app.py

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()