Update Ruff to latest Version (#10919)

* update

* update

* update

* update

examples/research_projects/flux_lora_quantization/train_dreambooth_lora_flux_miniature.py

@@ -783,7 +783,7 @@ 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/research_projects/gligen/demo.ipynb

@@ -26,8 +26,7 @@
     "%load_ext autoreload\n",
     "%autoreload 2\n",
     "\n",
-    "import torch\n",
-    "from diffusers import StableDiffusionGLIGENTextImagePipeline, StableDiffusionGLIGENPipeline"
+    "from diffusers import StableDiffusionGLIGENPipeline"
    ]
   },
   {
@@ -36,28 +35,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
+    "from transformers import CLIPTextModel, CLIPTokenizer\n",
+    "\n",
     "import diffusers\n",
     "from diffusers import (\n",
     "    AutoencoderKL,\n",
     "    DDPMScheduler,\n",
-    "    UNet2DConditionModel,\n",
-    "    UniPCMultistepScheduler,\n",
     "    EulerDiscreteScheduler,\n",
+    "    UNet2DConditionModel,\n",
     ")\n",
-    "from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer\n",
+    "\n",
+    "\n",
     "# pretrained_model_name_or_path = 'masterful/gligen-1-4-generation-text-box'\n",
     "\n",
-    "pretrained_model_name_or_path = '/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83'\n",
+    "pretrained_model_name_or_path = \"/root/data/zhizhonghuang/checkpoints/models--masterful--gligen-1-4-generation-text-box/snapshots/d2820dc1e9ba6ca082051ce79cfd3eb468ae2c83\"\n",
     "\n",
     "tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder=\"tokenizer\")\n",
     "noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder=\"scheduler\")\n",
-    "text_encoder = CLIPTextModel.from_pretrained(\n",
-    "    pretrained_model_name_or_path, subfolder=\"text_encoder\"\n",
-    ")\n",
-    "vae = AutoencoderKL.from_pretrained(\n",
-    "    pretrained_model_name_or_path, subfolder=\"vae\"\n",
-    ")\n",
+    "text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder=\"text_encoder\")\n",
+    "vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder=\"vae\")\n",
     "# unet = UNet2DConditionModel.from_pretrained(\n",
     "#     pretrained_model_name_or_path, subfolder=\"unet\"\n",
     "# )\n",
@@ -71,9 +67,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "unet = UNet2DConditionModel.from_pretrained(\n",
-    "    '/root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO'\n",
-    ")"
+    "unet = UNet2DConditionModel.from_pretrained(\"/root/data/zhizhonghuang/ckpt/GLIGEN_Text_Retrain_COCO\")"
    ]
   },
   {
@@ -108,6 +102,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
+    "\n",
     "# prompt = 'A realistic image of landscape scene depicting a green car parking on the left of a blue truck, with a red air balloon and a bird in the sky'\n",
     "# gen_boxes = [('a green car', [21, 281, 211, 159]), ('a blue truck', [269, 283, 209, 160]), ('a red air balloon', [66, 8, 145, 135]), ('a bird', [296, 42, 143, 100])]\n",
     "\n",
@@ -117,10 +114,8 @@
     "# prompt = 'A realistic scene of three skiers standing in a line on the snow near a palm tree'\n",
     "# gen_boxes = [('a skier', [5, 152, 139, 168]), ('a skier', [278, 192, 121, 158]), ('a skier', [148, 173, 124, 155]), ('a palm tree', [404, 105, 103, 251])]\n",
     "\n",
-    "prompt = 'An oil painting of a pink dolphin jumping on the left of a steam boat on the sea'\n",
-    "gen_boxes = [('a steam boat', [232, 225, 257, 149]), ('a jumping pink dolphin', [21, 249, 189, 123])]\n",
-    "\n",
-    "import numpy as np\n",
+    "prompt = \"An oil painting of a pink dolphin jumping on the left of a steam boat on the sea\"\n",
+    "gen_boxes = [(\"a steam boat\", [232, 225, 257, 149]), (\"a jumping pink dolphin\", [21, 249, 189, 123])]\n",
     "\n",
     "boxes = np.array([x[1] for x in gen_boxes])\n",
     "boxes = boxes / 512\n",
@@ -166,7 +161,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "diffusers.utils.make_image_grid(images, 4, len(images)//4)"
+    "diffusers.utils.make_image_grid(images, 4, len(images) // 4)"
    ]
   },
   {
@@ -179,7 +174,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "densecaption",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -197,5 +192,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

examples/research_projects/instructpix2pix_lora/train_instruct_pix2pix_lora.py

@@ -15,8 +15,8 @@
 # limitations under the License.
 
