up

README.md

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+---
+tags:
+- ddpm_diffusion
+---
+
+# Denoising Diffusion Probabilistic Models (DDPM)
+
+**Paper**: [Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2006.11239)
+
+**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.*
+
+## Usage
+
+```python
+# !pip install diffusers
+from diffusers import DiffusionPipeline
+import PIL.Image
+import numpy as np
+
+model_id = "fusing/ddpm-celeba-hq"
+
+# load model and scheduler
+ddpm = DiffusionPipeline.from_pretrained(model_id)
+
+# run pipeline in inference (sample random noise and denoise)
+image = ddpm()
+
+# process image to PIL
+image_processed = image.cpu().permute(0, 2, 3, 1)
+image_processed = (image_processed + 1.0) * 127.5
+image_processed = image_processed.numpy().astype(np.uint8)
+image_pil = PIL.Image.fromarray(image_processed[0])
+
+# save image
+image_pil.save("test.png")
+```
+
+## Samples
+
+1. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-celeba-hq/image_0.png)
+2. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-celeba-hq/image_1.png)
+3. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-celeba-hq/image_2.png)
+4. ![sample_1](https://huggingface.co/datasets/patrickvonplaten/images/resolve/main/hf/ddpm-celeba-hq/image_3.png)

config.json

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+{
+  "_class_name": "UNetModel",
+  "attn_resolutions": [
+    16
+  ],
+  "ch": 128,
+  "ch_mult": [
+    1,
+    1,
+    2,
+    2,
+    4,
+    4
+  ],
+  "dropout": 0.0,
+  "in_channels": 3,
+  "name_or_path": "fusing/ddpm-celeba-hq",
+  "num_res_blocks": 2,
+  "out_ch": 3,
+  "resamp_with_conv": true,
+  "resolution": 256
+}

diffusion_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b752ba96bcbbf21db1bee1d1bc3d1bb6db541d71fdd303e2a4a127ec0ea5abef
+size 454843005

model_index.json

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+{
+    "_class_name": "DDIM",
+    "_module": "modeling_ddim.py",
+    "noise_scheduler": [
+      "diffusers",
+      "GaussianDDPMScheduler"
+    ],
+    "unet": [
+      "diffusers",
+      "UNetModel"
+    ]
+  }

modeling_ddim.py

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+# Copyright 2022 The HuggingFace 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 diffusers import DiffusionPipeline
+import tqdm
+import torch
+
+
+class DDIM(DiffusionPipeline):
+
+    def __init__(self, unet, noise_scheduler):
+        super().__init__()
+        self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
+
+    def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
+        # eta corresponds to η in paper and should be between [0, 1]
+        if torch_device is None:
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        num_trained_timesteps = self.noise_scheduler.num_timesteps
+        inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
+
+        self.unet.to(torch_device)
+        image = self.noise_scheduler.sample_noise((batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution), device=torch_device, generator=generator)
+
+        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
+            # get actual t and t-1
+            train_step = inference_step_times[t]
+            prev_train_step = inference_step_times[t - 1] if t > 0 else - 1
+
+            # compute alphas
+            alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
+            alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
+            alpha_prod_t_rsqrt = 1 / alpha_prod_t.sqrt()
+            alpha_prod_t_prev_rsqrt = 1 / alpha_prod_t_prev.sqrt()
+            beta_prod_t_sqrt = (1 - alpha_prod_t).sqrt()
+            beta_prod_t_prev_sqrt = (1 - alpha_prod_t_prev).sqrt()
+
+            # compute relevant coefficients
+            coeff_1 = (alpha_prod_t_prev - alpha_prod_t).sqrt() * alpha_prod_t_prev_rsqrt * beta_prod_t_prev_sqrt / beta_prod_t_sqrt * eta
+            coeff_2 = ((1 - alpha_prod_t_prev) - coeff_1 ** 2).sqrt()
+
+            # model forward
+            with torch.no_grad():
+                noise_residual = self.unet(image, train_step)
+
+            # predict mean of prev image
+            pred_mean = alpha_prod_t_rsqrt * (image - beta_prod_t_sqrt * noise_residual)
+            pred_mean = (1 / alpha_prod_t_prev_rsqrt) * pred_mean + coeff_2 * noise_residual
+
+            # if eta > 0.0 add noise. Note eta = 1.0 essentially corresponds to DDPM
+            if eta > 0.0:
+                noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
+                image = pred_mean + coeff_1 * noise
+            else:
+                image = pred_mean
+
+        return image

scheduler_config.json

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+{
+  "_class_name": "GaussianDDPMScheduler",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "timesteps": 1000,
+  "variance_type": "fixed_small"
+}