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Avoid nested fix-copies (#1332) 

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

* style
This commit is contained in:
Anton Lozhkov 2022-11-18 15:33:57 +01:00 committed by GitHub
parent 195e437ac5
commit 81fa2d688d
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 4 additions and 11 deletions
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11
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
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@ -81,7 +81,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
Model that extracts features from generated images to be used as inputs for the `safety_checker`. 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__( def __init__(
self, self,
vae: AutoencoderKL, vae: AutoencoderKL,
@ -148,7 +147,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
feature_extractor=feature_extractor, 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"): def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
r""" r"""
Enable sliced attention computation. Enable sliced attention computation.
@ -168,7 +166,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
slice_size = self.unet.config.attention_head_dim // 2 slice_size = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(slice_size) 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): def disable_attention_slicing(self):
r""" r"""
Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go 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` # set slice_size = `None` to disable `attention slicing`
self.enable_attention_slicing(None) 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): def enable_sequential_cpu_offload(self, gpu_id=0):
r""" r"""
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet, 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) cpu_offload(cpu_offloaded_model, device)
@property @property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline._execution_device
def _execution_device(self): def _execution_device(self):
r""" r"""
Returns the device on which the pipeline's models will be executed. After calling 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 torch.device(module._hf_hook.execution_device)
return self.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): def enable_xformers_memory_efficient_attention(self):
r""" r"""
Enable memory efficient attention as implemented in xformers. Enable memory efficient attention as implemented in xformers.
@ -227,14 +221,12 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
""" """
self.unet.set_use_memory_efficient_attention_xformers(True) 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): def disable_xformers_memory_efficient_attention(self):
r""" r"""
Disable memory efficient attention as implemented in xformers. Disable memory efficient attention as implemented in xformers.
""" """
self.unet.set_use_memory_efficient_attention_xformers(False) 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): def _encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
r""" r"""
Encodes the prompt into text encoder hidden states. Encodes the prompt into text encoder hidden states.
@ -340,7 +332,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
return text_embeddings return text_embeddings
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None: if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device) 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 has_nsfw_concept = None
return image, has_nsfw_concept return image, has_nsfw_concept
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.decode_latents
def decode_latents(self, latents): def decode_latents(self, latents):
latents = 1 / 0.18215 * latents latents = 1 / 0.18215 * latents
image = self.vae.decode(latents).sample image = self.vae.decode(latents).sample
@ -360,7 +350,6 @@ class AltDiffusionImg2ImgPipeline(DiffusionPipeline):
image = image.cpu().permute(0, 2, 3, 1).float().numpy() image = image.cpu().permute(0, 2, 3, 1).float().numpy()
return image return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.AltDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta): def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # 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. # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.

4
utils/check_copies.py
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@ -153,6 +153,10 @@ def is_copy_consistent(filename, overwrite=False):
observed_code_lines = lines[start_index:line_index] observed_code_lines = lines[start_index:line_index]
observed_code = "".join(observed_code_lines) 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. # Before comparing, use the `replace_pattern` on the original code.
if len(replace_pattern) > 0: if len(replace_pattern) > 0:
patterns = replace_pattern.replace("with", "").split(",") patterns = replace_pattern.replace("with", "").split(",")