 """
-    Script to fine-tune Stable Diffusion for LORA InstructPix2Pix.
-    Base code referred from: https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py
+Script to fine-tune Stable Diffusion for LORA InstructPix2Pix.
+Base code referred from: https://github.com/huggingface/diffusers/blob/main/examples/instruct_pix2pix/train_instruct_pix2pix.py
 """
 
 import argparse

examples/research_projects/multi_subject_dreambooth/train_multi_subject_dreambooth.py

@@ -763,9 +763,9 @@ def main(args):
         # Parse instance and class inputs, and double check that lengths match
         instance_data_dir = args.instance_data_dir.split(",")
         instance_prompt = args.instance_prompt.split(",")
-        assert all(
-            x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)]
-        ), "Instance data dir and prompt inputs are not of the same length."
+        assert all(x == len(instance_data_dir) for x in [len(instance_data_dir), len(instance_prompt)]), (
+            "Instance data dir and prompt inputs are not of the same length."
+        )
 
         if args.with_prior_preservation:
             class_data_dir = args.class_data_dir.split(",")
@@ -788,9 +788,9 @@ def main(args):
                     negative_validation_prompts.append(None)
             args.validation_negative_prompt = negative_validation_prompts
 
-            assert num_of_validation_prompts == len(
-                negative_validation_prompts
-            ), "The length of negative prompts for validation is greater than the number of validation prompts."
+            assert num_of_validation_prompts == len(negative_validation_prompts), (
+                "The length of negative prompts for validation is greater than the number of validation prompts."
+            )
             args.validation_inference_steps = [args.validation_inference_steps] * num_of_validation_prompts
             args.validation_guidance_scale = [args.validation_guidance_scale] * num_of_validation_prompts
 

examples/research_projects/multi_token_textual_inversion/textual_inversion.py

@@ -830,9 +830,9 @@ def main():
                 # Let's make sure we don't update any embedding weights besides the newly added token
                 index_no_updates = get_mask(tokenizer, accelerator)
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
+                        orig_embeds_params[index_no_updates]
+                    )
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

examples/research_projects/onnxruntime/textual_inversion/textual_inversion.py

@@ -886,9 +886,9 @@ def main():
                 index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
 
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
+                        orig_embeds_params[index_no_updates]
+                    )
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

examples/research_projects/promptdiffusion/pipeline_prompt_diffusion.py

@@ -663,8 +663,7 @@ class PromptDiffusionPipeline(
                     self.check_image(image, prompt, prompt_embeds)
         else:
             raise ValueError(
-                f"You have passed a list of images of length {len(image_pair)}."
-                f"Make sure the list size equals to two."
+                f"You have passed a list of images of length {len(image_pair)}.Make sure the list size equals to two."
             )
 
         # Check `controlnet_conditioning_scale`

examples/research_projects/pytorch_xla/training/text_to_image/train_text_to_image_xla.py

@@ -173,7 +173,7 @@ class TrainSD:
         if not dataloader_exception:
             xm.wait_device_ops()
             total_time = time.time() - last_time
-            print(f"Average step time: {total_time/(self.args.max_train_steps-measure_start_step)}")
+            print(f"Average step time: {total_time / (self.args.max_train_steps - measure_start_step)}")
         else:
             print("dataloader exception happen, skip result")
             return
@@ -622,7 +622,7 @@ def main(args):
     num_devices_per_host = num_devices // num_hosts
     if xm.is_master_ordinal():
         print("***** Running training *****")
-        print(f"Instantaneous batch size per device = {args.train_batch_size // num_devices_per_host }")
+        print(f"Instantaneous batch size per device = {args.train_batch_size // num_devices_per_host}")
         print(
             f"Total train batch size (w. parallel, distributed & accumulation) = {args.train_batch_size * num_hosts}"
         )

examples/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth.py

@@ -1057,7 +1057,7 @@ 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/research_projects/scheduled_huber_loss_training/dreambooth/train_dreambooth_lora.py

@@ -1021,7 +1021,7 @@ 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/research_projects/scheduled_huber_loss_training/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 />
 
@@ -1336,7 +1336,7 @@ 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/research_projects/scheduled_huber_loss_training/text_to_image/train_text_to_image_lora_sdxl.py

