Fix Copying Mechanism typo/bug

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -758,7 +758,7 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
 
             idx += 1
 
-    # copied from train_dreambooth_lora_sdxl_advanced.py
+    # Copied from train_dreambooth_lora_sdxl_advanced.py
     def save_embeddings(self, file_path: str):
         assert self.train_ids is not None, "Initialize new tokens before saving embeddings."
         tensors = {}

examples/community/llm_grounded_diffusion.py

@@ -1524,35 +1524,35 @@ def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32
         assert emb.shape == (w.shape[0], embedding_dim)
         return emb
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_scale
     def guidance_scale(self):
         return self._guidance_scale
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_rescale
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.guidance_rescale
     def guidance_rescale(self):
         return self._guidance_rescale
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.clip_skip
     def clip_skip(self):
         return self._clip_skip
 
     # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
     # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
     # corresponds to doing no classifier free guidance.
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.do_classifier_free_guidance
     def do_classifier_free_guidance(self):
         return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.cross_attention_kwargs
     def cross_attention_kwargs(self):
         return self._cross_attention_kwargs
 
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps
     @property
+    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.num_timesteps
     def num_timesteps(self):
         return self._num_timesteps

src/diffusers/models/controlnet_xs.py

@@ -851,8 +851,8 @@ def _set_gradient_checkpointing(self, module, value=False):
         if hasattr(module, "gradient_checkpointing"):
             module.gradient_checkpointing = value
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel
     @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
         r"""
         Returns:
@@ -911,7 +911,7 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
         for name, module in self.named_children():
             fn_recursive_attn_processor(name, module, processor)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
     def set_default_attn_processor(self):
         """
         Disables custom attention processors and sets the default attention implementation.
@@ -927,7 +927,7 @@ def set_default_attn_processor(self):
 
         self.set_attn_processor(processor)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
     def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
         r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
 
@@ -952,7 +952,7 @@ def enable_freeu(self, s1: float, s2: float, b1: float, b2: float):
             setattr(upsample_block, "b1", b1)
             setattr(upsample_block, "b2", b2)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
     def disable_freeu(self):
         """Disables the FreeU mechanism."""
         freeu_keys = {"s1", "s2", "b1", "b2"}
@@ -961,7 +961,7 @@ def disable_freeu(self):
                 if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
                     setattr(upsample_block, k, None)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
     def fuse_qkv_projections(self):
         """
         Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
@@ -985,7 +985,7 @@ def fuse_qkv_projections(self):
             if isinstance(module, Attention):
                 module.fuse_projections(fuse=True)
 
-    # copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
     def unfuse_qkv_projections(self):
         """Disables the fused QKV projection if enabled.
 

src/diffusers/pipelines/deepfloyd_if/watermark.py

@@ -17,7 +17,7 @@ def __init__(self):
         self.watermark_image_as_pil = None
 
     def apply_watermark(self, images: List[PIL.Image.Image], sample_size=None):
-        # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
+        # Copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287
 
         h = images[0].height
         w = images[0].width

src/diffusers/schedulers/scheduling_dpmsolver_sde.py

@@ -370,7 +370,7 @@ def t_fn(_sigma):
         timesteps = np.array([self._sigma_to_t(sigma, log_sigmas) for sigma in sig_proposed])
         return timesteps
 
-    # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
         log_sigma = np.log(np.maximum(sigma, 1e-10))
@@ -394,7 +394,7 @@ def _sigma_to_t(self, sigma, log_sigmas):
         t = t.reshape(sigma.shape)
         return t
 
-    # copied from diffusers.schedulers.scheduling_euler_discrete._convert_to_karras
+    # copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
     def _convert_to_karras(self, in_sigmas: torch.Tensor) -> torch.Tensor:
         """Constructs the noise schedule of Karras et al. (2022)."""
 

src/diffusers/schedulers/scheduling_lms_discrete.py

@@ -324,7 +324,7 @@ def _init_step_index(self, timestep):
         else:
             self._step_index = self._begin_index
 
-    # copied from diffusers.schedulers.scheduling_euler_discrete._sigma_to_t
+    # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._sigma_to_t
     def _sigma_to_t(self, sigma, log_sigmas):
         # get log sigma
         log_sigma = np.log(np.maximum(sigma, 1e-10))
@@ -348,7 +348,7 @@ def _sigma_to_t(self, sigma, log_sigmas):
         t = t.reshape(sigma.shape)
         return t
 
