Update Ruff to latest Version (#10919)

* update

* update

* update

* update

examples/consistency_distillation/train_lcm_distill_lora_sdxl_wds.py

@@ -95,7 +95,7 @@ def get_module_kohya_state_dict(module, prefix: str, dtype: torch.dtype, adapter
 
         # Set alpha parameter
         if "lora_down" in kohya_key:
-            alpha_key = f'{kohya_key.split(".")[0]}.alpha'
+            alpha_key = f"{kohya_key.split('.')[0]}.alpha"
             kohya_ss_state_dict[alpha_key] = torch.tensor(module.peft_config[adapter_name].lora_alpha).to(dtype)
 
     return kohya_ss_state_dict

examples/custom_diffusion/retrieve.py

@@ -50,9 +50,11 @@ def retrieve(class_prompt, class_data_dir, num_class_images):
     total = 0
     pbar = tqdm(desc="downloading real regularization images", total=num_class_images)
 
-    with open(f"{class_data_dir}/caption.txt", "w") as f1, open(f"{class_data_dir}/urls.txt", "w") as f2, open(
-        f"{class_data_dir}/images.txt", "w"
-    ) as f3:
+    with (
+        open(f"{class_data_dir}/caption.txt", "w") as f1,
+        open(f"{class_data_dir}/urls.txt", "w") as f2,
+        open(f"{class_data_dir}/images.txt", "w") as f3,
+    ):
         while total < num_class_images:
             images = class_images[count]
             count += 1

examples/custom_diffusion/train_custom_diffusion.py

@@ -731,18 +731,18 @@ def main(args):
             if not class_images_dir.exists():
                 class_images_dir.mkdir(parents=True, exist_ok=True)
             if args.real_prior:
-                assert (
-                    class_images_dir / "images"
-                ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}"
-                assert (
-                    len(list((class_images_dir / "images").iterdir())) == args.num_class_images
-                ), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}"
-                assert (
-                    class_images_dir / "caption.txt"
-                ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}"
-                assert (
-                    class_images_dir / "images.txt"
-                ).exists(), f"Please run: python retrieve.py --class_prompt \"{concept['class_prompt']}\" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}"
+                assert (class_images_dir / "images").exists(), (
+                    f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
+                )
+                assert len(list((class_images_dir / "images").iterdir())) == args.num_class_images, (
+                    f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
+                )
+                assert (class_images_dir / "caption.txt").exists(), (
+                    f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
+                )
+                assert (class_images_dir / "images.txt").exists(), (
+                    f'Please run: python retrieve.py --class_prompt "{concept["class_prompt"]}" --class_data_dir {class_images_dir} --num_class_images {args.num_class_images}'
+                )
                 concept["class_prompt"] = os.path.join(class_images_dir, "caption.txt")
                 concept["class_data_dir"] = os.path.join(class_images_dir, "images.txt")
                 args.concepts_list[i] = concept

examples/dreambooth/train_dreambooth.py

@@ -1014,7 +1014,7 @@ def main(args):
 
     if args.train_text_encoder and unwrap_model(text_encoder).dtype != torch.float32:
         raise ValueError(
-            f"Text encoder loaded as datatype {unwrap_model(text_encoder).dtype}." f" {low_precision_error_string}"
+            f"Text encoder loaded as datatype {unwrap_model(text_encoder).dtype}. {low_precision_error_string}"
         )
 
     # Enable TF32 for faster training on Ampere GPUs,

examples/dreambooth/train_dreambooth_lora.py

@@ -982,7 +982,7 @@ def main(args):
 
         lora_state_dict, network_alphas = StableDiffusionLoraLoaderMixin.lora_state_dict(input_dir)
 
-        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = {f"{k.replace('unet.', '')}": v for k, v in lora_state_dict.items() if k.startswith("unet.")}
         unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
         incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
 

examples/dreambooth/train_dreambooth_lora_flux.py

@@ -1294,7 +1294,7 @@ def main(args):
         lora_state_dict = FluxPipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
-            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
+            f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
         transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
         incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")

examples/dreambooth/train_dreambooth_lora_lumina2.py

@@ -1053,7 +1053,7 @@ def main(args):
         lora_state_dict = Lumina2Text2ImgPipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
-            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
+            f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
         transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
         incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")

