa471dc39078c7e5f55cb4ab53
--- /dev/null
+++ b/Meissonic/train/test_cosmos_vqvae.py
@@ -0,0 +1,500 @@
+#!/usr/bin/env python3
+"""
+Test script for Cosmos VQ-VAE performance.
+
+This script:
+1. Loads a video from the training dataset
+2. Encodes it using CosmosVideoTokenizer
+3. Decodes it back
+4. Computes metrics (PSNR, SSIM, MSE)
+5. Creates a side-by-side comparison video
+6. Saves the results
+"""
+
+import argparse
+import os
+import sys
+sys.path.append(os.getcwd())
+
+import torch
+import numpy as np
+from PIL import Image
+import cv2
+from torchvision import transforms
+from torchvision.utils import make_grid, save_image
+
+from src.pipeline_video import CosmosVideoTokenizer
+from train.dataset_utils import OpenVid1MDataset, TinyOpenVid1MDataset
+from transformers import T5Tokenizer
+
+
+def calculate_psnr(img1, img2, max_val=1.0):
+ """Calculate PSNR between two images."""
+ mse = torch.mean((img1 - img2) ** 2)
+ if mse == 0:
+ return float('inf')
+ psnr = 20 * torch.log10(max_val / torch.sqrt(mse))
+ return psnr.item()
+
+
+def calculate_mse(img1, img2):
+ """Calculate MSE between two images."""
+ return torch.mean((img1 - img2) ** 2).item()
+
+
+def calculate_ssim(img1, img2, window_size=11):
+ """Calculate SSIM between two images (simplified version)."""
+ # Simple SSIM approximation
+ C1 = 0.01 ** 2
+ C2 = 0.03 ** 2
+
+ mu1 = img1.mean()
+ mu2 = img2.mean()
+
+ sigma1_sq = img1.var()
+ sigma2_sq = img2.var()
+ sigma12 = ((img1 - mu1) * (img2 - mu2)).mean()
+
+ ssim = ((2 * mu1 * mu2 + C1) * (2 * sigma12 + C2)) / ((mu1**2 + mu2**2 + C1) * (sigma1_sq + sigma2_sq + C2))
+ return ssim.item()
+
+
+def video_to_numpy(video_tensor):
+ """
+ Convert video tensor [C, F, H, W] in [0, 1] to numpy array [F, H, W, C] in [0, 255] (RGB).
+ """
+ if isinstance(video_tensor, torch.Tensor):
+ # [C, F, H, W] -> [F, C, H, W] -> [F, H, W, C]
+ # First move frame dimension to front, then transpose channels to last
+ video_np = video_tensor.permute(1, 0, 2, 3).cpu().numpy() # [F, C, H, W]
+ video_np = np.transpose(video_np, (0, 2, 3, 1)) # [F, H, W, C]
+ # Clamp to [0, 1] and convert to [0, 255]
+ video_np = np.clip(video_np, 0, 1)
+ video_np = (video_np * 255).astype(np.uint8)
+ else:
+ video_np = np.array(video_tensor)
+ return video_np
+
+
+def create_side_by_side_video(original, reconstructed, output_path, fps=8):
+ """
+ Create a side-by-side comparison video.
+
+ Args:
+ original: Original video tensor [C, F, H, W] or numpy array
+ reconstructed: Reconstructed video tensor [C, F, H, W] or numpy array
+ output_path: Path to save the output video
+ fps: Frames per second
+ """
+ # Convert to numpy (RGB format: [F, H, W, C])
+ orig_np = video_to_numpy(original)
+ recon_np = video_to_numpy(reconstructed)
+
+ # Get dimensions
+ F, H, W, C = orig_np.shape
+ F_recon, H_recon, W_recon, C_recon = recon_np.shape
+
+ # Ensure same number of frames
+ F_min = min(F, F_recon)
+ orig_np = orig_np[:F_min]
+ recon_np = recon_np[:F_min]
+
+ # Resize if dimensions don't match
+ if H != H_recon or W != W_recon:
+ print(f"Resizing reconstructed video from ({H_recon}, {W_recon}) to ({H}, {W})")
+ recon_np_resized = np.zeros((F_min, H, W, C), dtype=np.uint8)
+ for f in range(F_min):
+ # cv2.resize expects (width, height) for size parameter
+ recon_np_resized[f] = cv2.resize(recon_np[f], (W, H), interpolation=cv2.INTER_LINEAR)
+ recon_np = recon_np_resized
+
+ # Add text labels to frames
+ from PIL import Image, ImageDraw, ImageFont
+ side_by_side_frames = []
+ for f in range(F_min):
+ # Original frame with label
+ orig_frame_pil = Image.fromarray(orig_np[f])
+ draw = ImageDraw.Draw(orig_frame_pil)
+ try:
+ font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 32)
+ except:
+ try:
+ font = ImageFont.truetype("/System/Library/Fonts/Helvetica.ttc", 32)
+ except:
+ font = ImageFont.load_default()
+ # Draw text with outline for visibility
+ text = "Original"
+ x, y = 20, 20
+ for adj in [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]:
+ draw.text((x + adj[0], y + adj[1]), text, font=font, fill=(0, 0, 0))
+ draw.text((x, y), text, font=font, fill=(255, 255, 255))
+ orig_frame = np.array(orig_frame_pil)
+
+ # Reconstructed frame with label
+ recon_frame_pil = Image.fromarray(recon_np[f])
+ draw = ImageDraw.Draw(recon_frame_pil)
+ text = "Reconstructed"
+ x, y = 20, 20
+ for adj in [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]:
+ draw.text((x + adj[0], y + adj[1]), text, font=font, fill=(0, 0, 0))
+ draw.text((x, y), text, font=font, fill=(255, 255, 0)) # Yellow text
+ recon_frame = np.array(recon_frame_pil)
+
+ # Concatenate horizontally
+ frame = np.concatenate([orig_frame, recon_frame], axis=1)
+ side_by_side_frames.append(frame)
+
+ # Write video using OpenCV (needs BGR format)
+ fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+ out = cv2.VideoWriter(output_path, fourcc, fps, (W * 2, H))
+
+ if not out.isOpened():
+ print(f"Warning: Could not open video writer with mp4v codec, trying XVID...")
+ fourcc = cv2.VideoWriter_fourcc(*'XVID')
+ out = cv2.VideoWriter(output_path, fourcc, fps, (W * 2, H))
+
+ for frame in side_by_side_frames:
+ # Convert RGB to BGR for OpenCV
+ frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+ out.write(frame_bgr)
+
+ out.release()
+ print(f"Saved side-by-side video to: {output_path}")
+
+
+def add_text_to_image(image_tensor, text, position=(10, 30)):
+ """
+ Add text label to an image tensor.
+
+ Args:
+ image_tensor: Image tensor [C, H, W] in [0, 1]
+ text: Text to add
+ position: (x, y) position for text
+ Returns:
+ Image tensor with text [C, H, W]
+ """
+ # Convert to PIL Image
+ image_np = image_tensor.permute(1, 2, 0).cpu().numpy() # [H, W, C]
+ image_np = np.clip(image_np, 0, 1)
+ image_np = (image_np * 255).astype(np.uint8)
+ pil_image = Image.fromarray(image_np)
+
+ # Add text
+ from PIL import ImageDraw, ImageFont
+ draw = ImageDraw.Draw(pil_image)
+ try:
+ font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 24)
+ except:
+ try:
+ font = ImageFont.truetype("/System/Library/Fonts/Helvetica.ttc", 24)
+ except:
+ font = ImageFont.load_default()
+
+ # Draw white text with black outline
+ x, y = position
+ # Draw outline
+ for adj in [(-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)]:
+ draw.text((x + adj[0], y + adj[1]), text, font=font, fill=(0, 0, 0))
+ # Draw main text
+ draw.text((x, y), text, font=font, fill=(255, 255, 255))
+
+ # Convert back to tensor
+ image_tensor = transforms.ToTensor()(pil_image)
+ return image_tensor
+
+
+def create_comparison_grid(original, reconstructed, output_path, nrow=4):
+ """
+ Create a grid image comparing original and reconstructed frames.
+
+ Args:
+ original: Original video tensor [C, F, H, W]
+ reconstructed: Reconstructed video tensor [C, F, H, W]
+ output_path: Path to save the grid image
+ nrow: Number of frames per row
+ """
+ # Get number of frames
+ F = min(original.shape[1], reconstructed.shape[1])
+
+ # Select frames to display
+ num_frames_to_show = min(8, F)
+ frame_indices = np.linspace(0, F - 1, num_frames_to_show, dtype=int)
+
+ frames_list = []
+ for idx in frame_indices:
+ # Original frame with label
+ orig_frame = original[:, idx, :, :].clone() # [C, H, W]
+ orig_frame = add_text_to_image(orig_frame, "Original", position=(10, 10))
+ frames_list.append(orig_frame)
+
+ # Reconstructed frame with label
+ recon_frame = reconstructed[:, idx, :, :].clone() # [C, H, W]
+ recon_frame = add_text_to_image(recon_frame, "Reconstructed", position=(10, 10))
+ frames_list.append(recon_frame)
+
+ # Create grid
+ frames_tensor = torch.stack(frames_list, dim=0)
+ grid = make_grid(frames_tensor, nrow=nrow * 2, padding=2, pad_value=1.0)
+
+ save_image(grid, output_path)
+ print(f"Saved comparison grid to: {output_path}")
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Test Cosmos VQ-VAE performance")
+
+ parser.add_argument(
+ "--csv_path",
+ type=str,
+ required=True,
+ help="Path to OpenVid1M CSV file"
+ )
+ parser.add_argument(
+ "--video_root_dir",
+ type=str,
+ default=None,
+ help="Root directory for videos (auto-detected if not provided)"
+ )
+ parser.add_argument(
+ "--video_index",
+ type=int,
+ default=0,
+ help="Index of video to test (default: 0)"
+ )
+ parser.add_argument(
+ "--video_tokenizer_model_id",
+ type=str,
+ default="Cosmos-1.0-Tokenizer-DV8x16x16",
+ help="Cosmos tokenizer model ID"
+ )
+ parser.add_argument(
+ "--num_frames",
+ type=int,
+ default=16,
+ help="Number of frames"
+ )
+ parser.add_argument(
+ "--height",
+ type=int,
+ default=480,
+ help="Video height"
+ )
+ parser.add_argument(
+ "--width",
+ type=int,
+ default=848,
+ help="Video width"
+ )
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="./cosmos_test_output",
+ help="Output directory for results"
+ )
+ parser.add_argument(
+ "--device",
+ type=str,
+ default="cuda" if torch.cuda.is_available() else "cpu",
+ help="Device to use"
+ )
+ parser.add_argument(
+ "--dtype",
+ type=str,
+ default="float32",
+ choices=["float32", "float16", "bfloat16"],
+ help="Data type"
+ )
+
+ return parser.parse_args()
+
+
+def main():
+ args = parse_args()
+
+ # Create output directory
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ # Set device and dtype
+ device = torch.device(args.device)
+ if args.dtype == "float16":
+ dtype = torch.float16
+ elif args.dtype == "bfloat16":
+ dtype = torch.bfloat16
+ else:
+ dtype = torch.float32
+
+ print(f"Using device: {device}, dtype: {dtype}")
+
+ # Initialize tokenizer
+ print("Initializing CosmosVideoTokenizer...")
+ video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=device,
+ dtype=dtype
+ )
+ print(f"Codebook size: {video_tokenizer.codebook_size}")
+ print(f"Downsampling factors: t={video_tokenizer.t_downsample}, "
+ f"h={video_tokenizer.h_downsample}, w={video_tokenizer.w_downsample}")
+
+ # Load dataset
+ print(f"Loading dataset from: {args.csv_path}")
+
+ # Auto-detect video_root_dir if not provided
+ video_root_dir = args.video_root_dir
+ if video_root_dir is None:
+ csv_dir = os.path.dirname(args.csv_path)
+ if os.path.exists(os.path.join(csv_dir, 'video_reorg')):
+ video_root_dir = os.path.join(csv_dir, 'video_reorg')
+ elif os.path.exists(os.path.join(os.path.dirname(csv_dir), 'video_reorg')):
+ video_root_dir = os.path.join(os.path.dirname(csv_dir), 'video_reorg')
+ else:
+ video_root_dir = csv_dir
+ print(f"Warning: Video directory not found, using CSV directory: {video_root_dir}")
+
+ # Initialize tokenizer for dataset (needed for OpenVid1MDataset)
+ tokenizer = T5Tokenizer.from_pretrained("google/umt5-base")
+
+ # Create dataset
+ dataset = OpenVid1MDataset(
+ csv_path=args.csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=args.num_frames,
+ height=args.height,
+ width=args.width,
+ text_encoder_architecture="umt5-base",
+ )
+
+ print(f"Dataset size: {len(dataset)}")
+
+ # Load video
+ if args.video_index >= len(dataset):
+ print(f"Error: video_index {args.video_index} >= dataset size {len(dataset)}")
+ return
+
+ print(f"Loading video at index {args.video_index}...")
+ sample = dataset[args.video_index]
+ original_video = sample["video"] # [C, F, H, W]
+
+ # Get video info from dataset
+ row = dataset.data[args.video_index]
+ video_path = row.get('video', 'unknown')
+ caption = row.get('caption', 'no caption')
+
+ print(f"Video path: {video_path}")
+ print(f"Caption: {caption}")
+ print(f"Original video shape: {original_video.shape}")
+ print(f"Original video range: [{original_video.min():.3f}, {original_video.max():.3f}]")
+
+ # Move to device
+ original_video = original_video.to(device=device, dtype=dtype)
+
+ # Encode
+ print("\nEncoding video...")
+ with torch.no_grad():
+ codes = video_tokenizer.encode(original_video.unsqueeze(0)) # [1, F', H', W']
+
+ print(f"Encoded codes shape: {codes.shape}")
+ print(f"Codes range: [{codes.min().item()}, {codes.max().item()}]")
+ print(f"Codebook size: {video_tokenizer.codebook_size}")
+
+ # Decode
+ print("\nDecoding video...")
+ with torch.no_grad():
+ reconstructed_video = video_tokenizer.decode(codes) # [1, C, F, H, W]
+ reconstructed_video = reconstructed_video.squeeze(0) # [C, F, H, W]
+
+ print(f"Reconstructed video shape: {reconstructed_video.shape}")
+ print(f"Reconstructed video range: [{reconstructed_video.min():.3f}, {reconstructed_video.max():.3f}]")
+
+ # Ensure same number of frames for comparison
+ F_orig = original_video.shape[1]
+ F_recon = reconstructed_video.shape[1]
+ F_min = min(F_orig, F_recon)
+
+ original_video = original_video[:, :F_min, :, :]
+ reconstructed_video = reconstructed_video[:, :F_min, :, :]
+
+ # Resize if spatial dimensions don't match
+ if original_video.shape[2:] != reconstructed_video.shape[2:]:
+ print(f"Resizing reconstructed video from {reconstructed_video.shape[2:]} to {original_video.shape[2:]}")
+ # Use interpolation to resize
+ reconstructed_video_resized = torch.zeros_like(original_video)
+ for f in range(F_min):
+ frame = reconstructed_video[:, f, :, :].unsqueeze(0) # [1, C, H, W]
+ frame_resized = torch.nn.functional.interpolate(
+ frame, size=original_video.shape[2:], mode='bilinear', align_corners=False
+ )
+ reconstructed_video_resized[:, f, :, :] = frame_resized.squeeze(0)
+ reconstructed_video = reconstructed_video_resized
+
+ # Calculate metrics
+ print("\nCalculating metrics...")
+
+ # Convert to float32 for metric calculation
+ orig_f32 = original_video.to(torch.float32)
+ recon_f32 = reconstructed_video.to(torch.float32)
+
+ # Frame-wise metrics
+ psnr_values = []
+ mse_values = []
+ ssim_values = []
+
+ for f in range(F_min):
+ orig_frame = orig_f32[:, f, :, :] # [C, H, W]
+ recon_frame = recon_f32[:, f, :, :] # [C, H, W]
+
+ psnr = calculate_psnr(orig_frame, recon_frame)
+ mse = calculate_mse(orig_frame, recon_frame)
+ ssim = calculate_ssim(orig_frame, recon_frame)
+
+ psnr_values.append(psnr)
+ mse_values.append(mse)
+ ssim_values.append(ssim)
+
+ # Overall metrics
+ avg_psnr = np.mean(psnr_values)
+ avg_mse = np.mean(mse_values)
+ avg_ssim = np.mean(ssim_values)
+
+ print(f"\n=== Metrics ===")
+ print(f"PSNR: {avg_psnr:.2f} dB (per frame: {psnr_values})")
+ print(f"MSE: {avg_mse:.6f} (per frame: {mse_values})")
+ print(f"SSIM: {avg_ssim:.4f} (per frame: {ssim_values})")
+
+ # Save metrics to file
+ metrics_file = os.path.join(args.output_dir, f"metrics_video_{args.video_index}.txt")
+ with open(metrics_file, 'w') as f:
+ f.write(f"Video Index: {args.video_index}\n")
+ f.write(f"Video Path: {video_path}\n")
+ f.write(f"Caption: {caption}\n")
+ f.write(f"\n=== Metrics ===\n")
+ f.write(f"Average PSNR: {avg_psnr:.2f} dB\n")
+ f.write(f"Average MSE: {avg_mse:.6f}\n")
+ f.write(f"Average SSIM: {avg_ssim:.4f}\n")
+ f.write(f"\nPer-frame PSNR: {psnr_values}\n")
+ f.write(f"Per-frame MSE: {mse_values}\n")
+ f.write(f"Per-frame SSIM: {ssim_values}\n")
+
+ print(f"Saved metrics to: {metrics_file}")
+
+ # Create side-by-side video
+ print("\nCreating side-by-side comparison video...")
+ video_output_path = os.path.join(args.output_dir, f"comparison_video_{args.video_index}.mp4")
+ create_side_by_side_video(original_video, reconstructed_video, video_output_path, fps=8)
+
+ # Create comparison grid
+ print("Creating comparison grid...")
+ grid_output_path = os.path.join(args.output_dir, f"comparison_grid_video_{args.video_index}.png")
+ create_comparison_grid(original_video, reconstructed_video, grid_output_path, nrow=4)
+
+ print(f"\n=== Test Complete ===")
+ print(f"Results saved to: {args.output_dir}")
+ print(f" - Metrics: {metrics_file}")
+ print(f" - Side-by-side video: {video_output_path}")
+ print(f" - Comparison grid: {grid_output_path}")
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/Meissonic/train/test_cosmos_vqvae.sh b/Meissonic/train/test_cosmos_vqvae.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c6bdaea7cda7b6bdaa1e4a56de74443202793331
--- /dev/null
+++ b/Meissonic/train/test_cosmos_vqvae.sh
@@ -0,0 +1,62 @@
+#!/bin/bash
+# Test script for Cosmos VQ-VAE performance
+
+
+python train/test_cosmos_vqvae.py \
+ --csv_path "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+ --video_index 0 \
+ --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+ --num_frames 17 \
+ --height 128 \
+ --width 128 \
+ --output_dir "./cosmos_test_output" \
+ --device cuda \
+ --dtype float32
+
+# python train/test_cosmos_vqvae.py \
+# --csv_path "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --video_index 0 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --num_frames 16 \
+# --height 480 \
+# --width 848 \
+# --output_dir "./cosmos_test_output" \
+# --device cuda \
+# --dtype float32
+
+
+# python train/test_cosmos_vqvae.py \
+# --csv_path "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --video_index 1 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --num_frames 16 \
+# --height 480 \
+# --width 848 \
+# --output_dir "./cosmos_test_output" \
+# --device cuda \
+# --dtype float32
+
+# python train/test_cosmos_vqvae.py \
+# --csv_path "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --video_index 2 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --num_frames 16 \
+# --height 480 \
+# --width 848 \
+# --output_dir "./cosmos_test_output" \
+# --device cuda \
+# --dtype float32
+
+
+# python train/test_cosmos_vqvae.py \
+# --csv_path "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --video_index 3 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --num_frames 16 \
+# --height 480 \
+# --width 848 \
+# --output_dir "./cosmos_test_output" \
+# --device cuda \
+# --dtype float32
+
+
diff --git a/Meissonic/train/train.sh b/Meissonic/train/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6667ecd895a97862e05dba000d775a8849e54f13
--- /dev/null
+++ b/Meissonic/train/train.sh
@@ -0,0 +1,33 @@
+# bash it in root path
+PYTHON_PATH='./' accelerate launch --multi_gpu --gpu_ids '0,1,2,3' --main_process_port 25011 --num_processes 4 train/train_meissonic.py \
+ --output_dir "../CKPT_OUTPUT_PATH" \
+ --train_batch_size 4 \
+ --gradient_accumulation_steps 2 \
+ --learning_rate 1e-4 \
+ --max_grad_norm 10 \
+ --pretrained_model_name_or_path "meissonflow/meissonic" \
+ --text_encoder_architecture 'open_clip' \
+ --pretrained_model_architecture 'Meissonic' \
+ --training_from_scratch True \
+ --instance_dataset 'DATA_TYPE' \
+ --instance_data_dir '../parquets_father_dir/' \
+ --resolution 1024 \
+ --mixed_precision bf16 \
+ --lr_scheduler constant \
+ --use_8bit_adam \
+ --dataloader_num_workers 64 \
+ --validation_prompts \
+ 'a boy' \
+ 'A serene mountain landscape with towering snow-capped peaks, a crystal-clear blue lake reflecting the mountains, dense pine forests, and a vibrant orange sunrise illuminating the sky.' \
+ 'A playful golden retriever puppy with a shiny coat, bounding through a meadow filled with colorful wildflowers, under a bright, clear blue sky.' \
+ 'A bustling city street at night, illuminated by vibrant neon signs in various colors, with busy pedestrians, street vendors, and a light rain creating reflective puddles on the pavement.' \
+ 'A majestic, medieval castle perched on a rugged cliffside, overlooking a vast, calm ocean at sunset, with the sky painted in hues of pink, orange, and purple.' \
+ 'An elegant ballerina in a white tutu, dancing gracefully on a grand stage with ornate, gold-trimmed curtains, under a spotlight that casts a soft glow.' \
+ 'A cozy, rustic log cabin nestled in a snow-covered forest, with smoke rising from the stone chimney, warm lights glowing from the windows, and a path of footprints leading to the front door.'\
+ 'A Cute Cat' \
+ 'A Snow Mountain'\
+ --max_train_steps 100000 \
+ --checkpointing_steps 1000 \
+ --validation_steps 200 \
+ --report_to 'wandb' \
+ --logging_steps 10
diff --git a/Meissonic/train/train_mei_video.py b/Meissonic/train/train_mei_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..c392f50ad44cf6625ef2f6823c5730f128501a62
--- /dev/null
+++ b/Meissonic/train/train_mei_video.py
@@ -0,0 +1,1928 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import copy
+import logging
+import math
+import os
+os.environ["WANDB_DISABLED"] = "true"
+from contextlib import nullcontext
+from pathlib import Path
+import sys
+sys.path.append(os.getcwd())
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from peft import LoraConfig
+from peft.utils import get_peft_model_state_dict
+from torch.utils.data import DataLoader, default_collate
+from torchvision import transforms
+# Video training uses T5/UMT5, not CLIP
+import diffusers.optimization
+# EMA not used for video training
+from src.scheduler_video import Scheduler
+# Video training only - no image scheduler needed
+from diffusers.loaders import LoraLoaderMixin
+from diffusers.utils import is_wandb_available
+from src.pipeline_video import Pipeline as VideoPipeline
+from torchvision.utils import save_image,make_grid
+from datasets import load_dataset
+from train.trainer_utils import save_checkpoint
+from train.dataset_utils import VideoDataset, OpenVid1MDataset, PrecomputedFeatureDataset, PrecomputedVideoOnlyDataset
+from train.dataset_utils import tokenize_prompt, encode_prompt
+# Transformer2DModel removed - video training only uses WanDiscreteVideoTransformer
+from src.transformer_video import WanDiscreteVideoTransformer, WanModel
+from src.pipeline_video import CosmosVideoTokenizer
+from transformers import T5Tokenizer, T5EncoderModel
+
+if is_wandb_available():
+ import wandb
+ wandb.login(key="a96e0066098e5f64211a77b604ba2b1dd7bd7834")
+
+# t5_model_path = "/inspire/hdd/project/multimodal-discrete-diffusion/xinyi-253308120310/maskgit-video/43/Meissonic/google_umt5_xxl"
+# # cosmos_model_path = ""
+# wan_model_path = "/inspire/hdd/project/multimodal-discrete-diffusion/xinyi-253308120310/maskgit-video/43/Meissonic/Wan2.1-T2V-1.3B"
+
+logger = get_logger(__name__, log_level="INFO")
+
+import torch._dynamo
+torch._dynamo.config.verbose = True
+
+# Optionally suppress errors to fall back to eager execution
+torch._dynamo.config.suppress_errors = True
+
+def parse_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--text_encoder_architecture",
+ type=str,
+ default="open_clip",
+ required=False,
+ help="The architecture of the text encoder. For video training, must be 'umt5-base', 'umt5-xxl', or 't5'",
+ )
+ parser.add_argument(
+ "--instance_dataset",
+ type=str,
+ default=None,
+ required=False,
+ help="The dataset to use for training. One of ['MSCOCO600K', 'PickaPicV2']",
+ )
+ parser.add_argument("--training_from_scratch", action="store_true")
+
+ parser.add_argument(
+ "--pretrained_model_name_or_path",
+ type=str,
+ default=None,
+ required=True,
+ help="Path to pretrained model or model identifier from huggingface.co/models.",
+ )
+ parser.add_argument(
+ "--revision",
+ type=str,
+ default=None,
+ required=False,
+ help="Revision of pretrained model identifier from huggingface.co/models.",
+ )
+ parser.add_argument(
+ "--variant",
+ type=str,
+ default=None,
+ help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+ )
+ parser.add_argument(
+ "--instance_data_dataset",
+ type=str,
+ default=None,
+ required=False,
+ help="A Hugging Face dataset containing the training images",
+ )
+ parser.add_argument(
+ "--instance_data_dir",
+ type=str,
+ default=None,
+ required=False,
+ help="A folder containing the training data of instance images.",
+ )
+ parser.add_argument(
+ "--instance_data_image", type=str, default=None, required=False, help="A single training image"
+ )
+ parser.add_argument(
+ "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
+ )
+ parser.add_argument(
+ "--dataloader_num_workers",
+ type=int,
+ default=4,
+ help=(
+ "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process. "
+ "Recommended: 4-8 for video loading. Set to 0 if you encounter issues with multiprocessing."
+ ),
+ )
+ parser.add_argument(
+ "--dataloader_prefetch_factor",
+ type=int,
+ default=2,
+ help=(
+ "Number of batches loaded in advance by each worker. Higher values can improve GPU utilization "
+ "but use more memory. Default: 2."
+ ),
+ )
+ parser.add_argument(
+ "--allow_tf32",
+ action="store_true",
+ help=(
+ "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+ " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+ ),
+ )
+ parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
+ parser.add_argument("--ema_decay", type=float, default=0.9999)
+ parser.add_argument("--ema_update_after_step", type=int, default=0)
+ parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+ parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+ parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+ parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="muse_training",
+ help="The output directory where the model predictions and checkpoints will be written.",
+ )
+ parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+ parser.add_argument(
+ "--logging_dir",
+ type=str,
+ default="logs",
+ help=(
+ "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+ " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+ ),
+ )
+ parser.add_argument(
+ "--max_train_steps",
+ type=int,
+ default=None,
+ help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+ )
+ parser.add_argument(
+ "--checkpointing_steps",
+ type=int,
+ default=500,
+ help=(
+ "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
+ "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
+ "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
+ "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
+ "instructions."
+ ),
+ )
+ parser.add_argument(
+ "--logging_steps",
+ type=int,
+ default=50,
+ )
+ parser.add_argument(
+ "--checkpoints_total_limit",
+ type=int,
+ default=None,
+ help=(
+ "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
+ " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
+ " for more details"
+ ),
+ )
+ parser.add_argument(
+ "--resume_from_checkpoint",
+ type=str,
+ default=None,
+ help=(
+ "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+ ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+ ),
+ )
+ parser.add_argument(
+ "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+ )
+ parser.add_argument(
+ "--gradient_accumulation_steps",
+ type=int,
+ default=1,
+ help="Number of updates steps to accumulate before performing a backward/update pass.",
+ )
+ parser.add_argument(
+ "--learning_rate",
+ type=float,
+ default=0.0003,
+ help="Initial learning rate (after the potential warmup period) to use.",
+ )
+ parser.add_argument(
+ "--scale_lr",
+ action="store_true",
+ default=False,
+ help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+ )
+ parser.add_argument(
+ "--lr_scheduler",
+ type=str,
+ default="constant",
+ help=(
+ 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+ ' "constant", "constant_with_warmup"]'
+ ),
+ )
+ parser.add_argument(
+ "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+ )
+ parser.add_argument(
+ "--validation_steps",
+ type=int,
+ default=100,
+ help=(
+ "Run validation every X steps. Validation consists of running the prompt"
+ " `args.validation_prompt` multiple times: `args.num_validation_images`"
+ " and logging the images."
