fix LatentDiffusion

src/diffusers/pipelines/pipeline_latent_diffusion.py

@@ -900,11 +900,12 @@ class LatentDiffusion(DiffusionPipeline):
         num_trained_timesteps = self.noise_scheduler.timesteps
         inference_step_times = range(0, num_trained_timesteps, num_trained_timesteps // num_inference_steps)
 
-        image = torch.randn(
+        image = self.noise_scheduler.sample_noise(
             (batch_size, self.unet.in_channels, self.unet.image_size, self.unet.image_size),
+            device=torch_device,
             generator=generator,
         )
-        image = image.to(torch_device)
+
         # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
         # Ideally, read DDIM paper in-detail understanding
 
@@ -937,46 +938,17 @@ class LatentDiffusion(DiffusionPipeline):
                 pred_noise_t_uncond, pred_noise_t = pred_noise_t.chunk(2)
                 pred_noise_t = pred_noise_t_uncond + guidance_scale * (pred_noise_t - pred_noise_t_uncond)
 
-            # 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 = 1 - alpha_prod_t
-            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()
-
-            # Second: Compute variance: "sigma_t(η)" -> see formula (16)
-            # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
-            std_dev_t = (beta_prod_t_prev / beta_prod_t).sqrt() * (1 - alpha_prod_t / alpha_prod_t_prev).sqrt()
-            std_dev_t = eta * std_dev_t
-
-            # Third: 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
-
-            # Forth: Compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-            pred_prev_image = alpha_prod_t_prev.sqrt() * pred_original_image + pred_image_direction
-
-            # 5. Sample x_t-1 image optionally if η > 0.0 by adding noise to pred_prev_image
-            # Note: eta = 1.0 essentially corresponds to DDPM
-            if eta > 0.0:
-                noise = torch.randn(
-                    (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
-                    generator=generator,
-                )
-                noise = noise.to(torch_device)
-                prev_image = pred_prev_image + std_dev_t * noise
-            else:
-                prev_image = pred_prev_image
+            # 2. predict previous mean of image x_t-1
+            pred_prev_image = self.noise_scheduler.step(pred_noise_t, image, t, num_inference_steps, eta)
+
+            # 3. optionally sample variance
+            variance = 0
+            if eta > 0:
+                noise = self.noise_scheduler.sample_noise(image.shape, device=image.device, generator=generator)
+                variance = self.noise_scheduler.get_variance(t, num_inference_steps).sqrt() * eta * noise
 
-            # 6. Set current image to prev_image: x_t -> x_t-1
-            image = prev_image
+            # 4. set current image to prev_image: x_t -> x_t-1
+            image = pred_prev_image + variance
 
         # scale and decode image with vae
         image = 1 / 0.18215 * image