Update

model_index.json

@@ -1,11 +1,12 @@
 {
-  "_class_name": "DDPM",
-  "noise_scheduler": [
-    "diffusers",
-    "GaussianDDPMScheduler"
-  ],
-  "unet": [
-    "diffusers",
-    "UNetModel"
-  ]
-}
+    "_class_name": "DDPM",
+    "_module": "modeling_ddpm.py",
+    "noise_scheduler": [
+      "diffusers",
+      "GaussianDDPMScheduler"
+    ],
+    "unet": [
+      "diffusers",
+      "UNetModel"
+    ]
+  }

modeling_ddpm.py

@@ -0,0 +1,61 @@
+# 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 DDPM(DiffusionPipeline):
+
+    modeling_file = "modeling_ddpm.py"
+
+    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):
+        if torch_device is None:
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.unet.to(torch_device)
+        # 1. Sample gaussian noise
+        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(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
+            # i) define coefficients for time step t
+            clip_image_coeff = 1 / torch.sqrt(self.noise_scheduler.get_alpha_prod(t))
+            clip_noise_coeff = torch.sqrt(1 / self.noise_scheduler.get_alpha_prod(t) - 1)
+            image_coeff = (1 - self.noise_scheduler.get_alpha_prod(t - 1)) * torch.sqrt(self.noise_scheduler.get_alpha(t)) / (1 - self.noise_scheduler.get_alpha_prod(t))
+            clip_coeff = torch.sqrt(self.noise_scheduler.get_alpha_prod(t - 1)) * self.noise_scheduler.get_beta(t) / (1 - self.noise_scheduler.get_alpha_prod(t))
+
+            # ii) predict noise residual
+            with torch.no_grad():
+                noise_residual = self.unet(image, t)
+
+            # iii) compute predicted image from residual
+            # See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
+            pred_mean = clip_image_coeff * image - clip_noise_coeff * noise_residual
+            pred_mean = torch.clamp(pred_mean, -1, 1)
+            prev_image = clip_coeff * pred_mean + image_coeff * image
+
+            # iv) sample variance
+            prev_variance = self.noise_scheduler.sample_variance(t, prev_image.shape, device=torch_device, generator=generator)
+
+            # v) sample  x_{t-1} ~ N(prev_image, prev_variance)
+            sampled_prev_image = prev_image + prev_variance
+            image = sampled_prev_image
+
+        return image