@@ -750,7 +750,7 @@ def main(args):
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
         lora_state_dict, _ = 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/research_projects/sd3_lora_colab/train_dreambooth_lora_sd3_miniature.py

@@ -765,7 +765,7 @@ 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/text_to_image/train_text_to_image_lora_sdxl.py

@@ -767,7 +767,7 @@ def main(args):
                 raise ValueError(f"unexpected save model: {model.__class__}")
 
         lora_state_dict, _ = 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/textual_inversion/textual_inversion.py

@@ -910,9 +910,9 @@ def main():
                 index_no_updates[min(placeholder_token_ids) : max(placeholder_token_ids) + 1] = False
 
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
+                        orig_embeds_params[index_no_updates]
+                    )
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

examples/textual_inversion/textual_inversion_sdxl.py

@@ -965,12 +965,12 @@ def main():
                 index_no_updates_2[min(placeholder_token_ids_2) : max(placeholder_token_ids_2) + 1] = False
 
                 with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
-                    accelerator.unwrap_model(text_encoder_2).get_input_embeddings().weight[
-                        index_no_updates_2
-                    ] = orig_embeds_params_2[index_no_updates_2]
+                    accelerator.unwrap_model(text_encoder_1).get_input_embeddings().weight[index_no_updates] = (
+                        orig_embeds_params[index_no_updates]
+                    )
+                    accelerator.unwrap_model(text_encoder_2).get_input_embeddings().weight[index_no_updates_2] = (
+                        orig_embeds_params_2[index_no_updates_2]
+                    )
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

examples/vqgan/test_vqgan.py

@@ -177,7 +177,7 @@ class TextToImage(ExamplesTestsAccelerate):
                 --model_config_name_or_path {vqmodel_config_path}
                 --discriminator_config_name_or_path {discriminator_config_path}
                 --checkpointing_steps=1
-                --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
+                --resume_from_checkpoint={os.path.join(tmpdir, "checkpoint-4")}
                 --output_dir {tmpdir}
                 --seed=0
                 """.split()
@@ -262,7 +262,7 @@ class TextToImage(ExamplesTestsAccelerate):
                 --model_config_name_or_path {vqmodel_config_path}
                 --discriminator_config_name_or_path {discriminator_config_path}
                 --checkpointing_steps=1
-                --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
+                --resume_from_checkpoint={os.path.join(tmpdir, "checkpoint-4")}
                 --output_dir {tmpdir}
                 --use_ema
                 --seed=0
@@ -377,7 +377,7 @@ class TextToImage(ExamplesTestsAccelerate):
                 --discriminator_config_name_or_path {discriminator_config_path}
                 --output_dir {tmpdir}
                 --checkpointing_steps=2
-                --resume_from_checkpoint={os.path.join(tmpdir, 'checkpoint-4')}
+                --resume_from_checkpoint={os.path.join(tmpdir, "checkpoint-4")}
                 --checkpoints_total_limit=2
                 --seed=0
                 """.split()

examples/vqgan/train_vqgan.py

@@ -653,15 +653,15 @@ def main():
     try:
         # Gets the resolution of the timm transformation after centercrop
         timm_centercrop_transform = timm_transform.transforms[1]
-        assert isinstance(
-            timm_centercrop_transform, transforms.CenterCrop
-        ), f"Timm model {timm_model} is currently incompatible with this script. Try vgg19."
+        assert isinstance(timm_centercrop_transform, transforms.CenterCrop), (
+            f"Timm model {timm_model} is currently incompatible with this script. Try vgg19."
+        )
         timm_model_resolution = timm_centercrop_transform.size[0]
         # Gets final normalization
         timm_model_normalization = timm_transform.transforms[-1]
-        assert isinstance(
-            timm_model_normalization, transforms.Normalize
-        ), f"Timm model {timm_model} is currently incompatible with this script. Try vgg19."
+        assert isinstance(timm_model_normalization, transforms.Normalize), (
+            f"Timm model {timm_model} is currently incompatible with this script. Try vgg19."
+        )
     except AssertionError as e:
         raise NotImplementedError(e)
     # Enable flash attention if asked

pyproject.toml

@@ -3,7 +3,7 @@ line-length = 119
 
 [tool.ruff.lint]
 # Never enforce `E501` (line length violations).
-ignore = ["C901", "E501", "E741", "F402", "F823"]
+ignore = ["C901", "E501", "E721", "E741", "F402", "F823"]
 select = ["C", "E", "F", "I", "W"]
 
 # Ignore import violations in all `__init__.py` files.