-    # copied from diffusers.schedulers.scheduling_euler_discrete._convert_to_karras
+    # copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
     def _convert_to_karras(self, in_sigmas: torch.Tensor) -> torch.Tensor:
         """Constructs the noise schedule of Karras et al. (2022)."""
 

tests/lora/utils.py

@@ -156,7 +156,7 @@ def get_dummy_inputs(self, with_generator=True):
 
         return noise, input_ids, pipeline_inputs
 
-    # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
+    # Copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb
     def get_dummy_tokens(self):
         max_seq_length = 77
 

tests/pipelines/controlnet/test_controlnet_sdxl.py

@@ -294,7 +294,7 @@ def test_stable_diffusion_xl_multi_prompts(self):
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
-    # copied from test_stable_diffusion_xl.py
+    # Copied from test_stable_diffusion_xl.py
     def test_stable_diffusion_xl_prompt_embeds(self):
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
@@ -375,7 +375,7 @@ def test_controlnet_sdxl_lcm(self):
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
-    # copied from test_stable_diffusion_xl.py:test_stable_diffusion_two_xl_mixture_of_denoiser_fast
+    # Copied from test_stable_diffusion_xl.py:test_stable_diffusion_two_xl_mixture_of_denoiser_fast
     # with `StableDiffusionXLControlNetPipeline` instead of `StableDiffusionXLPipeline`
     def test_controlnet_sdxl_two_mixture_of_denoiser_fast(self):
         components = self.get_dummy_components()

tests/pipelines/controlnet/test_controlnet_sdxl_img2img.py

@@ -322,7 +322,7 @@ def test_stable_diffusion_xl_multi_prompts(self):
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
-    # copied from test_stable_diffusion_xl.py
+    # Copied from test_stable_diffusion_xl.py
     def test_stable_diffusion_xl_prompt_embeds(self):
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)

tests/pipelines/controlnet_xs/test_controlnetxs_sdxl.py

@@ -151,7 +151,7 @@ def get_dummy_components(self):
         }
         return components
 
-    # copied from test_controlnet_sdxl.py
+    # Copied from test_controlnet_sdxl.py
     def get_dummy_inputs(self, device, seed=0):
         if str(device).startswith("mps"):
             generator = torch.manual_seed(seed)
@@ -176,24 +176,24 @@ def get_dummy_inputs(self, device, seed=0):
 
         return inputs
 
-    # copied from test_controlnet_sdxl.py
+    # Copied from test_controlnet_sdxl.py
     def test_attention_slicing_forward_pass(self):
         return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)
 
-    # copied from test_controlnet_sdxl.py
     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available(),
         reason="XFormers attention is only available with CUDA and `xformers` installed",
     )
+    # Copied from test_controlnet_sdxl.py
     def test_xformers_attention_forwardGenerator_pass(self):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)
 
-    # copied from test_controlnet_sdxl.py
+    # Copied from test_controlnet_sdxl.py
     def test_inference_batch_single_identical(self):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3)
 
-    # copied from test_controlnet_sdxl.py
     @require_torch_gpu
+    # Copied from test_controlnet_sdxl.py
     def test_stable_diffusion_xl_offloads(self):
         pipes = []
         components = self.get_dummy_components()
@@ -222,7 +222,7 @@ def test_stable_diffusion_xl_offloads(self):
         assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
         assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
 
-    # copied from test_controlnet_sdxl.py
+    # Copied from test_controlnet_sdxl.py
     def test_stable_diffusion_xl_multi_prompts(self):
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components).to(torch_device)
@@ -276,7 +276,7 @@ def test_stable_diffusion_xl_multi_prompts(self):
         # ensure the results are not equal
         assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
 
-    # copied from test_stable_diffusion_xl.py
+    # Copied from test_stable_diffusion_xl.py
     def test_stable_diffusion_xl_prompt_embeds(self):
         components = self.get_dummy_components()
         sd_pipe = self.pipeline_class(**components)
@@ -315,11 +315,11 @@ def test_stable_diffusion_xl_prompt_embeds(self):
         # make sure that it's equal
         assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1.1e-4
 
-    # copied from test_stable_diffusion_xl.py
+    # Copied from test_stable_diffusion_xl.py
     def test_save_load_optional_components(self):
         self._test_save_load_optional_components()
 
-    # copied from test_controlnetxs.py
+    # Copied from test_controlnetxs.py
     def test_to_dtype(self):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)