Files upload

ip_ckpt/README.md

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+---
+license: apache-2.0
+---
+<div align="center">
+
+<h1> lyraDiff: An Out-of-the-box Acceleration Engine for Diffusion and DiT Models</h1>
+
+</div> 
+
+
+`lyraDiff` introduces a **recompilation-free** inference engine for Diffusion and DiT models, achieving **state-of-the-art speed**, **extensive model support**, and **pixel-level image consistency**.
+
+## Highlights
+- **State-of-the-art Inference Speed**: `lyraDiff` utilizes multiple techniques to achieve up to **6.1x** speedup of the model inference, including **Quantization**, **Fused GEMM Kernels**, **Flash Attention**, and **NHWC & Fused GroupNorm**.
+- **Memory Efficiency**: `lyraDiff` utilizes buffer-based DRAM reuse strategy and multiple types of quantizations (FP8/INT8/INT4) to save **10-40%** of DRAM usage.
+- **Extensive Model Support**: `lyraDiff` supports a wide range of top Generative/SR models such as **SD1.5, SDXL, FLUX, S3Diff, etc.**, and those most commonly used plugins such as **LoRA, ControlNet and Ip-Adapter**.
+- **Zero Compilation Deployment**: Unlike **TensorRT** or **AITemplate**, which takes minutes to compile, `lyraDiff` eliminates runtime recompilation overhead even with model inputs of dynamic shapes.
+- **Image Gen Consistency**: The outputs of `lyraDiff` are aligned with the ones of [HF diffusers](https://github.com/huggingface/diffusers) at the pixel level, even under LoRA switch in quantization mode.
+- **Fast Plugin Hot-swap**: `lyraDiff` provides **Super Fast Model Hot-swap for ControlNet and LoRA** which can hugely benefit a real-time image gen service.
+
+## Usage
+
+`lyraDiff-IP-Adapters` is converted from the standard [IP-Adapter](https://huggingface.co/h94/IP-Adapter) weights using this [script](https://github.com/TMElyralab/lyraDiff/blob/main/lyradiff/convert_model_scripts/convert_ipadapter.py) to be compatiable with [lyraDiff](https://github.com/TMElyralab/lyraDiff), and contains both SD1.5 and SDXL version of converted IP-Adapter
+
+We provide a reference implementation of lyraDiff version of SD1.5/SDXL, as well as sampling code, in a dedicated [github repository](https://github.com/TMElyralab/lyraDiff).
+
+### Example
+We provide minimal [script](https://github.com/TMElyralab/lyraDiff/blob/main/examples/SDXL/ipadapter_demo.py) for running SDXL models + IP-Adapter with lyraDiff as follows:
+
+```python
+import torch
+import time
+import sys, os
+from diffusers import StableDiffusionXLPipeline
+from lyradiff.lyradiff_model.module.lyradiff_ip_adapter import LyraIPAdapter
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextModelWithProjection
+from lyradiff.lyradiff_model.lyradiff_unet_model import LyraDiffUNet2DConditionModel
+from lyradiff.lyradiff_model.lyradiff_vae_model import LyraDiffVaeModel
+from diffusers import EulerAncestralDiscreteScheduler
+from PIL import Image
+from diffusers.utils import load_image
+import GPUtil
+
+model_path = "/path/to/sdxl/model/"
+vae_model_path = "/path/to/sdxl/sdxl-vae-fp16-fix"
+
+text_encoder = CLIPTextModel.from_pretrained(model_path, subfolder="text_encoder").to(torch.float16).to(torch.device("cuda"))
+text_encoder_2 = CLIPTextModelWithProjection.from_pretrained(model_path, subfolder="text_encoder_2").to(torch.float16).to(torch.device("cuda"))
+tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer")
+tokenizer_2 = CLIPTokenizer.from_pretrained( model_path, subfolder="tokenizer_2")
+
+unet = LyraDiffUNet2DConditionModel(is_sdxl=True)
+vae = LyraDiffVaeModel(scaling_factor=0.13025, is_upcast=False)
+
+unet.load_from_diffusers_model(os.path.join(model_path, "unet"))
+vae.load_from_diffusers_model(vae_model_path)
+
+scheduler = EulerAncestralDiscreteScheduler.from_pretrained(model_path, subfolder="scheduler", timestep_spacing="linspace")
+
+pipe = StableDiffusionXLPipeline(
+    vae=vae,
+    unet=unet,
+    text_encoder=text_encoder,
+    text_encoder_2=text_encoder_2,
+    tokenizer=tokenizer,
+    tokenizer_2=tokenizer_2,
+    scheduler=scheduler
+)
+
+ip_ckpt = "/path/to/sdxl/ip_ckpt/ip-adapter-plus_sdxl_vit-h.bin"
+image_encoder_path = "/path/to/sdxl/ip_ckpt/image_encoder"
+
+# Create LyraIPAdapter
+ip_adapter = LyraIPAdapter(unet_model=unet.model, sdxl=True, device=torch.device("cuda"), ip_ckpt=ip_ckpt, ip_plus=True, image_encoder_path=image_encoder_path, num_ip_tokens=16, ip_projection_dim=1024)
+
+# load ip_adapter image
+ip_image = load_image("https://cdn-uploads.huggingface.co/production/uploads/6461b412846a6c8c8305319d/8U6yNHTPLaOC3gIWJZWGL.png")
+ip_scale = 0.5
+
+# get ip image embedding and pass it to the pipeline
+ip_image_embedding = [ip_adapter.get_image_embeds_lyradiff(ip_image)['ip_hidden_states']]
+# unet set ip adapter scale in unet model obj, since we cannot set ip_adapter_scale through diffusers pipeline
+unet.set_ip_adapter_scale(ip_scale)
+
+for i in range(3):
+    generator = torch.Generator("cuda").manual_seed(123)
+    start = time.perf_counter()
+    images = pipe(prompt="a beautiful girl, cartoon style",
+                   height=1024,
+                   width=1024,
+                   num_inference_steps=20,
+                   num_images_per_prompt=1,
+                   guidance_scale=7.5,
+                   negative_prompt="NSFW",
+                   generator=torch.Generator("cuda").manual_seed(123),
+                   ip_adapter_image_embeds=ip_image_embedding
+                   )[0]
+    images[0].save(f"sdxl_ip_{i}.png")
+```
+
+
+## Citation
+``` bibtex
+@Misc{lyraDiff_2025,
+  author =       {Kangjian Wu, Zhengtao Wang, Yibo Lu, Haoxiong Su, Sa Xiao, Qiwen Mao, Mian Peng, Bin Wu, Wenjiang Zhou},
+  title =        {lyraDiff: Accelerating Diffusion Models with best flexibility},
+  howpublished = {\url{https://github.com/TMElyralab/lyraDiff}},
+  year =         {2025}
+}

ip_ckpt/lyra_tran/models/image_encoder/config.json

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+{
+  "_name_or_path": "./image_encoder",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
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+  "dropout": 0.0,
+  "hidden_act": "gelu",
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+  "patch_size": 14,
+  "projection_dim": 1024,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.0.dev0"
+}

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ip_ckpt/lyra_tran/sdxl_models/image_encoder/config.json

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ip_ckpt/lyra_tran/sdxl_models/image_encoder/model.safetensors

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sdxl-vae-fp16-fix/config.json

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sdxl_unet/config.json

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