1 files changed, 26 insertions, 23 deletions

diff --git a/pipelines/stable_diffusion/vlpn_stable_diffusion.py b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
index 242be29..2251848 100644
--- a/pipelines/stable_diffusion/vlpn_stable_diffusion.py
+++ b/pipelines/stable_diffusion/vlpn_stable_diffusion.py
@@ -295,16 +295,14 @@ class VlpnStableDiffusion(DiffusionPipeline):
 
     def get_timesteps(self, num_inference_steps, strength, device):
         # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
+        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
 
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        t_start = max(num_inference_steps - init_timestep, 0)
         timesteps = self.scheduler.timesteps[t_start:]
 
         timesteps = timesteps.to(device)
 
-        return timesteps
+        return timesteps, num_inference_steps - t_start
 
     def prepare_image(self, batch_size, width, height, max_offset, dtype, device, generator=None):
         offset = (max_offset * (2 * torch.rand(
@@ -312,12 +310,16 @@ class VlpnStableDiffusion(DiffusionPipeline):
             dtype=dtype,
             device=device,
             generator=generator
-        ) - 1)).expand(batch_size, 3, width, height)
-        image = (.1 * torch.normal(
-            mean=offset,
-            std=1,
-            generator=generator
-        )).clamp(-1, 1)
+        ) - 1)).expand(batch_size, 1, 2, 2)
+        image = F.interpolate(
+            torch.normal(
+                mean=offset,
+                std=0.3,
+                generator=generator
+            ).clamp(-1, 1),
+            size=(width, height),
+            mode="bicubic"
+        ).expand(batch_size, 3, width, height)
         return image
 
     def prepare_latents(self, init_image, timestep, batch_size, dtype, device, generator=None):
@@ -382,7 +384,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
         eta: float = 0.0,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         image: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
-        max_image_offset: float = 1.0,
+        max_init_offset: float = 0.7,
         output_type: str = "pil",
         return_dict: bool = True,
         callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
@@ -464,11 +466,7 @@ class VlpnStableDiffusion(DiffusionPipeline):
             device
         )
 
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.get_timesteps(num_inference_steps, strength, device)
-
-        # 5. Prepare latent variables
+        # 4. Prepare latent variables
         if isinstance(image, PIL.Image.Image):
             image = preprocess(image)
         elif image is None:
@@ -476,13 +474,18 @@ class VlpnStableDiffusion(DiffusionPipeline):
                 batch_size * num_images_per_prompt,
                 width,
                 height,
-                max_image_offset,
+                max_init_offset,
                 prompt_embeds.dtype,
                 device,
                 generator
             )
 
+        # 5. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
         latents = self.prepare_latents(
             image,
             latent_timestep,
@@ -492,10 +495,10 @@ class VlpnStableDiffusion(DiffusionPipeline):
             generator
         )
 
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
         extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
 
-        # 7. Denoising loo
+        # 8. Denoising loo
         if do_self_attention_guidance:
             store_processor = CrossAttnStoreProcessor()
             self.unet.mid_block.attentions[0].transformer_blocks[0].attn1.processor = store_processor
@@ -559,13 +562,13 @@ class VlpnStableDiffusion(DiffusionPipeline):
                     if callback is not None and i % callback_steps == 0:
                         callback(i, t, latents)
 
-        # 8. Post-processing
+        # 9. Post-processing
         image = self.decode_latents(latents)
 
-        # 9. Run safety checker
+        # 10. Run safety checker
         has_nsfw_concept = None
 
-        # 10. Convert to PIL
+        # 11. Convert to PIL
         if output_type == "pil":
             image = self.numpy_to_pil(image)