+ ),
+ )
+ parser.add_argument(
+ "--mixed_precision",
+ type=str,
+ default=None,
+ choices=["no", "fp16", "bf16"],
+ help=(
+ "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+ " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
+ " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+ ),
+ )
+ parser.add_argument(
+ "--report_to",
+ type=str,
+ default="wandb",
+ help=(
+ 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+ ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+ ),
+ )
+ parser.add_argument("--validation_prompts", type=str, nargs="*")
+ parser.add_argument(
+ "--resolution",
+ type=int,
+ default=512,
+ help=(
+ "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+ " resolution"
+ ),
+ )
+ parser.add_argument("--split_vae_encode", type=int, required=False, default=None)
+ parser.add_argument("--min_masking_rate", type=float, default=0.0)
+ parser.add_argument("--cond_dropout_prob", type=float, default=0.0)
+ parser.add_argument("--max_grad_norm", default=50.0, type=float, help="Max gradient norm.", required=False)
+ parser.add_argument("--use_lora", action="store_true", help="Fine tune the model using LoRa")
+ parser.add_argument("--text_encoder_use_lora", action="store_true", help="Fine tune the model using LoRa")
+ parser.add_argument("--lora_r", default=16, type=int)
+ parser.add_argument("--lora_alpha", default=32, type=int)
+ parser.add_argument("--lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
+ parser.add_argument("--text_encoder_lora_r", default=16, type=int)
+ parser.add_argument("--text_encoder_lora_alpha", default=32, type=int)
+ parser.add_argument("--text_encoder_lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
+ parser.add_argument("--train_text_encoder", action="store_true")
+ parser.add_argument("--image_key", type=str, required=False)
+ parser.add_argument("--prompt_key", type=str, required=False)
+ parser.add_argument(
+ "--gradient_checkpointing",
+ action="store_true",
+ help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+ )
+ parser.add_argument("--prompt_prefix", type=str, required=False, default=None)
+
+ # Video training specific arguments
+ # Video training only - model_type is always "video"
+ parser.add_argument(
+ "--num_frames",
+ type=int,
+ default=16,
+ help="Number of frames in the video (for video training only)",
+ )
+ parser.add_argument(
+ "--video_height",
+ type=int,
+ default=480,
+ help="Height of the video in pixels (for video training only)",
+ )
+ parser.add_argument(
+ "--video_width",
+ type=int,
+ default=848,
+ help="Width of the video in pixels (for video training only)",
+ )
+ parser.add_argument(
+ "--video_tokenizer_model_id",
+ type=str,
+ default="Cosmos-1.0-Tokenizer-DV8x16x16",
+ help="HuggingFace model ID for Cosmos video tokenizer (for video training only)",
+ )
+ parser.add_argument(
+ "--wan_pretrained_path",
+ type=str,
+ default=None,
+ help="Path or HuggingFace model ID to Wan pretrained weights. If provided, will load Wan weights into the backbone.",
+ )
+ parser.add_argument(
+ "--freeze_wan_backbone",
+ action="store_true",
+ help="Freeze Wan backbone weights (set lr=0). If set, --wan_backbone_lr_ratio is ignored.",
+ )
+ parser.add_argument(
+ "--wan_backbone_lr_ratio",
+ type=float,
+ default=0.1,
+ help="Learning rate ratio for Wan backbone relative to other parts (token_embedding, logits_head). Default: 0.1 (backbone lr = base_lr * 0.1). Ignored if --freeze_wan_backbone is set.",
+ )
+ parser.add_argument(
+ "--use_precomputed_features",
+ action="store_true",
+ help="Use pre-extracted features (video codes and text embeddings) instead of encoding on-the-fly.",
+ )
+ parser.add_argument(
+ "--use_precomputed_video_only",
+ action="store_true",
+ help="Use pre-extracted video codes only, encode text with UMT5-XXL at runtime.",
+ )
+ parser.add_argument(
+ "--features_dir",
+ type=str,
+ default=None,
+ help="Directory containing pre-extracted features (required if --use_precomputed_features is set).",
+ )
+ parser.add_argument(
+ "--empty_embeds_path",
+ type=str,
+ default=None,
+ help="Path to pre-extracted empty_embeds .pt file (required if --use_precomputed_features and --cond_dropout_prob > 0).",
+ )
+
+ args = parser.parse_args()
+
+ # Validate precomputed features arguments
+ if args.use_precomputed_features and args.use_precomputed_video_only:
+ raise ValueError("Cannot set both --use_precomputed_features and --use_precomputed_video_only")
+
+ if args.use_precomputed_features or args.use_precomputed_video_only:
+ if args.features_dir is None:
+ raise ValueError("--features_dir is required when --use_precomputed_features or --use_precomputed_video_only is set")
+ if not os.path.exists(args.features_dir):
+ raise ValueError(f"Features directory not found: {args.features_dir}")
+ # Check if empty_embeds is needed
+ if args.cond_dropout_prob > 0.0:
+ # Try to get empty_embeds_path from metadata.json if not provided
+ metadata_file = os.path.join(args.features_dir, "metadata.json")
+ if args.empty_embeds_path is None and os.path.exists(metadata_file):
+ import json
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+ if metadata.get("empty_embeds_path"):
+ args.empty_embeds_path = os.path.join(args.features_dir, metadata["empty_embeds_path"])
+ # Use print instead of logger since Accelerator is not initialized yet
+ print(f"Found empty_embeds_path in metadata: {args.empty_embeds_path}")
+
+ if args.empty_embeds_path is None:
+ raise ValueError(
+ "--empty_embeds_path is required when --use_precomputed_features or --use_precomputed_video_only is set "
+ "and --cond_dropout_prob > 0.0. "
+ "Please run extract_features.py with --extract_text to generate the empty_embeds file."
+ )
+ if not os.path.exists(args.empty_embeds_path):
+ raise ValueError(f"Empty embeds file not found: {args.empty_embeds_path}")
+
+ if args.report_to == "wandb":
+ if not is_wandb_available():
+ raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+
+ num_datasources = sum(
+ [x is not None for x in [args.instance_data_dir, args.instance_data_image, args.instance_data_dataset]]
+ )
+
+ # if num_datasources != 1:
+ # raise ValueError(
+ # "provide one and only one of `--instance_data_dir`, `--instance_data_image`, or `--instance_data_dataset`"
+ # )
+
+ if args.instance_data_dir is not None:
+ if not os.path.exists(args.instance_data_dir):
+ raise ValueError(f"Does not exist: `--args.instance_data_dir` {args.instance_data_dir}")
+
+ if args.instance_data_image is not None:
+ if not os.path.exists(args.instance_data_image):
+ raise ValueError(f"Does not exist: `--args.instance_data_image` {args.instance_data_image}")
+
+ if args.instance_data_dataset is not None and (args.image_key is None or args.prompt_key is None):
+ raise ValueError("`--instance_data_dataset` requires setting `--image_key` and `--prompt_key`")
+
+ return args
+
+# _prepare_latent_image_ids removed - not used for video training
+
+def safe_unwrap_model(model, accelerator):
+ """
+ Safely unwrap model from accelerate/distributed wrapper, handling torch.compile.
+
+ The unwrapping order for a compiled + distributed model:
+ 1. DistributedDataParallel (DDP) → wraps OptimizedModule
+ 2. OptimizedModule (torch.compile) → has _orig_mod attribute
+ 3. Original model (WanDiscreteVideoTransformer)
+
+ Args:
+ model: The model (may be wrapped by accelerate and/or torch.compile)
+ accelerator: Accelerator instance
+
+ Returns:
+ The unwrapped model
+ """
+ unwrapped = model
+
+ # Step 1: Unwrap DDP/accelerate wrapper
+ try:
+ unwrapped = accelerator.unwrap_model(model)
+ except (KeyError, AttributeError):
+ pass
+
+ # Step 2: Handle DDP directly if accelerator.unwrap_model didn't work
+ if hasattr(unwrapped, 'module'):
+ unwrapped = unwrapped.module
+
+ # Step 3: Unwrap torch.compile (OptimizedModule has _orig_mod)
+ if hasattr(unwrapped, '_orig_mod'):
+ unwrapped = unwrapped._orig_mod
+
+ # Step 4: Handle nested _orig_mod (in case of multiple compile calls)
+ while hasattr(unwrapped, '_orig_mod'):
+ unwrapped = unwrapped._orig_mod
+
+ return unwrapped
+
+def main(args):
+ if args.allow_tf32:
+ torch.backends.cuda.matmul.allow_tf32 = True
+
+ logging_dir = Path(args.output_dir, args.logging_dir)
+
+ accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+ accelerator = Accelerator(
+ gradient_accumulation_steps=args.gradient_accumulation_steps,
+ mixed_precision=args.mixed_precision,
+ log_with=args.report_to,
+ project_config=accelerator_project_config,
+ )
+
+ if accelerator.is_main_process:
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ # Make one log on every process with the configuration for debugging.
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+
+ if accelerator.is_main_process:
+ accelerator.init_trackers("meissonic", config=vars(copy.deepcopy(args)))
+
+ if args.seed is not None:
+ set_seed(args.seed)
+
+ # Initialize text encoder and tokenizer for video training (T5/UMT5 only)
+ # Skip loading if using precomputed features (will load only during validation)
+ text_encoder = None
+ tokenizer = None
+
+ if args.use_precomputed_features:
+ logger.info("Using precomputed features - skipping text encoder and video tokenizer loading during training")
+ logger.info("Text encoder and video tokenizer will be loaded only during validation/inference")
+ elif args.use_precomputed_video_only:
+ logger.info("Using precomputed video codes only - will encode text with UMT5-XXL at runtime")
+ logger.info("Video tokenizer will be loaded only during validation/inference")
+ # Force text encoder architecture to umt5-xxl for this mode
+ if args.text_encoder_architecture != "umt5-xxl":
+ logger.info(f"Forcing text_encoder_architecture to 'umt5-xxl' for --use_precomputed_video_only mode (was '{args.text_encoder_architecture}')")
+ args.text_encoder_architecture = "umt5-xxl"
+
+ # Load text encoder and tokenizer for training (since we need to encode text at runtime)
+ if args.text_encoder_architecture in ["umt5-base", "umt5-xxl", "t5"]:
+ if args.resume_from_checkpoint:
+ text_encoder = T5EncoderModel.from_pretrained(
+ args.resume_from_checkpoint, subfolder="text_encoder", variant=args.variant
+ )
+ tokenizer = T5Tokenizer.from_pretrained(
+ args.resume_from_checkpoint, subfolder="tokenizer", variant=args.variant
+ )
+ else:
+ # Map architecture to model ID
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base"
+
+ text_encoder = T5EncoderModel.from_pretrained(model_id, variant=args.variant)
+ tokenizer = T5Tokenizer.from_pretrained(model_id, variant=args.variant)
+ text_encoder.eval()
+ text_encoder.requires_grad_(False)
+ text_encoder.config.use_cache = False
+
+ text_encoder.to(accelerator.device)
+ logger.info(f"Loaded text encoder: {model_id} (d_model={text_encoder.config.d_model})")
+
+
+ # Get mask_token_id and codebook_size from metadata (required)
+ metadata_file = os.path.join(args.features_dir, "metadata.json")
+ if not os.path.exists(metadata_file):
+ raise ValueError(f"Metadata file not found: {metadata_file}. Please ensure features were extracted with extract_features.py.")
+
+ import json
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+
+ codebook_size = metadata.get("codebook_size")
+ mask_token_id = metadata.get("mask_token_id")
+
+ if codebook_size is None or mask_token_id is None:
+ raise ValueError(
+ f"codebook_size and mask_token_id must be in metadata.json. "
+ f"Found: codebook_size={codebook_size}, mask_token_id={mask_token_id}. "
+ f"Please re-run extract_features.py to ensure metadata is complete."
+ )
+
+ logger.info(f"Loaded from metadata: codebook_size={codebook_size}, mask_token_id={mask_token_id}")
+
+ # Create a minimal object with just the attributes we need
+ class MinimalTokenizer:
+ def __init__(self, mask_token_id, codebook_size):
+ self.mask_token_id = mask_token_id
+ self.codebook_size = codebook_size
+ video_tokenizer = MinimalTokenizer(mask_token_id, codebook_size)
+ logger.info(f"Minimal tokenizer created: mask_token_id={mask_token_id}, codebook_size={codebook_size}")
+ else:
+ # Load text encoder and tokenizer normally
+ if args.text_encoder_architecture in ["umt5-base", "umt5-xxl", "t5"]:
+ if args.resume_from_checkpoint:
+ text_encoder = T5EncoderModel.from_pretrained(
+ args.resume_from_checkpoint, subfolder="text_encoder", variant=args.variant
+ )
+ tokenizer = T5Tokenizer.from_pretrained(
+ args.resume_from_checkpoint, subfolder="tokenizer", variant=args.variant
+ )
+ else:
+ # Map architecture to model ID
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base" # or "google/t5-v1_1-base" depending on your needs
+ else:
+ raise ValueError(f"Unknown text encoder architecture: {args.text_encoder_architecture}")
+
+ # text_encoder = T5EncoderModel.from_pretrained(model_id)
+ # tokenizer = T5Tokenizer.from_pretrained(model_id)
+ text_encoder = T5EncoderModel.from_pretrained(model_path, local_files_only=True)
+ tokenizer = T5Tokenizer.from_pretrained(model_path, local_files_only=True)
+ logger.info(f"Loaded text encoder: {model_id} (d_model={text_encoder.config.d_model})")
+
+ # Initialize video tokenizer for video training
+ device = accelerator.device
+ dtype = torch.float32
+ if accelerator.mixed_precision == "fp16":
+ dtype = torch.float16
+ elif accelerator.mixed_precision == "bf16":
+ dtype = torch.bfloat16
+ text_encoder.to(accelerator.device)
+ tokenizer.to(accelerator.device)
+
+ video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=device,
+ dtype=dtype
+ )
+ video_tokenizer.requires_grad_(False)
+
+ if not args.use_precomputed_features and not args.use_precomputed_video_only and args.train_text_encoder:
+ if args.text_encoder_use_lora:
+ lora_config = LoraConfig(
+ r=args.text_encoder_lora_r,
+ lora_alpha=args.text_encoder_lora_alpha,
+ target_modules=args.text_encoder_lora_target_modules,
+ )
+ text_encoder.add_adapter(lora_config)
+ text_encoder.train()
+ text_encoder.requires_grad_(True)
+ else:
+ text_encoder.eval()
+ text_encoder.requires_grad_(False)
+
+ # Initialize video transformer model
+ if args.training_from_scratch:
+ # Calculate compressed dimensions based on Cosmos tokenizer
+ # Cosmos compresses: F' = F // 8, H' = H // 16, W' = W // 16
+ # However, actual encoding may have slight variations due to padding/rounding
+ # So we test with a dummy video to get the exact dimensions
+ if args.use_precomputed_features or args.use_precomputed_video_only:
+ # For precomputed features, get dimensions from metadata or sample file
+ logger.info("Getting compressed dimensions from precomputed features...")
+ metadata_file = os.path.join(args.features_dir, "metadata.json")
+
+ # Try to get dimensions from metadata first
+ F_prime, H_prime, W_prime = None, None, None
+ if os.path.exists(metadata_file):
+ import json
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+ # Check if metadata has sample shape info
+ if 'samples' in metadata and len(metadata['samples']) > 0:
+ sample = metadata['samples'][0]
+ if 'video_code_shape' in sample:
+ shape = sample['video_code_shape']
+ if len(shape) == 3: # [F', H', W']
+ F_prime, H_prime, W_prime = shape[0], shape[1], shape[2]
+ logger.info(f"Got dimensions from metadata: F'={F_prime}, H'={H_prime}, W'={W_prime}")
+
+ # If not in metadata, load a sample file
+ # if F_prime is None or H_prime is None or W_prime is None:
+ # logger.info("Loading a sample file to get dimensions...")
+ # # Try to find the first available sample
+ # video_codes_dir = os.path.join(args.features_dir, "video_codes")
+ # sample_path = None
+ # for level1 in range(1000):
+ # level1_dir = os.path.join(video_codes_dir, f"{level1:03d}")
+ # if not os.path.exists(level1_dir):
+ # continue
+ # for level2 in range(1000):
+ # level2_dir = os.path.join(level1_dir, f"{level2:03d}")
+ # if not os.path.exists(level2_dir):
+ # continue
+ # for level3 in range(1000):
+ # level3_dir = os.path.join(level2_dir, f"{level3:03d}")
+ # if not os.path.exists(level3_dir):
+ # continue
+ # # Find first .npy file
+ # for filename in os.listdir(level3_dir):
+ # if filename.endswith('.npy'):
+ # sample_path = os.path.join(level3_dir, filename)
+ # break
+ # if sample_path:
+ # break
+ # if sample_path:
+ # break
+ # if sample_path:
+ # break
+
+ # if sample_path:
+ # import numpy as np
+ # sample_tokens = np.load(sample_path) # [F', H', W']
+ # F_prime, H_prime, W_prime = sample_tokens.shape[0], sample_tokens.shape[1], sample_tokens.shape[2]
+ # logger.info(f"Got dimensions from sample file: F'={F_prime}, H'={H_prime}, W'={W_prime}")
+ # else:
+ # raise FileNotFoundError(f"Could not find any sample files in {video_codes_dir} to determine dimensions")
+ else:
+ # For non-precomputed features, use tokenizer to encode dummy video
+ dummy_video = torch.zeros(1, 3, args.num_frames, args.video_height, args.video_width,
+ device=accelerator.device, dtype=torch.float32)
+ with torch.no_grad():
+ dummy_tokens = video_tokenizer.encode(dummy_video) # [1, F', H', W']
+ F_prime, H_prime, W_prime = dummy_tokens.shape[1], dummy_tokens.shape[2], dummy_tokens.shape[3]
+ logger.info(f"Actual compressed dimensions from tokenizer: F'={F_prime}, H'={H_prime}, W'={W_prime}")
+ logger.info(f"Theoretical dimensions: F'={args.num_frames // video_tokenizer.t_downsample}, "
+ f"H'={args.video_height // video_tokenizer.h_downsample}, "
+ f"W'={args.video_width // video_tokenizer.w_downsample}")
+
+ # Get text encoder dimension
+ if args.use_precomputed_features:
+ # For precomputed features, get text_dim from metadata or use default
+ text_dim_actual = None
+ metadata_file = os.path.join(args.features_dir, "metadata.json")
+ if os.path.exists(metadata_file):
+ import json
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+ # Try to get from a sample
+ if 'samples' in metadata and len(metadata['samples']) > 0:
+ sample = metadata['samples'][0]
+ if 'text_embedding_shape' in sample:
+ shape = sample['text_embedding_shape']
+ if len(shape) == 2: # [L, D]
+ text_dim_actual = shape[1]
+ logger.info(f"Got text_dim from metadata: {text_dim_actual}")
+
+ # If not found, use default based on architecture
+ if text_dim_actual is None:
+ if args.text_encoder_architecture == "umt5-base":
+ text_dim_actual = 768
+ elif args.text_encoder_architecture == "umt5-xxl":
+ text_dim_actual = 4096
+ elif args.text_encoder_architecture == "t5":
+ text_dim_actual = 768
+ else:
+ text_dim_actual = 768 # default
+ logger.info(f"Using default text_dim for {args.text_encoder_architecture}: {text_dim_actual}")
+ elif args.use_precomputed_video_only:
+ # For video-only mode, use the actual text encoder dimension (UMT5-XXL)
+ text_dim_actual = text_encoder.config.d_model
+ logger.info(f"Using actual text encoder dimension for {args.text_encoder_architecture}: {text_dim_actual}")
+ else:
+ text_dim_actual = text_encoder.config.d_model
+
+ # If Wan pretrained path is provided, load config from it first
+ wan_config = None
+ if args.wan_pretrained_path is not None:
+ logger.info(f"Loading Wan config from: {args.wan_pretrained_path}")
+ try:
+ # Try to load WanModel config
+ # Try loading config.json directly
+ import json
+ config_path = os.path.join(args.wan_pretrained_path, "config.json")
+ if os.path.exists(config_path):
+ with open(config_path, 'r') as f:
+ wan_config_dict = json.load(f)
+ # Create a simple config object
+ from types import SimpleNamespace
+ wan_config = SimpleNamespace(**wan_config_dict)
+ else:
+ logger.warning(f"Could not find config in {args.wan_pretrained_path}, using default values")
+
+ if wan_config is not None:
+ logger.info(f"Loaded Wan config: dim={getattr(wan_config, 'dim', 'N/A')}, "
+ f"ffn_dim={getattr(wan_config, 'ffn_dim', 'N/A')}, "
+ f"num_layers={getattr(wan_config, 'num_layers', 'N/A')}, "
+ f"num_heads={getattr(wan_config, 'num_heads', 'N/A')}")
+ except Exception as e:
+ logger.warning(f"Failed to load Wan config: {e}, using default values")
+
+
+ # Use Wan config if available, otherwise use defaults
+ dim = getattr(wan_config, 'dim', 2048) if wan_config else 2048
+ ffn_dim = getattr(wan_config, 'ffn_dim', 8192) if wan_config else 8192
+ num_layers = getattr(wan_config, 'num_layers', 32) if wan_config else 32
+ num_heads = getattr(wan_config, 'num_heads', 16) if wan_config else 16
+ freq_dim = getattr(wan_config, 'freq_dim', 256) if wan_config else 256
+ in_dim = getattr(wan_config, 'in_dim', 16) if wan_config else 16
+ out_dim = getattr(wan_config, 'out_dim', 16) if wan_config else 16
+
+ # text_dim: Use Wan's text_dim if available, but warn if it doesn't match text encoder
+ wan_text_dim = getattr(wan_config, 'text_dim', None) if wan_config else None
+ if wan_text_dim is not None and wan_text_dim != text_dim_actual:
+ logger.warning(f"Wan config text_dim ({wan_text_dim}) doesn't match text encoder dimension ({text_dim_actual}). "
+ f"Will use text encoder dimension and skip loading text_embedding weights.")
+ text_dim_for_model = text_dim_actual
+ else:
+ # Use Wan's text_dim if it matches, or use text encoder dimension
+ text_dim_for_model = wan_text_dim if wan_text_dim is not None else text_dim_actual
+
+ model = WanDiscreteVideoTransformer(
+ codebook_size=video_tokenizer.codebook_size,
+ vocab_size=video_tokenizer.codebook_size + 1,
+ num_frames=F_prime,
+ height=H_prime,
+ width=W_prime,
+ model_type='t2v',
+ patch_size=(1, 2, 2),
+ text_len=512,
+ in_dim=in_dim,
+ dim=dim,
+ ffn_dim=ffn_dim,
+ freq_dim=freq_dim,
+ text_dim=text_dim_for_model,
+ out_dim=out_dim,
+ num_heads=num_heads,
+ num_layers=num_layers,
+ window_size=(-1, -1),
+ qk_norm=True,
+ cross_attn_norm=True,
+ eps=1e-6
+ )
+
+ # Load Wan pretrained weights into backbone if provided
+ if args.wan_pretrained_path is not None:
+ logger.info(f"Loading Wan pretrained weights from: {args.wan_pretrained_path}")
+ try:
+ # Check if it's a local path or HuggingFace model ID
+ is_local_path = os.path.exists(args.wan_pretrained_path) and os.path.isdir(args.wan_pretrained_path)
+
+ if is_local_path:
+ # Local path: find the state dict file
+ state_dict_path = None
+ possible_paths = [
+ os.path.join(args.wan_pretrained_path, "diffusion_pytorch_model.safetensors"),
+ os.path.join(args.wan_pretrained_path, "diffusion_pytorch_model.bin"),
+ os.path.join(args.wan_pretrained_path, "pytorch_model.bin"),
+ os.path.join(args.wan_pretrained_path, "model.safetensors"),
+ ]
+ for p in possible_paths:
+ if os.path.exists(p):
+ state_dict_path = p
+ break
+
+ if state_dict_path is None:
+ raise FileNotFoundError(f"Could not find state dict in {args.wan_pretrained_path}")
+
+ logger.info(f"Loading weights from local path: {state_dict_path}")
+
+ # Load state dict from local file
+ if state_dict_path.endswith('.safetensors'):
+ from safetensors import safe_open
+ wan_state_dict = {}
+ with safe_open(state_dict_path, framework="pt", device="cpu") as f:
+ for k in f.keys():
+ wan_state_dict[k] = f.get_tensor(k)
+ else:
+ wan_state_dict = torch.load(state_dict_path, map_location="cpu")
+ else:
+ # HuggingFace model ID: try to load using from_pretrained
+ logger.info(f"Loading weights from HuggingFace Hub: {args.wan_pretrained_path}")
+ try:
+ # Try loading as WanModel first
+ temp_model = WanModel.from_pretrained(
+ args.wan_pretrained_path,
+ subfolder=None,
+ low_cpu_mem_usage=False,
+ device_map=None
+ )
+ wan_state_dict = temp_model.state_dict()
+ del temp_model
+ except:
+ # If that fails, try with 'backbone' subfolder
+ try:
+ temp_model = WanModel.from_pretrained(
+ args.wan_pretrained_path,
+ subfolder="backbone",
+ low_cpu_mem_usage=False,
+ device_map=None
+ )
+ wan_state_dict = temp_model.state_dict()
+ del temp_model
+ except:
+ # Last resort: try to download and load state dict directly
+ from huggingface_hub import hf_hub_download
+ import tempfile
+ with tempfile.TemporaryDirectory() as tmpdir:
+ # Try different possible filenames
+ possible_files = [
+ "diffusion_pytorch_model.safetensors",
+ "diffusion_pytorch_model.bin",
+ "pytorch_model.bin",
+ "model.safetensors",
+ ]
+ state_dict_path = None
+ for filename in possible_files:
+ try:
+ state_dict_path = hf_hub_download(
+ repo_id=args.wan_pretrained_path,
+ filename=filename,
+ cache_dir=tmpdir
+ )
+ break
+ except:
+ continue
+
+ if state_dict_path is None:
+ raise FileNotFoundError(
+ f"Could not find state dict file in HuggingFace model {args.wan_pretrained_path}"
+ )
+
+ # Load state dict
+ if state_dict_path.endswith('.safetensors'):
+ from safetensors import safe_open
+ wan_state_dict = {}
+ with safe_open(state_dict_path, framework="pt", device="cpu") as f:
+ for k in f.keys():
+ wan_state_dict[k] = f.get_tensor(k)
+ else:
+ wan_state_dict = torch.load(state_dict_path, map_location="cpu")
+
+ # Remove text_embedding weights if input dimension doesn't match
+ # This is necessary when using a different text encoder (e.g., UMT5-base with 768 dim
+ # vs Wan's original 4096 dim)
+ # Check the first text_embedding layer's input dimension (text_embedding.0.weight shape[1])
+ text_embedding_key = 'text_embedding.0.weight'
+ if text_embedding_key in wan_state_dict:
+ pretrained_text_dim = wan_state_dict[text_embedding_key].shape[1] # Input dimension
+ model_text_dim = model.backbone.text_embedding[0].weight.shape[1] # Model's expected input dimension
+
+ if pretrained_text_dim != model_text_dim:
+ # Remove all text_embedding related keys
+ keys_to_remove = [k for k in wan_state_dict.keys() if 'text_embedding' in k]
+ for k in keys_to_remove:
+ del wan_state_dict[k]
+ logger.info(f"Removed {len(keys_to_remove)} text_embedding keys due to input dimension mismatch "
+ f"(pretrained: {pretrained_text_dim}, model: {model_text_dim})")
+
+ # Load into model's backbone
+ missing_keys, unexpected_keys = model.backbone.load_state_dict(wan_state_dict, strict=False)
+
+ # Log results
+ if missing_keys:
+ # Filter out expected missing keys (text_embedding if removed)
+ actual_missing = [k for k in missing_keys if 'text_embedding' not in k]
+ if actual_missing:
+ logger.warning(f"Missing keys when loading Wan weights: {actual_missing[:10]}..."
+ if len(actual_missing) > 10 else f"Missing keys: {actual_missing}")
+ else:
+ logger.info(f"Only text_embedding keys are missing (expected due to text_dim mismatch)")
+ if unexpected_keys:
+ logger.warning(f"Unexpected keys when loading Wan weights: {unexpected_keys[:10]}..."
+ if len(unexpected_keys) > 10 else f"Unexpected keys: {unexpected_keys}")
+
+ logger.info("✓ Successfully loaded Wan pretrained weights into backbone (excluding text_embedding)")
+
+ except Exception as e:
+ logger.warning(f"Failed to load Wan pretrained weights: {e}")
+ import traceback
+ traceback.print_exc()
+ logger.warning("Continuing with random initialization")
+ else:
+ # Load from pretrained checkpoint
+ model = WanDiscreteVideoTransformer.from_pretrained(
+ args.pretrained_model_name_or_path, subfolder="transformer", low_cpu_mem_usage=False, device_map=None
+ )
+
+ # Save vocab_size before torch.compile (for use in training loop)
+ # This avoids issues with accelerate.unwrap_model when using torch.compile
+ vocab_size = model.vocab_size
+
+ # Convert model to correct dtype before torch.compile
+ # This ensures all layers (especially text_embedding which is randomly initialized) are on the right dtype
+ if accelerator.mixed_precision == "fp16":
+ model = model.to(dtype=torch.float16)
+ elif accelerator.mixed_precision == "bf16":
+ model = model.to(dtype=torch.bfloat16)
+ # else: keep float32
+
+ model = torch.compile(model)
+
+ if args.use_lora:
+ lora_config = LoraConfig(
+ r=args.lora_r,
+ lora_alpha=args.lora_alpha,
+ target_modules=args.lora_target_modules,
+ )
+ model.add_adapter(lora_config)
+
+ model.train()
+
+ # Freeze Wan backbone if requested
+ if args.freeze_wan_backbone:
+ for name, param in model.named_parameters():
+ if 'backbone' in name:
+ param.requires_grad = False
+ logger.info("Wan backbone parameters are frozen (requires_grad=False)")
+
+ if args.gradient_checkpointing:
+ model.enable_gradient_checkpointing()
+ if args.train_text_encoder and not args.use_precomputed_features and not args.use_precomputed_video_only:
+ # Only enable gradient checkpointing for text_encoder if it's loaded
+ text_encoder.gradient_checkpointing_enable()
+ elif args.use_precomputed_video_only:
+ # For video-only mode, enable gradient checkpointing for text encoder to save memory
+ text_encoder.gradient_checkpointing_enable()
+ logger.info("Enabled gradient checkpointing for text encoder to save memory")
+
+ # EMA is not used for video training
+ ema = None
+
+ def save_model_hook(models, weights, output_dir):
+ if accelerator.is_main_process:
+ transformer_lora_layers_to_save = None
+ text_encoder_lora_layers_to_save = None
+
+ for model_ in models:
+ # Unwrap model_ to get the actual model type (handles torch.compile wrapping)
+ unwrapped_model_ = safe_unwrap_model(model_, accelerator)
+
+ # Use class name comparison for more robust type checking
+ # This handles cases where the same class might be loaded from different modules
+ model_class_name = unwrapped_model_.__class__.__name__
+
+ if model_class_name == "WanDiscreteVideoTransformer":
+ if args.use_lora:
+ transformer_lora_layers_to_save = get_peft_model_state_dict(model_)
+ else:
+ # Unwrap before saving to avoid torch.compile issues
+ unwrapped_model_.save_pretrained(os.path.join(output_dir, "transformer"))
+ elif model_class_name in ["T5EncoderModel", "T5Model"]:
+ if args.text_encoder_use_lora:
+ text_encoder_lora_layers_to_save = get_peft_model_state_dict(model_)
+ else:
+ # Unwrap before saving to avoid torch.compile issues
+ unwrapped_model_.save_pretrained(os.path.join(output_dir, "text_encoder"))
+ else:
+ raise ValueError(f"unexpected save model: {model_.__class__}, unwrapped: {unwrapped_model_.__class__.__name__}")
+
+ # make sure to pop weight so that corresponding model is not saved again
+ weights.pop()
+
+ if transformer_lora_layers_to_save is not None or text_encoder_lora_layers_to_save is not None:
+ LoraLoaderMixin.save_lora_weights(
+ output_dir,
+ unet_lora_layers=transformer_lora_layers_to_save,
+ text_encoder_lora_layers=text_encoder_lora_layers_to_save,
+ )
+
+ # EMA not used for video training
+
+ def load_model_hook(models, input_dir):
+ transformer = None
+ text_encoder_ = None
+
+ # this part is added for keep consistency when add model.compile() in the model
+ def adap_compile(ori_dict):#add '_orig_mod.' to each key
+ new_dict = {}
+ for k,v in ori_dict.items():
+ new_dict['_orig_mod.'+k] = v
+ return new_dict
+
+ while len(models) > 0:
+ model_ = models.pop()
+
+ # Unwrap model to get the actual class name
+ unwrapped_model_ = safe_unwrap_model(model_, accelerator)
+ model_class_name = unwrapped_model_.__class__.__name__
+
+ if model_class_name == "WanDiscreteVideoTransformer":
+ if args.use_lora:
+ transformer = model_
+ else:
+ load_model = WanDiscreteVideoTransformer.from_pretrained(os.path.join(input_dir, "transformer"), low_cpu_mem_usage=False, device_map=None)
+ model_.load_state_dict(adap_compile(load_model.state_dict()))
+ del load_model
+ elif model_class_name in ["T5EncoderModel", "T5Model"]:
+ if args.text_encoder_use_lora:
+ text_encoder_ = model_
+ else:
+ try:
+ load_model = T5EncoderModel.from_pretrained(os.path.join(input_dir, "text_encoder"))
+ model_.load_state_dict(load_model.state_dict())
+ except:
+ print('Not found text-encoder model in current folder. Loading default UMT5-base.')
