scale_noise->add_noise

Author: hlky

examples/community/pipeline_flux_differential_img2img.py

@@ -582,7 +582,7 @@ def prepare_latents(
 
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+            latents = self.scheduler.add_noise(image_latents, noise, timestep)
         else:
             noise = latents.to(device)
             latents = noise
@@ -976,8 +976,8 @@ def __call__(
 
                 if i < len(timesteps) - 1:
                     noise_timestep = timesteps[i + 1]
-                    image_latent = self.scheduler.scale_noise(
-                        original_image_latents, torch.tensor([noise_timestep]), noise
+                    image_latent = self.scheduler.add_noise(
+                        original_image_latents, noise, torch.tensor([noise_timestep])
                     )
 
                     # start diff diff

examples/community/pipeline_stable_diffusion_3_differential_img2img.py

@@ -640,7 +640,7 @@ def prepare_latents(
         shape = init_latents.shape
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
 
-        init_latents = self.scheduler.scale_noise(init_latents, timestep, noise)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
         latents = init_latents.to(device=device, dtype=dtype)
 
         return latents

src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py

@@ -580,7 +580,7 @@ def prepare_latents(
             image_latents = torch.cat([image_latents], dim=0)
 
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+        latents = self.scheduler.add_noise(image_latents, noise, timestep)
         latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
         return latents, latent_image_ids
 

src/diffusers/pipelines/flux/pipeline_flux_control_inpaint.py

@@ -646,7 +646,7 @@ def prepare_latents(
             image_latents = torch.cat([image_latents], dim=0)
 
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+        latents = self.scheduler.add_noise(image_latents, noise, timestep)
         latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
         return latents, noise, image_latents, latent_image_ids
 
@@ -1091,8 +1091,8 @@ def __call__(
                 init_mask = mask
                 if i < len(timesteps) - 1:
                     noise_timestep = timesteps[i + 1]
-                    init_latents_proper = self.scheduler.scale_noise(
-                        image_latents, torch.tensor([noise_timestep]), noise
+                    init_latents_proper = self.scheduler.add_noise(
+                        image_latents, noise, torch.tensor([noise_timestep])
                     )
                 else:
                     init_latents_proper = image_latents

src/diffusers/pipelines/flux/pipeline_flux_controlnet_image_to_image.py

@@ -579,7 +579,7 @@ def prepare_latents(
             image_latents = torch.cat([image_latents], dim=0)
 
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+        latents = self.scheduler.add_noise(image_latents, noise, timestep)
         latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
         return latents, latent_image_ids
 

src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py

@@ -605,7 +605,7 @@ def prepare_latents(
 
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+            latents = self.scheduler.add_noise(image_latents, noise, timestep)
         else:
             noise = latents.to(device)
             latents = noise
@@ -1159,8 +1159,8 @@ def __call__(
 
                 if i < len(timesteps) - 1:
                     noise_timestep = timesteps[i + 1]
-                    init_latents_proper = self.scheduler.scale_noise(
-                        init_latents_proper, torch.tensor([noise_timestep]), noise
+                    init_latents_proper = self.scheduler.add_noise(
+                        init_latents_proper, noise, torch.tensor([noise_timestep])
                     )
 
                 latents = (1 - init_mask) * init_latents_proper + init_mask * latents

src/diffusers/pipelines/flux/pipeline_flux_inpaint.py

@@ -582,7 +582,7 @@ def prepare_latents(
 
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-            latents = self.scheduler.scale_noise(image_latents, timestep, noise)
+            latents = self.scheduler.add_noise(image_latents, noise, timestep)
         else:
             noise = latents.to(device)
             latents = noise
@@ -977,8 +977,8 @@ def __call__(
 
                 if i < len(timesteps) - 1:
                     noise_timestep = timesteps[i + 1]
-                    init_latents_proper = self.scheduler.scale_noise(
-                        init_latents_proper, torch.tensor([noise_timestep]), noise
+                    init_latents_proper = self.scheduler.add_noise(
+                        init_latents_proper, noise, torch.tensor([noise_timestep])
                     )
 
                 latents = (1 - init_mask) * init_latents_proper + init_mask * latents

src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py

@@ -702,7 +702,7 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
 
         # get latents
-        init_latents = self.scheduler.scale_noise(init_latents, timestep, noise)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
         latents = init_latents.to(device=device, dtype=dtype)
 
         return latents

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py

@@ -708,7 +708,7 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt
         noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
 
         # get latents
-        init_latents = self.scheduler.scale_noise(init_latents, timestep, noise)
+        init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
         latents = init_latents.to(device=device, dtype=dtype)
 
         return latents

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -717,7 +717,7 @@ def prepare_latents(
         if latents is None:
             noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
             # if strength is 1. then initialise the latents to noise, else initial to image + noise
-            latents = noise if is_strength_max else self.scheduler.scale_noise(image_latents, timestep, noise)
+            latents = noise if is_strength_max else self.scheduler.add_noise(image_latents, noise, timestep)
         else:
             noise = latents.to(device)
             latents = noise
@@ -1203,8 +1203,8 @@ def __call__(
 
                     if i < len(timesteps) - 1:
                         noise_timestep = timesteps[i + 1]
-                        init_latents_proper = self.scheduler.scale_noise(
-                            init_latents_proper, torch.tensor([noise_timestep]), noise
+                        init_latents_proper = self.scheduler.add_noise(
+                            init_latents_proper, noise, torch.tensor([noise_timestep])
                         )
 
                     latents = (1 - init_mask) * init_latents_proper + init_mask * latents