examples/dreambooth/train_dreambooth_lora_sana.py

@@ -1064,7 +1064,7 @@ def main(args):
         lora_state_dict = SanaPipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
-            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
+            f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
         transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
         incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")

examples/dreambooth/train_dreambooth_lora_sd3.py

@@ -1355,7 +1355,7 @@ def main(args):
         lora_state_dict = StableDiffusion3Pipeline.lora_state_dict(input_dir)
 
         transformer_state_dict = {
-            f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.")
+            f"{k.replace('transformer.', '')}": v for k, v in lora_state_dict.items() if k.startswith("transformer.")
         }
         transformer_state_dict = convert_unet_state_dict_to_peft(transformer_state_dict)
         incompatible_keys = set_peft_model_state_dict(transformer_, transformer_state_dict, adapter_name="default")

examples/dreambooth/train_dreambooth_lora_sdxl.py

@@ -118,7 +118,7 @@ def save_model_card(
             )
 
     model_description = f"""
-# {'SDXL' if 'playground' not in base_model else 'Playground'} LoRA DreamBooth - {repo_id}
+# {"SDXL" if "playground" not in base_model else "Playground"} LoRA DreamBooth - {repo_id}
 
 <Gallery />
 
@@ -1286,7 +1286,7 @@ def main(args):
 
         lora_state_dict, network_alphas = StableDiffusionLoraLoaderMixin.lora_state_dict(input_dir)
 
-        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = {f"{k.replace('unet.', '')}": v for k, v in lora_state_dict.items() if k.startswith("unet.")}
         unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
         incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
         if incompatible_keys is not None:

examples/flux-control/train_control_lora_flux.py

@@ -91,9 +91,9 @@ def log_validation(flux_transformer, args, accelerator, weight_dtype, step, is_f
             torch_dtype=weight_dtype,
         )
         pipeline.load_lora_weights(args.output_dir)
-        assert (
-            pipeline.transformer.config.in_channels == initial_channels * 2
-        ), f"{pipeline.transformer.config.in_channels=}"
+        assert pipeline.transformer.config.in_channels == initial_channels * 2, (
+            f"{pipeline.transformer.config.in_channels=}"
+        )
 
     pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
@@ -954,7 +954,7 @@ def main(args):
 
             lora_state_dict = FluxControlPipeline.lora_state_dict(input_dir)
             transformer_lora_state_dict = {
-                f'{k.replace("transformer.", "")}': v
+                f"{k.replace('transformer.', '')}": v
                 for k, v in lora_state_dict.items()
                 if k.startswith("transformer.") and "lora" in k
             }

examples/model_search/pipeline_easy.py

@@ -1081,9 +1081,9 @@ class AutoConfig:
                     f"textual_inversion_path: {search_word} -> {textual_inversion_path.model_status.site_url}"
                 )
 
-                pretrained_model_name_or_paths[
-                    pretrained_model_name_or_paths.index(search_word)
-                ] = textual_inversion_path.model_path
+                pretrained_model_name_or_paths[pretrained_model_name_or_paths.index(search_word)] = (
+                    textual_inversion_path.model_path
+                )
 
         self.load_textual_inversion(
             pretrained_model_name_or_paths, token=tokens, tokenizer=tokenizer, text_encoder=text_encoder, **kwargs

examples/research_projects/anytext/anytext.py

@@ -187,9 +187,9 @@ def get_clip_token_for_string(tokenizer, string):
         return_tensors="pt",
     )
     tokens = batch_encoding["input_ids"]
-    assert (
-        torch.count_nonzero(tokens - 49407) == 2
-    ), f"String '{string}' maps to more than a single token. Please use another string"
+    assert torch.count_nonzero(tokens - 49407) == 2, (
+        f"String '{string}' maps to more than a single token. Please use another string"
+    )
     return tokens[0, 1]
 