+ load_model = T5EncoderModel.from_pretrained("google/umt5-base")
+ model_.load_state_dict(load_model.state_dict())
+ del load_model
+ else:
+ raise ValueError(f"unexpected load model: {model_.__class__}, unwrapped: {model_class_name}")
+
+ if transformer is not None or text_encoder_ is not None:
+ lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
+ LoraLoaderMixin.load_lora_into_text_encoder(
+ lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_
+ )
+ LoraLoaderMixin.load_lora_into_transformer(
+ lora_state_dict, network_alphas=network_alphas, transformer=transformer
+ )
+
+ # EMA not used for video training
+
+ accelerator.register_load_state_pre_hook(load_model_hook)
+ accelerator.register_save_state_pre_hook(save_model_hook)
+
+ if args.scale_lr:
+ args.learning_rate = (
+ args.learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+ )
+
+ if args.use_8bit_adam:
+ try:
+ import bitsandbytes as bnb
+ except ImportError:
+ raise ImportError(
+ "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+ )
+
+ optimizer_cls = bnb.optim.AdamW8bit
+ else:
+ optimizer_cls = torch.optim.AdamW
+
+ # Separate Wan backbone parameters from other parameters (token_embedding, logits_head)
+ # This allows different learning rates for backbone vs head/tail
+ backbone_params = []
+ other_params = []
+
+ for name, param in model.named_parameters():
+ if 'backbone' in name:
+ backbone_params.append((name, param))
+ else:
+ other_params.append((name, param))
+
+ # Log parameter counts
+ backbone_param_count = sum(p.numel() for _, p in backbone_params)
+ other_param_count = sum(p.numel() for _, p in other_params)
+ total_param_count = sum(p.numel() for _, p in model.named_parameters())
+ logger.info(f"Parameter counts: backbone={backbone_param_count:,}, other={other_param_count:,}, total={total_param_count:,}")
+
+ # no decay on bias and layernorm and embedding
+ no_decay = ["bias", "layer_norm.weight", "mlm_ln.weight", "embeddings.weight"]
+
+ # Calculate backbone lr
+ if args.freeze_wan_backbone:
+ backbone_lr = 0.0
+ logger.info("Wan backbone is frozen (lr=0)")
+ else:
+ backbone_lr = args.learning_rate * args.wan_backbone_lr_ratio
+ logger.info(f"Wan backbone lr = {backbone_lr:.6f} (base_lr * {args.wan_backbone_lr_ratio})")
+
+ logger.info(f"Other parts (token_embedding, logits_head) lr = {args.learning_rate:.6f}")
+
+ # Group parameters: backbone and other parts, each with decay and no_decay
+ optimizer_grouped_parameters = []
+
+ # Backbone parameters with weight decay
+ if backbone_params:
+ backbone_with_decay = [p for n, p in backbone_params if not any(nd in n for nd in no_decay)]
+ if backbone_with_decay:
+ optimizer_grouped_parameters.append({
+ "params": backbone_with_decay,
+ "lr": backbone_lr,
+ "weight_decay": args.adam_weight_decay,
+ })
+
+ # Backbone parameters without weight decay
+ backbone_no_decay = [p for n, p in backbone_params if any(nd in n for nd in no_decay)]
+ if backbone_no_decay:
+ optimizer_grouped_parameters.append({
+ "params": backbone_no_decay,
+ "lr": backbone_lr,
+ "weight_decay": 0.0,
+ })
+
+ # Other parameters (token_embedding, logits_head) with weight decay
+ if other_params:
+ other_with_decay = [p for n, p in other_params if not any(nd in n for nd in no_decay)]
+ if other_with_decay:
+ optimizer_grouped_parameters.append({
+ "params": other_with_decay,
+ "lr": args.learning_rate,
+ "weight_decay": args.adam_weight_decay,
+ })
+
+ # Other parameters without weight decay
+ other_no_decay = [p for n, p in other_params if any(nd in n for nd in no_decay)]
+ if other_no_decay:
+ optimizer_grouped_parameters.append({
+ "params": other_no_decay,
+ "lr": args.learning_rate,
+ "weight_decay": 0.0,
+ })
+
+ if args.train_text_encoder and not args.use_precomputed_features and not args.use_precomputed_video_only:
+ # Only add text_encoder to optimizer if it's loaded and not using precomputed features
+ optimizer_grouped_parameters.append(
+ {"params": text_encoder.parameters(), "weight_decay": args.adam_weight_decay}
+ )
+
+ optimizer = optimizer_cls(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ betas=(args.adam_beta1, args.adam_beta2),
+ weight_decay=args.adam_weight_decay,
+ eps=args.adam_epsilon,
+ )
+
+ logger.info("Creating dataloaders and lr_scheduler")
+
+ total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+ # Video training datasets
+ if args.use_precomputed_features:
+ # Use pre-extracted features
+ logger.info(f"Using pre-extracted features from: {args.features_dir}")
+ dataset = PrecomputedFeatureDataset(
+ features_dir=args.features_dir,
+ )
+ elif args.use_precomputed_video_only:
+ # Use pre-extracted video codes only, process text at runtime
+ logger.info(f"Using pre-extracted video codes from: {args.features_dir}")
+ logger.info("Text will be encoded with UMT5-XXL at runtime")
+ # We'll need to create a dataset that loads video codes but processes text
+ # For now, let's create a modified version of PrecomputedFeatureDataset
+ dataset = PrecomputedVideoOnlyDataset(
+ features_dir=args.features_dir,
+ )
+ # Set up tokenizer for dataset
+ dataset.tokenizer = tokenizer
+ dataset.text_encoder_architecture = args.text_encoder_architecture
+ elif args.instance_dataset == 'OpenVid1MDataset':
+ # OpenVid1M dataset from CSV file
+ csv_path = args.instance_data_dir
+ if not os.path.exists(csv_path):
+ raise FileNotFoundError(f"CSV file not found: {csv_path}")
+
+ # Video root directory: assume videos are in the same directory as CSV or in a 'video_reorg' subdirectory
+ csv_dir = os.path.dirname(csv_path)
+ # Try to find video directory
+ if os.path.exists(os.path.join(csv_dir, 'video_reorg')):
+ video_root_dir = os.path.join(csv_dir, 'video_reorg')
+ elif os.path.exists(os.path.join(os.path.dirname(csv_dir), 'video_reorg')):
+ video_root_dir = os.path.join(os.path.dirname(csv_dir), 'video_reorg')
+ else:
+ # Fallback: use CSV directory
+ video_root_dir = csv_dir
+ logger.warning(f"Video directory not found, using CSV directory: {video_root_dir}")
+
+ dataset = OpenVid1MDataset(
+ csv_path=csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ text_encoder_architecture=args.text_encoder_architecture,
+ prompt_prefix=args.prompt_prefix,
+ )
+ elif args.instance_dataset == 'HuggingFaceDataset' or args.instance_dataset == 'VideoDataset':
+ dataset = VideoDataset(
+ hf_dataset=load_dataset(args.instance_data_dir, split="train"),
+ tokenizer=tokenizer,
+ video_key=args.image_key if args.image_key else "video",
+ prompt_key=args.prompt_key if args.prompt_key else "caption",
+ prompt_prefix=args.prompt_prefix,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ text_encoder_architecture=args.text_encoder_architecture
+ )
+ else:
+ raise ValueError(f"For video training, instance_dataset must be 'OpenVid1MDataset', 'HuggingFaceDataset' or 'VideoDataset', got '{args.instance_dataset}'")
+
+ # Adjust DataLoader settings for precomputed features to reduce memory usage
+ if args.use_precomputed_features or args.use_precomputed_video_only:
+ # For precomputed features, reduce prefetch to save memory
+ # Features are already extracted, so we don't need as much prefetching
+ prefetch_factor = min(args.dataloader_prefetch_factor, 1) if args.dataloader_num_workers > 0 else None
+ # Consider disabling pin_memory for precomputed features if memory is tight
+ # pin_memory=True is faster but uses more memory
+ pin_memory = True # Keep True for performance, but can be set to False if OOM persists
+ logger.info(f"Using precomputed features - DataLoader settings: prefetch_factor={prefetch_factor}, pin_memory={pin_memory}")
+ else:
+ prefetch_factor = args.dataloader_prefetch_factor if args.dataloader_num_workers > 0 else None
+ pin_memory = True
+
+ train_dataloader = DataLoader(
+ dataset,
+ batch_size=args.train_batch_size,
+ shuffle=True,
+ num_workers=args.dataloader_num_workers,
+ collate_fn=default_collate,
+ pin_memory=pin_memory,
+ prefetch_factor=prefetch_factor,
+ persistent_workers=args.dataloader_num_workers > 0, # Keep workers alive between epochs
+ )
+ train_dataloader.num_batches = len(train_dataloader)
+
+ # Log dataloader configuration for performance monitoring
+ if accelerator.is_main_process:
+ logger.info(f"Dataloader configuration:")
+ logger.info(f" - num_workers: {args.dataloader_num_workers} (0 = single-threaded, recommended: 4-8 for video)")
+ logger.info(f" - prefetch_factor: {args.dataloader_prefetch_factor if args.dataloader_num_workers > 0 else 'N/A (num_workers=0)'}")
+ logger.info(f" - persistent_workers: {args.dataloader_num_workers > 0}")
+ logger.info(f" - pin_memory: True")
+ if args.dataloader_num_workers == 0:
+ logger.warning(
+ "⚠️ num_workers=0 may cause GPU underutilization. "
+ "Consider setting --dataloader_num_workers 4-8 to improve GPU utilization."
+ )
+
+ # Calculate max_train_steps if not provided
+ if args.max_train_steps is None:
+ # Default to 1 epoch if not specified
+ num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
+ args.max_train_steps = num_update_steps_per_epoch
+ logger.warning(f"max_train_steps not specified, defaulting to 1 epoch ({args.max_train_steps} steps)")
+
+ lr_scheduler = diffusers.optimization.get_scheduler(
+ args.lr_scheduler,
+ optimizer=optimizer,
+ num_training_steps=args.max_train_steps * accelerator.num_processes,
+ num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+ )
+
+ logger.info("Preparing model, optimizer and dataloaders")
+
+ if args.use_precomputed_features:
+ # Don't prepare text_encoder if using precomputed features
+ model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader
+ )
+ elif args.use_precomputed_video_only:
+ # Prepare text_encoder since we need it for training
+ model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader
+ )
+ elif args.train_text_encoder:
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder
+ )
+ else:
+ model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader
+ )
+
+ train_dataloader.num_batches = len(train_dataloader)
+
+ weight_dtype = torch.float32
+ if accelerator.mixed_precision == "fp16":
+ weight_dtype = torch.float16
+ elif accelerator.mixed_precision == "bf16":
+ weight_dtype = torch.bfloat16
+
+ if not args.use_precomputed_features and not args.use_precomputed_video_only:
+ if not args.train_text_encoder:
+ text_encoder.to(device=accelerator.device, dtype=weight_dtype)
+ # Video tokenizer is already on the correct device
+ elif args.use_precomputed_video_only:
+ # Text encoder is already prepared by accelerator
+ logger.info("Text encoder prepared by accelerator for video-only precomputed mode")
+ else:
+ # For precomputed features, text_encoder is None, so skip
+ logger.info("Skipping text_encoder.to() - using precomputed features")
+
+ # EMA not used for video training
+
+ if not args.use_precomputed_features and not args.use_precomputed_video_only:
+ with nullcontext() if args.train_text_encoder else torch.no_grad():
+ # T5/UMT5 doesn't have cond_embeds, only encoder_hidden_states
+ input_ids_empty, attention_mask_empty = tokenize_prompt(tokenizer, "", args.text_encoder_architecture)
+ empty_embeds, _, _ = encode_prompt(
+ text_encoder, input_ids_empty.to(accelerator.device, non_blocking=True), args.text_encoder_architecture, attention_mask_empty.to(accelerator.device, non_blocking=True)
+ )
+ empty_clip_embeds = None # Not used for T5
+
+ # Video training doesn't use instance_data_image
+ elif args.use_precomputed_video_only:
+ # Generate empty_embeds at runtime using loaded text encoder
+ with torch.no_grad():
+ input_ids_empty, attention_mask_empty = tokenize_prompt(tokenizer, "", args.text_encoder_architecture)
+ empty_embeds, _, _ = encode_prompt(
+ text_encoder, input_ids_empty.to(accelerator.device, non_blocking=True), args.text_encoder_architecture, attention_mask_empty.to(accelerator.device, non_blocking=True)
+ )
+ logger.info(f"Generated empty_embeds at runtime: shape={empty_embeds.shape}, dtype={empty_embeds.dtype}")
+ else:
+ # For precomputed features, load empty_embeds from file if needed
+ empty_clip_embeds = None # Not used for T5
+ if args.cond_dropout_prob > 0.0:
+ if args.empty_embeds_path is None:
+ raise ValueError(
+ "--empty_embeds_path is required when --use_precomputed_features is set "
+ "and --cond_dropout_prob > 0.0"
+ )
+ logger.info(f"Loading empty_embeds from: {args.empty_embeds_path}")
+
+ # Load empty_embeds from .npy file (more space-efficient than .pt)
+ import numpy as np
+ empty_embeds_np = np.load(args.empty_embeds_path) # Load as numpy array
+ empty_embeds = torch.from_numpy(empty_embeds_np).to(dtype=weight_dtype) # Convert to tensor
+
+ # Load metadata to verify compatibility
+ metadata_file = os.path.join(args.features_dir, "metadata.json")
+ if os.path.exists(metadata_file):
+ import json
+ with open(metadata_file, 'r') as f:
+ metadata = json.load(f)
+ logger.info(f"Empty embeds info from metadata: shape={metadata.get('empty_embeds_shape')}")
+ # Verify text encoder architecture matches
+ if metadata.get("text_encoder_architecture") != args.text_encoder_architecture:
+ logger.warning(
+ f"Text encoder architecture mismatch: "
+ f"empty_embeds was extracted with {metadata.get('text_encoder_architecture')}, "
+ f"but training uses {args.text_encoder_architecture}"
+ )
+
+ # Note: We don't move to device here, will move during training loop if needed
+ logger.info(f"Loaded empty_embeds: shape={empty_embeds.shape}, dtype={empty_embeds.dtype}")
+ else:
+ empty_embeds = None
+ logger.info("Skipping empty_embeds loading - cond_dropout_prob is 0.0")
+
+ # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+ num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
+ # Afterwards we recalculate our number of training epochs.
+ # Note: We are not doing epoch based training here, but just using this for book keeping and being able to
+ # reuse the same training loop with other datasets/loaders.
+ # max_train_steps should already be set above if it was None
+ num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) if num_update_steps_per_epoch > 0 else 1
+
+ # Train!
+ logger.info("***** Running training *****")
+ logger.info(f" Num training steps = {args.max_train_steps}")
+ logger.info(f" Instantaneous batch size per device = { args.train_batch_size}")
+ logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+ logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+
+ resume_from_checkpoint = args.resume_from_checkpoint
+ if resume_from_checkpoint:
+ if resume_from_checkpoint == "latest":
+ # Get the most recent checkpoint
+ dirs = os.listdir(args.output_dir)
+ dirs = [d for d in dirs if d.startswith("checkpoint")]
+ dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+ if len(dirs) > 0:
+ resume_from_checkpoint = os.path.join(args.output_dir, dirs[-1])
+ else:
+ resume_from_checkpoint = None
+
+ if resume_from_checkpoint is None:
+ accelerator.print(
+ f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+ )
+ else:
+ accelerator.print(f"Resuming from checkpoint {resume_from_checkpoint}")
+
+ if resume_from_checkpoint is None:
+ global_step = 0
+ first_epoch = 0
+ else:
+ accelerator.load_state(resume_from_checkpoint)
+ global_step = int(os.path.basename(resume_from_checkpoint).split("-")[1])
+ first_epoch = global_step // num_update_steps_per_epoch
+
+ # EMA not used for video training
+
+ # As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to
+ # reuse the same training loop with other datasets/loaders.
+ for epoch in range(first_epoch, num_train_epochs):
+ for batch in train_dataloader:
+ with torch.no_grad():
+ if args.use_precomputed_features:
+ # Use pre-extracted features (both video and text)
+ # Features are already on CPU with correct dtypes (video_codes: int32, text_embedding: float16/bfloat16)
+ # Move to device and convert dtype in one step to avoid intermediate copies
+ weight_dtype = {
+ "fp16": torch.float16,
+ "bf16": torch.bfloat16,
+ }.get(accelerator.mixed_precision, torch.float32)
+
+ video_tokens = batch["video_codes"].to(
+ device=accelerator.device,
+ non_blocking=False, # CPU->GPU
+ ) # [B, F', H', W'], int32/int64, on GPU
+
+ encoder_hidden_states = batch["text_embedding"].to(
+ device=accelerator.device,
+ dtype=weight_dtype,
+ non_blocking=False,
+ ) # [B, L, D], float16/bfloat16, on GPU
+
+ # Get context lengths for precomputed features
+ # batch["context_len"] is a tensor from DataLoader collation [B]
+ if "context_len" in batch and batch["context_len"] is not None:
+ context_lens = batch["context_len"].to(dtype=torch.long, device=accelerator.device)
+ else:
+ # Fallback: assume all sequences have full length (512 for T5)
+ context_lens = torch.full((batch_size,), 512, dtype=torch.long, device=accelerator.device)
+ elif args.use_precomputed_video_only:
+ # Use pre-extracted video codes only, encode text at runtime
+ video_tokens = batch["video_codes"].to(
+ device=accelerator.device,
+ non_blocking=False, # CPU->GPU
+ ) # [B, F', H', W'], int32/int64, on GPU
+
+ # Encode text at runtime with UMT5-XXL
+ prompt_input_ids = batch["prompt_input_ids"].to(accelerator.device, non_blocking=True)
+ prompt_attention_mask = batch["prompt_attention_mask"].to(accelerator.device, non_blocking=True)
+
+ encoder_hidden_states, cond_embeds, context_lens = encode_prompt(
+ text_encoder, prompt_input_ids, args.text_encoder_architecture, prompt_attention_mask
+ )
+
+ del prompt_input_ids, prompt_attention_mask
+ if cond_embeds is not None:
+ del cond_embeds
+
+ batch_size = video_tokens.shape[0]
+ # with torch.no_grad():
+ # t_min = video_tokens.min().item()
+ # t_max = video_tokens.max().item()
+ # # if accelerator.is_main_process:
+ # # print(f"[DEBUG] tokens range = [{t_min}, {t_max}]")
+ # # try:
+ # # vocab = model.backbone.token_embed.num_embeddings
+ # # except Exception:
+ # # vocab = 16000
+ # # print(f"[DEBUG] model vocab_size = {vocab}")
+
+ # assert t_min >= 0, f"Found negative token id: {t_min}"
+
+ # assert t_max < 16000, f"Found token id {t_max} >= vocab_size 16000"
+ # Delete batch after moving data to GPU to free CPU memory
+ del batch
+
+ # Debug print (only on main process, first batch)
+ if accelerator.is_main_process and global_step == 0 and epoch == first_epoch:
+ print(f"[DEBUG] video_tokens: shape={video_tokens.shape}, dtype={video_tokens.dtype}, device={video_tokens.device}")
+ print(f"[DEBUG] encoder_hidden_states: shape={encoder_hidden_states.shape}, dtype={encoder_hidden_states.dtype}, device={encoder_hidden_states.device}")
+ else:
+ # Video training path - encode on-the-fly
+ video_values = batch["video"].to(accelerator.device, non_blocking=True) # [B, C, F, H, W]
+ batch_size = video_values.shape[0]
+
+ # Encode video to discrete tokens using CosmosVideoTokenizer
+ split_batch_size = args.split_vae_encode if args.split_vae_encode is not None else batch_size
+ num_splits = math.ceil(batch_size / split_batch_size)
+ video_tokens = []
+ for i in range(num_splits):
+ start_idx = i * split_batch_size
+ end_idx = min((i + 1) * split_batch_size, batch_size)
+ # video_values: [B, C, F, H, W]
+ tokens = video_tokenizer.encode(video_values[start_idx:end_idx]) # [B, F', H', W']
+ video_tokens.append(tokens)
+ video_tokens = torch.cat(video_tokens, dim=0) # [B, F', H', W']
+
+ if "prompt_input_ids" in batch:
+ with nullcontext() if args.train_text_encoder else torch.no_grad():
+ # Get attention_mask from batch or assume full length
+ attention_mask = batch.get("prompt_attention_mask", None)
+ if attention_mask is not None:
+ attention_mask = attention_mask.to(accelerator.device, non_blocking=True)
+ encoder_hidden_states, cond_embeds, context_lens = encode_prompt(
+ text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True), args.text_encoder_architecture, attention_mask
+ )
+
+ # Flatten video tokens for masking: [B, F', H', W'] -> [B, F'*H'*W']
+ B, F_prime, H_prime, W_prime = video_tokens.shape
+ seq_len = F_prime * H_prime * W_prime
+ video_tokens_flat = video_tokens.view(B, seq_len) # [B, seq_len]
+
+ timesteps = torch.rand(batch_size, device=video_tokens_flat.device)
+ mask_prob = torch.cos(timesteps * math.pi * 0.5)
+ mask_prob = mask_prob.clip(args.min_masking_rate)
+
+ num_token_masked = (seq_len * mask_prob).round().clamp(min=1)
+ batch_randperm = torch.rand(batch_size, seq_len, device=video_tokens_flat.device).argsort(dim=-1)
+ mask = batch_randperm < num_token_masked.unsqueeze(-1)
+
+ mask_id = video_tokenizer.mask_token_id # codebook_size
+ input_ids_flat = torch.where(mask, mask_id, video_tokens_flat)
+ labels_flat = torch.where(mask, video_tokens_flat, -100)
+
+ # Reshape back to [B, F', H', W'] for model forward
+ input_ids = input_ids_flat.view(B, F_prime, H_prime, W_prime)
+ labels = labels_flat.view(B, F_prime, H_prime, W_prime)
+
+ if args.cond_dropout_prob > 0.0:
+ assert encoder_hidden_states is not None
+ assert empty_embeds is not None, "empty_embeds must be loaded when cond_dropout_prob > 0.0"
+
+ batch_size = encoder_hidden_states.shape[0]
+
+ # Move empty_embeds to device if needed (for precomputed features case)
+ if empty_embeds.device != encoder_hidden_states.device:
+ empty_embeds = empty_embeds.to(encoder_hidden_states.device)
+
+ mask = (
+ torch.zeros((batch_size, 1, 1), device=encoder_hidden_states.device).float().uniform_(0, 1)
+ < args.cond_dropout_prob
+ )
+
+ empty_embeds_ = empty_embeds.expand(batch_size, -1, -1)
+ encoder_hidden_states = torch.where(
+ (encoder_hidden_states * mask).bool(), encoder_hidden_states, empty_embeds_
+ )
+
+ # Handle cond_embeds dropout (only for CLIP, not for T5)
+ # For T5/UMT5, cond_embeds is None, so skip this step
+
+ # Video tokens are already in [B, F', H', W'] format, no need to reshape
+
+ # if not args.use_precomputed_features and "prompt_input_ids" in batch:
+ # with nullcontext() if args.train_text_encoder else torch.no_grad():
+ # # Get attention_mask from batch or assume full length
+ # attention_mask = batch.get("prompt_attention_mask", None)
+ # if attention_mask is not None:
+ # attention_mask = attention_mask.to(accelerator.device, non_blocking=True)
+ # encoder_hidden_states, cond_embeds, context_lens = encode_prompt(
+ # text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True), args.text_encoder_architecture, attention_mask
+ # )
+
+ # Train Step
+ with accelerator.accumulate(model):
+ # Video training: use WanDiscreteVideoTransformer
+ # vocab_size is already saved before torch.compile
+
+ # Prepare timesteps: [B] -> [B] (scalar timesteps for video)
+ timesteps_tensor = (mask_prob * 1000).long().to(input_ids.device)
+
+ # Ensure encoder_hidden_states is on correct dtype
+ # Note: For precomputed features, dtype is already converted above
+ if encoder_hidden_states is not None and not args.use_precomputed_features:
+ # Only convert if not using precomputed features (already converted above)
+ if accelerator.mixed_precision == "fp16" and encoder_hidden_states.dtype != torch.float16:
+ encoder_hidden_states = encoder_hidden_states.to(dtype=torch.float16)
+ elif accelerator.mixed_precision == "bf16" and encoder_hidden_states.dtype != torch.bfloat16:
+ encoder_hidden_states = encoder_hidden_states.to(dtype=torch.bfloat16)
+ elif accelerator.mixed_precision == "no" and encoder_hidden_states.dtype != torch.float32:
+ encoder_hidden_states = encoder_hidden_states.to(dtype=torch.float32)
+
+ # Forward pass: input_ids is [B, F', H', W'], encoder_hidden_states is [B, L, D]
+ logits = model(
+ tokens=input_ids, # [B, F', H', W']
+ timesteps=timesteps_tensor, # [B]
+ encoder_hidden_states=encoder_hidden_states, # [B, L, D]
+ context_lens=context_lens, # [B] - effective text lengths
+ y=None,
+ ) # Returns [B, vocab_size, F', H', W']
+
+ # Reshape logits and labels for loss computation
+ # logits: [B, vocab_size, F', H', W'] -> [B*F'*H'*W', vocab_size]
+ B, vocab_size, F_prime_logits, H_prime_logits, W_prime_logits = logits.shape
+ # logits = logits.permute(0, 2, 3, 4, 1).reshape(B * F_prime_logits * H_prime_logits * W_prime_logits, vocab_size)
+
+ # # labels: [B, F', H', W'] - may have different dimensions due to patch/unpatch operations
+ # # Crop labels to match logits dimensions if needed
+ # B_labels, F_prime_labels, H_prime_labels, W_prime_labels = labels.shape
+ # assert B == B_labels, f"Batch size mismatch: logits {B} vs labels {B_labels}"
+
+ # # # Crop labels to match logits spatial dimensions
+ # # if F_prime_labels != F_prime_logits or H_prime_labels != H_prime_logits or W_prime_labels != W_prime_logits:
+ # # # Crop labels to match logits dimensions
+ # # labels = labels[:, :F_prime_logits, :H_prime_logits, :W_prime_logits]
+
+ # # labels: [B, F', H', W'] -> [B*F'*H'*W']
+ # labels_flat = labels.reshape(-1)
+
+ # # Convert to long (int64) for cross_entropy (required by CUDA kernel)
+ # # video_tokens might be int32 from precomputed features
+ # labels_flat = labels_flat.long()
+
+ # loss = F.cross_entropy(
+ # logits,
+ # labels_flat,
+ # ignore_index=-100,
+ # reduction="mean",
+ # )
+ loss = F.cross_entropy(
+ logits, # [B, vocab, F, H, W]
+ labels.long(), # [B, F, H, W]
+ ignore_index=-100,
+ )
+
+
+ # Gather the losses across all processes for logging (if we use distributed training).
+ avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
+ avg_masking_rate = accelerator.gather(mask_prob.repeat(args.train_batch_size)).mean()
+
+ accelerator.backward(loss)
+
+ if args.max_grad_norm is not None and accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+
+ optimizer.step()
+ lr_scheduler.step()
+
+ optimizer.zero_grad(set_to_none=True)
+
+ # Checks if the accelerator has performed an optimization step behind the scenes
+ if accelerator.sync_gradients:
+ # EMA not used for video training
+
+ if (global_step + 1) % args.logging_steps == 0:
+ logs = {
+ "step_loss": avg_loss.item(),
+ "lr": lr_scheduler.get_last_lr()[0],
+ "avg_masking_rate": avg_masking_rate.item(),
+ }
+ accelerator.log(logs, step=global_step + 1)
+
+ logger.info(
+ f"Step: {global_step + 1} "
+ f"Loss: {avg_loss.item():0.4f} "
+ f"LR: {lr_scheduler.get_last_lr()[0]:0.6f}"
+ )
+
+ if (global_step + 1) % args.checkpointing_steps == 0:
+ save_checkpoint(args, accelerator, global_step + 1, logger)
+
+ if (global_step + 1) % args.validation_steps == 0 and accelerator.is_main_process:
+ # EMA not used for video training
+
+ with torch.no_grad():
+ logger.info("Generating videos for validation...")
+
+ model.eval()
+
+ # Load text encoder and video tokenizer for validation if using precomputed features
+ # Use different variable names to avoid shadowing global variables
+ val_text_encoder = None
+ val_tokenizer = None
+ val_video_tokenizer = None
+
+ if args.use_precomputed_features:
+ logger.info("Loading text encoder and video tokenizer for validation...")
