make code cleaner

models/vision/ddim/modeling_ddim.py

@@ -42,7 +42,7 @@ class DDIM(DiffusionPipeline):
             generator=generator,
         )
 
-        # See formulas (9), (10) and (7) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
+        # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding
 
         # Notation (<variable name> -> <name in paper>
@@ -68,7 +68,6 @@ class DDIM(DiffusionPipeline):
             beta_prod_t_prev = (1 - alpha_prod_t_prev)
 
             # 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()

models/vision/ddpm/modeling_ddpm.py

@@ -41,33 +41,45 @@ class DDPM(DiffusionPipeline):
         for t in tqdm.tqdm(reversed(range(len(self.noise_scheduler))), total=len(self.noise_scheduler)):
             # 1. predict noise residual
             with torch.no_grad():
-                noise_residual = self.unet(image, t)
+                pred_noise_t = self.unet(image, t)
 
             # 2. compute alphas, betas
             alpha_prod_t = self.noise_scheduler.get_alpha_prod(t)
             alpha_prod_t_prev = self.noise_scheduler.get_alpha_prod(t - 1)
-            beta_prod_t = 1 - alpha_prod_t
-            beta_prod_t_prev = 1 - alpha_prod_t_prev
+            beta_prod_t = (1 - alpha_prod_t)
+            beta_prod_t_prev = (1 - alpha_prod_t_prev)
 
             # 3. compute predicted image from residual
-            # See 2nd formula at https://github.com/hojonathanho/diffusion/issues/5#issue-896554416 for comparison
-            # First: Compute inner formula
-            pred_mean = (1 / alpha_prod_t.sqrt()) * (image - beta_prod_t.sqrt() * noise_residual)
-            # Second: Clip
-            pred_mean = torch.clamp(pred_mean, -1, 1)
-            # Third: Compute outer coefficients
-            pred_mean_coeff = (alpha_prod_t_prev.sqrt() * self.noise_scheduler.get_beta(t)) / beta_prod_t
-            image_coeff = (beta_prod_t_prev * self.noise_scheduler.get_alpha(t).sqrt()) / beta_prod_t 
-            # Fourth: Compute outer formula
-            prev_image = pred_mean_coeff * pred_mean + image_coeff * image
-
-            # 4. sample variance
-            prev_variance = self.noise_scheduler.sample_variance(
-                t, prev_image.shape, device=torch_device, generator=generator
-            )
+            # First: compute predicted original image from predicted noise also called
+            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.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 coefficients for pred_original_image x_0 and current image x_t
+            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+            pred_original_image_coeff = (alpha_prod_t_prev.sqrt() * self.noise_scheduler.get_beta(t)) / beta_prod_t
+            current_image_coeff = self.noise_scheduler.get_alpha(t).sqrt() * beta_prod_t_prev / beta_prod_t
+            # Fourth: Compute predicted previous image µ_t
+            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+            pred_prev_image = pred_original_image_coeff * pred_original_image + current_image_coeff * image
+
+            # 5. For t > 0, compute predicted variance βt (see formala (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
+            # and sample from it to get previous image
+            # x_{t-1} ~ N(pred_prev_image, variance) == add variane to pred_image
+            if t > 0:
+                variance = ((1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.noise_scheduler.get_beta(t)).sqrt()
+                noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
+                sampled_variance = variance * noise
+#                sampled_variance = self.noise_scheduler.sample_variance(
+#                    t, pred_prev_image.shape, device=torch_device, generator=generator
+#                )
+                prev_image = pred_prev_image + sampled_variance
+            else:
+                prev_image = pred_prev_image
 
-            # 5. sample  x_{t-1} ~ N(prev_image, prev_variance) = add variance to predicted image
-            sampled_prev_image = prev_image + prev_variance
-            image = sampled_prev_image
+            # 6. Set current image to prev_image: x_t -> x_t-1
+            image = prev_image
 
         return image