import gradio as gr import numpy as np import spaces import torch import random from PIL import Image from pipeline_flux_kontext import FluxKontextPipeline from diffusers.utils import load_image # --- LoRA 配置 --- # 结构: "LoRA显示名称": {"file": "LoRA文件名.safetensors", "adapter_name": "唯一的适配器名称"} LORA_REPO_ID = "IdlecloudX/Flux_and_Wan_Lora" LORA_SETS = { "Remove Clothes": { "file": "change_clothes_to_nothing_000012800.safetensors", "adapter_name": "remove_clothes" }, "Pencil Drawing": { "file": "pencil_drawing.safetensors", "adapter_name": "pencil_drawing" }, "Pixelize": { "file": "kontext-pixelize-v1.1_000001500.safetensors", "adapter_name": "pixelize" }, "Fluffy Art": { "file": "fluffy-kontext-lora.safetensors", "adapter_name": "fluffy" }, "Reality Transform": { "file": "aldniki_reality_transform_v01.safetensors", "adapter_name": "reality_transform" }, "Figure Maker": { "file": "aldniki_figure_maker_v01.safetensors", "adapter_name": "figure_maker" } } # ------------------------- # 加载 Kontext 模型 MAX_SEED = np.iinfo(np.int32).max print("正在加载 FLUX Kontext pipeline...") pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda") print("Pipeline 加载完成。") # --- 加载所有定义的 LoRA 权重 --- for name, lora_config in LORA_SETS.items(): print(f"--- 正在加载 LoRA: {name} ---") try: pipe.load_lora_weights( LORA_REPO_ID, weight_name=lora_config['file'], adapter_name=lora_config['adapter_name'] ) print(f"'{name}' LoRA 加载成功。") except Exception as e: print(f"加载 LoRA '{name}' ({lora_config['file']}) 失败: {e}") print("请检查 LORA_REPO_ID 和文件名是否正确，或者 LoRA 是否与当前模型兼容。") # ------------------------------------ def concatenate_images(images, direction="horizontal"): """ 将多个PIL图像水平或垂直拼接。 """ 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: # vertical 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 def infer(input_images, prompt, selected_loras, seed=42, randomize_seed=False, guidance_scale=2.5, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) if input_images is None: raise gr.Error("请至少上传一张图片。") 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("请上传至少一张有效的图片。") concatenated_image = concatenate_images(valid_images, "horizontal") if concatenated_image is None: raise gr.Error("处理输入图片失败。") 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." # --- LoRA 应用逻辑 --- active_adapters = [] if selected_loras: for lora_name in selected_loras: if lora_name in LORA_SETS: active_adapters.append(LORA_SETS[lora_name]["adapter_name"]) if active_adapters: print(f"正在启用选择的 LoRA 适配器: {active_adapters}") pipe.set_adapters(active_adapters, adapter_weights=[1.0] * len(active_adapters)) else: pipe.disable_lora() image = pipe( image=concatenated_image, prompt=final_prompt, guidance_scale=guidance_scale, width=concatenated_image.size[0], height=concatenated_image.size[1], generator=torch.Generator().manual_seed(seed), ).images[0] if active_adapters: print("推理完成，正在禁用 LoRA 适配器。") pipe.disable_lora() return image, seed, gr.update(visible=True) css=""" #col-container { margin: 0 auto; max-width: 960px; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# FLUX.1 Kontext [dev] - Multi-Image with LoRA 使用 FLUX.1 Kontext [dev] 将多张图片中的元素组合成一张新图，并支持应用自定义 LoRA 风格。 """) with gr.Row(): with gr.Column(): input_images = gr.Gallery( label="上传用于编辑的图片", 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, info = "描述期望的输出构图", max_lines=1, placeholder="例如：左边图片里的狗坐在右边图片的长椅上", container=False, ) run_button = gr.Button("Run", scale=0) lora_selection = gr.CheckboxGroup( choices=list(LORA_SETS.keys()), label="选择 LoRA 风格 (可多选)", info="选择一个或多个风格进行叠加。" ) with gr.Accordion("高级设置", open=False): seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="随机种子", value=True) guidance_scale = gr.Slider( label="引导系数 (Guidance Scale)", minimum=1, maximum=10, step=0.1, value=2.5, ) with gr.Column(): result = gr.Image(label="结果", show_label=False, interactive=False) reuse_button = gr.Button("复用此图", visible=False) inputs = [input_images, prompt, lora_selection, seed, randomize_seed, guidance_scale] outputs = [result, seed, reuse_button] gr.on( triggers=[run_button.click, prompt.submit], fn = infer, inputs = inputs, outputs = outputs ) reuse_button.click( fn = lambda image: [image] if image is not None else [], inputs = [result], outputs = [input_images] ) demo.launch()