+
+ # Load text encoder
+ if args.text_encoder_architecture == "umt5-base":
+ model_id = "google/umt5-base"
+ elif args.text_encoder_architecture == "umt5-xxl":
+ model_id = "google/umt5-xxl"
+ elif args.text_encoder_architecture == "t5":
+ model_id = "t5-base"
+ else:
+ raise ValueError(f"Unknown text encoder architecture: {args.text_encoder_architecture}")
+
+ val_text_encoder = T5EncoderModel.from_pretrained(model_id, torch_dtype=weight_dtype)
+ val_tokenizer = T5Tokenizer.from_pretrained(model_id)
+
+ val_text_encoder.eval()
+ val_text_encoder.requires_grad_(False)
+ elif args.use_precomputed_video_only:
+ logger.info("Reusing already loaded text encoder for validation...")
+ # Reuse the text encoder and tokenizer already loaded for training
+ # Need to unwrap from accelerator wrapper first
+ val_text_encoder = safe_unwrap_model(text_encoder, accelerator)
+ val_text_encoder.eval()
+ val_text_encoder.requires_grad_(False)
+ val_tokenizer = tokenizer
+
+ # Load video tokenizer
+ val_video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=accelerator.device,
+ dtype=weight_dtype
+ )
+ val_video_tokenizer.requires_grad_(False)
+ val_video_tokenizer.eval()
+
+ logger.info("Text encoder and video tokenizer loaded for validation")
+ else:
+ # Use global variables when not using precomputed features
+ val_text_encoder = text_encoder
+ val_tokenizer = tokenizer
+ val_video_tokenizer = video_tokenizer
+ if args.train_text_encoder:
+ val_text_encoder.eval()
+
+ # Video pipeline validation
+ logger.info("Generating videos for validation...")
+
+ # For video, create scheduler with mask_token_id
+ scheduler = Scheduler(
+ mask_token_id=val_video_tokenizer.mask_token_id,
+ masking_schedule="cosine"
+ )
+ scheduler.set_timesteps(num_inference_steps=48, device=accelerator.device)
+
+ # Get unwrapped transformer and ensure it's on correct dtype
+ unwrapped_transformer = safe_unwrap_model(model, accelerator)
+ # Ensure transformer is on the correct dtype (text_embedding was randomly initialized as float32)
+ unwrapped_transformer = unwrapped_transformer.to(dtype=weight_dtype)
+
+ pipe = VideoPipeline(
+ tokenizer=val_tokenizer,
+ text_encoder=val_text_encoder,
+ transformer=unwrapped_transformer,
+ scheduler=scheduler,
+ video_tokenizer=val_video_tokenizer,
+ text_len=512,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ )
+
+ # Generate videos
+ try:
+ videos = pipe(
+ prompt=args.validation_prompts,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ guidance_scale=9.0,
+ num_inference_steps=48,
+ output_type="pil",
+ ).videos
+
+ # Log videos to wandb (save first frame of each video)
+ if is_wandb_available():
+ wandb_images = []
+ for i, video in enumerate(videos):
+ if isinstance(video, list) and len(video) > 0:
+ first_frame = video[0]
+ elif isinstance(video, torch.Tensor):
+ first_frame = transforms.ToPILImage()(video[:, 0, :, :].clamp(0, 1))
+ else:
+ first_frame = video
+ if first_frame is not None:
+ prompt_caption = args.validation_prompts[i] if i < len(args.validation_prompts) else f"video_{i}"
+ wandb_images.append(wandb.Image(first_frame, caption=prompt_caption))
+ if wandb_images:
+ wandb.log({"generated_videos_first_frame": wandb_images}, step=global_step + 1)
+
+ # Save video frames as grid
+ for i, video in enumerate(videos):
+ if isinstance(video, list):
+ frames = [transforms.ToTensor()(frame) for frame in video]
+ if frames:
+ frames_tensor = torch.stack(frames, dim=0)
+ grid = make_grid(frames_tensor, nrow=min(4, len(frames)))
+ grid_path = os.path.join(args.output_dir, f"{global_step}_video_{i}_CFG-9.png")
+ save_image(grid, grid_path)
+ if is_wandb_available():
+ wandb.log(
+ {"generated_videos_grid": wandb.Image(grid, caption=f"video_{i}_grid")},
+ step=global_step + 1,
+ )
+ elif isinstance(video, torch.Tensor):
+ C, num_frames_video, H, W = video.shape
+ frames_list = [video[:, f, :, :] for f in range(num_frames_video)]
+ frames_tensor = torch.stack(frames_list, dim=0)
+ grid = make_grid(frames_tensor, nrow=min(4, num_frames_video))
+ grid_path = os.path.join(args.output_dir, f"{global_step}_video_{i}_CFG-9.png")
+ save_image(grid, grid_path)
+ if is_wandb_available():
+ wandb.log(
+ {"generated_videos_grid": wandb.Image(grid, caption=f"video_{i}_grid")},
+ step=global_step + 1,
+ )
+
+ logger.info(f"Validation videos saved to {args.output_dir}")
+ except Exception as e:
+ logger.error(f"Video validation failed: {e}")
+ import traceback
+ traceback.print_exc()
+ finally:
+ # Clean up models loaded for validation (if using precomputed features)
+ if args.use_precomputed_features:
+ # Delete validation models to free GPU memory
+ if 'val_text_encoder' in locals():
+ del val_text_encoder
+ if 'val_tokenizer' in locals():
+ del val_tokenizer
+ if 'val_video_tokenizer' in locals():
+ del val_video_tokenizer
+ if 'pipe' in locals():
+ del pipe
+ if 'scheduler' in locals():
+ del scheduler
+ # Clear CUDA cache
+ torch.cuda.empty_cache()
+ logger.info("Cleaned up validation models and freed GPU memory")
+ elif args.use_precomputed_video_only:
+ # Don't delete text_encoder/tokenizer as they are reused from training
+ # Only clean up validation-specific models
+ if 'val_video_tokenizer' in locals():
+ del val_video_tokenizer
+ if 'pipe' in locals():
+ del pipe
+ if 'scheduler' in locals():
+ del scheduler
+ # Clear CUDA cache
+ torch.cuda.empty_cache()
+ logger.info("Cleaned up validation models (kept text_encoder for reuse)")
+
+ model.train()
+
+ if args.train_text_encoder and not args.use_precomputed_features and not args.use_precomputed_video_only:
+ # Only set train mode if text_encoder is still loaded (not using precomputed features)
+ if text_encoder is not None:
+ text_encoder.train()
+
+ # EMA not used for video training
+
+ global_step += 1
+
+ # Stop training if max steps is reached
+ if global_step >= args.max_train_steps:
+ break
+ # End for
+
+ accelerator.wait_for_everyone()
+
+ # Evaluate and save checkpoint at the end of training
+ save_checkpoint(args, accelerator, global_step, logger)
+
+ # Save the final trained checkpoint
+ if accelerator.is_main_process:
+ model = safe_unwrap_model(model, accelerator)
+ # EMA not used for video training
+ model.save_pretrained(args.output_dir)
+
+ accelerator.end_training()
+
+
+
+
+
+if __name__ == "__main__":
+ main(parse_args())
+
+
diff --git a/Meissonic/train/train_meissonic.py b/Meissonic/train/train_meissonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..432ff946f0fcf1a020e417a12d6afa1a048374f2
--- /dev/null
+++ b/Meissonic/train/train_meissonic.py
@@ -0,0 +1,1085 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import copy
+import logging
+import math
+import os
+from contextlib import nullcontext
+from pathlib import Path
+import sys
+sys.path.append(os.getcwd())
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import ProjectConfiguration, set_seed
+from peft import LoraConfig
+from peft.utils import get_peft_model_state_dict
+from torch.utils.data import DataLoader, default_collate
+from torchvision import transforms
+from transformers import (
+ CLIPTextModelWithProjection,
+ CLIPTokenizer,
+)
+import diffusers.optimization
+from diffusers import EMAModel, VQModel
+from src.scheduler import Scheduler
+from diffusers.loaders import LoraLoaderMixin
+from diffusers.utils import is_wandb_available
+from src.pipeline import Pipeline
+from torchvision.utils import save_image,make_grid
+from datasets import load_dataset
+from train.trainer_utils import save_checkpoint
+from train.dataset_utils import MyParquetDataset, HuggingFaceDataset
+from train.dataset_utils import tokenize_prompt, encode_prompt
+from src.transformer import Transformer2DModel
+
+if is_wandb_available():
+ import wandb
+ # wandb.login(key="")
+
+logger = get_logger(__name__, log_level="INFO")
+
+import torch._dynamo
+torch._dynamo.config.verbose = True
+
+# Optionally suppress errors to fall back to eager execution
+torch._dynamo.config.suppress_errors = True
+
+def parse_args():
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--pretrained_model_architecture",
+ type=str,
+ default="Meissonic",
+ required=False
+ )
+ parser.add_argument(
+ "--text_encoder_architecture",
+ type=str,
+ default="open_clip",
+ required=False,
+ help="The architecture of the text encoder. One of ['CLIP', 'open_clip', 'flan-t5-base','Qwen2-0.5B','gemini-2b',long_CLIP_T5_base','CLIP_T5_base']",
+ )
+ parser.add_argument(
+ "--instance_dataset",
+ type=str,
+ default=None,
+ required=False,
+ help="The dataset to use for training. One of ['MSCOCO600K', 'PickaPicV2']",
+ )
+ parser.add_argument(
+ "--training_from_scratch",
+ type=bool,
+ default=False,
+ required=False
+ )
+ parser.add_argument(
+ "--pretrained_model_name_or_path",
+ type=str,
+ default=None,
+ required=True,
+ help="Path to pretrained model or model identifier from huggingface.co/models.",
+ )
+ parser.add_argument(
+ "--revision",
+ type=str,
+ default=None,
+ required=False,
+ help="Revision of pretrained model identifier from huggingface.co/models.",
+ )
+ parser.add_argument(
+ "--variant",
+ type=str,
+ default=None,
+ help="Variant of the model files of the pretrained model identifier from huggingface.co/models, 'e.g.' fp16",
+ )
+ parser.add_argument(
+ "--instance_data_dataset",
+ type=str,
+ default=None,
+ required=False,
+ help="A Hugging Face dataset containing the training images",
+ )
+ parser.add_argument(
+ "--instance_data_dir",
+ type=str,
+ default=None,
+ required=False,
+ help="A folder containing the training data of instance images.",
+ )
+ parser.add_argument(
+ "--instance_data_image", type=str, default=None, required=False, help="A single training image"
+ )
+ parser.add_argument(
+ "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
+ )
+ parser.add_argument(
+ "--dataloader_num_workers",
+ type=int,
+ default=0,
+ help=(
+ "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
+ ),
+ )
+ parser.add_argument(
+ "--allow_tf32",
+ action="store_true",
+ help=(
+ "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+ " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+ ),
+ )
+ parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.")
+ parser.add_argument("--ema_decay", type=float, default=0.9999)
+ parser.add_argument("--ema_update_after_step", type=int, default=0)
+ parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+ parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+ parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+ parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+ parser.add_argument(
+ "--output_dir",
+ type=str,
+ default="muse_training",
+ help="The output directory where the model predictions and checkpoints will be written.",
+ )
+ parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+ parser.add_argument(
+ "--logging_dir",
+ type=str,
+ default="logs",
+ help=(
+ "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+ " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+ ),
+ )
+ parser.add_argument(
+ "--max_train_steps",
+ type=int,
+ default=None,
+ help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
+ )
+ parser.add_argument(
+ "--checkpointing_steps",
+ type=int,
+ default=500,
+ help=(
+ "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
+ "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
+ "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
+ "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
+ "instructions."
+ ),
+ )
+ parser.add_argument(
+ "--logging_steps",
+ type=int,
+ default=50,
+ )
+ parser.add_argument(
+ "--checkpoints_total_limit",
+ type=int,
+ default=None,
+ help=(
+ "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
+ " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
+ " for more details"
+ ),
+ )
+ parser.add_argument(
+ "--resume_from_checkpoint",
+ type=str,
+ default=None,
+ help=(
+ "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+ ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+ ),
+ )
+ parser.add_argument(
+ "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+ )
+ parser.add_argument(
+ "--gradient_accumulation_steps",
+ type=int,
+ default=1,
+ help="Number of updates steps to accumulate before performing a backward/update pass.",
+ )
+ parser.add_argument(
+ "--learning_rate",
+ type=float,
+ default=0.0003,
+ help="Initial learning rate (after the potential warmup period) to use.",
+ )
+ parser.add_argument(
+ "--scale_lr",
+ action="store_true",
+ default=False,
+ help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+ )
+ parser.add_argument(
+ "--lr_scheduler",
+ type=str,
+ default="constant",
+ help=(
+ 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+ ' "constant", "constant_with_warmup"]'
+ ),
+ )
+ parser.add_argument(
+ "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+ )
+ parser.add_argument(
+ "--validation_steps",
+ type=int,
+ default=100,
+ help=(
+ "Run validation every X steps. Validation consists of running the prompt"
+ " `args.validation_prompt` multiple times: `args.num_validation_images`"
+ " and logging the images."
+ ),
+ )
+ parser.add_argument(
+ "--mixed_precision",
+ type=str,
+ default=None,
+ choices=["no", "fp16", "bf16"],
+ help=(
+ "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
+ " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
+ " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
+ ),
+ )
+ parser.add_argument(
+ "--report_to",
+ type=str,
+ default="wandb",
+ help=(
+ 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+ ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+ ),
+ )
+ parser.add_argument("--validation_prompts", type=str, nargs="*")
+ parser.add_argument(
+ "--resolution",
+ type=int,
+ default=512,
+ help=(
+ "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+ " resolution"
+ ),
+ )
+ parser.add_argument("--split_vae_encode", type=int, required=False, default=None)
+ parser.add_argument("--min_masking_rate", type=float, default=0.0)
+ parser.add_argument("--cond_dropout_prob", type=float, default=0.0)
+ parser.add_argument("--max_grad_norm", default=50.0, type=float, help="Max gradient norm.", required=False)
+ parser.add_argument("--use_lora", action="store_true", help="Fine tune the model using LoRa")
+ parser.add_argument("--text_encoder_use_lora", action="store_true", help="Fine tune the model using LoRa")
+ parser.add_argument("--lora_r", default=16, type=int)
+ parser.add_argument("--lora_alpha", default=32, type=int)
+ parser.add_argument("--lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
+ parser.add_argument("--text_encoder_lora_r", default=16, type=int)
+ parser.add_argument("--text_encoder_lora_alpha", default=32, type=int)
+ parser.add_argument("--text_encoder_lora_target_modules", default=["to_q", "to_k", "to_v"], type=str, nargs="+")
+ parser.add_argument("--train_text_encoder", action="store_true")
+ parser.add_argument("--image_key", type=str, required=False)
+ parser.add_argument("--prompt_key", type=str, required=False)
+ parser.add_argument(
+ "--gradient_checkpointing",
+ action="store_true",
+ help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+ )
+ parser.add_argument("--prompt_prefix", type=str, required=False, default=None)
+
+ args = parser.parse_args()
+
+ if args.report_to == "wandb":
+ if not is_wandb_available():
+ raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
+
+ num_datasources = sum(
+ [x is not None for x in [args.instance_data_dir, args.instance_data_image, args.instance_data_dataset]]
+ )
+
+ if num_datasources != 1:
+ raise ValueError(
+ "provide one and only one of `--instance_data_dir`, `--instance_data_image`, or `--instance_data_dataset`"
+ )
+
+ if args.instance_data_dir is not None:
+ if not os.path.exists(args.instance_data_dir):
+ raise ValueError(f"Does not exist: `--args.instance_data_dir` {args.instance_data_dir}")
+
+ if args.instance_data_image is not None:
+ if not os.path.exists(args.instance_data_image):
+ raise ValueError(f"Does not exist: `--args.instance_data_image` {args.instance_data_image}")
+
+ if args.instance_data_dataset is not None and (args.image_key is None or args.prompt_key is None):
+ raise ValueError("`--instance_data_dataset` requires setting `--image_key` and `--prompt_key`")
+
+ return args
+
+def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+ latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+ latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+ latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+
+ latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
+
+ latent_image_ids = latent_image_ids.reshape(
+ latent_image_id_height * latent_image_id_width, latent_image_id_channels
+ )
+ # latent_image_ids = latent_image_ids.unsqueeze(0).repeat(batch_size, 1, 1)
+
+ return latent_image_ids.to(device=device, dtype=dtype)
+
+def main(args):
+ if args.allow_tf32:
+ torch.backends.cuda.matmul.allow_tf32 = True
+
+ # if args.pretrained_model_architecture == "Meissonic":
+ # from src.pipeline import Pipeline
+ # else:
+ # raise ValueError(f"Unknown model architecture: {args.pretrained_model_architecture}")
+
+
+ logging_dir = Path(args.output_dir, args.logging_dir)
+
+ accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
+
+ accelerator = Accelerator(
+ gradient_accumulation_steps=args.gradient_accumulation_steps,
+ mixed_precision=args.mixed_precision,
+ log_with=args.report_to,
+ project_config=accelerator_project_config,
+ )
+
+ if accelerator.is_main_process:
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ # Make one log on every process with the configuration for debugging.
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+
+ if accelerator.is_main_process:
+ accelerator.init_trackers("meissonic", config=vars(copy.deepcopy(args)))
+
+ if args.seed is not None:
+ set_seed(args.seed)
+
+ if args.text_encoder_architecture == "open_clip":
+ if args.resume_from_checkpoint:
+ text_encoder = CLIPTextModelWithProjection.from_pretrained(
+ args.resume_from_checkpoint, subfolder="text_encoder", variant=args.variant
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(
+ args.resume_from_checkpoint, subfolder="tokenizer", variant=args.variant
+ )
+ else:
+ text_encoder = CLIPTextModelWithProjection.from_pretrained(
+ args.pretrained_model_name_or_path, subfolder="text_encoder", variant=args.variant
+ )
+ tokenizer = CLIPTokenizer.from_pretrained(
+ args.pretrained_model_name_or_path, subfolder="tokenizer", variant=args.variant
+ )
+
+ # elif args.text_encoder_architecture == "CLIP_T5_base":
+ # text_encoder_clip = CLIPTextModelWithProjection.from_pretrained(
+ # args.pretrained_model_name_or_path, subfolder="text_encoder", variant=args.variant
+ # )
+ # tokenizer_clip = CLIPTokenizer.from_pretrained(
+ # args.pretrained_model_name_or_path, subfolder="tokenizer", variant=args.variant
+ # )
+ # from transformers import T5Tokenizer, T5ForConditionalGeneration
+ # text_encoder_t5 = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base",torch_dtype=torch.float16)
+ # tokenizer_t5 = T5Tokenizer.from_pretrained("google/flan-t5-base",torch_dtype=torch.float16)
+ # text_encoder = [text_encoder_clip,text_encoder_t5]
+ # tokenizer = [tokenizer_clip,tokenizer_t5]
+ # elif args.text_encoder_architecture == "flan-t5-base":
+ # from transformers import T5Tokenizer, T5ForConditionalGeneration
+ # text_encoder = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base",torch_dtype=torch.float16)
+ # tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base",torch_dtype=torch.float16)
+ # elif args.text_encoder_architecture == "gemini-2b":
+ # raise NotImplementedError("Gemini-2b is not yet supported")
+ # elif args.text_encoder_architecture == "Qwen2-0.5B":
+ # raise NotImplementedError("Qwen2-0.5B is not yet supported")
+ else:
+ raise ValueError(f"Unknown text encoder architecture: {args.text_encoder_architecture}")
+
+ vq_model = VQModel.from_pretrained(
+ args.pretrained_model_name_or_path, subfolder="vqvae", revision=args.revision, variant=args.variant
+ )
+
+ if args.train_text_encoder:
+ if args.text_encoder_use_lora:
+ lora_config = LoraConfig(
+ r=args.text_encoder_lora_r,
+ lora_alpha=args.text_encoder_lora_alpha,
+ target_modules=args.text_encoder_lora_target_modules,
+ )
+ text_encoder.add_adapter(lora_config)
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ text_encoder[0].train()
+ text_encoder[0].requires_grad_(True)
+ text_encoder[1].train()
+ text_encoder[1].requires_grad_(True)
+ else:
+ text_encoder.train()
+ text_encoder.requires_grad_(True)
+ else:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ text_encoder[0].eval()
+ text_encoder[0].requires_grad_(False)
+ text_encoder[1].eval()
+ text_encoder[1].requires_grad_(False)
+ else:
+ text_encoder.eval()
+ text_encoder.requires_grad_(False)
+
+ vq_model.requires_grad_(False)
+
+ if args.pretrained_model_architecture == "Meissonic":
+ if args.training_from_scratch:
+ model = Transformer2DModel(
+ patch_size = 1,
+ in_channels = 64,
+ num_layers = 14,
+ num_single_layers = 28,
+ attention_head_dim = 128,
+ num_attention_heads = 8,
+ joint_attention_dim = 1024,
+ pooled_projection_dim = 1024,
+ guidance_embeds = False,
+ axes_dims_rope = (16, 56, 56),
+ downsample= True,
+ upsample= True,
+ )
+
+ # model_tmp = Transformer2DModel.from_pretrained("LAST_STAGE_CKPT_PATH", low_cpu_mem_usage=False, device_map=None)
+ # model.load_state_dict(model_tmp.state_dict(), strict=False)
+ # del model_tmp
+ else:
+ model = Transformer2DModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="transformer", low_cpu_mem_usage=False, device_map=None)
+ else:
+ raise ValueError(f"Unknown model architecture: {args.pretrained_model_architecture}")
+
+ model = torch.compile(model)
+
+ if args.use_lora:
+ lora_config = LoraConfig(
+ r=args.lora_r,
+ lora_alpha=args.lora_alpha,
+ target_modules=args.lora_target_modules,
+ )
+ model.add_adapter(lora_config)
+
+ model.train()
+
+ if args.gradient_checkpointing:
+ model.enable_gradient_checkpointing()
+ if args.train_text_encoder:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ text_encoder[0].gradient_checkpointing_enable()
+ text_encoder[1].gradient_checkpointing_enable()
+ else:
+ text_encoder.gradient_checkpointing_enable()
+
+ if args.use_ema: # Not verify the robostness of this part
+ ema = EMAModel(
+ model.parameters(),
+ decay=args.ema_decay,
+ update_after_step=args.ema_update_after_step,
+ model_cls= Transformer2DModel,
+ model_config=model.config,
+ )
+
+ def save_model_hook(models, weights, output_dir):
+ if accelerator.is_main_process:
+ transformer_lora_layers_to_save = None
+ text_encoder_lora_layers_to_save = None
+
+ for model_ in models:
+ if isinstance(model_, type(accelerator.unwrap_model(model))):
+ if args.use_lora:
+ transformer_lora_layers_to_save = get_peft_model_state_dict(model_)
+ else:
+ model_.save_pretrained(os.path.join(output_dir, "transformer"))
+ elif isinstance(model_, type(accelerator.unwrap_model(text_encoder))):
+ if args.text_encoder_use_lora:
+ text_encoder_lora_layers_to_save = get_peft_model_state_dict(model_)
+ else:
+ model_.save_pretrained(os.path.join(output_dir, "text_encoder"))
+ else:
+ raise ValueError(f"unexpected save model: {model_.__class__}")
+
+ # make sure to pop weight so that corresponding model is not saved again
+ weights.pop()
+
+ if transformer_lora_layers_to_save is not None or text_encoder_lora_layers_to_save is not None:
+ LoraLoaderMixin.save_lora_weights(
+ output_dir,
+ unet_lora_layers=transformer_lora_layers_to_save,
+ text_encoder_lora_layers=text_encoder_lora_layers_to_save,
+ )
+
+ if args.use_ema:
+ ema.save_pretrained(os.path.join(output_dir, "ema_model"))
+
+ def load_model_hook(models, input_dir):
+ transformer = None
+ text_encoder_ = None
+
+ # this part is added for keep consistency when add model.compile() in the model
+ def adap_compile(ori_dict):#add '_orig_mod.' to each key
+ new_dict = {}
+ for k,v in ori_dict.items():
+ new_dict['_orig_mod.'+k] = v
+ return new_dict
+
+ while len(models) > 0:
+ model_ = models.pop()
+
+ if isinstance(model_, type(accelerator.unwrap_model(model))):
+ if args.use_lora:
+ transformer = model_
+ else:
+ if args.pretrained_model_architecture == "Meissonic":
+ load_model = Transformer2DModel.from_pretrained(os.path.join(input_dir, "transformer"), low_cpu_mem_usage=False, device_map=None)
+ else:
+ raise ValueError(f"Unknown model architecture: {args.pretrained_model_architecture}")
+ model_.load_state_dict(adap_compile(load_model.state_dict()))
+ del load_model
+ elif isinstance(model_, type(accelerator.unwrap_model(text_encoder))):
+ if args.text_encoder_use_lora:
+ text_encoder_ = model_
+ else:
+ try:
+ load_model = CLIPTextModelWithProjection.from_pretrained(os.path.join(input_dir, "text_encoder"))
+ model_.load_state_dict(load_model.state_dict())
+ # print('finished loading text encoder!')
+ except:
+ print('Not found text-encoder model in current folder. So we download one text encoder from Internet.')
+ load_model = CLIPTextModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+ model_.load_state_dict(load_model.state_dict())
+ del load_model
+ else:
+ raise ValueError(f"unexpected save model: {model.__class__}")
+
+ if transformer is not None or text_encoder_ is not None:
+ lora_state_dict, network_alphas = LoraLoaderMixin.lora_state_dict(input_dir)
+ LoraLoaderMixin.load_lora_into_text_encoder(
+ lora_state_dict, network_alphas=network_alphas, text_encoder=text_encoder_
+ )
+ LoraLoaderMixin.load_lora_into_transformer(
+ lora_state_dict, network_alphas=network_alphas, transformer=transformer
+ )
+
+ if args.use_ema:
+ load_from = EMAModel.from_pretrained(os.path.join(input_dir, "ema_model"), model_cls=Transformer2DModel)
+ ema.load_state_dict(adap_compile(load_from.state_dict()))
+ del load_from
+
+ accelerator.register_load_state_pre_hook(load_model_hook)
+ accelerator.register_save_state_pre_hook(save_model_hook)
+
+ if args.scale_lr:
+ args.learning_rate = (
+ args.learning_rate * args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+ )
+
+ if args.use_8bit_adam:
+ try:
+ import bitsandbytes as bnb
+ except ImportError:
+ raise ImportError(
+ "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`"
+ )
+
+ optimizer_cls = bnb.optim.AdamW8bit
+ else:
+ optimizer_cls = torch.optim.AdamW
+
+ # no decay on bias and layernorm and embedding
+ no_decay = ["bias", "layer_norm.weight", "mlm_ln.weight", "embeddings.weight"]
+ optimizer_grouped_parameters = [
+ {
+ "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
+ "weight_decay": args.adam_weight_decay,
+ },
+ {
+ "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
+ "weight_decay": 0.0,
+ },
+ ]
+
+ if args.train_text_encoder:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ optimizer_grouped_parameters.append(
+ {"params": text_encoder[0].parameters(), "weight_decay": args.adam_weight_decay}
+ )
+ optimizer_grouped_parameters.append(
+ {"params": text_encoder[1].parameters(), "weight_decay": args.adam_weight_decay}
+ )
+ else:
+ optimizer_grouped_parameters.append(
+ {"params": text_encoder.parameters(), "weight_decay": args.adam_weight_decay}
+ )
+
+ optimizer = optimizer_cls(
+ optimizer_grouped_parameters,
+ lr=args.learning_rate,
+ betas=(args.adam_beta1, args.adam_beta2),
+ weight_decay=args.adam_weight_decay,
+ eps=args.adam_epsilon,
+ )
+
+ logger.info("Creating dataloaders and lr_scheduler")
+
+ total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+ if args.instance_dataset == "MyParquetDataset":
+ dataset = MyParquetDataset(
+ root_dir=args.instance_data_dir, # something like '../parquets_father_dir/'
+ tokenizer=tokenizer,
+ size=args.resolution,
+ text_encoder_architecture=args.text_encoder_architecture
+ )
+ elif args.instance_dataset == 'HuggingFaceDataset': # you can try this first, just download dataset from huggingface
+ dataset = HuggingFaceDataset(
+ hf_dataset=load_dataset(args.instance_data_dir, split="train"), # something like '../parquets_father_dir/'
+ tokenizer=tokenizer,
+ image_key='image',
+ prompt_key='caption',
+ prompt_prefix=args.prompt_prefix,
+ size=args.resolution,
+ text_encoder_architecture=args.text_encoder_architecture
+ )
+ elif args.instance_dataset == "DATA_TYPE":
+ raise NotImplementedError("DATA_TYPE is not yet supported")
+ # Some instructions
+ # Origanize your text-image pairs in the following way:
+ # when apply __getitem__ method, return a dictionary with keys 'image', 'micro_conds' and 'prompt_input_ids'
+ # For more details to follow, please refer to the implementation of MyParquetDataset class
+ else:
+ assert False
+
+ train_dataloader = DataLoader(
+ dataset,
+ batch_size=args.train_batch_size,
+ shuffle=True,
+ num_workers=args.dataloader_num_workers,
+ collate_fn=default_collate,
+ pin_memory=True,
+ )
+ train_dataloader.num_batches = len(train_dataloader)
+
+ lr_scheduler = diffusers.optimization.get_scheduler(
+ args.lr_scheduler,
+ optimizer=optimizer,
+ num_training_steps=args.max_train_steps * accelerator.num_processes,
+ num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
+ )
+
+ logger.info("Preparing model, optimizer and dataloaders")
+
+ if args.train_text_encoder:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder[0], text_encoder[1] = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder[0], text_encoder[1]
+ )
+ else:
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder
+ )
+ else:
+ model, optimizer, lr_scheduler, train_dataloader = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader
+ )
+
+ train_dataloader.num_batches = len(train_dataloader)
+
+ weight_dtype = torch.float32
+ if accelerator.mixed_precision == "fp16":
+ weight_dtype = torch.float16
+ elif accelerator.mixed_precision == "bf16":
+ weight_dtype = torch.bfloat16
+
+ if not args.train_text_encoder:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ text_encoder[0].to(device=accelerator.device, dtype=weight_dtype)
+ text_encoder[1].to(device=accelerator.device, dtype=weight_dtype)
+ else:
+ text_encoder.to(device=accelerator.device, dtype=weight_dtype)
+
+ vq_model.to(device=accelerator.device)
+
+ if args.use_ema:
+ ema.to(accelerator.device)
+
+ with nullcontext() if args.train_text_encoder else torch.no_grad():
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ _input_ids_tmp_ = tokenize_prompt(tokenizer, "", args.text_encoder_architecture)
+ _input_ids_tmp_[0] = _input_ids_tmp_[0].to(accelerator.device, non_blocking=True)
+ _input_ids_tmp_[1] = _input_ids_tmp_[1].to(accelerator.device, non_blocking=True)
+ empty_embeds, empty_clip_embeds = encode_prompt(
+ text_encoder, _input_ids_tmp_, args.text_encoder_architecture
+ )
+ else:
+ empty_embeds, empty_clip_embeds = encode_prompt(
+ text_encoder, tokenize_prompt(tokenizer, "", args.text_encoder_architecture).to(accelerator.device, non_blocking=True), args.text_encoder_architecture
+ )
+
+ # There is a single image, we can just pre-encode the single prompt
+ if args.instance_data_image is not None:
+ prompt = os.path.splitext(os.path.basename(args.instance_data_image))[0]
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ _input_ids_tmp_ = tokenize_prompt(tokenizer, prompt, args.text_encoder_architecture)
+ _input_ids_tmp_[0] = _input_ids_tmp_[0].to(accelerator.device, non_blocking=True)
+ _input_ids_tmp_[1] = _input_ids_tmp_[1].to(accelerator.device, non_blocking=True)
+ empty_embeds, empty_clip_embeds = encode_prompt(
+ text_encoder, _input_ids_tmp_, args.text_encoder_architecture
+ )
+ else:
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder, tokenize_prompt(tokenizer, prompt, args.text_encoder_architecture).to(accelerator.device, non_blocking=True), args.text_encoder_architecture
+ )
+ encoder_hidden_states = encoder_hidden_states.repeat(args.train_batch_size, 1, 1)
+ cond_embeds = cond_embeds.repeat(args.train_batch_size, 1)
+
+ # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+ num_update_steps_per_epoch = math.ceil(train_dataloader.num_batches / args.gradient_accumulation_steps)
+ # Afterwards we recalculate our number of training epochs.
