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ddpm-church/README.md

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+---
+license: apache-2.0
+tags:
+- pytorch
+- diffusers
+- unconditional-image-generation
+---
+
+# Denoising Diffusion Probabilistic Models (DDPM)
+
+**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
+
+**Authors**: Jonathan Ho, Ajay Jain, Pieter Abbeel
+
+**Abstract**:
+
+*We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.*
+
+## Inference
+
+**DDPM** models can use *discrete noise schedulers* such as:
+
+- [scheduling_ddpm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddpm.py)
+- [scheduling_ddim](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)
+- [scheduling_pndm](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py)
+
+for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest.
+For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.
+
+See the following code:
+
+```python
+# !pip install diffusers
+from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
+
+model_id = "google/ddpm-church-256"
+
+# load model and scheduler
+ddpm = DDPMPipeline.from_pretrained(model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
+
+# run pipeline in inference (sample random noise and denoise)
+image = ddpm().images[0]
+
+
+# save image
+image.save("ddpm_generated_image.png")
+```
+
+For more in-detail information, please have a look at the [official inference example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb)
+
+## Training
+
+If you want to train your own model, please have a look at the [official training example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+
+## Samples
+1. ![sample_1](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_0.png)
+2. ![sample_2](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_1.png)
+3. ![sample_3](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_2.png)
+4. ![sample_4](https://huggingface.co/google/ddpm-church-256/resolve/main/images/generated_image_3.png)

ddpm-church/config.json

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+{
+  "_class_name": "UNet2DModel",
+  "_diffusers_version": "0.0.4",
+  "act_fn": "silu",
+  "attention_head_dim": null,
+  "block_out_channels": [
+    128,
+    128,
+    256,
+    256,
+    512,
+    512
+  ],
+  "center_input_sample": false,
+  "down_block_types": [
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "DownBlock2D",
+    "AttnDownBlock2D",
+    "DownBlock2D"
+  ],
+  "downsample_padding": 0,
+  "flip_sin_to_cos": false,
+  "freq_shift": 1,
+  "in_channels": 3,
+  "layers_per_block": 2,
+  "mid_block_scale_factor": 1,
+  "norm_eps": 1e-06,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 256,
+  "time_embedding_type": "positional",
+  "up_block_types": [
+    "UpBlock2D",
+    "AttnUpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D",
+    "UpBlock2D"
+  ]
+}

ddpm-church/diffusion_pytorch_model.bin

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+oid sha256:3111c176c9848d8d9b91d6fb8e2aa2945c220845aced8f47efb44f9deadd412c
+size 454853117

ddpm-church/model_index.json

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+{
+    "_class_name": "DDPMPipeline",
+    "scheduler": [
+      "diffusers",
+      "DDPMScheduler"
+    ],
+    "unet": [
+      "diffusers",
+      "UNet2DModel"
+    ]
+  }

ddpm-church/scheduler_config.json

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+{
+  "_class_name": "DDPMScheduler",
+  "_diffusers_version": "0.1.1",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "clip_sample": true,
+  "num_train_timesteps": 1000,
+  "trained_betas": null,
+  "variance_type": "fixed_small"
+}