update

Author: DN6

examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py

@@ -839,9 +839,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
         idx = 0
         for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
             assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings."
-            assert all(isinstance(tok, str) for tok in inserting_toks), (
-                "All elements in inserting_toks should be strings."
-            )
+            assert all(
+                isinstance(tok, str) for tok in inserting_toks
+            ), "All elements in inserting_toks should be strings."
 
             self.inserting_toks = inserting_toks
             special_tokens_dict = {"additional_special_tokens": self.inserting_toks}

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -725,9 +725,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
         idx = 0
         for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
             assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings."
-            assert all(isinstance(tok, str) for tok in inserting_toks), (
-                "All elements in inserting_toks should be strings."
-            )
+            assert all(
+                isinstance(tok, str) for tok in inserting_toks
+            ), "All elements in inserting_toks should be strings."
 
             self.inserting_toks = inserting_toks
             special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
@@ -747,9 +747,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
                 .to(dtype=self.dtype)
                 * std_token_embedding
             )
-            self.embeddings_settings[f"original_embeddings_{idx}"] = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
-            )
+            self.embeddings_settings[
+                f"original_embeddings_{idx}"
+            ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
             self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
 
             inu = torch.ones((len(tokenizer),), dtype=torch.bool)

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -890,9 +890,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
         idx = 0
         for tokenizer, text_encoder in zip(self.tokenizers, self.text_encoders):
             assert isinstance(inserting_toks, list), "inserting_toks should be a list of strings."
-            assert all(isinstance(tok, str) for tok in inserting_toks), (
-                "All elements in inserting_toks should be strings."
-            )
+            assert all(
+                isinstance(tok, str) for tok in inserting_toks
+            ), "All elements in inserting_toks should be strings."
 
             self.inserting_toks = inserting_toks
             special_tokens_dict = {"additional_special_tokens": self.inserting_toks}
@@ -912,9 +912,9 @@ def initialize_new_tokens(self, inserting_toks: List[str]):
                 .to(dtype=self.dtype)
                 * std_token_embedding
             )
-            self.embeddings_settings[f"original_embeddings_{idx}"] = (
-                text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
-            )
+            self.embeddings_settings[
+                f"original_embeddings_{idx}"
+            ] = text_encoder.text_model.embeddings.token_embedding.weight.data.clone()
             self.embeddings_settings[f"std_token_embedding_{idx}"] = std_token_embedding
 
             inu = torch.ones((len(tokenizer),), dtype=torch.bool)

examples/community/pipeline_prompt2prompt.py

@@ -907,12 +907,12 @@ def create_controller(
 
     # reweight
     if edit_type == "reweight":
-        assert equalizer_words is not None and equalizer_strengths is not None, (
-            "To use reweight edit, please specify equalizer_words and equalizer_strengths."
-        )
-        assert len(equalizer_words) == len(equalizer_strengths), (
-            "equalizer_words and equalizer_strengths must be of same length."
-        )
+        assert (
+            equalizer_words is not None and equalizer_strengths is not None
+        ), "To use reweight edit, please specify equalizer_words and equalizer_strengths."
+        assert len(equalizer_words) == len(
+            equalizer_strengths
+        ), "equalizer_words and equalizer_strengths must be of same length."
         equalizer = get_equalizer(prompts[1], equalizer_words, equalizer_strengths, tokenizer=tokenizer)
         return AttentionReweight(
             prompts,

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/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)

examples/model_search/pipeline_easy.py

@@ -1081,9 +1081,9 @@ def auto_load_textual_inversion(
                     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 @@ def __init__(self, img_size=(32, 100), in_channels=3, embed_dim=768, sub_num=2):
 
     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/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