Upload folder using huggingface_hub

README.md

@@ -0,0 +1,130 @@
+---
+license: apache-2.0
+tags:
+- diffusers
+- image-generation
+- unconditional-image-generation
+- diffusion-models
+- ddpm
+- ema
+- cifar10
+datasets:
+- cifar10
+pipeline_tag: image-generation
+---
+
+# DDPM EMA CIFAR-10
+
+## Model Description
+
+This model is an EMA (Exponential Moving Average) version of the DDPM (Denoising Diffusion Probabilistic Models) trained on CIFAR-10 dataset. It's based on the original [DDPM](https://github.com/hojonathanho/diffusion) model but uses exponential moving averages of model parameters for improved stability and quality.
+
+**Model Type**: Unconditional Image Generation  
+**Architecture**: DDPM  
+**Training Dataset**: CIFAR-10  
+**Image Resolution**: 32×32 pixels  
+**License**: Apache-2.0
+
+## Model Details
+
+This model implements the DDPM approach described in the paper ["Denoising Diffusion Probabilistic Models"](https://arxiv.org/abs/2006.11239) by Jonathan Ho, Ajay Jain, and Pieter Abbeel. The EMA version provides more stable training and often better sample quality by maintaining exponentially weighted averages of model parameters.
+
+### Key Features:
+- **EMA Training**: Uses exponential moving averages for improved model stability
+- **High Quality Generation**: Produces high-quality 32×32 pixel images
+- **CIFAR-10 Classes**: Generates images from all 10 CIFAR-10 categories (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck)
+- **Diffusers Compatible**: Fully compatible with Hugging Face Diffusers library
+
+## Usage
+
+### Basic Usage
+
+```python
+from diffusers import DDPMPipeline
+
+# Load the model
+model_id = "FrankCCCCC/ddpm-ema-cifar10"  # Replace with actual repo ID
+pipeline = DDPMPipeline.from_pretrained(model_id)
+
+# Generate an image
+image = pipeline().images[0]
+image.save("generated_cifar10.png")
+```
+
+### Generate Multiple Images
+
+```python
+from diffusers import DDPMPipeline
+
+pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/ddpm-ema-cifar10")
+
+# Generate batch of images
+images = pipeline(batch_size=4).images
+
+# Save images
+for i, image in enumerate(images):
+    image.save(f"generated_cifar10_{i}.png")
+```
+
+### Advanced Usage with Different Schedulers
+
+```python
+from diffusers import DDPMPipeline, DDIMScheduler, PNDMScheduler
+
+pipeline = DDPMPipeline.from_pretrained("FrankCCCCC/ddpm-ema-cifar10")
+
+# Use DDIM scheduler for faster inference
+ddim_scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
+pipeline.scheduler = ddim_scheduler
+
+# Generate with fewer inference steps
+image = pipeline(num_inference_steps=50).images[0]
+image.save("generated_ddim.png")
+```
+
+## Training Details
+
+- **Dataset**: CIFAR-10 (50,000 training images, 32×32 RGB)
+- **Training Procedure**: EMA version of standard DDPM training
+- **Model Architecture**: U-Net
+- **Parameter Updates**: Exponential moving averages applied to model weights
+- **Training Objective**: Variational lower bound on negative log likelihood
+
+## Model Performance
+
+The EMA version typically provides:
+- **Improved Stability**: More consistent training dynamics
+- **Better Sample Quality**: Often achieves better FID scores compared to non-EMA versions
+- **Reduced Mode Collapse**: More diverse sample generation
+
+Expected performance metrics (approximate):
+- **FID Score**: 
+  - 4.5216 (50K ``.png`` Samples are generated by the DDIM with 100 sampling steps)
+  - 6.5398 (10K ``.png`` Samples are generated by the DDIM with 100 sampling steps)
+
+## Inference Examples
+
+The model generates diverse samples across all CIFAR-10 categories:
+- Airplanes, automobiles, birds, cats, deer
+- Dogs, frogs, horses, ships, trucks
+
+All generated images are 32×32 pixels in RGB format.
+
+## Citation
+
+If you use this model, please cite the original DDPM paper:
+
+```bibtex
+@article{ho2020denoising,
+  title={Denoising Diffusion Probabilistic Models},
+  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  journal={Advances in Neural Information Processing Systems},
+  volume={33},
+  pages={6840--6851},
+  year={2020}
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 License.

model_index.json

@@ -0,0 +1,12 @@
+{
+  "_class_name": "DDPMPipeline",
+  "_diffusers_version": "0.34.0",
+  "scheduler": [
+    "diffusers",
+    "DDPMScheduler"
+  ],
+  "unet": [
+    "diffusers",
+    "UNet2DModel"
+  ]
+}

scheduler/scheduler_config.json

@@ -0,0 +1,19 @@
+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.34.0",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "clip_sample_range": 1.0,
+  "dynamic_thresholding_ratio": 0.995,
+  "num_train_timesteps": 1000,
+  "prediction_type": "epsilon",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "leading",
+  "trained_betas": null,
+  "variance_type": "fixed_large"
+}

unet/config.json

@@ -0,0 +1,47 @@
+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.34.0",
+  "act_fn": "silu",
+  "add_attention": true,
+  "attention_head_dim": null,
+  "attn_norm_num_groups": null,
+  "block_out_channels": [
+    128,
+    256,
+    256,
+    256
+  ],
+  "center_input_sample": false,
+  "class_embed_type": null,
+  "down_block_types": [
+    "DownBlock2D",
+    "AttnDownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 0,
+  "downsample_type": "conv",
+  "dropout": 0.0,
+  "flip_sin_to_cos": false,
+  "freq_shift": 1,
+  "in_channels": 3,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "mid_block_type": "UNetMidBlock2D",
+  "norm_eps": 1e-06,
+  "norm_num_groups": 32,
+  "num_class_embeds": null,
+  "num_train_timesteps": null,
+  "out_channels": 3,
+  "resnet_time_scale_shift": "default",
+  "sample_size": 32,
+  "time_embedding_dim": null,
+  "time_embedding_type": "positional",
+  "up_block_types": [
+    "UpBlock2D",
+    "UpBlock2D",
+    "AttnUpBlock2D",
+    "UpBlock2D"
+  ],
+  "upsample_type": "conv"
+}

unet/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fd1376952ca4403185abb572190bdc54797444b41d98dd26ee0c1e6fc970c55
+size 143020060