 

examples/research_projects/anytext/ocr_recog/RecSVTR.py

@@ -312,9 +312,9 @@ class PatchEmbed(nn.Module):
 
     def forward(self, x):
         B, C, H, W = x.shape
-        assert (
-            H == self.img_size[0] and W == self.img_size[1]
-        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        assert H == self.img_size[0] and W == self.img_size[1], (
+            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        )
         x = self.proj(x).flatten(2).permute(0, 2, 1)
         return x
 

examples/research_projects/colossalai/train_dreambooth_colossalai.py

@@ -619,7 +619,7 @@ def main(args):
 
             optimizer.step()
             lr_scheduler.step()
-            logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
+            logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated() / 2**20} MB", ranks=[0])
             # Checks if the accelerator has performed an optimization step behind the scenes
             progress_bar.update(1)
             global_step += 1

examples/research_projects/controlnet/train_controlnet_webdataset.py

@@ -803,21 +803,20 @@ def parse_args(input_args=None):
         "--control_type",
         type=str,
         default="canny",
-        help=("The type of controlnet conditioning image to use. One of `canny`, `depth`" " Defaults to `canny`."),
+        help=("The type of controlnet conditioning image to use. One of `canny`, `depth` Defaults to `canny`."),
     )
     parser.add_argument(
         "--transformer_layers_per_block",
         type=str,
         default=None,
-        help=("The number of layers per block in the transformer. If None, defaults to" " `args.transformer_layers`."),
+        help=("The number of layers per block in the transformer. If None, defaults to `args.transformer_layers`."),
     )
     parser.add_argument(
         "--old_style_controlnet",
         action="store_true",
         default=False,
         help=(
-            "Use the old style controlnet, which is a single transformer layer with"
-            " a single head. Defaults to False."
+            "Use the old style controlnet, which is a single transformer layer with a single head. Defaults to False."
         ),
     )
 

examples/research_projects/diffusion_dpo/train_diffusion_dpo.py

@@ -86,7 +86,7 @@ def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: st
 
 
 def log_validation(args, unet, accelerator, weight_dtype, epoch, is_final_validation=False):
-    logger.info(f"Running validation... \n Generating images with prompts:\n" f" {VALIDATION_PROMPTS}.")
+    logger.info(f"Running validation... \n Generating images with prompts:\n {VALIDATION_PROMPTS}.")
 
     # create pipeline
     pipeline = DiffusionPipeline.from_pretrained(

examples/research_projects/diffusion_dpo/train_diffusion_dpo_sdxl.py

@@ -91,7 +91,7 @@ def import_model_class_from_model_name_or_path(
 
 
 def log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):
-    logger.info(f"Running validation... \n Generating images with prompts:\n" f" {VALIDATION_PROMPTS}.")
+    logger.info(f"Running validation... \n Generating images with prompts:\n {VALIDATION_PROMPTS}.")
 
     if is_final_validation:
         if args.mixed_precision == "fp16":

examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora.py

@@ -91,7 +91,7 @@ def import_model_class_from_model_name_or_path(
 
 
 def log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):
-    logger.info(f"Running validation... \n Generating images with prompts:\n" f" {VALIDATION_PROMPTS}.")
+    logger.info(f"Running validation... \n Generating images with prompts:\n {VALIDATION_PROMPTS}.")
 
     if is_final_validation:
         if args.mixed_precision == "fp16":
@@ -683,7 +683,7 @@ def main(args):
 
         lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.lora_state_dict(input_dir)
 
-        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = {f"{k.replace('unet.', '')}": v for k, v in lora_state_dict.items() if k.startswith("unet.")}
         unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
         incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
         if incompatible_keys is not None:

examples/research_projects/diffusion_orpo/train_diffusion_orpo_sdxl_lora_wds.py

@@ -89,7 +89,7 @@ def import_model_class_from_model_name_or_path(
 
 
 def log_validation(args, unet, vae, accelerator, weight_dtype, epoch, is_final_validation=False):
-    logger.info(f"Running validation... \n Generating images with prompts:\n" f" {VALIDATION_PROMPTS}.")
+    logger.info(f"Running validation... \n Generating images with prompts:\n {VALIDATION_PROMPTS}.")
 
     if is_final_validation:
         if args.mixed_precision == "fp16":
@@ -790,7 +790,7 @@ def main(args):
 
         lora_state_dict, network_alphas = StableDiffusionXLLoraLoaderMixin.lora_state_dict(input_dir)
 
-        unet_state_dict = {f'{k.replace("unet.", "")}': v for k, v in lora_state_dict.items() if k.startswith("unet.")}
+        unet_state_dict = {f"{k.replace('unet.', '')}": v for k, v in lora_state_dict.items() if k.startswith("unet.")}
         unet_state_dict = convert_unet_state_dict_to_peft(unet_state_dict)
         incompatible_keys = set_peft_model_state_dict(unet_, unet_state_dict, adapter_name="default")
         if incompatible_keys is not None: