cleanup LDM

models/vision/latent_diffusion/modeling_latent_diffusion.py

@@ -863,6 +863,7 @@ class LatentDiffusion(DiffusionPipeline):
         super().__init__()
         self.register_modules(vqvae=vqvae, bert=bert, tokenizer=tokenizer, unet=unet, noise_scheduler=noise_scheduler)
 
+    @torch.no_grad()
     def __call__(self, prompt, batch_size=1, generator=None, torch_device=None, eta=0.0, guidance_scale=1.0, num_inference_steps=50):
         # eta corresponds to η in paper and should be between [0, 1]
 
@@ -873,6 +874,7 @@ class LatentDiffusion(DiffusionPipeline):
         self.vqvae.to(torch_device)
         self.bert.to(torch_device)
         
+        # get unconditional embeddings for classifier free guidence
         if guidance_scale != 1.0:
             uncond_input = self.tokenizer([""], padding="max_length", max_length=77, return_tensors='pt').to(torch_device)
             uncond_embeddings = self.bert(uncond_input.input_ids)[0]
@@ -901,19 +903,23 @@ class LatentDiffusion(DiffusionPipeline):
         # - pred_image_direction -> "direction pointingc to x_t"
         # - pred_prev_image -> "x_t-1"
         for t in tqdm.tqdm(reversed(range(num_inference_steps)), total=num_inference_steps):
-            # 1. predict noise residual
+            # guidance_scale of 1 means no guidance
             if guidance_scale == 1.0:
-                timesteps = torch.tensor([inference_step_times[t]] * image.shape[0], device=torch_device)
-                context = text_embedding
                 image_in = image
+                context = text_embedding
+                timesteps = torch.tensor([inference_step_times[t]] * image.shape[0], device=torch_device)
             else:
+                # for classifier free guidance, we need to do two forward passes
+                # here we concanate embedding and unconditioned embedding in a single batch 
+                # to avoid doing two forward passes
                 image_in = torch.cat([image] * 2)
-                timesteps = torch.tensor([inference_step_times[t]] * image.shape[0], device=torch_device)
                 context = torch.cat([uncond_embeddings, text_embedding])
+                timesteps = torch.tensor([inference_step_times[t]] * image.shape[0], device=torch_device)
+
+            # 1. predict noise residual
+            pred_noise_t = self.unet(image_in, timesteps, context=context)
             
-            with torch.no_grad():
-                pred_noise_t = self.unet(image_in, timesteps, context=context)
-            
+            # perform guidance
             if guidance_scale != 1.0:
                 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)
@@ -933,18 +939,15 @@ class LatentDiffusion(DiffusionPipeline):
             # "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(η)" -> see formula (16)
+            # 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
 
-            # Fourth: Compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            # 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
 
-            # Fifth: Compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
+            # 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
@@ -958,9 +961,9 @@ class LatentDiffusion(DiffusionPipeline):
             # 6. Set current image to prev_image: x_t -> x_t-1
             image = prev_image
 
+        # scale and decode image with vae
         image = 1 /  0.18215 * image
         image = self.vqvae.decode(image)
         image = torch.clamp((image+1.0)/2.0, min=0.0, max=1.0)
-        image = 255. * image
 
         return image