+ # Note: We are not doing epoch based training here, but just using this for book keeping and being able to
+ # reuse the same training loop with other datasets/loaders.
+ num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+ # Train!
+ logger.info("***** Running training *****")
+ logger.info(f" Num training steps = {args.max_train_steps}")
+ logger.info(f" Instantaneous batch size per device = { args.train_batch_size}")
+ logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+ logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+
+ resume_from_checkpoint = args.resume_from_checkpoint
+ if resume_from_checkpoint:
+ if resume_from_checkpoint == "latest":
+ # Get the most recent checkpoint
+ dirs = os.listdir(args.output_dir)
+ dirs = [d for d in dirs if d.startswith("checkpoint")]
+ dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+ if len(dirs) > 0:
+ resume_from_checkpoint = os.path.join(args.output_dir, dirs[-1])
+ else:
+ resume_from_checkpoint = None
+
+ if resume_from_checkpoint is None:
+ accelerator.print(
+ f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
+ )
+ else:
+ accelerator.print(f"Resuming from checkpoint {resume_from_checkpoint}")
+
+ if resume_from_checkpoint is None:
+ global_step = 0
+ first_epoch = 0
+ else:
+ accelerator.load_state(resume_from_checkpoint)
+ global_step = int(os.path.basename(resume_from_checkpoint).split("-")[1])
+ first_epoch = global_step // num_update_steps_per_epoch
+
+ # This is to solve the inconsistent tensor device issue
+ if args.use_ema:
+ ema.shadow_params = [p.to(accelerator.device) for p in ema.shadow_params]
+
+ # As stated above, we are not doing epoch based training here, but just using this for book keeping and being able to
+ # reuse the same training loop with other datasets/loaders.
+ for epoch in range(first_epoch, num_train_epochs):
+ for batch in train_dataloader:
+ torch.cuda.empty_cache()
+ with torch.no_grad():
+ micro_conds = batch["micro_conds"].to(accelerator.device, non_blocking=True)
+ pixel_values = batch["image"].to(accelerator.device, non_blocking=True)
+
+ batch_size = pixel_values.shape[0]
+
+ split_batch_size = args.split_vae_encode if args.split_vae_encode is not None else batch_size
+ num_splits = math.ceil(batch_size / split_batch_size)
+ image_tokens = []
+ for i in range(num_splits):
+ start_idx = i * split_batch_size
+ end_idx = min((i + 1) * split_batch_size, batch_size)
+ bs = pixel_values.shape[0]
+ image_tokens.append(
+ vq_model.quantize(vq_model.encode(pixel_values[start_idx:end_idx]).latents)[2][2].reshape(
+ split_batch_size, -1
+ )
+ )
+ image_tokens = torch.cat(image_tokens, dim=0)
+
+ batch_size, seq_len = image_tokens.shape
+
+ timesteps = torch.rand(batch_size, device=image_tokens.device)
+ mask_prob = torch.cos(timesteps * math.pi * 0.5)
+ mask_prob = mask_prob.clip(args.min_masking_rate)
+
+ num_token_masked = (seq_len * mask_prob).round().clamp(min=1)
+ batch_randperm = torch.rand(batch_size, seq_len, device=image_tokens.device).argsort(dim=-1)
+ mask = batch_randperm < num_token_masked.unsqueeze(-1)
+
+ mask_id = accelerator.unwrap_model(model).config.vocab_size - 1
+ input_ids = torch.where(mask, mask_id, image_tokens)
+ labels = torch.where(mask, image_tokens, -100)
+
+ if "prompt_input_ids" in batch:
+ with nullcontext() if args.train_text_encoder else torch.no_grad():
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ batch["prompt_input_ids"][0] = batch["prompt_input_ids"][0].to(accelerator.device, non_blocking=True)
+ batch["prompt_input_ids"][1] = batch["prompt_input_ids"][1].to(accelerator.device, non_blocking=True)
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder, batch["prompt_input_ids"], args.text_encoder_architecture
+ )
+ else:
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True), args.text_encoder_architecture
+ )
+
+ if args.cond_dropout_prob > 0.0:
+ assert encoder_hidden_states is not None
+
+ batch_size = encoder_hidden_states.shape[0]
+
+ mask = (
+ torch.zeros((batch_size, 1, 1), device=encoder_hidden_states.device).float().uniform_(0, 1)
+ < args.cond_dropout_prob
+ )
+
+ empty_embeds_ = empty_embeds.expand(batch_size, -1, -1)
+ encoder_hidden_states = torch.where(
+ (encoder_hidden_states * mask).bool(), encoder_hidden_states, empty_embeds_
+ )
+
+ empty_clip_embeds_ = empty_clip_embeds.expand(batch_size, -1)
+ cond_embeds = torch.where((cond_embeds * mask.squeeze(-1)).bool(), cond_embeds, empty_clip_embeds_)
+
+ bs = input_ids.shape[0]
+ vae_scale_factor = 2 ** (len(vq_model.config.block_out_channels) - 1)
+ resolution = args.resolution // vae_scale_factor
+ input_ids = input_ids.reshape(bs, resolution, resolution)
+
+ if "prompt_input_ids" in batch:
+ with nullcontext() if args.train_text_encoder else torch.no_grad():
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ batch["prompt_input_ids"][0] = batch["prompt_input_ids"][0].to(accelerator.device, non_blocking=True)
+ batch["prompt_input_ids"][1] = batch["prompt_input_ids"][1].to(accelerator.device, non_blocking=True)
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder, batch["prompt_input_ids"],args.text_encoder_architecture
+ )
+ else:
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder, batch["prompt_input_ids"].to(accelerator.device, non_blocking=True),args.text_encoder_architecture
+ )
+
+ # Train Step
+ with accelerator.accumulate(model):
+ codebook_size = accelerator.unwrap_model(model).config.codebook_size
+
+ if args.pretrained_model_architecture == 'Meissonic':
+
+ if args.resolution == 1024: # only stage 3 and stage 4 do not apply 2*
+ img_ids = _prepare_latent_image_ids(input_ids.shape[0], input_ids.shape[-2],input_ids.shape[-1],input_ids.device,input_ids.dtype)
+ else:
+ img_ids = _prepare_latent_image_ids(input_ids.shape[0],2*input_ids.shape[-2],2*input_ids.shape[-1],input_ids.device,input_ids.dtype)
+
+ txt_ids = torch.zeros(encoder_hidden_states.shape[1],3).to(device = input_ids.device, dtype = input_ids.dtype)
+
+ logits = (
+ model(
+ hidden_states=input_ids, # should be (batch size, channel, height, width)
+ encoder_hidden_states=encoder_hidden_states, # should be (batch size, sequence_len, embed_dims)
+ micro_conds=micro_conds, #
+ pooled_projections=cond_embeds, # should be (batch_size, projection_dim)
+ img_ids = img_ids,
+ txt_ids = txt_ids,
+ # timestep = timesteps * 20,
+ timestep = mask_prob * 1000,
+ # guidance = 9,
+ )
+ .reshape(bs, codebook_size, -1)
+ .permute(0, 2, 1)
+ .reshape(-1, codebook_size)
+ )
+ else:
+ raise ValueError(f"Unknown model architecture: {args.pretrained_model_architecture}")
+
+ loss = F.cross_entropy(
+ logits,
+ labels.view(-1),
+ ignore_index=-100,
+ reduction="mean",
+ )
+
+ # Gather the losses across all processes for logging (if we use distributed training).
+ avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
+ avg_masking_rate = accelerator.gather(mask_prob.repeat(args.train_batch_size)).mean()
+
+ accelerator.backward(loss)
+
+ if args.max_grad_norm is not None and accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
+
+ optimizer.step()
+ lr_scheduler.step()
+
+ optimizer.zero_grad(set_to_none=True)
+
+ # Checks if the accelerator has performed an optimization step behind the scenes
+ if accelerator.sync_gradients:
+ if args.use_ema:
+ ema.step(model.parameters())
+
+ if (global_step + 1) % args.logging_steps == 0:
+ logs = {
+ "step_loss": avg_loss.item(),
+ "lr": lr_scheduler.get_last_lr()[0],
+ "avg_masking_rate": avg_masking_rate.item(),
+ }
+ accelerator.log(logs, step=global_step + 1)
+
+ logger.info(
+ f"Step: {global_step + 1} "
+ f"Loss: {avg_loss.item():0.4f} "
+ f"LR: {lr_scheduler.get_last_lr()[0]:0.6f}"
+ )
+
+ if (global_step + 1) % args.checkpointing_steps == 0:
+ save_checkpoint(args, accelerator, global_step + 1, logger)
+
+ if (global_step + 1) % args.validation_steps == 0 and accelerator.is_main_process:
+ if args.use_ema:
+ ema.store(model.parameters())
+ ema.copy_to(model.parameters())
+
+ with torch.no_grad():
+ logger.info("Generating images...")
+
+ model.eval()
+
+ if args.train_text_encoder:
+ text_encoder.eval()
+
+ scheduler = Scheduler.from_pretrained(
+ args.pretrained_model_name_or_path,
+ subfolder="scheduler",
+ revision=args.revision,
+ variant=args.variant,
+ )
+ if args.text_encoder_architecture == "CLIP" or args.text_encoder_architecture == "open_clip":
+ pipe = Pipeline(
+ transformer=accelerator.unwrap_model(model),
+ tokenizer=tokenizer,
+ text_encoder=text_encoder,
+ vqvae=vq_model,
+ scheduler=scheduler,
+ )
+ else:
+ pipe = Pipeline(
+ transformer=accelerator.unwrap_model(model),
+ tokenizer=tokenizer[0],
+ text_encoder=text_encoder[0],
+ vqvae=vq_model,
+ scheduler=scheduler,
+ text_encoder_t5=text_encoder[1],
+ tokenizer_t5=tokenizer[1]
+ )
+
+
+
+
+
+ pil_images = pipe(prompt=args.validation_prompts,height=args.resolution,width=args.resolution,guidance_scale=9,num_inference_steps=64).images
+ wandb_images = [
+ wandb.Image(image, caption=args.validation_prompts[i])
+ for i, image in enumerate(pil_images)
+ ]
+
+ wandb.log({"generated_images": wandb_images}, step=global_step + 1)
+
+ result=[]
+ for img in pil_images:
+ if not isinstance(img, torch.Tensor):
+ img = transforms.ToTensor()(img)
+ result.append(img.unsqueeze(0))
+ result = torch.cat(result,dim=0)
+ result = make_grid(result, nrow=3)
+ save_image(result,os.path.join(args.output_dir,str(global_step)+'_text2image_1024_CFG-9.png'))
+
+
+ # pil_images = pipe(prompt=args.validation_prompts,height=args.resolution,width=args.resolution,guidance_scale=9).images
+ # result=[]
+ # for img in pil_images:
+ # if not isinstance(img, torch.Tensor):
+ # img = transforms.ToTensor()(img)
+ # result.append(img.unsqueeze(0))
+ # result = torch.cat(result,dim=0)
+ # result = make_grid(result, nrow=3)
+ # save_image(result,os.path.join(args.output_dir,str(global_step)+'_text2image_1024_CFG-9.png'))
+
+
+
+ model.train()
+
+ if args.train_text_encoder:
+ if args.text_encoder_architecture == "CLIP_T5_base": # Not support yet. Only support open_clip
+ text_encoder[0].train()
+ text_encoder[1].trian()
+ else:
+ text_encoder.train()
+
+ if args.use_ema:
+ ema.restore(model.parameters())
+
+ global_step += 1
+
+ # Stop training if max steps is reached
+ if global_step >= args.max_train_steps:
+ break
+ # End for
+
+ accelerator.wait_for_everyone()
+
+ # Evaluate and save checkpoint at the end of training
+ save_checkpoint(args, accelerator, global_step, logger)
+
+ # Save the final trained checkpoint
+ if accelerator.is_main_process:
+ model = accelerator.unwrap_model(model)
+ if args.use_ema:
+ ema.copy_to(model.parameters())
+ model.save_pretrained(args.output_dir)
+
+ accelerator.end_training()
+
+
+
+
+
+if __name__ == "__main__":
+ main(parse_args())
+
+
diff --git a/Meissonic/train/train_overfit.py b/Meissonic/train/train_overfit.py
new file mode 100644
index 0000000000000000000000000000000000000000..386a82b4cefcf1e5c1742d44cba6efd3e5b444b9
--- /dev/null
+++ b/Meissonic/train/train_overfit.py
@@ -0,0 +1,624 @@
+#!/usr/bin/env python3
+"""
+Overfitting experiment script to verify implementation correctness.
+
+This script trains on a tiny subset (128-256 videos) with:
+- High learning rate (5e-4 to 1e-3)
+- Simple constant/warmup scheduler
+- Small batch size (4-8)
+- Fixed seed for reproducibility
+- 2k-5k steps
+
+Expected behavior if implementation is correct:
+- Loss should drop to 5-6 or even lower (0.x)
+- Loss should continue decreasing, indicating ability to overfit
+
+If loss stays high (9-10) or diverges, there's likely a bug in:
+- mask_token logic
+- scheduler
+- label alignment
+- logits reshaping
+"""
+
+import argparse
+import logging
+import os
+import sys
+sys.path.append(os.getcwd())
+
+import torch
+import torch.nn.functional as F
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from torch.utils.data import DataLoader, default_collate
+from transformers import T5Tokenizer, T5EncoderModel
+
+from src.scheduler_video import Scheduler
+from src.pipeline_video import CosmosVideoTokenizer, Pipeline as VideoPipeline
+from src.transformer_video import WanDiscreteVideoTransformer, WanModel
+from train.dataset_utils import TinyOpenVid1MDataset, tokenize_prompt, encode_prompt
+from torchvision import transforms
+from torchvision.utils import save_image, make_grid
+
+logger = get_logger(__name__, log_level="INFO")
+
+
+def parse_args():
+ parser = argparse.ArgumentParser(description="Overfitting experiment for video diffusion model")
+
+ # Model args
+ parser.add_argument("--text_encoder_architecture", type=str, default="umt5-base")
+ parser.add_argument("--video_tokenizer_model_id", type=str, default="Cosmos-1.0-Tokenizer-DV8x16x16")
+ parser.add_argument("--wan_pretrained_path", type=str, default=None, help="Path to pretrained Wan weights")
+
+ # Dataset args
+ parser.add_argument("--instance_data_dir", type=str, required=True, help="Path to OpenVid1M CSV file")
+ parser.add_argument("--max_samples", type=int, default=256, help="Number of samples for overfitting (128-256)")
+ parser.add_argument("--num_frames", type=int, default=16)
+ parser.add_argument("--video_height", type=int, default=480)
+ parser.add_argument("--video_width", type=int, default=848)
+
+ # Training args
+ parser.add_argument("--train_batch_size", type=int, default=4, help="Batch size (4-8 for overfitting)")
+ parser.add_argument("--learning_rate", type=float, default=5e-4, help="High LR for overfitting (5e-4 to 1e-3)")
+ parser.add_argument("--max_train_steps", type=int, default=3000, help="Steps for overfitting (2k-5k)")
+ parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
+ parser.add_argument("--lr_warmup_steps", type=int, default=100, help="Small warmup for overfitting")
+ parser.add_argument("--gradient_checkpointing", action="store_true")
+ parser.add_argument("--mixed_precision", type=str, default="bf16", choices=["no", "fp16", "bf16"])
+
+ # Other args
+ parser.add_argument("--seed", type=int, default=42, help="Fixed seed for reproducibility")
+ parser.add_argument("--output_dir", type=str, default="./output_overfit")
+ parser.add_argument("--logging_steps", type=int, default=50)
+ parser.add_argument("--save_steps", type=int, default=500)
+ parser.add_argument("--inference_steps", type=int, default=500, help="Steps interval for inference (default: 500)")
+ parser.add_argument("--num_inference_samples", type=int, default=4, help="Number of prompts to use for inference")
+ parser.add_argument("--num_inference_steps", type=int, default=48, help="Number of inference steps for generation")
+ parser.add_argument("--dataloader_num_workers", type=int, default=4)
+
+ return parser.parse_args()
+
+
+def save_video_frames(video, output_path, prompt_text=""):
+ """
+ Save video frames as a grid image and individual frames.
+
+ Args:
+ video: Can be list of PIL Images or torch.Tensor [C, F, H, W]
+ output_path: Base path for saving (without extension)
+ prompt_text: Prompt text for filename
+ """
+ import numpy as np
+ from PIL import Image
+
+ # Convert to list of PIL Images if needed
+ if isinstance(video, torch.Tensor):
+ # video: [C, F, H, W] in [0, 1]
+ C, F, H, W = video.shape
+ frames = []
+ for f in range(F):
+ frame = video[:, f, :, :].cpu().numpy() # [C, H, W]
+ frame = np.transpose(frame, (1, 2, 0)) # [H, W, C]
+ frame = (frame * 255).astype(np.uint8)
+ frames.append(Image.fromarray(frame))
+ video = frames
+ elif isinstance(video, list):
+ frames = video
+ else:
+ logger.warning(f"Unknown video type: {type(video)}")
+ return
+
+ if not frames:
+ logger.warning(f"No frames to save for {output_path}")
+ return
+
+ # Save grid of all frames
+ frames_tensor = torch.stack([transforms.ToTensor()(frame) for frame in frames], dim=0)
+ grid = make_grid(frames_tensor, nrow=min(4, len(frames)))
+ grid_path = f"{output_path}_grid.png"
+ save_image(grid, grid_path)
+ logger.info(f"Saved video grid to {grid_path}")
+
+ # Save individual frames
+ frames_dir = f"{output_path}_frames"
+ os.makedirs(frames_dir, exist_ok=True)
+ for i, frame in enumerate(frames):
+ frame_path = os.path.join(frames_dir, f"frame_{i:03d}.png")
+ frame.save(frame_path)
+
+ # Save as GIF
+ try:
+ gif_path = f"{output_path}.gif"
+ frames[0].save(
+ gif_path,
+ save_all=True,
+ append_images=frames[1:],
+ duration=200, # 200ms per frame
+ loop=0
+ )
+ logger.info(f"Saved video GIF to {gif_path}")
+ except Exception as e:
+ logger.warning(f"Failed to save GIF: {e}")
+
+
+def main():
+ args = parse_args()
+
+ # Set seed for reproducibility
+ set_seed(args.seed)
+
+ # Initialize accelerator
+ accelerator = Accelerator(
+ gradient_accumulation_steps=args.gradient_accumulation_steps,
+ mixed_precision=args.mixed_precision,
+ log_with=None, # Disable wandb for overfitting experiment
+ )
+
+ # Setup logging
+ logging.basicConfig(
+ format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+ datefmt="%m/%d/%Y %H:%M:%S",
+ level=logging.INFO,
+ )
+ logger.info(accelerator.state, main_process_only=False)
+
+ if accelerator.is_local_main_process:
+ os.makedirs(args.output_dir, exist_ok=True)
+
+ # Initialize tokenizer and text encoder
+ logger.info("Initializing text encoder...")
+ if args.text_encoder_architecture in ["umt5-base", "t5"]:
+ tokenizer = T5Tokenizer.from_pretrained("google/umt5-base")
+ text_encoder = T5EncoderModel.from_pretrained("google/umt5-base")
+ else:
+ raise ValueError(f"Unsupported text encoder: {args.text_encoder_architecture}")
+
+ text_encoder.requires_grad_(False)
+ text_encoder.eval()
+ text_dim_actual = text_encoder.config.d_model
+
+ # Initialize video tokenizer
+ logger.info("Initializing video tokenizer...")
+ device = accelerator.device
+ dtype = torch.bfloat16 if args.mixed_precision == "bf16" else (torch.float16 if args.mixed_precision == "fp16" else torch.float32)
+ video_tokenizer = CosmosVideoTokenizer(
+ model_id=args.video_tokenizer_model_id,
+ device=device,
+ dtype=dtype,
+ )
+ video_tokenizer.requires_grad_(False)
+
+ # Calculate compressed dimensions
+ t_ds = video_tokenizer.t_downsample
+ h_ds = video_tokenizer.h_downsample
+ w_ds = video_tokenizer.w_downsample
+ F_prime = args.num_frames // t_ds
+ H_prime = args.video_height // h_ds
+ W_prime = args.video_width // w_ds
+
+ # Initialize transformer model
+ logger.info("Initializing transformer model...")
+
+ # Try to load Wan config from pretrained weights if provided
+ wan_config = None
+ if args.wan_pretrained_path:
+ try:
+ if os.path.isdir(args.wan_pretrained_path):
+ # Local directory
+ config_path = os.path.join(args.wan_pretrained_path, "config.json")
+ if os.path.exists(config_path):
+ import json
+ with open(config_path, 'r') as f:
+ wan_config = json.load(f)
+ else:
+ # HuggingFace Hub - try to load config
+ try:
+ from types import SimpleNamespace
+ temp_model = WanModel.from_pretrained(args.wan_pretrained_path, subfolder=None)
+ wan_config = SimpleNamespace(
+ dim=temp_model.dim,
+ ffn_dim=temp_model.ffn_dim,
+ num_layers=temp_model.num_layers,
+ num_heads=temp_model.num_heads,
+ freq_dim=temp_model.freq_dim,
+ in_dim=temp_model.in_dim,
+ out_dim=temp_model.out_dim,
+ text_dim=getattr(temp_model, 'text_dim', None),
+ )
+ del temp_model
+ except:
+ pass
+ except Exception as e:
+ logger.warning(f"Failed to load Wan config: {e}")
+
+ # Use Wan config if available, otherwise use defaults
+ if wan_config:
+ dim = wan_config.dim
+ ffn_dim = wan_config.ffn_dim
+ num_layers = wan_config.num_layers
+ num_heads = wan_config.num_heads
+ freq_dim = wan_config.freq_dim
+ in_dim = wan_config.in_dim
+ out_dim = wan_config.out_dim
+ text_dim_for_model = wan_config.text_dim if wan_config.text_dim else text_dim_actual
+ else:
+ # Default values
+ dim = 2048
+ ffn_dim = 8192
+ num_layers = 32
+ num_heads = 16
+ freq_dim = 256
+ in_dim = 16
+ out_dim = 16
+ text_dim_for_model = text_dim_actual
+
+ # Override text_dim with actual text encoder dimension
+ if text_dim_for_model != text_dim_actual:
+ logger.warning(f"Wan config text_dim ({text_dim_for_model}) != text encoder dim ({text_dim_actual}), using {text_dim_actual}")
+ text_dim_for_model = text_dim_actual
+
+ model = WanDiscreteVideoTransformer(
+ codebook_size=video_tokenizer.codebook_size,
+ vocab_size=video_tokenizer.codebook_size + 1,
+ num_frames=F_prime,
+ height=H_prime,
+ width=W_prime,
+ text_dim=text_dim_for_model,
+ dim=dim,
+ ffn_dim=ffn_dim,
+ num_layers=num_layers,
+ num_heads=num_heads,
+ freq_dim=freq_dim,
+ in_dim=in_dim,
+ out_dim=out_dim,
+ )
+
+ # Load pretrained weights if provided
+ if args.wan_pretrained_path:
+ logger.info(f"Loading pretrained weights from {args.wan_pretrained_path}...")
+ try:
+ if os.path.isdir(args.wan_pretrained_path):
+ state_dict_path = os.path.join(args.wan_pretrained_path, "diffusion_pytorch_model.safetensors")
+ if not os.path.exists(state_dict_path):
+ state_dict_path = os.path.join(args.wan_pretrained_path, "pytorch_model.bin")
+
+ if os.path.exists(state_dict_path):
+ from safetensors import safe_open
+ wan_state_dict = {}
+ if state_dict_path.endswith('.safetensors'):
+ with safe_open(state_dict_path, framework="pt", device="cpu") as f:
+ for k in f.keys():
+ wan_state_dict[k] = f.get_tensor(k)
+ else:
+ wan_state_dict = torch.load(state_dict_path, map_location="cpu")
+ else:
+ raise FileNotFoundError(f"State dict not found in {args.wan_pretrained_path}")
+ else:
+ # HuggingFace Hub
+ temp_model = WanModel.from_pretrained(args.wan_pretrained_path, subfolder=None)
+ wan_state_dict = temp_model.state_dict()
+ del temp_model
+
+ # Remove text_embedding weights if shape doesn't match
+ text_embedding_key = 'text_embedding.0.weight'
+ if text_embedding_key in wan_state_dict:
+ pretrained_text_dim = wan_state_dict[text_embedding_key].shape[1]
+ model_text_dim = model.backbone.text_embedding[0].weight.shape[1]
+
+ if pretrained_text_dim != model_text_dim:
+ keys_to_remove = [k for k in wan_state_dict.keys() if 'text_embedding' in k]
+ for k in keys_to_remove:
+ del wan_state_dict[k]
+ logger.info(f"Removed {len(keys_to_remove)} text_embedding keys due to dimension mismatch")
+
+ missing_keys, unexpected_keys = model.backbone.load_state_dict(wan_state_dict, strict=False)
+ if missing_keys:
+ logger.warning(f"Missing keys: {missing_keys[:10]}...")
+ if unexpected_keys:
+ logger.warning(f"Unexpected keys: {unexpected_keys[:10]}...")
+ logger.info("Successfully loaded pretrained weights")
+ except Exception as e:
+ logger.warning(f"Failed to load pretrained weights: {e}")
+
+ # Initialize scheduler
+ logger.info("Initializing scheduler...")
+ scheduler = Scheduler(
+ mask_token_id=video_tokenizer.mask_token_id,
+ masking_schedule="cosine",
+ )
+
+ # Setup optimizer
+ logger.info("Setting up optimizer...")
+ optimizer = torch.optim.AdamW(
+ model.parameters(),
+ lr=args.learning_rate,
+ betas=(0.9, 0.999),
+ weight_decay=0.01,
+ eps=1e-8,
+ )
+
+ # Simple constant scheduler with warmup
+ def lr_lambda(current_step):
+ if current_step < args.lr_warmup_steps:
+ return float(current_step) / float(max(1, args.lr_warmup_steps))
+ return 1.0
+
+ lr_scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda)
+
+ # Create tiny dataset
+ logger.info(f"Creating tiny dataset with {args.max_samples} samples...")
+
+ # Auto-detect video_root_dir if not provided
+ csv_path = args.instance_data_dir
+ csv_dir = os.path.dirname(csv_path)
+ if os.path.exists(os.path.join(csv_dir, 'video_reorg')):
+ video_root_dir = os.path.join(csv_dir, 'video_reorg')
+ elif os.path.exists(os.path.join(os.path.dirname(csv_dir), 'video_reorg')):
+ video_root_dir = os.path.join(os.path.dirname(csv_dir), 'video_reorg')
+ else:
+ # Fallback: use CSV directory
+ video_root_dir = csv_dir
+ logger.warning(f"Video directory not found, using CSV directory: {video_root_dir}")
+
+ dataset = TinyOpenVid1MDataset(
+ csv_path=csv_path,
+ video_root_dir=video_root_dir,
+ tokenizer=tokenizer,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ text_encoder_architecture=args.text_encoder_architecture,
+ max_samples=args.max_samples,
+ seed=args.seed,
+ )
+
+ # Create dataloader
+ train_dataloader = DataLoader(
+ dataset,
+ batch_size=args.train_batch_size,
+ shuffle=True,
+ num_workers=args.dataloader_num_workers,
+ collate_fn=default_collate,
+ pin_memory=True,
+ prefetch_factor=2 if args.dataloader_num_workers > 0 else None,
+ persistent_workers=args.dataloader_num_workers > 0,
+ )
+
+ logger.info(f"Dataset size: {len(dataset)}")
+ logger.info(f"Dataloader batches: {len(train_dataloader)}")
+
+ # Enable gradient checkpointing if requested (before prepare)
+ if args.gradient_checkpointing:
+ model.enable_gradient_checkpointing()
+
+ # Prepare with accelerator
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder = accelerator.prepare(
+ model, optimizer, lr_scheduler, train_dataloader, text_encoder
+ )
+
+ # Training loop
+ logger.info("Starting overfitting experiment...")
