Avoid nested fix-copies (#1332)

* Avoid nested `# Copied from` statements during `make fix-copies`

* style

src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py

@@ -81,7 +81,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             Model that extracts features from generated images to be used as inputs for the `safety_checker`.
     """
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.__init__
     def __init__(
         self,
         vae: AutoencoderKL,
@@ -148,7 +147,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             feature_extractor=feature_extractor,
         )
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.enable_attention_slicing
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
         Enable sliced attention computation.
@@ -168,7 +166,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             slice_size = self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(slice_size)
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.disable_attention_slicing
     def disable_attention_slicing(self):
         r"""
         Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
@@ -177,7 +174,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         # set slice_size = `None` to disable `attention slicing`
         self.enable_attention_slicing(None)
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.enable_sequential_cpu_offload
     def enable_sequential_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
@@ -196,7 +192,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
                 cpu_offload(cpu_offloaded_model, device)
 
     @property
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline._execution_device
     def _execution_device(self):
         r"""
         Returns the device on which the pipeline's models will be executed. After calling
@@ -214,7 +209,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
                 return torch.device(module._hf_hook.execution_device)
         return self.device
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.enable_xformers_memory_efficient_attention
     def enable_xformers_memory_efficient_attention(self):
         r"""
         Enable memory efficient attention as implemented in xformers.
@@ -227,14 +221,12 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         """
         self.unet.set_use_memory_efficient_attention_xformers(True)
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.disable_xformers_memory_efficient_attention
     def disable_xformers_memory_efficient_attention(self):
         r"""
         Disable memory efficient attention as implemented in xformers.
         """
         self.unet.set_use_memory_efficient_attention_xformers(False)
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline._encode_prompt
     def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
         r"""
         Encodes the prompt into text encoder hidden states.
@@ -340,7 +332,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
 
         return text_embeddings
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.run_safety_checker
     def run_safety_checker(self, image, device, dtype):
         if self.safety_checker is not None:
             safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
@@ -351,7 +342,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
             has_nsfw_concept = None
         return image, has_nsfw_concept
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.decode_latents
     def decode_latents(self, latents):
         latents = 1 / 0.18215 * latents
         image = self.vae.decode(latents).sample
@@ -360,7 +350,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()
         return image
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.prepare_extra_step_kwargs
     def prepare_extra_step_kwargs(self, generator, eta):
         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.

utils/check_copies.py

@@ -153,6 +153,10 @@ def is_copy_consistent(filename, overwrite=False):
         observed_code_lines = lines[start_index:line_index]
         observed_code = "".join(observed_code_lines)
 
+        # Remove any nested `Copied from` comments to avoid circular copies
+        theoretical_code = [line for line in theoretical_code.split("\n") if _re_copy_warning.search(line) is None]
+        theoretical_code = "\n".join(theoretical_code)
+
         # Before comparing, use the `replace_pattern` on the original code.
         if len(replace_pattern) > 0:
             patterns = replace_pattern.replace("with", "").split(",")