import gradio as gr import numpy as np import spaces import torch import random from PIL import Image import math # --- nag_app.pyから移植した機能 --- # 翻訳ライブラリのインポート from deep_translator import GoogleTranslator from langdetect import detect # NAG対応パイプラインのインポート # 注: このコードを実行するには、nag_app.pyのHugging Face Spaceから # `src`ディレクトリ（pipeline_flux_kontext_nag.pyとtransformer_flux.pyを含む）を # このファイルと同じ階層に配置する必要があります。 from src.pipeline_flux_kontext_nag import NAGFluxKontextPipeline from src.transformer_flux import NAGFluxTransformer2DModel # --- ここまでが移植部分 --- # エラー解決のためにdiffusersの内部マッピングをインポート from diffusers.loaders.peft import _SET_ADAPTER_SCALE_FN_MAPPING # 定数の設定 MAX_SEED = np.iinfo(np.int32).max DEFAULT_NAG_NEGATIVE_PROMPT = "Low resolution, blurry, lack of details, big head" OUTPUT_RESOLUTION = 1024 # --- nag_app.pyから移植したモデル読み込み処理 --- # NAG対応のKontextモデルをロード transformer = NAGFluxTransformer2DModel.from_pretrained( "black-forest-labs/FLUX.1-Kontext-dev", subfolder="transformer", torch_dtype=torch.bfloat16, ) pipe = NAGFluxKontextPipeline.from_pretrained( "black-forest-labs/FLUX.1-Kontext-dev", transformer=transformer, torch_dtype=torch.bfloat16, ) pipe = pipe.to("cuda") # --- ここまでが移植部分 --- # --- LoRAの読み込み処理 (5つ) --- print("Loading LoRA weights...") # LoRA名とアダプター名のマッピング LORA_MAPPING = { "Hyper-SD": "hyper", "Relighting": "relight", "LoRA 3": "lora_3", "LoRA 4": "lora_4", "LoRA 5": "lora_5", } # 1. Hyper-SD LoRA pipe.load_lora_weights( "ByteDance/Hyper-SD", weight_name="Hyper-FLUX.1-dev-8steps-lora.safetensors", adapter_name=LORA_MAPPING["Hyper-SD"] ) # 2. Relighting LoRA pipe.load_lora_weights( "linoyts/relighting-kontext-dev-lora", weight_name="relighting-kontext-dev-lora.safetensors", adapter_name=LORA_MAPPING["Relighting"] ) # 3. 追加のLoRA 3 (後で設定) # ★ 注意: 以下のリポジトリ名とファイル名は仮のものです。後で正しいものに置き換えてください。 try: pipe.load_lora_weights( "author/repo_name_3", # 例: "cagliostrolab/animagine-xl-3.0" weight_name="lora_file_3.safetensors", # 例: "animagine-xl-3.0.safetensors" adapter_name=LORA_MAPPING["LoRA 3"] ) except Exception as e: print(f"Warning: Could not load {list(LORA_MAPPING.keys())[2]}. Please check repository and file names. Error:", e) # 4. 追加のLoRA 4 (後で設定) try: pipe.load_lora_weights( "author/repo_name_4", weight_name="lora_file_4.safetensors", adapter_name=LORA_MAPPING["LoRA 4"] ) except Exception as e: print(f"Warning: Could not load {list(LORA_MAPPING.keys())[3]}. Please check repository and file names. Error:", e) # 5. 追加のLoRA 5 (後で設定) try: pipe.load_lora_weights( "author/repo_name_5", weight_name="lora_file_5.safetensors", adapter_name=LORA_MAPPING["LoRA 5"] ) except Exception as e: print(f"Warning: Could not load {list(LORA_MAPPING.keys())[4]}. Please check repository and file names. Error:", e) print("LoRA weights loading process finished.") # --- ここまでが変更部分 --- # カスタムモデルをdiffusersのLoRA対応表に登録する _SET_ADAPTER_SCALE_FN_MAPPING[NAGFluxTransformer2DModel.__name__] = _SET_ADAPTER_SCALE_FN_MAPPING["FluxTransformer2DModel"] print("Custom model 'NAGFluxTransformer2DModel' registered for LoRA.") def round_to_multiple(number, multiple=8): return multiple * round(number / multiple) def concatenate_images(images, direction="horizontal"): if not images: return None valid_images = [img for img in images if img is not None] if not valid_images: return None if len(valid_images) == 1: return valid_images[0].convert("RGB") valid_images = [img.convert("RGB") for img in valid_images] if direction == "horizontal": total_width = sum(img.width for img in valid_images) max_height = max(img.height for img in valid_images) concatenated = Image.new('RGB', (total_width, max_height), (255, 255, 255)) x_offset = 0 for img in valid_images: y_offset = (max_height - img.height) // 2 concatenated.paste(img, (x_offset, y_offset)) x_offset += img.width else: max_width = max(img.width for img in valid_images) total_height = sum(img.height for img in valid_images) concatenated = Image.new('RGB', (max_width, total_height), (255, 255, 255)) y_offset = 0 for img in valid_images: x_offset = (max_width - img.width) // 2 concatenated.paste(img, (x_offset, y_offset)) y_offset += img.height return concatenated @spaces.GPU(duration=25) # ★ infer関数の引数に negative_prompt を追加 def infer(input_images, prompt, negative_prompt, seed, randomize_seed, guidance_scale, nag_negative_prompt, nag_scale, num_inference_steps, # LoRAの有効/無効と強度を個別に受け取る enable_lora1, weight_lora1, enable_lora2, weight_lora2, enable_lora3, weight_lora3, enable_lora4, weight_lora4, enable_lora5, weight_lora5, progress=gr.Progress(track_tqdm=True)): active_adapters = [] active_weights = [] lora_params = [ (enable_lora1, weight_lora1, "Hyper-SD"), (enable_lora2, weight_lora2, "Relighting"), (enable_lora3, weight_lora3, "LoRA 3"), (enable_lora4, weight_lora4, "LoRA 4"), (enable_lora5, weight_lora5, "LoRA 5"), ] for is_enabled, weight, name in lora_params: if is_enabled: adapter_name = LORA_MAPPING[name] active_adapters.append(adapter_name) active_weights.append(weight) print(f"Applying {name} LoRA with weight {weight}") if active_adapters: pipe.set_adapters(active_adapters, adapter_weights=active_weights) else: print("No LoRA selected. Running without LoRA.") pipe.disable_lora() prompt = prompt.strip() if prompt: print(f"Original prompt: {prompt}") try: detected_lang = detect(prompt) if detected_lang != 'en': print(f"Detected language: {detected_lang}. Translating to English...") translated_prompt = GoogleTranslator(source=detected_lang, target='en').translate(prompt) prompt = translated_prompt print(f"Translated prompt: {prompt}") else: print("Prompt is already in English.") except Exception as e: print(f"Warning: Translation or language detection failed: {e}. Using original prompt.") # ★ negative_promptを処理するコードを追加 negative_prompt = negative_prompt.strip() if negative_prompt and negative_prompt.strip() else None if randomize_seed: seed = random.randint(0, MAX_SEED) if input_images is None: raise gr.Error("Please upload at least one image.") if not isinstance(input_images, list): input_images = [input_images] valid_images = [img[0] for img in input_images if img is not None] if not valid_images: raise gr.Error("Please upload at least one valid image.") if len(valid_images) == 1: print("Single image detected. Calculating aspect-ratio aware dimensions.") input_for_pipe = valid_images[0] input_width, input_height = input_for_pipe.size aspect_ratio = input_width / input_height target_pixels = OUTPUT_RESOLUTION * OUTPUT_RESOLUTION final_height = int(math.sqrt(target_pixels / aspect_ratio)) final_width = int(aspect_ratio * final_height) final_width = round_to_multiple(final_width, 8) final_height = round_to_multiple(final_height, 8) print(f"Output dimensions set to: {final_width}x{final_height}") else: print(f"Multiple ({len(valid_images)}) images detected. Using fixed 1024x1024 output.") input_for_pipe = concatenate_images(valid_images, "horizontal") if input_for_pipe is None: raise gr.Error("Failed to process the input images.") final_width = OUTPUT_RESOLUTION final_height = OUTPUT_RESOLUTION final_prompt = f"From the provided reference images, create a unified, cohesive image such that {prompt}. Maintain the identity and characteristics of each subject while adjusting their proportions, scale, and positioning to create a harmonious, naturally balanced composition. Blend and integrate all elements seamlessly with consistent lighting, perspective, and style.the final result should look like a single naturally captured scene where all subjects are properly sized and positioned relative to each other, not assembled from multiple sources." # ★ pipe()呼び出しに negative_prompt を追加 image = pipe( image=input_for_pipe, prompt=final_prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, nag_negative_prompt=nag_negative_prompt, nag_scale=nag_scale, width=final_width, height=final_height, num_inference_steps=num_inference_steps, generator=torch.Generator().manual_seed(seed), ).images[0] pipe.disable_lora() return image, seed, gr.update(visible=True) css=""" #col-container { margin: 0 auto; max-width: 960px; } .lora-row { align-items: center; margin-bottom: 8px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# FLUX.1 Kontext [dev] - Multi-Image with NAG Compose a new image from multiple images using FLUX.1 Kontext, enhanced with Normalized Attention Guidance (NAG) and automatic prompt translation. - **Single Image Input**: Output will match the input aspect ratio. - **Multiple Image Inputs**: Output will be a fixed 1024x1024 resolution. """) with gr.Row(): with gr.Column(): input_images = gr.Gallery( label="Upload image(s) for editing", show_label=True, elem_id="gallery_input", columns=3, rows=2, object_fit="contain", height="auto", file_types=['image'], type='pil' ) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt (auto-translates to English)", container=False, ) run_button = gr.Button("Run", scale=0) with gr.Accordion("Advanced Settings", open=False): # --- ★ UIを修正: 各LoRAコンポーネントを個別の変数として定義 --- gr.Markdown("### LoRA Settings") with gr.Row(elem_classes="lora-row"): enable_lora1 = gr.Checkbox(label="Hyper-SD", value=True, scale=1) weight_lora1 = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.02, value=0.12, scale=3, visible=True) with gr.Row(elem_classes="lora-row"): enable_lora2 = gr.Checkbox(label="Relighting", value=False, scale=1) weight_lora2 = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=1.0, scale=3, visible=False) with gr.Row(elem_classes="lora-row"): enable_lora3 = gr.Checkbox(label="LoRA 3", value=False, scale=1) weight_lora3 = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=0.8, scale=3, visible=False) with gr.Row(elem_classes="lora-row"): enable_lora4 = gr.Checkbox(label="LoRA 4", value=False, scale=1) weight_lora4 = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=0.8, scale=3, visible=False) with gr.Row(elem_classes="lora-row"): enable_lora5 = gr.Checkbox(label="LoRA 5", value=False, scale=1) weight_lora5 = gr.Slider(label="Weight", minimum=0.0, maximum=2.0, step=0.05, value=0.8, scale=3, visible=False) # --- ★ ここまでが変更部分 --- gr.Markdown("### Generation Settings") # ★ UIに negative_prompt を追加 negative_prompt = gr.Text( label="Negative Prompt (Standard)", placeholder="Enter concepts to avoid (e.g., ugly, deformed)", max_lines=2, ) num_inference_steps = gr.Slider( label="Inference Steps", minimum=8, maximum=50, step=1, value=8, ) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=10, step=0.25, value=4.5, ) nag_negative_prompt = gr.Text( label="Negative Prompt for NAG", value=DEFAULT_NAG_NEGATIVE_PROMPT, max_lines=2, placeholder="Enter concepts to avoid with NAG", ) nag_scale = gr.Slider( label="NAG Scale", minimum=0.0, maximum=20.0, step=0.25, value=3.5 ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) with gr.Column(): result = gr.Image(label="Result", show_label=False, interactive=False, format="png") reuse_button = gr.Button("Reuse this image", visible=False) # ★ イベントハンドラを更新: all_inputsに negative_prompt を追加 all_inputs = [ input_images, prompt, negative_prompt, seed, randomize_seed, guidance_scale, nag_negative_prompt, nag_scale, num_inference_steps, enable_lora1, weight_lora1, enable_lora2, weight_lora2, enable_lora3, weight_lora3, enable_lora4, weight_lora4, enable_lora5, weight_lora5, ] gr.on( triggers=[run_button.click, prompt.submit], fn = infer, inputs = all_inputs, outputs = [result, seed, reuse_button] ) # --- ★ ここまでが変更部分 --- reuse_button.click( fn = lambda image: [image] if image is not None else [], inputs = [result], outputs = [input_images] ) # --- ★ 各チェックボックスとスライダーの表示を個別に連動させる --- def update_visibility(is_checked): return gr.update(visible=is_checked) enable_lora1.change(fn=update_visibility, inputs=enable_lora1, outputs=weight_lora1) enable_lora2.change(fn=update_visibility, inputs=enable_lora2, outputs=weight_lora2) enable_lora3.change(fn=update_visibility, inputs=enable_lora3, outputs=weight_lora3) enable_lora4.change(fn=update_visibility, inputs=enable_lora4, outputs=weight_lora4) enable_lora5.change(fn=update_visibility, inputs=enable_lora5, outputs=weight_lora5) # --- ★ ここまでが変更部分 --- demo.launch()