+ logger.info(f"Target: Loss should drop to 5-6 or lower within {args.max_train_steps} steps")
+ logger.info(f"If loss stays high (9-10) or diverges, there's likely a bug")
+
+ model.train()
+ global_step = 0
+
+ for epoch in range(1000): # Large number, will break on step limit
+ for batch in train_dataloader:
+ with accelerator.accumulate(model):
+ # Get video and encode
+ video_values = batch["video"].to(device=accelerator.device, dtype=torch.float32)
+ video_tokens = video_tokenizer.encode(video_values) # [B, F', H', W']
+
+ # Flatten for masking
+ B, F_prime_vid, H_prime_vid, W_prime_vid = video_tokens.shape
+ video_tokens_flat = video_tokens.view(B, -1)
+ seq_len = video_tokens_flat.shape[1]
+
+ # Apply masking: use per-sample masking like train_mei_video.py
+ mask_prob = torch.rand(B, device=video_tokens_flat.device) * 0.5 + 0.1 # 0.1 to 0.6 per sample
+ num_token_masked = (seq_len * mask_prob).round().clamp(min=1)
+ batch_randperm = torch.rand(B, seq_len, device=video_tokens_flat.device).argsort(dim=-1)
+ mask = batch_randperm < num_token_masked.unsqueeze(-1)
+
+ # Create input_ids: masked positions get mask_token_id, others keep original tokens
+ mask_id = video_tokenizer.mask_token_id # codebook_size
+ input_ids_flat = torch.where(mask, mask_id, video_tokens_flat)
+ # Create labels: masked positions get original tokens (for loss), others get -100 (ignored)
+ labels_flat = torch.where(mask, video_tokens_flat, -100)
+
+ # Reshape back to [B, F', H', W'] for model forward
+ input_ids = input_ids_flat.view(B, F_prime_vid, H_prime_vid, W_prime_vid)
+ labels = labels_flat.view(B, F_prime_vid, H_prime_vid, W_prime_vid)
+
+ # Use average mask ratio for timestep
+ mask_ratio = mask_prob.mean().item()
+
+ # Encode text
+ encoder_hidden_states, cond_embeds = encode_prompt(
+ text_encoder,
+ batch["prompt_input_ids"].to(device=accelerator.device),
+ args.text_encoder_architecture
+ )
+
+ # Forward pass
+ logits = model(
+ tokens=input_ids,
+ timesteps=torch.full((B,), int(mask_ratio * 1000), device=accelerator.device, dtype=torch.long),
+ encoder_hidden_states=encoder_hidden_states,
+ y=None,
+ )
+
+ # Reshape for loss
+ # logits: [B, vocab_size, F', H', W'] -> [B*F'*H'*W', vocab_size]
+ B_logits, vocab_size, F_prime_logits, H_prime_logits, W_prime_logits = logits.shape
+ logits = logits.permute(0, 2, 3, 4, 1).reshape(B_logits * F_prime_logits * H_prime_logits * W_prime_logits, vocab_size)
+
+ # labels: [B, F', H', W'] - crop to match logits dimensions if needed
+ B_labels, F_prime_labels, H_prime_labels, W_prime_labels = labels.shape
+ assert B_logits == B_labels, f"Batch size mismatch: logits {B_logits} vs labels {B_labels}"
+
+ # Crop labels to match logits spatial dimensions
+ if F_prime_labels != F_prime_logits or H_prime_labels != H_prime_logits or W_prime_labels != W_prime_logits:
+ labels = labels[:, :F_prime_logits, :H_prime_logits, :W_prime_logits]
+
+ # labels: [B, F', H', W'] -> [B*F'*H'*W']
+ labels_flat = labels.reshape(-1)
+
+ # Verify label values are in valid range [0, codebook_size-1] or -100 (ignored)
+ codebook_size = video_tokenizer.codebook_size
+ valid_labels = labels_flat[(labels_flat >= 0) & (labels_flat != -100)]
+ if len(valid_labels) > 0:
+ assert valid_labels.min() >= 0 and valid_labels.max() < codebook_size, (
+ f"Label values out of range: min={valid_labels.min()}, max={valid_labels.max()}, "
+ f"expected [0, {codebook_size-1}]"
+ )
+
+ # Compute loss: only on masked positions (labels != -100), ignore unmasked positions
+ # vocab_size = codebook_size + 1 (includes mask_token_id = codebook_size)
+ # labels are in [0, codebook_size-1] range (Cosmos tokens), which map directly to logits indices [0, codebook_size-1]
+ loss = F.cross_entropy(
+ logits,
+ labels_flat,
+ ignore_index=-100, # Ignore unmasked positions
+ reduction="mean",
+ )
+
+ # Backward
+ accelerator.backward(loss)
+ if accelerator.sync_gradients:
+ accelerator.clip_grad_norm_(model.parameters(), 1.0)
+ optimizer.step()
+ lr_scheduler.step()
+ optimizer.zero_grad()
+
+ if accelerator.sync_gradients:
+ global_step += 1
+
+ if global_step % args.logging_steps == 0:
+ logger.info(f"Step {global_step}/{args.max_train_steps}, Loss: {loss.item():.4f}, LR: {lr_scheduler.get_last_lr()[0]:.2e}")
+
+ if global_step % args.save_steps == 0:
+ if accelerator.is_main_process:
+ checkpoint_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+ os.makedirs(checkpoint_path, exist_ok=True)
+ unwrapped_model = accelerator.unwrap_model(model)
+ unwrapped_model.save_pretrained(checkpoint_path)
+ logger.info(f"Saved checkpoint to {checkpoint_path}")
+
+ # Inference: generate videos using training data prompts
+ if global_step % args.inference_steps == 0 and global_step > 0:
+ if accelerator.is_main_process:
+ logger.info(f"Step {global_step}: Generating videos for inference...")
+
+ # Sample prompts from training dataset (get original captions from dataset.data)
+ inference_indices = torch.randperm(len(dataset), generator=torch.Generator().manual_seed(args.seed))[:args.num_inference_samples].tolist()
+ inference_prompts = []
+ for idx in inference_indices:
+ # Get original caption from dataset
+ row = dataset.data[idx]
+ prompt_text = row['caption']
+ if dataset.prompt_prefix is not None:
+ prompt_text = dataset.prompt_prefix + prompt_text
+ inference_prompts.append(prompt_text)
+
+ logger.info(f"Using prompts: {inference_prompts[:2]}...") # Log first 2 prompts
+
+ try:
+ # Create inference pipeline
+ model.eval()
+ text_encoder.eval()
+
+ # Get unwrapped model and ensure correct dtype
+ unwrapped_model = accelerator.unwrap_model(model)
+ unwrapped_text_encoder = accelerator.unwrap_model(text_encoder)
+ weight_dtype = torch.bfloat16 if args.mixed_precision == "bf16" else (torch.float16 if args.mixed_precision == "fp16" else torch.float32)
+ unwrapped_model = unwrapped_model.to(dtype=weight_dtype)
+ unwrapped_text_encoder = unwrapped_text_encoder.to(dtype=weight_dtype)
+
+ # Create scheduler for inference
+ inference_scheduler = Scheduler(
+ mask_token_id=video_tokenizer.mask_token_id,
+ masking_schedule="cosine"
+ )
+ inference_scheduler.set_timesteps(
+ num_inference_steps=args.num_inference_steps,
+ device=accelerator.device
+ )
+
+ # Create pipeline
+ pipe = VideoPipeline(
+ tokenizer=tokenizer,
+ text_encoder=unwrapped_text_encoder,
+ transformer=unwrapped_model,
+ scheduler=inference_scheduler,
+ video_tokenizer=video_tokenizer,
+ text_len=512,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ )
+ pipe = pipe.to(accelerator.device)
+
+ # Generate videos
+ with torch.no_grad():
+ videos = pipe(
+ prompt=inference_prompts,
+ num_frames=args.num_frames,
+ height=args.video_height,
+ width=args.video_width,
+ guidance_scale=9.0,
+ num_inference_steps=args.num_inference_steps,
+ output_type="pil",
+ ).videos
+
+ # Save videos
+ inference_dir = os.path.join(args.output_dir, f"inference_step_{global_step}")
+ os.makedirs(inference_dir, exist_ok=True)
+
+ for i, (video, prompt) in enumerate(zip(videos, inference_prompts)):
+ # Sanitize prompt for filename (remove special chars, limit length)
+ safe_prompt = "".join(c for c in prompt[:50] if c.isalnum() or c in (' ', '-', '_')).strip().replace(' ', '_')[:30]
+ if not safe_prompt:
+ safe_prompt = f"prompt_{i}"
+ output_path = os.path.join(inference_dir, f"step{global_step}_video{i}_{safe_prompt}")
+ save_video_frames(video, output_path, prompt_text=prompt)
+
+ logger.info(f"Saved inference videos to {inference_dir}")
+ logger.info(f"=" * 80)
+ logger.info(f"Step {global_step} Inference Results:")
+ logger.info(f" - Videos saved to: {inference_dir}")
+ logger.info(f" - Prompts used: {inference_prompts}")
+ logger.info(f" - Check videos to observe:")
+ logger.info(f" * Structure: When do coherent shapes/objects appear?")
+ logger.info(f" * Motion: When does temporal consistency emerge?")
+ logger.info(f" * Condition alignment: When do videos match prompts?")
+ logger.info(f"=" * 80)
+
+ # Set model back to training mode
+ model.train()
+
+ except Exception as e:
+ logger.error(f"Inference failed at step {global_step}: {e}")
+ import traceback
+ traceback.print_exc()
+ model.train() # Ensure model is back in training mode
+
+ if global_step >= args.max_train_steps:
+ break
+
+ if global_step >= args.max_train_steps:
+ break
+
+ logger.info("Overfitting experiment completed!")
+ logger.info(f"Final loss: {loss.item():.4f}")
+ logger.info("If loss dropped to 5-6 or lower, implementation is likely correct.")
+ logger.info("If loss stayed high (9-10) or diverged, check for bugs in mask_token, scheduler, or label alignment.")
+
+
+if __name__ == "__main__":
+ main()
+
diff --git a/Meissonic/train/train_video.sh b/Meissonic/train/train_video.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1b022fe8de1780ad1e75eecbb08f6dadb7077113
--- /dev/null
+++ b/Meissonic/train/train_video.sh
@@ -0,0 +1,82 @@
+#!/bin/bash
+# 8-GPU training script for video diffusion model
+# Usage: bash train/train_video.sh
+
+accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 \
+ train/train_mei_video.py \
+ --use_precomputed_features \
+ --features_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128_with_mask_attn_debug \
+ --text_encoder_architecture umt5-xxl \
+ --wan_pretrained_path /mnt/Wan2.1-T2V-1.3B \
+ --training_from_scratch True \
+ --pretrained_model_name_or_path "dummy" \
+ --wan_backbone_lr_ratio 0.2 \
+ --num_frames 17 \
+ --video_height 128 \
+ --video_width 128 \
+ --dataloader_num_workers 8 \
+ --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+ --instance_dataset OpenVid1MDataset \
+ --instance_data_dir "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+ --train_batch_size 8 \
+ --gradient_accumulation_steps 4 \
+ --learning_rate 3e-4 \
+ --max_train_steps 10000 \
+ --checkpointing_steps 500 \
+ --validation_steps 500 \
+ --logging_steps 10 \
+ --validation_prompts "a cat playing" "a girl walking" \
+ --output_dir "./output_128x128_17f_2*4bs_4*8*8vqvae_0_2_ratio" \
+ --mixed_precision bf16 \
+ --lr_scheduler constant \
+ --lr_warmup_steps 0 \
+ --use_8bit_adam \
+ --gradient_checkpointing \
+ --min_masking_rate 0.0 \
+ --cond_dropout_prob 0.1 \
+ --split_vae_encode 1 \
+ --allow_tf32 \
+ --seed 42 \
+ --report_to wandb
+
+ # --use_precomputed_features \
+ # --features_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features \
+
+# accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 \
+# train/train_mei_video.py \
+# --use_precomputed_features \
+# --features_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features \
+# --text_encoder_architecture umt5-xxl \
+# --wan_pretrained_path Wan-AI/Wan2.1-T2V-1.3B \
+# --training_from_scratch True \
+# --pretrained_model_name_or_path "dummy" \
+# --wan_backbone_lr_ratio 1 \
+# --num_frames 4 \
+# --video_height 256 \
+# --video_width 448 \
+# --dataloader_num_workers 8 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --instance_dataset OpenVid1MDataset \
+# --instance_data_dir "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --train_batch_size 1 \
+# --gradient_accumulation_steps 1 \
+# --learning_rate 3e-4 \
+# --max_train_steps 10000 \
+# --checkpointing_steps 500 \
+# --validation_steps 500 \
+# --logging_steps 10 \
+# --validation_prompts "a cat playing" "a girl walking" \
+# --output_dir "./output_256x448_4f_2bs_4*8*8vqvae_0_00_ratio_continue_tmp" \
+# --mixed_precision bf16 \
+# --lr_scheduler constant \
+# --lr_warmup_steps 0 \
+# --use_8bit_adam \
+# --gradient_checkpointing \
+# --min_masking_rate 0.0 \
+# --cond_dropout_prob 0.1 \
+# --split_vae_encode 1 \
+# --allow_tf32 \
+# --seed 42 \
+# --report_to wandb
+
+# --pretrained_model_name_or_path "/mnt/Meissonic/output_256x448_4f_2bs_4*8*8vqvae_0_00_ratio/checkpoint-4000" \
\ No newline at end of file
diff --git a/Meissonic/train/train_video_stage_1.sh b/Meissonic/train/train_video_stage_1.sh
new file mode 100644
index 0000000000000000000000000000000000000000..d40479f24c275e43d3ccbb2d9ffa05a3915b6e6d
--- /dev/null
+++ b/Meissonic/train/train_video_stage_1.sh
@@ -0,0 +1,83 @@
+
+
+# Overall Batch size: 8 (per GPU) * 4 (gradient accumulation) * 96 (GPU cards) = 3072
+# We set max steps to 100k, but we may not need so much, depends on actual performace
+# typically 20s for 10 steps, so we can run 1800 steps per hour and 43.2k steps per day, which equals to 132 epochs
+
+
+export WAN_DISABLE_FLASH_ATTN=1
+
+accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 \
+ train/train_mei_video.py \
+ --use_precomputed_video_only \
+ --features_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128 \
+ --text_encoder_architecture umt5-xxl \
+ --wan_pretrained_path /mnt/Wan2.1-T2V-1.3B \
+ --training_from_scratch \
+ --pretrained_model_name_or_path "dummy" \
+ --wan_backbone_lr_ratio 0.2 \
+ --num_frames 17 \
+ --video_height 128 \
+ --video_width 128 \
+ --dataloader_num_workers 8 \
+ --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+ --instance_dataset OpenVid1MDataset \
+ --instance_data_dir "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+ --train_batch_size 1 \
+ --gradient_accumulation_steps 1 \
+ --learning_rate 3e-3 \
+ --max_train_steps 100000 \
+ --checkpointing_steps 500 \
+ --validation_steps 100 \
+ --logging_steps 10 \
+ --validation_prompts "a cat playing" "a girl walking" "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution." \
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner." \
+ --output_dir "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3" \
+ --mixed_precision bf16 \
+ --lr_scheduler constant \
+ --lr_warmup_steps 0 \
+ --use_8bit_adam \
+ --gradient_checkpointing \
+ --min_masking_rate 0.0 \
+ --cond_dropout_prob 0.0 \
+ --split_vae_encode 1 \
+ --allow_tf32 \
+ --seed 42 \
+ --report_to wandb
+
+# accelerate launch --multi_gpu --gpu_ids '0,1,2,3,4,5,6,7' --main_process_port 25011 --num_processes 8 \
+# train/train_mei_video.py \
+# --use_precomputed_features \
+# --features_dir /mnt/VideoGen/dataset/OpenVid1M/extracted_features \
+# --text_encoder_architecture umt5-xxl \
+# --wan_pretrained_path /mnt/Wan2.1-T2V-1.3B \
+# --training_from_scratch True \
+# --pretrained_model_name_or_path "dummy" \
+# --wan_backbone_lr_ratio 0.2 \
+# --num_frames 17 \
+# --video_height 128 \
+# --video_width 128 \
+# --dataloader_num_workers 8 \
+# --video_tokenizer_model_id "Cosmos-0.1-Tokenizer-DV4x8x8" \
+# --instance_dataset OpenVid1MDataset \
+# --instance_data_dir "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv" \
+# --train_batch_size 8 \
+# --gradient_accumulation_steps 4 \
+# --learning_rate 3e-4 \
+# --max_train_steps 100000 \
+# --checkpointing_steps 500 \
+# --validation_steps 500 \
+# --logging_steps 10 \
+# --validation_prompts "a cat playing" "a girl walking" \
+# --output_dir "./output_128x128_17f_8*4bs_4*8*8vqvae_0_2_ratio" \
+# --mixed_precision bf16 \
+# --lr_scheduler constant \
+# --lr_warmup_steps 0 \
+# --use_8bit_adam \
+# --gradient_checkpointing \
+# --min_masking_rate 0.0 \
+# --cond_dropout_prob 0.1 \
+# --split_vae_encode 1 \
+# --allow_tf32 \
+# --seed 42 \
+# --report_to wandb
\ No newline at end of file
diff --git a/Meissonic/train/trainer_utils.py b/Meissonic/train/trainer_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4956a83dd94a140cab80c0baedd906713523f82
--- /dev/null
+++ b/Meissonic/train/trainer_utils.py
@@ -0,0 +1,45 @@
+# Copyright 2024 The HuggingFace Team and The MeissonFlow Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import shutil
+from pathlib import Path
+
+
+def save_checkpoint(args, accelerator, global_step, logger):
+ output_dir = args.output_dir
+
+ # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
+ if accelerator.is_main_process and args.checkpoints_total_limit is not None:
+ checkpoints = os.listdir(output_dir)
+ checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
+ checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
+
+ # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
+ if len(checkpoints) >= args.checkpoints_total_limit:
+ num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
+ removing_checkpoints = checkpoints[0:num_to_remove]
+
+ logger.info(
+ f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
+ )
+ logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
+
+ for removing_checkpoint in removing_checkpoints:
+ removing_checkpoint = os.path.join(output_dir, removing_checkpoint)
+ shutil.rmtree(removing_checkpoint)
+
+ save_path = Path(output_dir) / f"checkpoint-{global_step}"
+ accelerator.save_state(save_path)
+ logger.info(f"Saved state to {save_path}")
diff --git a/Meissonic/wandb/run-20251219_095320-szkwvitk/files/config.yaml b/Meissonic/wandb/run-20251219_095320-szkwvitk/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Meissonic/wandb/run-20251219_095320-szkwvitk/files/media/images/generated_videos_first_frame_10100_b2eb0bb48e9b123906cb.png b/Meissonic/wandb/run-20251219_095320-szkwvitk/files/media/images/generated_videos_first_frame_10100_b2eb0bb48e9b123906cb.png
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diff --git a/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/config.yaml b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/config.yaml
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+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ atfv17l5d8rtbeeoodvcjytuxj5qt2ye:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "256"
+ - --video_width
+ - "256"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "2"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576127954944"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:16:34.855351Z"
+ writerId: atfv17l5d8rtbeeoodvcjytuxj5qt2ye
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 2
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 256
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 256
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/requirements.txt b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/requirements.txt
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+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
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+cycler==0.12.1
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+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
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+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
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+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+simple-websocket==1.1.0
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+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..c79ab9dbf3d1b8fc2c70ca259a6f0d77802f6ee8
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:16:34.855351Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "256",
+ "--video_width",
+ "256",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "2",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576127954944"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "atfv17l5d8rtbeeoodvcjytuxj5qt2ye"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-summary.json b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..8afb95f49483c85658a334253ad61c5e4b5851ef
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":2},"_runtime":2}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_081634-hjn0m6c2/run-hjn0m6c2.wandb b/Meissonic/wandb/run-20251229_081634-hjn0m6c2/run-hjn0m6c2.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..3724cb3010fe6d0a165d913cfbe63fa26068d4cf
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diff --git a/Meissonic/wandb/run-20251229_081752-78ojckdj/files/config.yaml b/Meissonic/wandb/run-20251229_081752-78ojckdj/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..572b73e141e35b802ce1d73e632dc7184931285b
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081752-78ojckdj/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ zkex3lp5g2z9pkl87qlphzoninilz5fa:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "256"
+ - --video_width
+ - "256"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "2"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576128081920"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:17:52.346764Z"
+ writerId: zkex3lp5g2z9pkl87qlphzoninilz5fa
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 2
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 256
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 256
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_081752-78ojckdj/files/requirements.txt b/Meissonic/wandb/run-20251229_081752-78ojckdj/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081752-78ojckdj/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
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+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
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+dominate==2.9.1
+attrs==25.4.0
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+shellingham==1.5.4
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+nvidia-cuda-runtime-cu12==12.8.90
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+pfzy==0.3.4
+httpcore==1.0.9
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+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
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+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
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new file mode 100644
index 0000000000000000000000000000000000000000..4958e288497f13b4c9e13f847231e3a89ccde134
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+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
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+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "256",
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+ "256",
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+ "8",
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+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "2",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
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+ "100",
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+ "a cat playing",
+ "a girl walking",
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+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
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+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
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+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
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+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
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+ "gpu": "NVIDIA A100-SXM4-40GB",
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diff --git a/Meissonic/wandb/run-20251229_081959-tvb7bjux/files/requirements.txt b/Meissonic/wandb/run-20251229_081959-tvb7bjux/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
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diff --git a/Meissonic/wandb/run-20251229_081959-tvb7bjux/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_081959-tvb7bjux/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..cbab7264e1963cac0307a473cff1a120a918e76e
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_081959-tvb7bjux/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
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+ "startedAt": "2025-12-29T08:19:59.376492Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "256",
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+ "256",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "2",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
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+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
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+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
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+ "architecture": "Ampere",
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+ "architecture": "Ampere",
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+ },
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+ },
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+}
\ No newline at end of file
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diff --git a/Meissonic/wandb/run-20251229_082208-d5bens3y/files/config.yaml b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..83ac83bb3dc8ed70b65f8c7f7c45dd4f44fa5f22
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ 0do11cxq84ks57gwyzlkq3oixg2jsfez:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "256"
+ - --video_width
+ - "256"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "2"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576128356352"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:22:08.563808Z"
+ writerId: 0do11cxq84ks57gwyzlkq3oixg2jsfez
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 2
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 256
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 256
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_082208-d5bens3y/files/requirements.txt b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
+mdurl==0.1.2
+pandas==2.3.3
+modelscope==1.32.0
+ftfy==6.3.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+beartype==0.22.8
+dominate==2.9.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
+networkx==3.3
+wheel==0.45.1
+timm==1.0.22
+pyparsing==3.2.5
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
+httpcore==1.0.9
+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
+antlr4-python3-runtime==4.9.3
+sentry-sdk==2.46.0
+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
+tifffile==2025.5.10
+safetensors==0.7.0
+gitdb==4.0.12
+blinker==1.9.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+typer-slim==0.20.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..629f2020affa57938d72beecd00033b7c63f3510
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:22:08.563808Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "256",
+ "--video_width",
+ "256",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "2",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576128356352"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "0do11cxq84ks57gwyzlkq3oixg2jsfez"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-summary.json b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..984aba10a00cb8a981bbca1cbc3e577ebdef58cf
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082208-d5bens3y/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_runtime":60,"_wandb":{"runtime":60}}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_082208-d5bens3y/run-d5bens3y.wandb b/Meissonic/wandb/run-20251229_082208-d5bens3y/run-d5bens3y.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..e3764f07a11ce17505e4ee2ceeba126d8d4de410
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diff --git a/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/config.yaml b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dc8bf399a27719e2e594a2004135aee6d3b1ffc8
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ cexoq7e8axe3b6nnx8tjxfhoa3q5j093:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "256"
+ - --video_width
+ - "256"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576128708608"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:23:48.413885Z"
+ writerId: cexoq7e8axe3b6nnx8tjxfhoa3q5j093
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 256
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 256
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/requirements.txt b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
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+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
+mdurl==0.1.2
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+ftfy==6.3.1
+transformers==4.57.3
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+Jinja2==3.1.6
+click==8.3.1
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+dominate==2.9.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
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+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
+networkx==3.3
+wheel==0.45.1
+timm==1.0.22
+pyparsing==3.2.5
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+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
+httpcore==1.0.9
+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
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+sentry-sdk==2.46.0
+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
+tifffile==2025.5.10
+safetensors==0.7.0
+gitdb==4.0.12
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+h11==0.16.0
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+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
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+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
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+zipp==3.23.0
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+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..2249996df9abedd0adab5693a42ae1d00676c3a2
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:23:48.413885Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_256_256_full_set",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "256",
+ "--video_width",
+ "256",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576128708608"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "cexoq7e8axe3b6nnx8tjxfhoa3q5j093"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-summary.json b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1a94005933617ca388ce16c6cb55ce2f06fcf658
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082348-xdcob8vv/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":62},"_runtime":62}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_082348-xdcob8vv/run-xdcob8vv.wandb b/Meissonic/wandb/run-20251229_082348-xdcob8vv/run-xdcob8vv.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..56313eabe98e3346cf20df4f17de88a9d5afe74f
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diff --git a/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/config.yaml b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3b6c59d67fcb113b4d1c2810c40b0d3784c2e98c
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ 23ej42n6czwqb5tfzy8zgnxhw87hp68m:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576128917504"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:27:35.178318Z"
+ writerId: 23ej42n6czwqb5tfzy8zgnxhw87hp68m
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/requirements.txt b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
+mdurl==0.1.2
+pandas==2.3.3
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+ftfy==6.3.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+beartype==0.22.8
+dominate==2.9.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
+networkx==3.3
+wheel==0.45.1
+timm==1.0.22
+pyparsing==3.2.5
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
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+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
+antlr4-python3-runtime==4.9.3
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+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
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+tifffile==2025.5.10
+safetensors==0.7.0
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+blinker==1.9.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
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+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
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+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0483236ec49757cd80d41befac8336014e5d14fc
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:27:35.178318Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576128917504"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
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+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
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+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
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+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
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+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
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+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
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+ "cudaCores": 6912,
+ "architecture": "Ampere",
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+ }
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+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/wandb-summary.json b/Meissonic/wandb/run-20251229_082735-s2rbngfj/files/wandb-summary.json
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@@ -0,0 +1 @@
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diff --git a/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/config.yaml b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6d80db42f6ab6bdcfedf99f49498fab7e910b017
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ klckxk39jaoiyfq1gchmx3az6t8cjds2:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576129069056"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:30:18.123071Z"
+ writerId: klckxk39jaoiyfq1gchmx3az6t8cjds2
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/requirements.txt b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
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+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+huggingface-hub==0.36.0
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+torchmetrics==1.8.2
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+torch==2.9.1
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+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..37640d405846139dc9a9019391cbd179f5cff95a
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:30:18.123071Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576129069056"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "klckxk39jaoiyfq1gchmx3az6t8cjds2"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-summary.json b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..41c80f51acbd739213ed6fbdd4077790252bc00d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083018-js6dhqj8/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":36},"_runtime":36}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083018-js6dhqj8/run-js6dhqj8.wandb b/Meissonic/wandb/run-20251229_083018-js6dhqj8/run-js6dhqj8.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..6b7d3d07d85c21d65f998a8d598cf6d4de39666e
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diff --git a/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/config.yaml b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b642f1da44fc7a50c30e259377583222dbb2c95c
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ cef545nzoydf3af57q8i0xeevxfhokhu:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "4"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576129302528"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:35:18.601369Z"
+ writerId: cef545nzoydf3af57q8i0xeevxfhokhu
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 4
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/requirements.txt b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/requirements.txt
@@ -0,0 +1,151 @@
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+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
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+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
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+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+frozenlist==1.8.0
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+protobuf==6.33.1
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+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
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diff --git a/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..98f3ea4d4d06b70d83ffb4db2f9ec9493dbb0e93
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:35:18.601369Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "4",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
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+ "used": "15576129302528"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
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+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "cef545nzoydf3af57q8i0xeevxfhokhu"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-summary.json b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..69e8a87828be38513e2aff6e058e4a9485d68a00
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+++ b/Meissonic/wandb/run-20251229_083518-un2j6o0e/files/wandb-summary.json
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\ No newline at end of file
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diff --git a/Meissonic/wandb/run-20251229_083628-ef52280g/files/config.yaml b/Meissonic/wandb/run-20251229_083628-ef52280g/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0cc8dffe2e5beec6f74d7d3b3d296ac375ceb440
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083628-ef52280g/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ gey79twhahqhiya7s0c4ou5v5d8w2hkj:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576129425408"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:36:28.442156Z"
+ writerId: gey79twhahqhiya7s0c4ou5v5d8w2hkj
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_083628-ef52280g/files/requirements.txt b/Meissonic/wandb/run-20251229_083628-ef52280g/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083628-ef52280g/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
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+xxhash==3.6.0
+tap==0.2
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+tokenizers==0.22.1
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+annotated-types==0.7.0
+GitPython==3.1.45
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+setuptools==80.9.0
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+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
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+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..b62b0211f87deb124625363da3efa86b4b3d5e6f
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:36:28.442156Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576129425408"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "gey79twhahqhiya7s0c4ou5v5d8w2hkj"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-summary.json b/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..3e4dda75148b11087b7ca4d383b6747633b42a57
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083628-ef52280g/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":37},"_runtime":37}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083628-ef52280g/run-ef52280g.wandb b/Meissonic/wandb/run-20251229_083628-ef52280g/run-ef52280g.wandb
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index 0000000000000000000000000000000000000000..55a0a30f099a1b558b296bed8b39842c94835419
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diff --git a/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/config.yaml b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b1b3064a79dd2d24ecc1e208146d580c39b8de4a
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ dany56ckri37c4cswzaw5bcanyqvfkvb:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576129572864"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:38:09.169418Z"
+ writerId: dany56ckri37c4cswzaw5bcanyqvfkvb
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/requirements.txt b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
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+modelscope==1.32.0
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+transformers==4.57.3
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+beartype==0.22.8
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+attrs==25.4.0
+hf-xet==1.2.0
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+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
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+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
+httpcore==1.0.9
+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
+antlr4-python3-runtime==4.9.3
+sentry-sdk==2.46.0
+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
+tifffile==2025.5.10
+safetensors==0.7.0
+gitdb==4.0.12
+blinker==1.9.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+typer-slim==0.20.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..e46cf05b85a9a094dc3e10050351436836397067
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:38:09.169418Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576129572864"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "dany56ckri37c4cswzaw5bcanyqvfkvb"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-summary.json b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f6a8d6fba6992723d0f745edbf01bee18eb2022
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":27},"_runtime":27}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_083809-sx4rkgm3/run-sx4rkgm3.wandb b/Meissonic/wandb/run-20251229_083809-sx4rkgm3/run-sx4rkgm3.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..6b355c465eadb796410037c31b001e42cc749b61
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diff --git a/Meissonic/wandb/run-20251229_084520-fzi541le/files/config.yaml b/Meissonic/wandb/run-20251229_084520-fzi541le/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5d69cb69f91a15be5f137b8a0be4c42dc70da3db
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_084520-fzi541le/files/config.yaml
@@ -0,0 +1,309 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ xdutcov5l8zjxveke9fy1hqfvc51vmd0:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - "True"
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576129806336"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:45:20.618023Z"
+ writerId: xdutcov5l8zjxveke9fy1hqfvc51vmd0
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_084520-fzi541le/files/requirements.txt b/Meissonic/wandb/run-20251229_084520-fzi541le/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_084520-fzi541le/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
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new file mode 100644
index 0000000000000000000000000000000000000000..e96947c0ffcb23084869a37b4f6fb69e6fb7e97c
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_084520-fzi541le/files/wandb-metadata.json
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+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
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+ "128",
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+ "128",
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+ "8",
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+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
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+ "500",
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+ "100",
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+ "10",
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+ "a cat playing",
+ "a girl walking",
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+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
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+ "wandb"
+ ],
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+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
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+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
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+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
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+ "/": {
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+ }
+ },
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+}
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diff --git a/Meissonic/wandb/run-20251229_084618-2l6k4nad/files/requirements.txt b/Meissonic/wandb/run-20251229_084618-2l6k4nad/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
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diff --git a/Meissonic/wandb/run-20251229_084618-2l6k4nad/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_084618-2l6k4nad/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..0b27e905b62e88c82c8f755038dd5300834d002d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_084618-2l6k4nad/files/wandb-metadata.json
@@ -0,0 +1,158 @@
+{
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+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:46:18.953823Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "True",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
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+ "100",
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+ "10",
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+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
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+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
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+ "architecture": "Ampere",
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+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
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+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
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+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
