fix copied from statement

Author: Trgtuan10

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_img2img.py

@@ -182,7 +182,7 @@ def __init__(
         self.prompt_template_encode_start_idx = 34
         self.default_sample_size = 128
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._extract_masked_hidden
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._extract_masked_hidden
     def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
         bool_mask = mask.bool()
         valid_lengths = bool_mask.sum(dim=1)
@@ -191,7 +191,7 @@ def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor
 
         return split_result
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._get_qwen_prompt_embeds
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._get_qwen_prompt_embeds
     def _get_qwen_prompt_embeds(
         self,
         prompt: Union[str, List[str]] = None,
@@ -265,7 +265,7 @@ def get_timesteps(self, num_inference_steps, strength, device):
 
         return timesteps, num_inference_steps - t_start
 
-    # Copied fromCopied from diffusers.pipelines.qwenimage.pipeline_qwenimage.encode_prompt
+    # Copied fromCopied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.encode_prompt
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
@@ -304,7 +304,7 @@ def encode_prompt(
 
         return prompt_embeds, prompt_embeds_mask
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.check_inputs
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -365,7 +365,7 @@ def check_inputs(
             raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._prepare_latent_image_ids
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
@@ -380,7 +380,7 @@ def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         return latent_image_ids.to(device=device, dtype=dtype)
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._pack_latents
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._pack_latents
     def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
         latents = latents.permute(0, 2, 4, 1, 3, 5)
@@ -389,7 +389,7 @@ def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         return latents
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._unpack_latents
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
     def _unpack_latents(latents, height, width, vae_scale_factor):
         batch_size, num_patches, channels = latents.shape
 

src/diffusers/pipelines/qwenimage/pipeline_qwenimage_inpaint.py

@@ -192,7 +192,7 @@ def __init__(
         self.prompt_template_encode_start_idx = 34
         self.default_sample_size = 128
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._extract_masked_hidden
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._extract_masked_hidden
     def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor):
         bool_mask = mask.bool()
         valid_lengths = bool_mask.sum(dim=1)
@@ -201,7 +201,7 @@ def _extract_masked_hidden(self, hidden_states: torch.Tensor, mask: torch.Tensor
 
         return split_result
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._get_qwen_prompt_embeds
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._get_qwen_prompt_embeds
     def _get_qwen_prompt_embeds(
         self,
         prompt: Union[str, List[str]] = None,
@@ -240,7 +240,7 @@ def _get_qwen_prompt_embeds(
 
         return prompt_embeds, encoder_attention_mask
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_img2img._encode_vae_image
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage_img2img.QwenImagePipeline._encode_vae_image
     def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
         if isinstance(generator, list):
             image_latents = [
@@ -276,7 +276,7 @@ def get_timesteps(self, num_inference_steps, strength, device):
 
         return timesteps, num_inference_steps - t_start
 
-    # Copied fromCopied from diffusers.pipelines.qwenimage.pipeline_qwenimage.encode_prompt
+    # Copied fromCopied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.encode_prompt
     def encode_prompt(
         self,
         prompt: Union[str, List[str]],
@@ -315,7 +315,7 @@ def encode_prompt(
 
         return prompt_embeds, prompt_embeds_mask
 
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.check_inputs
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline.check_inputs
     def check_inputs(
         self,
         prompt,
@@ -392,7 +392,7 @@ def check_inputs(
             raise ValueError(f"`max_sequence_length` cannot be greater than 1024 but is {max_sequence_length}")
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._prepare_latent_image_ids
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._prepare_latent_image_ids
     def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
         latent_image_ids = torch.zeros(height, width, 3)
         latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
@@ -416,7 +416,7 @@ def _pack_latents(latents, batch_size, num_channels_latents, height, width):
         return latents
 
     @staticmethod
-    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage._unpack_latents
+    # Copied from diffusers.pipelines.qwenimage.pipeline_qwenimage.QwenImagePipeline._unpack_latents
     def _unpack_latents(latents, height, width, vae_scale_factor):
         batch_size, num_patches, channels = latents.shape