Upload modeling_ddim.py

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.
+
+
+import torch
+
+import tqdm
+from diffusers import DiffusionPipeline
+
+
+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)
+
+        # Sample gaussian noise to begin loop
+        image = self.noise_scheduler.sample_noise(
+            (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
+            device=torch_device,
+            generator=generator,
+        )
+
+        # See formulas (9), (10) and (7) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+        # Ideally, read DDIM paper in-detail understanding
+
+        # Notation (<variable name> -> <name in paper>
+        # - pred_noise_t -> e_theta(x_t, t)
+        # - pred_original_image -> f_theta(x_t, t) or x_0
+        # - std_dev_t -> sigma_t
+
+        for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
+            # 1. predict noise residual
+            with torch.no_grad():
+                pred_noise_t = self.unet(image, inference_step_times[t])
+
+            # 2. 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
+
+            # 3. compute alphas, betas
+            alpha_prod_t = self.noise_scheduler.get_alpha_prod(train_step)
+            alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(prev_train_step)
+            beta_prod_t_sqrt = (1 - alpha_prod_t).sqrt()
+            beta_prod_t_prev_sqrt = (1 - alpha_prod_t_prev).sqrt()
+
+            # 4. Compute predicted previous image from predicted noise
+            # First: compute predicted original image from predicted noise also called
+            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_original_image = (image - beta_prod_t_sqrt * pred_noise_t) / alpha_prod_t.sqrt()
+            # Second: Clip "predicted x_0"
+            pred_original_image = torch.clamp(pred_original_image, -1, 1)
+            # Third: Compute variance: "sigma_t" -> see 
+#            std_dev_t = (1 - alpha_prod_t / alpha_prod_t_prev).sqrt() * beta_prod_t_prev_sqrt / beta_prod_t_sqrt
+            std_dev_t = (1 - alpha_prod_t / alpha_prod_t_prev).sqrt()
+            std_dev_t = std_dev_t * eta
+            # Fourth: Compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            pred_image_direction = (1 - alpha_prod_t_prev - std_dev_t**2).sqrt() * pred_noise_t
+
+            # Fourth: Compute outer formula (DDIM formula)
+            pred_prev_image = alpha_prod_t_prev.sqrt() * pred_original_image + pred_image_direction 
+
+            # 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)
+                prev_image = pred_prev_image + std_dev_t * noise
+            else:
+                prev_image = pred_prev_image
+
+            # Set current image to prev_image: x_t -> x_t-1
+            image = prev_image
+
+        return image