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diff --git a/Meissonic/wandb/run-20251229_084618-2l6k4nad/run-2l6k4nad.wandb b/Meissonic/wandb/run-20251229_084618-2l6k4nad/run-2l6k4nad.wandb
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diff --git a/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/requirements.txt b/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/requirements.txt
@@ -0,0 +1,151 @@
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+av==16.0.1
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+xxhash==3.6.0
+tap==0.2
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+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
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+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
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+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
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+lightning==2.6.0
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+bitsandbytes==0.48.2
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+nvidia-cublas-cu12==12.8.4.1
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+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
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+sentry-sdk==2.46.0
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+torchmetrics==1.8.2
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+MarkupSafe==2.1.5
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+tzdata==2025.2
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+torch==2.9.1
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diff --git a/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..13a2eae9a84e81ec403b77e924615cd19132f156
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085306-rfncwmtb/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:53:06.346699Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576130723840"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
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+ "architecture": "Ampere",
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+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
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+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
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+ "writerId": "088z9y3i6hpxv8yvsakvkmxfx7j2zud5"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_085306-rfncwmtb/run-rfncwmtb.wandb b/Meissonic/wandb/run-20251229_085306-rfncwmtb/run-rfncwmtb.wandb
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diff --git a/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/config.yaml b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..60d349d09563b6d635775d28509cca9b9b659a95
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/config.yaml
@@ -0,0 +1,308 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ 5phq83bjhmlxddgoilx7eg4m1m2huy0f:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576135294976"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T08:57:19.168946Z"
+ writerId: 5phq83bjhmlxddgoilx7eg4m1m2huy0f
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
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+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/requirements.txt b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
+mdurl==0.1.2
+pandas==2.3.3
+modelscope==1.32.0
+ftfy==6.3.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+beartype==0.22.8
+dominate==2.9.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
+networkx==3.3
+wheel==0.45.1
+timm==1.0.22
+pyparsing==3.2.5
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
+httpcore==1.0.9
+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
+antlr4-python3-runtime==4.9.3
+sentry-sdk==2.46.0
+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
+tifffile==2025.5.10
+safetensors==0.7.0
+gitdb==4.0.12
+blinker==1.9.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+typer-slim==0.20.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f0db55891fc9df6df88114ab08e82830fa56d648
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T08:57:19.168946Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576135294976"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "5phq83bjhmlxddgoilx7eg4m1m2huy0f"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-summary.json b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..bf55868a8b18bdd0746b03671b2168432b325fcb
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_runtime":28,"_wandb":{"runtime":28}}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_085719-9ezk0nqn/run-9ezk0nqn.wandb b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/run-9ezk0nqn.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..a748d3f99d280cd6757c0efefc770c2973621396
Binary files /dev/null and b/Meissonic/wandb/run-20251229_085719-9ezk0nqn/run-9ezk0nqn.wandb differ
diff --git a/Meissonic/wandb/run-20251229_090331-alguiic1/files/config.yaml b/Meissonic/wandb/run-20251229_090331-alguiic1/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a929393378c2be8a560ce5a9df8de02ad7700504
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090331-alguiic1/files/config.yaml
@@ -0,0 +1,308 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ emji49jn678obspjvv2gi3mj8a2k6sq4:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576135458816"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:03:31.275394Z"
+ writerId: emji49jn678obspjvv2gi3mj8a2k6sq4
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
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+ - 98
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+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_090331-alguiic1/files/requirements.txt b/Meissonic/wandb/run-20251229_090331-alguiic1/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090331-alguiic1/files/requirements.txt
@@ -0,0 +1,151 @@
+ImageIO==2.37.2
+typing-inspection==0.4.2
+av==16.0.1
+dill==0.4.0
+matplotlib==3.10.7
+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
+pydantic_core==2.41.5
+matrix-game-2.0==0.0.1
+wsproto==1.3.2
+psutil==7.1.3
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+nvidia-cusparselt-cu12==0.7.1
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+datasets==4.4.1
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+lightning==2.6.0
+Flask-SocketIO==5.5.1
+torchvision==0.24.1
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+fonttools==4.61.0
+open_clip_torch==3.2.0
+flash_attn==2.8.3
+mdurl==0.1.2
+pandas==2.3.3
+modelscope==1.32.0
+ftfy==6.3.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+beartype==0.22.8
+dominate==2.9.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+bitsandbytes==0.48.2
+lightning-utilities==0.15.2
+easydict==1.13
+networkx==3.3
+wheel==0.45.1
+timm==1.0.22
+pyparsing==3.2.5
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
+httpcore==1.0.9
+multidict==6.7.0
+pycparser==2.23
+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
+antlr4-python3-runtime==4.9.3
+sentry-sdk==2.46.0
+urllib3==2.5.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
+cffi==2.0.0
+packaging==25.0
+inquirerpy==0.3.4
+aiosignal==1.4.0
+MarkupSafe==2.1.5
+nvidia-cuda-nvrtc-cu12==12.8.93
+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
+sympy==1.14.0
+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
+tifffile==2025.5.10
+safetensors==0.7.0
+gitdb==4.0.12
+blinker==1.9.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+typer-slim==0.20.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
+six==1.17.0
+platformdirs==4.5.0
+starlette==0.50.0
+scipy==1.15.3
+pycocotools==2.0.10
+accelerate==1.12.0
+zipp==3.23.0
+propcache==0.4.1
+bidict==0.23.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+simple-websocket==1.1.0
+nvidia-curand-cu12==10.3.9.90
+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..f856e16c6173072367b2fcff139ec2cee04f4a83
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T09:03:31.275394Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576135458816"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "emji49jn678obspjvv2gi3mj8a2k6sq4"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-summary.json b/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..0c3c0567d4c23124b231f2b4ddb7fa8d30a38646
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090331-alguiic1/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_runtime":27,"_wandb":{"runtime":27}}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_090331-alguiic1/run-alguiic1.wandb b/Meissonic/wandb/run-20251229_090331-alguiic1/run-alguiic1.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..6688560bf96970ef25e40bac0fd4575b0b095dee
Binary files /dev/null and b/Meissonic/wandb/run-20251229_090331-alguiic1/run-alguiic1.wandb differ
diff --git a/Meissonic/wandb/run-20251229_090648-6beufw2w/files/config.yaml b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b2c3fec0dabe2b3f94f3dab9b5a59d4f5def2283
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/config.yaml
@@ -0,0 +1,308 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ fqod8s6y2ph1ko6q2mj7qiafxdgfb2xg:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576135680000"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
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+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:06:48.255954Z"
+ writerId: fqod8s6y2ph1ko6q2mj7qiafxdgfb2xg
+ m: []
+ python_version: 3.10.19
+ t:
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+ - 1
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+ - 41
+ - 49
+ - 51
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+ value: 0.9
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+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
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+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
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+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
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+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
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+ value: 32
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+ value: 16
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+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_090648-6beufw2w/files/requirements.txt b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/requirements.txt
@@ -0,0 +1,151 @@
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+filelock==3.19.1
+tokenizers==0.22.1
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+annotated-types==0.7.0
+GitPython==3.1.45
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+torchmetrics==1.8.2
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+lmdb==1.7.5
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+fsspec==2025.9.0
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+protobuf==6.33.1
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+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
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diff --git a/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..419a13ebf9b0f736f0f5343e6d558c65532a6840
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T09:06:48.255954Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15576135680000"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
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+ }
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+ "writerId": "fqod8s6y2ph1ko6q2mj7qiafxdgfb2xg"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-summary.json b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..334abaad9a4c576b8b414ca3a7804f2fbc807661
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_090648-6beufw2w/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":28},"_runtime":28}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_090648-6beufw2w/run-6beufw2w.wandb b/Meissonic/wandb/run-20251229_090648-6beufw2w/run-6beufw2w.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..aa7ad860a98a72d36b73a515e204f54146e7a2eb
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diff --git a/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/config.yaml b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5bd636c12b826126acbd9a1017ecf86f2a5560b6
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/config.yaml
@@ -0,0 +1,308 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ aepprjfcso077zyaqxob652staeuxwcx:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576135843840"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:11:41.395964Z"
+ writerId: aepprjfcso077zyaqxob652staeuxwcx
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
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+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/requirements.txt b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/requirements.txt
@@ -0,0 +1,151 @@
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+av==16.0.1
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+xxhash==3.6.0
+tap==0.2
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+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
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+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
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+websocket-client==1.9.0
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+nvidia-nvshmem-cu12==3.3.20
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+wsproto==1.3.2
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+peft==0.18.0
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+wcwidth==0.2.14
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+PyYAML==6.0.3
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+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
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+protobuf==6.33.1
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diff --git a/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..c0e80ce168baf4ec21205386e05f81c123614929
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091141-ka0jd7f5/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T09:11:41.395964Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
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+ "used": "15576135843840"
+ }
+ },
+ "memory": {
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+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
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+ "name": "NVIDIA A100-SXM4-40GB",
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+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
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+}
\ No newline at end of file
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\ No newline at end of file
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diff --git a/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/config.yaml b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..16ca22250fcbc32d343ddecd325b5ccb2950b4df
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/config.yaml
@@ -0,0 +1,308 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ 2s2ya1sag5knet8vv0nugmmk3kg2p4b4:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576136171520"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:15:48.674161Z"
+ writerId: 2s2ya1sag5knet8vv0nugmmk3kg2p4b4
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/requirements.txt b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1d01ecad871b6b3baba9900a3b3d370e9205a61d
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/requirements.txt
@@ -0,0 +1,151 @@
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+av==16.0.1
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+xxhash==3.6.0
+tap==0.2
+mc_bin_client==1.0.1
+exceptiongroup==1.3.1
+cycler==0.12.1
+einops==0.8.1
+opencv-python==4.12.0.88
+scikit-image==0.25.2
+dashscope==1.25.2
+charset-normalizer==3.4.4
+filelock==3.19.1
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+lazy_loader==0.4
+kiwisolver==1.4.9
+Flask==3.1.2
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+setuptools==80.9.0
+websocket-client==1.9.0
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+nvidia-nvshmem-cu12==3.3.20
+itsdangerous==2.2.0
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+matrix-game-2.0==0.0.1
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+wcwidth==0.2.14
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+PyYAML==6.0.3
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+beartype==0.22.8
+dominate==2.9.1
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+nvidia-cuda-runtime-cu12==12.8.90
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+nvidia-cublas-cu12==12.8.4.1
+pfzy==0.3.4
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+regex==2025.11.3
+importlib_metadata==8.7.0
+Werkzeug==3.1.4
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+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+cryptography==46.0.3
+omegaconf==2.3.0
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+packaging==25.0
+inquirerpy==0.3.4
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+MarkupSafe==2.1.5
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+tzdata==2025.2
+decord==0.6.0
+async-timeout==5.0.1
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+numpy==2.1.2
+torch==2.9.1
+diffusers==0.35.2
+nvidia-cuda-cupti-cu12==12.8.90
+smmap==5.0.2
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+safetensors==0.7.0
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+nvidia-cusparse-cu12==12.5.8.93
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+typer-slim==0.20.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+python-engineio==4.12.3
+lmdb==1.7.5
+nvidia-nvtx-cu12==12.8.90
+fsspec==2025.9.0
+markdown-it-py==4.0.0
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+propcache==0.4.1
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+simple-websocket==1.1.0
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+contourpy==1.3.2
+imageio-ffmpeg==0.6.0
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+prompt_toolkit==3.0.52
+pillow==11.3.0
+protobuf==6.33.1
+yarl==1.22.0
+clip==1.0
+nvidia-cudnn-cu12==9.10.2.21
+python-socketio==5.15.0
diff --git a/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..cef4197a13fe549f31b6e3aba7dda9d04e1d34ed
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.10.19",
+ "startedAt": "2025-12-29T09:15:48.674161Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
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+ "used": "15576136171520"
+ }
+ },
+ "memory": {
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+ },
+ "gpu_nvidia": [
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
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+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
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+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
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+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
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+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
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+ "architecture": "Ampere",
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+ }
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+ "cudaVersion": "12.8",
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+}
\ No newline at end of file
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new file mode 100644
index 0000000000000000000000000000000000000000..022646b35b7c26b16cc2e065b40d7c01e5cfafde
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+++ b/Meissonic/wandb/run-20251229_091548-1gbb0o27/files/wandb-summary.json
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\ No newline at end of file
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diff --git a/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/config.yaml b/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..816a53299f5c24ee098aad6bfb62b745f6e178ef
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/config.yaml
@@ -0,0 +1,310 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ kfxt7o6ujt71x9w49ztafzu7mrji6t6i:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15576136318976"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.10.19
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:17:49.878310Z"
+ writerId: kfxt7o6ujt71x9w49ztafzu7mrji6t6i
+ m: []
+ python_version: 3.10.19
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ "3":
+ - 61
+ "4": 3.10.19
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
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diff --git a/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/wandb-metadata.json
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+{
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+ "startedAt": "2025-12-29T09:17:49.878310Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
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+ "--lr_scheduler",
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+ "0.0",
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+ "1",
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+ "42",
+ "--report_to",
+ "wandb"
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+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
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+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
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+ "executable": "/home/ubuntu/miniconda3/envs/matrix-game2/bin/python3.10",
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+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "kfxt7o6ujt71x9w49ztafzu7mrji6t6i"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/wandb-summary.json b/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..447ffd19e2b55ec31fba41619192284b6bcf0048
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091749-rjwft5vo/files/wandb-summary.json
@@ -0,0 +1 @@
+{"generated_videos_first_frame":{"filenames":["media/images/generated_videos_first_frame_400_1d62580a9de1268911e7.png","media/images/generated_videos_first_frame_400_adfbcce8afb45b1c30a8.png","media/images/generated_videos_first_frame_400_35b6b89b5aac853dd909.png","media/images/generated_videos_first_frame_400_0f42b78b94bd3a593399.png"],"captions":["a cat playing","a girl walking","The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.","The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner."],"_type":"images/separated","width":128,"height":128,"format":"png","count":4},"lr":0.0006000000000000001,"_wandb":{"runtime":585},"_runtime":585.580046805,"_timestamp":1.7670003995006068e+09,"_step":500,"step_loss":10.336981773376465,"generated_videos_grid":{"size":326517,"caption":"video_3_grid","_type":"image-file","sha256":"5d2f188185d80ef48816605d781f7656520844d8ec8d681b7fc647ac7eb3a2b9","path":"media/images/generated_videos_grid_400_5d2f188185d80ef48816.png","format":"png","width":522,"height":522},"avg_masking_rate":0.12160906940698624}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_091749-rjwft5vo/run-rjwft5vo.wandb b/Meissonic/wandb/run-20251229_091749-rjwft5vo/run-rjwft5vo.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ed5283acafeb2ee6ccbdaf43586fc9c026aae814
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_091749-rjwft5vo/run-rjwft5vo.wandb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e049ad98ac15273d98a7cca440e40b83f486e7615b939df7172cc406427d4e4
+size 324088
diff --git a/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/config.yaml b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e8686544c627b3fd40d62cf6507c84385fef89f1
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/config.yaml
@@ -0,0 +1,310 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ bljc882in14q19pdqk4un5uzm8o9i1ww:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15584534986752"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/mei-video/bin/python3.13
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.13.11
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:23:38.591647Z"
+ writerId: bljc882in14q19pdqk4un5uzm8o9i1ww
+ m: []
+ python_version: 3.13.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "4": 3.13.11
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/requirements.txt b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a15fd034e55caa7ed0e61870cf7b5d86e79963b3
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/requirements.txt
@@ -0,0 +1,122 @@
+typing-inspection==0.4.2
+dill==0.4.0
+ffmpy==1.0.0
+xxhash==3.6.0
+partd==1.4.2
+brotli==1.2.0
+charset-normalizer==3.4.4
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+orjson==3.11.5
+numpy==2.4.0
+pydantic_core==2.41.5
+groovy==0.1.2
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+MarkupSafe==3.0.3
+protobuf==6.33.2
+nvidia-cusparselt-cu12==0.7.1
+locket==1.0.0
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+pydub==0.25.1
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+torchvision==0.24.1
+cloudpickle==3.1.2
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+open_clip_torch==3.2.0
+mdurl==0.1.2
+pandas==2.3.3
+toolz==1.1.0
+python-multipart==0.0.21
+ftfy==6.3.1
+platformdirs==4.5.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+typer==0.21.0
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+gradio_client==2.0.2
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+timm==1.0.22
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+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
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+bitsandbytes==0.49.0
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+torchmetrics==1.8.2
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+aiosignal==1.4.0
+nvidia-cuda-nvrtc-cu12==12.8.93
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+h11==0.16.0
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+idna==3.11
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+starlette==0.50.0
+audioop-lts==0.2.2
+urllib3==2.6.2
+accelerate==1.12.0
+psutil==7.2.1
+diffusers==0.36.0
+annotated-doc==0.0.4
+zipp==3.23.0
+propcache==0.4.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+nvidia-curand-cu12==10.3.9.90
+datasets==4.4.2
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+yarl==1.22.0
+nvidia-cudnn-cu12==9.10.2.21
+tomlkit==0.13.3
diff --git a/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..51c58f47591a4ade8344774bcf2461a00a272ae4
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.13.11",
+ "startedAt": "2025-12-29T09:23:38.591647Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/mei-video/bin/python3.13",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15584534986752"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "bljc882in14q19pdqk4un5uzm8o9i1ww"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-summary.json b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..75631e6e43bf8471ee6ca35a1ab1286f569677cb
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092338-9aiqswtw/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":3},"_runtime":3}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_092338-9aiqswtw/run-9aiqswtw.wandb b/Meissonic/wandb/run-20251229_092338-9aiqswtw/run-9aiqswtw.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..f8704d203fc1aa7f70dc9ef5ab095edf1b75241b
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diff --git a/Meissonic/wandb/run-20251229_092754-hkolswde/files/config.yaml b/Meissonic/wandb/run-20251229_092754-hkolswde/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b212c1c08c6c9165b0dbc5ed7df9a30e3d52c391
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092754-hkolswde/files/config.yaml
@@ -0,0 +1,310 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ qek4phtgw47r5nb6ur2jyeakhg2xjsmd:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15584537948160"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/mei-video/bin/python3.13
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.13.11
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:27:54.387328Z"
+ writerId: qek4phtgw47r5nb6ur2jyeakhg2xjsmd
+ m: []
+ python_version: 3.13.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "4": 3.13.11
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_092754-hkolswde/files/requirements.txt b/Meissonic/wandb/run-20251229_092754-hkolswde/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a15fd034e55caa7ed0e61870cf7b5d86e79963b3
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092754-hkolswde/files/requirements.txt
@@ -0,0 +1,122 @@
+typing-inspection==0.4.2
+dill==0.4.0
+ffmpy==1.0.0
+xxhash==3.6.0
+partd==1.4.2
+brotli==1.2.0
+charset-normalizer==3.4.4
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+orjson==3.11.5
+numpy==2.4.0
+pydantic_core==2.41.5
+groovy==0.1.2
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+MarkupSafe==3.0.3
+protobuf==6.33.2
+nvidia-cusparselt-cu12==0.7.1
+locket==1.0.0
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+pydub==0.25.1
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+torchvision==0.24.1
+cloudpickle==3.1.2
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+open_clip_torch==3.2.0
+mdurl==0.1.2
+pandas==2.3.3
+toolz==1.1.0
+python-multipart==0.0.21
+ftfy==6.3.1
+platformdirs==4.5.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+typer==0.21.0
+lightning-utilities==0.15.2
+gradio_client==2.0.2
+wheel==0.45.1
+timm==1.0.22
+semantic-version==2.10.0
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+importlib_metadata==8.7.1
+httpcore==1.0.9
+fsspec==2025.10.0
+multidict==6.7.0
+regex==2025.11.3
+bitsandbytes==0.49.0
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+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+uvicorn==0.40.0
+packaging==25.0
+aiosignal==1.4.0
+nvidia-cuda-nvrtc-cu12==12.8.93
+networkx==3.6.1
+setuptools==80.9.0
+sympy==1.14.0
+torch==2.9.1
+nvidia-cuda-cupti-cu12==12.8.90
+gradio==6.2.0
+smmap==5.0.2
+safetensors==0.7.0
+gitdb==4.0.12
+safehttpx==0.1.7
+fastapi==0.128.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+pillow==12.0.0
+sentry-sdk==2.48.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+tzdata==2025.3
+nvidia-nvtx-cu12==12.8.90
+filelock==3.20.1
+markdown-it-py==4.0.0
+six==1.17.0
+starlette==0.50.0
+audioop-lts==0.2.2
+urllib3==2.6.2
+accelerate==1.12.0
+psutil==7.2.1
+diffusers==0.36.0
+annotated-doc==0.0.4
+zipp==3.23.0
+propcache==0.4.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+nvidia-curand-cu12==10.3.9.90
+datasets==4.4.2
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+aiofiles==24.1.0
+dask==2025.12.0
+yarl==1.22.0
+nvidia-cudnn-cu12==9.10.2.21
+tomlkit==0.13.3
diff --git a/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..5d667483319987e9e2e6bf230de37719e4e17f61
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.13.11",
+ "startedAt": "2025-12-29T09:27:54.387328Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/mei-video/bin/python3.13",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15584537948160"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "qek4phtgw47r5nb6ur2jyeakhg2xjsmd"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-summary.json b/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..d36db3b5803a02ddbdedb9c4a80ca513af26e4ff
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_092754-hkolswde/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":45},"_runtime":45}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_092754-hkolswde/run-hkolswde.wandb b/Meissonic/wandb/run-20251229_092754-hkolswde/run-hkolswde.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..ad5a808e80ccf24cd4aaea1c8fe5547d1681622d
Binary files /dev/null and b/Meissonic/wandb/run-20251229_092754-hkolswde/run-hkolswde.wandb differ
diff --git a/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/config.yaml b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..fbe6a1ec62e0f0bf62480ed7ba6acd244c1f12de
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/config.yaml
@@ -0,0 +1,310 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ 24dmzc7sd9zzpl5g1sdza6vk0vkp4c4k:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15584538075136"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/mei-video/bin/python3.13
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.13.11
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:30:47.679442Z"
+ writerId: 24dmzc7sd9zzpl5g1sdza6vk0vkp4c4k
+ m: []
+ python_version: 3.13.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "4": 3.13.11
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/requirements.txt b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a15fd034e55caa7ed0e61870cf7b5d86e79963b3
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/requirements.txt
@@ -0,0 +1,122 @@
+typing-inspection==0.4.2
+dill==0.4.0
+ffmpy==1.0.0
+xxhash==3.6.0
+partd==1.4.2
+brotli==1.2.0
+charset-normalizer==3.4.4
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+orjson==3.11.5
+numpy==2.4.0
+pydantic_core==2.41.5
+groovy==0.1.2
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+MarkupSafe==3.0.3
+protobuf==6.33.2
+nvidia-cusparselt-cu12==0.7.1
+locket==1.0.0
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+pydub==0.25.1
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+torchvision==0.24.1
+cloudpickle==3.1.2
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+open_clip_torch==3.2.0
+mdurl==0.1.2
+pandas==2.3.3
+toolz==1.1.0
+python-multipart==0.0.21
+ftfy==6.3.1
+platformdirs==4.5.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+typer==0.21.0
+lightning-utilities==0.15.2
+gradio_client==2.0.2
+wheel==0.45.1
+timm==1.0.22
+semantic-version==2.10.0
+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
+importlib_metadata==8.7.1
+httpcore==1.0.9
+fsspec==2025.10.0
+multidict==6.7.0
+regex==2025.11.3
+bitsandbytes==0.49.0
+anyio==4.12.0
+nvidia-cusolver-cu12==11.7.3.90
+torchmetrics==1.8.2
+uvicorn==0.40.0
+packaging==25.0
+aiosignal==1.4.0
+nvidia-cuda-nvrtc-cu12==12.8.93
+networkx==3.6.1
+setuptools==80.9.0
+sympy==1.14.0
+torch==2.9.1
+nvidia-cuda-cupti-cu12==12.8.90
+gradio==6.2.0
+smmap==5.0.2
+safetensors==0.7.0
+gitdb==4.0.12
+safehttpx==0.1.7
+fastapi==0.128.0
+nvidia-cusparse-cu12==12.5.8.93
+multiprocess==0.70.18
+pillow==12.0.0
+sentry-sdk==2.48.0
+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+tzdata==2025.3
+nvidia-nvtx-cu12==12.8.90
+filelock==3.20.1
+markdown-it-py==4.0.0
+six==1.17.0
+starlette==0.50.0
+audioop-lts==0.2.2
+urllib3==2.6.2
+accelerate==1.12.0
+psutil==7.2.1
+diffusers==0.36.0
+annotated-doc==0.0.4
+zipp==3.23.0
+propcache==0.4.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+nvidia-curand-cu12==10.3.9.90
+datasets==4.4.2
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+aiofiles==24.1.0
+dask==2025.12.0
+yarl==1.22.0
+nvidia-cudnn-cu12==9.10.2.21
+tomlkit==0.13.3
diff --git a/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..84d580b153c5079e7952b13d957d329c1ef17f55
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.13.11",
+ "startedAt": "2025-12-29T09:30:47.679442Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/mei-video/bin/python3.13",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15584538075136"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
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+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "24dmzc7sd9zzpl5g1sdza6vk0vkp4c4k"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-summary.json b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..c06148192ebeffb55e0aca087559968faedf510f
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093047-tjwhycdm/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_wandb":{"runtime":78},"_runtime":78}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093047-tjwhycdm/run-tjwhycdm.wandb b/Meissonic/wandb/run-20251229_093047-tjwhycdm/run-tjwhycdm.wandb
new file mode 100644
index 0000000000000000000000000000000000000000..df97e0be126c82e3f76fe16d3515820c56e392c3
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diff --git a/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/config.yaml b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..219de3c10a01c14d9cb72e9e2ec34de106c60323
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/config.yaml
@@ -0,0 +1,310 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ a7q3mqc5fpt8eof7ipn9yioxo8tydpnv:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15584538189824"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/mei-video/bin/python3.13
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.13.11
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:33:32.970685Z"
+ writerId: a7q3mqc5fpt8eof7ipn9yioxo8tydpnv
+ m: []
+ python_version: 3.13.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "4": 3.13.11
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
diff --git a/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/requirements.txt b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..a15fd034e55caa7ed0e61870cf7b5d86e79963b3
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/requirements.txt
@@ -0,0 +1,122 @@
+typing-inspection==0.4.2
+dill==0.4.0
+ffmpy==1.0.0
+xxhash==3.6.0
+partd==1.4.2
+brotli==1.2.0
+charset-normalizer==3.4.4
+tokenizers==0.22.1
+aiohappyeyeballs==2.6.1
+python-dateutil==2.9.0.post0
+pyarrow==22.0.0
+annotated-types==0.7.0
+GitPython==3.1.45
+rich==14.2.0
+nvidia-cufile-cu12==1.13.1.3
+nvidia-nvshmem-cu12==3.3.20
+orjson==3.11.5
+numpy==2.4.0
+pydantic_core==2.41.5
+groovy==0.1.2
+peft==0.18.0
+typing_extensions==4.15.0
+wcwidth==0.2.14
+MarkupSafe==3.0.3
+protobuf==6.33.2
+nvidia-cusparselt-cu12==0.7.1
+locket==1.0.0
+PyYAML==6.0.3
+nvidia-nvjitlink-cu12==12.8.93
+pytorch-lightning==2.6.0
+frozenlist==1.8.0
+pydub==0.25.1
+huggingface-hub==0.36.0
+Pygments==2.19.2
+aiohttp==3.13.2
+torchvision==0.24.1
+cloudpickle==3.1.2
+wandb==0.23.1
+tqdm==4.67.1
+httpx==0.28.1
+open_clip_torch==3.2.0
+mdurl==0.1.2
+pandas==2.3.3
+toolz==1.1.0
+python-multipart==0.0.21
+ftfy==6.3.1
+platformdirs==4.5.1
+transformers==4.57.3
+requests==2.32.5
+pytz==2025.2
+Jinja2==3.1.6
+click==8.3.1
+attrs==25.4.0
+hf-xet==1.2.0
+shellingham==1.5.4
+nvidia-nccl-cu12==2.27.5
+nvidia-cuda-runtime-cu12==12.8.90
+typer==0.21.0
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+triton==3.5.1
+nvidia-cublas-cu12==12.8.4.1
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+regex==2025.11.3
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+h11==0.16.0
+certifi==2025.11.12
+idna==3.11
+tzdata==2025.3
+nvidia-nvtx-cu12==12.8.90
+filelock==3.20.1
+markdown-it-py==4.0.0
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+starlette==0.50.0
+audioop-lts==0.2.2
+urllib3==2.6.2
+accelerate==1.12.0
+psutil==7.2.1
+diffusers==0.36.0
+annotated-doc==0.0.4
+zipp==3.23.0
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+mpmath==1.3.0
+sentencepiece==0.2.1
+nvidia-curand-cu12==10.3.9.90
+datasets==4.4.2
+nvidia-cufft-cu12==11.3.3.83
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+dask==2025.12.0
+yarl==1.22.0
+nvidia-cudnn-cu12==9.10.2.21
+tomlkit==0.13.3
diff --git a/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..af975273a156cf2c906018605c429aa2f436943c
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.13.11",
+ "startedAt": "2025-12-29T09:33:32.970685Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/mei-video/bin/python3.13",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15584538189824"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "a7q3mqc5fpt8eof7ipn9yioxo8tydpnv"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-summary.json b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..36b39aeda94abcc7cd91a61f0f9fbfff2e31c16a
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/files/wandb-summary.json
@@ -0,0 +1 @@
+{"_runtime":71,"_wandb":{"runtime":71}}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093332-4lgcq9jf/run-4lgcq9jf.wandb b/Meissonic/wandb/run-20251229_093332-4lgcq9jf/run-4lgcq9jf.wandb
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diff --git a/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/config.yaml b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/config.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6bf790f39da16dd1a94c1e9b59e7ed0464b1f2fb
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/config.yaml
@@ -0,0 +1,312 @@
+_wandb:
+ value:
+ cli_version: 0.23.1
+ e:
+ vcdq559fpl4a8h6xzf7kxm6bwt39s12v:
+ args:
+ - --use_precomputed_video_only
+ - --features_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+ - --text_encoder_architecture
+ - umt5-xxl
+ - --wan_pretrained_path
+ - /mnt/Wan2.1-T2V-1.3B
+ - --training_from_scratch
+ - --pretrained_model_name_or_path
+ - dummy
+ - --wan_backbone_lr_ratio
+ - "0.2"
+ - --num_frames
+ - "17"
+ - --video_height
+ - "128"
+ - --video_width
+ - "128"
+ - --dataloader_num_workers
+ - "8"
+ - --video_tokenizer_model_id
+ - Cosmos-0.1-Tokenizer-DV4x8x8
+ - --instance_dataset
+ - OpenVid1MDataset
+ - --instance_data_dir
+ - /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+ - --train_batch_size
+ - "1"
+ - --gradient_accumulation_steps
+ - "1"
+ - --learning_rate
+ - "3e-3"
+ - --max_train_steps
+ - "100000"
+ - --checkpointing_steps
+ - "500"
+ - --validation_steps
+ - "100"
+ - --logging_steps
+ - "10"
+ - --validation_prompts
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+ - --output_dir
+ - ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+ - --mixed_precision
+ - bf16
+ - --lr_scheduler
+ - constant
+ - --lr_warmup_steps
+ - "0"
+ - --use_8bit_adam
+ - --gradient_checkpointing
+ - --min_masking_rate
+ - "0.0"
+ - --cond_dropout_prob
+ - "0.0"
+ - --split_vae_encode
+ - "1"
+ - --allow_tf32
+ - --seed
+ - "42"
+ - --report_to
+ - wandb
+ codePath: train/train_mei_video.py
+ codePathLocal: train/train_mei_video.py
+ cpu_count: 48
+ cpu_count_logical: 96
+ cudaVersion: "12.8"
+ disk:
+ /:
+ total: "16650112278528"
+ used: "15584538329088"
+ email: jinbin5bai@gmail.com
+ executable: /home/ubuntu/miniconda3/envs/mei-video/bin/python3.13
+ git:
+ commit: 6819d374ef1b86bdedad373aab1121a89687e5cf
+ remote: https://github.com/viiika/Meissonic.git
+ gpu: NVIDIA A100-SXM4-40GB
+ gpu_count: 8
+ gpu_nvidia:
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-71102f28-cd17-57e7-6181-120bf743d23d
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-303ab142-3206-9a14-c758-58ab97d7510e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e
+ - architecture: Ampere
+ cudaCores: 6912
+ memoryTotal: "42949672960"
+ name: NVIDIA A100-SXM4-40GB
+ uuid: GPU-efb2d1fc-1eed-653d-ed51-5273085154ba
+ host: ip-172-31-91-136
+ memory:
+ total: "1204521451520"
+ os: Linux-6.8.0-1027-aws-x86_64-with-glibc2.35
+ program: /mnt/Meissonic/train/train_mei_video.py
+ python: CPython 3.13.11
+ root: /mnt/Meissonic
+ startedAt: "2025-12-29T09:35:00.408966Z"
+ writerId: vcdq559fpl4a8h6xzf7kxm6bwt39s12v
+ m: []
+ python_version: 3.13.11
+ t:
+ "1":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "2":
+ - 1
+ - 11
+ - 41
+ - 49
+ - 51
+ - 71
+ - 83
+ - 98
+ - 105
+ "3":
+ - 61
+ "4": 3.13.11
+ "5": 0.23.1
+ "6": 4.57.3
+ "12": 0.23.1
+ "13": linux-x86_64
+adam_beta1:
+ value: 0.9
+adam_beta2:
+ value: 0.999
+adam_epsilon:
+ value: 1e-08
+adam_weight_decay:
+ value: 0.01
+allow_tf32:
+ value: true
+checkpointing_steps:
+ value: 500
+checkpoints_total_limit:
+ value: null
+cond_dropout_prob:
+ value: 0
+dataloader_num_workers:
+ value: 8
+dataloader_prefetch_factor:
+ value: 2
+ema_decay:
+ value: 0.9999
+ema_update_after_step:
+ value: 0
+empty_embeds_path:
+ value: null
+features_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128
+freeze_wan_backbone:
+ value: false
+gradient_accumulation_steps:
+ value: 1
+gradient_checkpointing:
+ value: true
+image_key:
+ value: null
+instance_data_dataset:
+ value: null
+instance_data_dir:
+ value: /mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv
+instance_data_image:
+ value: null
+instance_dataset:
+ value: OpenVid1MDataset
+learning_rate:
+ value: 0.003
+logging_dir:
+ value: logs
+logging_steps:
+ value: 10
+lora_alpha:
+ value: 32
+lora_r:
+ value: 16
+lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+lr_scheduler:
+ value: constant
+lr_warmup_steps:
+ value: 0
+max_grad_norm:
+ value: 50
+max_train_steps:
+ value: 100000
+min_masking_rate:
+ value: 0
+mixed_precision:
+ value: bf16
+num_frames:
+ value: 17
+output_dir:
+ value: ./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3
+pretrained_model_name_or_path:
+ value: dummy
+prompt_key:
+ value: null
+prompt_prefix:
+ value: null
+report_to:
+ value: wandb
+resolution:
+ value: 512
+resume_from_checkpoint:
+ value: null
+revision:
+ value: null
+scale_lr:
+ value: false
+seed:
+ value: 42
+split_vae_encode:
+ value: 1
+text_encoder_architecture:
+ value: umt5-xxl
+text_encoder_lora_alpha:
+ value: 32
+text_encoder_lora_r:
+ value: 16
+text_encoder_lora_target_modules:
+ value:
+ - to_q
+ - to_k
+ - to_v
+text_encoder_use_lora:
+ value: false
+train_batch_size:
+ value: 1
+train_text_encoder:
+ value: false
+training_from_scratch:
+ value: true
+use_8bit_adam:
+ value: true
+use_ema:
+ value: false
+use_lora:
+ value: false
+use_precomputed_features:
+ value: false
+use_precomputed_video_only:
+ value: true
+validation_prompts:
+ value:
+ - a cat playing
+ - a girl walking
+ - The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.
+ - The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.
+validation_steps:
+ value: 100
+variant:
+ value: null
+video_height:
+ value: 128
+video_tokenizer_model_id:
+ value: Cosmos-0.1-Tokenizer-DV4x8x8
+video_width:
+ value: 128
+wan_backbone_lr_ratio:
+ value: 0.2
+wan_pretrained_path:
+ value: /mnt/Wan2.1-T2V-1.3B
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+accelerate==1.12.0
+psutil==7.2.1
+diffusers==0.36.0
+annotated-doc==0.0.4
+zipp==3.23.0
+propcache==0.4.1
+mpmath==1.3.0
+sentencepiece==0.2.1
+nvidia-curand-cu12==10.3.9.90
+datasets==4.4.2
+nvidia-cufft-cu12==11.3.3.83
+pydantic==2.12.5
+pip==25.3
+aiofiles==24.1.0
+dask==2025.12.0
+yarl==1.22.0
+nvidia-cudnn-cu12==9.10.2.21
+tomlkit==0.13.3
diff --git a/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-metadata.json b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-metadata.json
new file mode 100644
index 0000000000000000000000000000000000000000..bdbf044dc4ee8f282dd2c84fb8cc5fb73684acd3
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-metadata.json
@@ -0,0 +1,157 @@
+{
+ "os": "Linux-6.8.0-1027-aws-x86_64-with-glibc2.35",
+ "python": "CPython 3.13.11",
+ "startedAt": "2025-12-29T09:35:00.408966Z",
+ "args": [
+ "--use_precomputed_video_only",
+ "--features_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/extracted_features_17_128_128",
+ "--text_encoder_architecture",
+ "umt5-xxl",
+ "--wan_pretrained_path",
+ "/mnt/Wan2.1-T2V-1.3B",
+ "--training_from_scratch",
+ "--pretrained_model_name_or_path",
+ "dummy",
+ "--wan_backbone_lr_ratio",
+ "0.2",
+ "--num_frames",
+ "17",
+ "--video_height",
+ "128",
+ "--video_width",
+ "128",
+ "--dataloader_num_workers",
+ "8",
+ "--video_tokenizer_model_id",
+ "Cosmos-0.1-Tokenizer-DV4x8x8",
+ "--instance_dataset",
+ "OpenVid1MDataset",
+ "--instance_data_dir",
+ "/mnt/VideoGen/dataset/OpenVid1M/video_reorg/OpenVid1M_reorganized.csv",
+ "--train_batch_size",
+ "1",
+ "--gradient_accumulation_steps",
+ "1",
+ "--learning_rate",
+ "3e-3",
+ "--max_train_steps",
+ "100000",
+ "--checkpointing_steps",
+ "500",
+ "--validation_steps",
+ "100",
+ "--logging_steps",
+ "10",
+ "--validation_prompts",
+ "a cat playing",
+ "a girl walking",
+ "The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.",
+ "The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner.",
+ "--output_dir",
+ "./output_256x256_17f_2*4bs_4*8*8vqvae_0_2_ratio_lr3e-3",
+ "--mixed_precision",
+ "bf16",
+ "--lr_scheduler",
+ "constant",
+ "--lr_warmup_steps",
+ "0",
+ "--use_8bit_adam",
+ "--gradient_checkpointing",
+ "--min_masking_rate",
+ "0.0",
+ "--cond_dropout_prob",
+ "0.0",
+ "--split_vae_encode",
+ "1",
+ "--allow_tf32",
+ "--seed",
+ "42",
+ "--report_to",
+ "wandb"
+ ],
+ "program": "/mnt/Meissonic/train/train_mei_video.py",
+ "codePath": "train/train_mei_video.py",
+ "codePathLocal": "train/train_mei_video.py",
+ "git": {
+ "remote": "https://github.com/viiika/Meissonic.git",
+ "commit": "6819d374ef1b86bdedad373aab1121a89687e5cf"
+ },
+ "email": "jinbin5bai@gmail.com",
+ "root": "/mnt/Meissonic",
+ "host": "ip-172-31-91-136",
+ "executable": "/home/ubuntu/miniconda3/envs/mei-video/bin/python3.13",
+ "cpu_count": 48,
+ "cpu_count_logical": 96,
+ "gpu": "NVIDIA A100-SXM4-40GB",
+ "gpu_count": 8,
+ "disk": {
+ "/": {
+ "total": "16650112278528",
+ "used": "15584538329088"
+ }
+ },
+ "memory": {
+ "total": "1204521451520"
+ },
+ "gpu_nvidia": [
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-54a50f05-7a41-8b8e-59c5-e1774ec42215"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-71102f28-cd17-57e7-6181-120bf743d23d"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-57dfac44-bb50-f9b6-1534-27fbe79dfd87"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-f55652c0-bdaf-e7bb-a876-8fce14c3f879"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-303ab142-3206-9a14-c758-58ab97d7510e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-1adf5c34-24d0-c5e2-b33b-783100bbd6c3"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-5b4a0e50-96a5-74bd-f595-14de5614cc6e"
+ },
+ {
+ "name": "NVIDIA A100-SXM4-40GB",
+ "memoryTotal": "42949672960",
+ "cudaCores": 6912,
+ "architecture": "Ampere",
+ "uuid": "GPU-efb2d1fc-1eed-653d-ed51-5273085154ba"
+ }
+ ],
+ "cudaVersion": "12.8",
+ "writerId": "vcdq559fpl4a8h6xzf7kxm6bwt39s12v"
+}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-summary.json b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..9dcca013a028c9c21d84e08e974a08f15e51ae43
--- /dev/null
+++ b/Meissonic/wandb/run-20251229_093500-yyrdgepk/files/wandb-summary.json
@@ -0,0 +1 @@
+{"generated_videos_first_frame":{"count":4,"filenames":["media/images/generated_videos_first_frame_400_daf2d6d4a92c1732b441.png","media/images/generated_videos_first_frame_400_7e274e88e8caf65dc595.png","media/images/generated_videos_first_frame_400_5dcdc12cd2058ba5a9ee.png","media/images/generated_videos_first_frame_400_a14b1826b3dd5a9eba69.png"],"captions":["a cat playing","a girl walking","The video features a man named David Schultz from Hamline University. He is dressed in a suit and tie, standing in front of a building with a tree in the background. The man appears to be speaking or presenting, as suggested by the context of the image. The style of the video is likely informative or educational, given the context of the man's attire and the setting. The video may be part of a news segment or a lecture series, as indicated by the man's professional appearance and the presence of a building that could be a university or academic institution.","The video captures the interior of a car at a car show. The car features a striking orange and black color scheme, with the seats upholstered in orange leather and the door panels in black leather. The car's interior is well-lit, highlighting the details of the upholstery and the design of the door panels. The car is on display, with people walking around and observing it. The car show setting is bustling with activity, with other cars and people visible in the background. The video is a close-up shot of the car's interior, focusing on the details of the upholstery and the design of the door panels. The style of the video is realistic, capturing the car's interior in a clear and detailed manner."],"_type":"images/separated","width":128,"height":128,"format":"png"},"generated_videos_grid":{"width":522,"height":522,"caption":"video_3_grid","sha256":"053d1da246a9155b89d3117d33ddf47552bd72d464f742afe62759ab3d43d1e3","format":"png","_type":"image-file","size":419777,"path":"media/images/generated_videos_grid_400_053d1da246a9155b89d3.png"},"_timestamp":1.767001316258984e+09,"step_loss":11.12689208984375,"avg_masking_rate":0.9541797637939453,"_wandb":{"runtime":421},"_step":420,"lr":0.0006000000000000001,"_runtime":421.494331581}
\ No newline at end of file
diff --git a/Meissonic/wandb/run-20251229_093500-yyrdgepk/run-yyrdgepk.wandb b/Meissonic/wandb/run-20251229_093500-yyrdgepk/run-yyrdgepk.wandb
new file mode 100644
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71ac9f3708d9bf33df07f5adffcc7a734cb4125d331d2cc0cbeda23bdf521b7d
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+assets/vben_vs_sota.png filter=lfs diff=lfs merge=lfs -text
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diff --git a/Wan2.1-T2V-1.3B/LICENSE.txt b/Wan2.1-T2V-1.3B/LICENSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/LICENSE.txt
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
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diff --git a/Wan2.1-T2V-1.3B/README.md b/Wan2.1-T2V-1.3B/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..fbbf7c13900d37f3179c546d09372aeb52e0fae1
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/README.md
@@ -0,0 +1,298 @@
+---
+license: apache-2.0
+language:
+- en
+- zh
+pipeline_tag: text-to-video
+library_name: diffusers
+tags:
+- video
+- video-generation
+---
+# Wan2.1
+
+
+
+
+
+
+ 💜 Wan ｜ 🖥️ GitHub | 🤗 Hugging Face | 🤖 ModelScope | 📑 Paper (Coming soon) | 📑 Blog | 💬 WeChat Group | 📖 Discord
+
+
+-----
+
+[**Wan: Open and Advanced Large-Scale Video Generative Models**]("#")
+
+In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features:
+- 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks.
+- 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models.
+- 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation.
+- 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications.
+- 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation.
+
+
+This repository hosts our T2V-1.3B model, a versatile solution for video generation that is compatible with nearly all consumer-grade GPUs. In this way, we hope that **Wan2.1** can serve as an easy-to-use tool for more creative teams in video creation, providing a high-quality foundational model for academic teams with limited computing resources. This will facilitate both the rapid development of the video creation community and the swift advancement of video technology.
+
+
+## Video Demos
+
+
+
+
+
+
+## 🔥 Latest News!!
+
+* Feb 25, 2025: 👋 We've released the inference code and weights of Wan2.1.
+
+
+## 📑 Todo List
+- Wan2.1 Text-to-Video
+ - [x] Multi-GPU Inference code of the 14B and 1.3B models
+ - [x] Checkpoints of the 14B and 1.3B models
+ - [x] Gradio demo
+ - [ ] Diffusers integration
+ - [ ] ComfyUI integration
+- Wan2.1 Image-to-Video
+ - [x] Multi-GPU Inference code of the 14B model
+ - [x] Checkpoints of the 14B model
+ - [x] Gradio demo
+ - [ ] Diffusers integration
+ - [ ] ComfyUI integration
+
+
+## Quickstart
+
+#### Installation
+Clone the repo:
+```
+git clone https://github.com/Wan-Video/Wan2.1.git
+cd Wan2.1
+```
+
+Install dependencies:
+```
+# Ensure torch >= 2.4.0
+pip install -r requirements.txt
+```
+
+
+#### Model Download
+
+| Models | Download Link | Notes |
+| --------------|-------------------------------------------------------------------------------|-------------------------------|
+| T2V-14B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-14B) | Supports both 480P and 720P
+| I2V-14B-720P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-720P) | Supports 720P
+| I2V-14B-480P | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-480P) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-I2V-14B-480P) | Supports 480P
+| T2V-1.3B | 🤗 [Huggingface](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B) 🤖 [ModelScope](https://www.modelscope.cn/models/Wan-AI/Wan2.1-T2V-1.3B) | Supports 480P
+
+
+> 💡Note: The 1.3B model is capable of generating videos at 720P resolution. However, due to limited training at this resolution, the results are generally less stable compared to 480P. For optimal performance, we recommend using 480P resolution.
+
+
+Download models using 🤗 huggingface-cli:
+```
+pip install "huggingface_hub[cli]"
+huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B
+```
+
+Download models using 🤖 modelscope-cli:
+```
+pip install modelscope
+modelscope download Wan-AI/Wan2.1-T2V-1.3B --local_dir ./Wan2.1-T2V-1.3B
+```
+
+#### Run Text-to-Video Generation
+
+This repository supports two Text-to-Video models (1.3B and 14B) and two resolutions (480P and 720P). The parameters and configurations for these models are as follows:
+
+
+
+
+
Task
+
Resolution
+
Model
+
+
+
480P
+
720P
+
+
+
+
+
t2v-14B
+
✔️
+
✔️
+
Wan2.1-T2V-14B
+
+
+
t2v-1.3B
+
✔️
+
❌
+
Wan2.1-T2V-1.3B
+
+
+
+
+
+##### (1) Without Prompt Extention
+
+To facilitate implementation, we will start with a basic version of the inference process that skips the [prompt extension](#2-using-prompt-extention) step.
+
+- Single-GPU inference
+
+```
+python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+If you encounter OOM (Out-of-Memory) issues, you can use the `--offload_model True` and `--t5_cpu` options to reduce GPU memory usage. For example, on an RTX 4090 GPU:
+
+```
+python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --offload_model True --t5_cpu --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+> 💡Note: If you are using the `T2V-1.3B` model, we recommend setting the parameter `--sample_guide_scale 6`. The `--sample_shift parameter` can be adjusted within the range of 8 to 12 based on the performance.
+
+- Multi-GPU inference using FSDP + xDiT USP
+
+```
+pip install "xfuser>=0.4.1"
+torchrun --nproc_per_node=8 generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --dit_fsdp --t5_fsdp --ulysses_size 8 --sample_shift 8 --sample_guide_scale 6 --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage."
+```
+
+
+##### (2) Using Prompt Extention
+
+Extending the prompts can effectively enrich the details in the generated videos, further enhancing the video quality. Therefore, we recommend enabling prompt extension. We provide the following two methods for prompt extension:
+
+- Use the Dashscope API for extension.
+ - Apply for a `dashscope.api_key` in advance ([EN](https://www.alibabacloud.com/help/en/model-studio/getting-started/first-api-call-to-qwen) | [CN](https://help.aliyun.com/zh/model-studio/getting-started/first-api-call-to-qwen)).
+ - Configure the environment variable `DASH_API_KEY` to specify the Dashscope API key. For users of Alibaba Cloud's international site, you also need to set the environment variable `DASH_API_URL` to 'https://dashscope-intl.aliyuncs.com/api/v1'. For more detailed instructions, please refer to the [dashscope document](https://www.alibabacloud.com/help/en/model-studio/developer-reference/use-qwen-by-calling-api?spm=a2c63.p38356.0.i1).
+ - Use the `qwen-plus` model for text-to-video tasks and `qwen-vl-max` for image-to-video tasks.
+ - You can modify the model used for extension with the parameter `--prompt_extend_model`. For example:
+```
+DASH_API_KEY=your_key python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'dashscope' --prompt_extend_target_lang 'ch'
+```
+
+- Using a local model for extension.
+
+ - By default, the Qwen model on HuggingFace is used for this extension. Users can choose based on the available GPU memory size.
+ - For text-to-video tasks, you can use models like `Qwen/Qwen2.5-14B-Instruct`, `Qwen/Qwen2.5-7B-Instruct` and `Qwen/Qwen2.5-3B-Instruct`
+ - For image-to-video tasks, you can use models like `Qwen/Qwen2.5-VL-7B-Instruct` and `Qwen/Qwen2.5-VL-3B-Instruct`.
+ - Larger models generally provide better extension results but require more GPU memory.
+ - You can modify the model used for extension with the parameter `--prompt_extend_model` , allowing you to specify either a local model path or a Hugging Face model. For example:
+
+```
+python generate.py --task t2v-1.3B --size 832*480 --ckpt_dir ./Wan2.1-T2V-1.3B --prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage" --use_prompt_extend --prompt_extend_method 'local_qwen' --prompt_extend_target_lang 'ch'
+```
+
+##### (3) Runing local gradio
+
+```
+cd gradio
+# if one uses dashscope’s API for prompt extension
+DASH_API_KEY=your_key python t2v_1.3B_singleGPU.py --prompt_extend_method 'dashscope' --ckpt_dir ./Wan2.1-T2V-1.3B
+
+# if one uses a local model for prompt extension
+python t2v_1.3B_singleGPU.py --prompt_extend_method 'local_qwen' --ckpt_dir ./Wan2.1-T2V-1.3B
+```
+
+
+
+## Evaluation
+
+We employ our **Wan-Bench** framework to evaluate the performance of the T2V-1.3B model, with the results displayed in the table below. The results indicate that our smaller 1.3B model surpasses the overall metrics of larger open-source models, demonstrating the effectiveness of **WanX2.1**'s architecture and the data construction pipeline.
+
+
+
+
+
+
+
+## Computational Efficiency on Different GPUs
+
+We test the computational efficiency of different **Wan2.1** models on different GPUs in the following table. The results are presented in the format: **Total time (s) / peak GPU memory (GB)**.
+
+
+
+
+
+
+> The parameter settings for the tests presented in this table are as follows:
+> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+
+
+
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+
+
+
+
+
+| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
+| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. Then we calculated the total score through a weighted average based on the importance of each dimension. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2.1,
+ title = {Wan: Open and Advanced Large-Scale Video Generative Models},
+ author = {Wan Team},
+ journal = {},
+ year = {2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
+
+
+
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
\ No newline at end of file
diff --git a/Wan2.1-T2V-1.3B/config.json b/Wan2.1-T2V-1.3B/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d203bef600b2f3c64fe1f5f53d70a2087f4ccd2f
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/config.json
@@ -0,0 +1,14 @@
+{
+ "_class_name": "WanModel",
+ "_diffusers_version": "0.30.0",
+ "dim": 1536,
+ "eps": 1e-06,
+ "ffn_dim": 8960,
+ "freq_dim": 256,
+ "in_dim": 16,
+ "model_type": "t2v",
+ "num_heads": 12,
+ "num_layers": 30,
+ "out_dim": 16,
+ "text_len": 512
+}
diff --git a/Wan2.1-T2V-1.3B/examples/i2v_input.JPG b/Wan2.1-T2V-1.3B/examples/i2v_input.JPG
new file mode 100644
index 0000000000000000000000000000000000000000..8c7fabd943752179587eb717362db32ce1eb4800
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/examples/i2v_input.JPG
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:077e3d965090c9028c69c00931675f42e1acc815c6eb450ab291b3b72d211a8e
+size 250628
diff --git a/Wan2.1-T2V-1.3B/google/umt5-xxl/special_tokens_map.json b/Wan2.1-T2V-1.3B/google/umt5-xxl/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..14855e7052ffbb595057dfd791d293c1c940db2c
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/google/umt5-xxl/special_tokens_map.json
@@ -0,0 +1,308 @@
+{
+ "additional_special_tokens": [
+ "",
+ "",
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+ "",
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+ "",
+ "",
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+ "",
+ "",
+ "",
+ "",
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+ "",
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+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "",
+ "
+
+
+
+
+> The parameter settings for the tests presented in this table are as follows:
+> (1) For the 1.3B model on 8 GPUs, set `--ring_size 8` and `--ulysses_size 1`;
+> (2) For the 14B model on 1 GPU, use `--offload_model True`;
+> (3) For the 1.3B model on a single 4090 GPU, set `--offload_model True --t5_cpu`;
+> (4) For all testings, no prompt extension was applied, meaning `--use_prompt_extend` was not enabled.
+
+-------
+
+## Introduction of Wan2.1
+
+**Wan2.1** is designed on the mainstream diffusion transformer paradigm, achieving significant advancements in generative capabilities through a series of innovations. These include our novel spatio-temporal variational autoencoder (VAE), scalable training strategies, large-scale data construction, and automated evaluation metrics. Collectively, these contributions enhance the model’s performance and versatility.
+
+
+##### (1) 3D Variational Autoencoders
+We propose a novel 3D causal VAE architecture, termed **Wan-VAE** specifically designed for video generation. By combining multiple strategies, we improve spatio-temporal compression, reduce memory usage, and ensure temporal causality. **Wan-VAE** demonstrates significant advantages in performance efficiency compared to other open-source VAEs. Furthermore, our **Wan-VAE** can encode and decode unlimited-length 1080P videos without losing historical temporal information, making it particularly well-suited for video generation tasks.
+
+
+
+
+
+
+
+##### (2) Video Diffusion DiT
+
+**Wan2.1** is designed using the Flow Matching framework within the paradigm of mainstream Diffusion Transformers. Our model's architecture uses the T5 Encoder to encode multilingual text input, with cross-attention in each transformer block embedding the text into the model structure. Additionally, we employ an MLP with a Linear layer and a SiLU layer to process the input time embeddings and predict six modulation parameters individually. This MLP is shared across all transformer blocks, with each block learning a distinct set of biases. Our experimental findings reveal a significant performance improvement with this approach at the same parameter scale.
+
+
+
+
+
+
+| Model | Dimension | Input Dimension | Output Dimension | Feedforward Dimension | Frequency Dimension | Number of Heads | Number of Layers |
+|--------|-----------|-----------------|------------------|-----------------------|---------------------|-----------------|------------------|
+| 1.3B | 1536 | 16 | 16 | 8960 | 256 | 12 | 30 |
+| 14B | 5120 | 16 | 16 | 13824 | 256 | 40 | 40 |
+
+
+
+##### Data
+
+We curated and deduplicated a candidate dataset comprising a vast amount of image and video data. During the data curation process, we designed a four-step data cleaning process, focusing on fundamental dimensions, visual quality and motion quality. Through the robust data processing pipeline, we can easily obtain high-quality, diverse, and large-scale training sets of images and videos.
+
+
+
+
+##### Comparisons to SOTA
+We compared **Wan2.1** with leading open-source and closed-source models to evaluate the performace. Using our carefully designed set of 1,035 internal prompts, we tested across 14 major dimensions and 26 sub-dimensions. Then we calculated the total score through a weighted average based on the importance of each dimension. The detailed results are shown in the table below. These results demonstrate our model's superior performance compared to both open-source and closed-source models.
+
+
+
+
+## Citation
+If you find our work helpful, please cite us.
+
+```
+@article{wan2.1,
+ title = {Wan: Open and Advanced Large-Scale Video Generative Models},
+ author = {Wan Team},
+ journal = {},
+ year = {2025}
+}
+```
+
+## License Agreement
+The models in this repository are licensed under the Apache 2.0 License. We claim no rights over the your generate contents, granting you the freedom to use them while ensuring that your usage complies with the provisions of this license. You are fully accountable for your use of the models, which must not involve sharing any content that violates applicable laws, causes harm to individuals or groups, disseminates personal information intended for harm, spreads misinformation, or targets vulnerable populations. For a complete list of restrictions and details regarding your rights, please refer to the full text of the [license](LICENSE.txt).
+
+
+## Acknowledgements
+
+We would like to thank the contributors to the [SD3](https://huggingface.co/stabilityai/stable-diffusion-3-medium), [Qwen](https://huggingface.co/Qwen), [umt5-xxl](https://huggingface.co/google/umt5-xxl), [diffusers](https://github.com/huggingface/diffusers) and [HuggingFace](https://huggingface.co) repositories, for their open research.
+
+
+
+## Contact Us
+If you would like to leave a message to our research or product teams, feel free to join our [Discord](https://discord.gg/p5XbdQV7) or [WeChat groups](https://gw.alicdn.com/imgextra/i2/O1CN01tqjWFi1ByuyehkTSB_!!6000000000015-0-tps-611-1279.jpg)!
\ No newline at end of file
diff --git a/Wan2.1-T2V-1.3B/config.json b/Wan2.1-T2V-1.3B/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d203bef600b2f3c64fe1f5f53d70a2087f4ccd2f
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/config.json
@@ -0,0 +1,14 @@
+{
+ "_class_name": "WanModel",
+ "_diffusers_version": "0.30.0",
+ "dim": 1536,
+ "eps": 1e-06,
+ "ffn_dim": 8960,
+ "freq_dim": 256,
+ "in_dim": 16,
+ "model_type": "t2v",
+ "num_heads": 12,
+ "num_layers": 30,
+ "out_dim": 16,
+ "text_len": 512
+}
diff --git a/Wan2.1-T2V-1.3B/examples/i2v_input.JPG b/Wan2.1-T2V-1.3B/examples/i2v_input.JPG
new file mode 100644
index 0000000000000000000000000000000000000000..8c7fabd943752179587eb717362db32ce1eb4800
--- /dev/null
+++ b/Wan2.1-T2V-1.3B/examples/i2v_input.JPG
@@ -0,0 +1,3 @@
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