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Update app.py
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app.py
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import
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import random
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import spaces
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import torch
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from
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from
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", subfolder="vae", torch_dtype=dtype).to(device)
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", torch_dtype=dtype, vae=taef1).to(device)
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torch.cuda.empty_cache()
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"""
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Generate an image using the Flux.1 Krea-Dev Image Generator
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"""
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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prompt=prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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output_type="pil",
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good_vae=good_vae,
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):
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yield img, seed
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"
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gr.Markdown(f"""# FLUX.1 Krea [dev]
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FLUX.1 Krea [dev] model further tuned and customized with [Krea](https://krea.ai)
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[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
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""")
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Run", scale=0)
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=768,
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=768,
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)
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with gr.Row():
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step=1,
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value=20,
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)
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gr.Examples(
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examples = examples,
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fn = infer,
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inputs = [prompt],
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outputs = [result, seed],
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cache_examples="lazy"
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)
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)
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import argparse
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import os
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import random
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import torch
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import numpy as np
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from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
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from PIL import Image
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import re
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def generate_image(pipe, good_vae, prompt, seed=42, randomize_seed=True, width=768, height=768, guidance_scale=4.5, num_inference_steps=20):
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"""
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使用 FLUX.1-Krea-dev 模型生成图像。
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Args:
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pipe: Diffusers pipeline.
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good_vae: 高质量的 VAE 解码器.
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prompt (str): 文本提示.
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seed (int): 随机种子.
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randomize_seed (bool): 是否随机化种子.
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width (int): 图像宽度.
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height (int): 图像高度.
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guidance_scale (float): 指导比例.
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num_inference_steps (int): 推理步数.
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Returns:
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tuple[Image.Image, int]: 返回生成的 PIL 图像和使用的种子.
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"""
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MAX_SEED = np.iinfo(np.int32).max
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator(device=pipe.device).manual_seed(seed)
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print(f"ℹ️ 使用种子: {seed}")
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print("1. 正在生成潜在向量 (latents)...")
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# 使用 pipeline 生成潜在向量
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latents = pipe(
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prompt=prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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output_type="latent"
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).images
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print("2. 使用高质量 VAE 解码图像...")
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# 使用高质量的 VAE 解码潜在向量
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# 需要根据 VAE 的配置进行缩放
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latents = latents / good_vae.config.scaling_factor
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image_tensor = good_vae.decode(latents, return_dict=False)[0]
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print("3. 后处理图像...")
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# 将张量转换为 PIL 图像
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image = pipe.image_processor.postprocess(image_tensor, output_type="pil")[0]
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return image, seed
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def main():
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"""
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主执行函数,用于解析参数和调用生成逻辑。
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"""
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# --- 参数解析 ---
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parser = argparse.ArgumentParser(description="使用 FLUX.1-Krea-dev 模型从文本提示生成图像。")
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parser.add_argument("--prompt", type=str, required=True, help="用于图像生成的文本提示。")
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parser.add_argument("--seed", type=int, default=None, help="随机种子。如果未提供,将随机生成。")
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parser.add_argument("--steps", type=int, default=20, help="推理步数。")
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parser.add_argument("--width", type=int, default=768, help="图像宽度。")
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parser.add_argument("--height", type=int, default=768, help="图像高度。")
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args = parser.parse_args()
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# --- 模型加载 ---
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print("⏳ 正在加载模型,请稍候...")
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dtype = torch.bfloat16
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device = "cuda" if torch.cuda.is_available() else "cpu"
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# 加载两个 VAE:一个用于快速预览(在 pipeline 中),一个用于高质量最终输出
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taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
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good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", subfolder="vae", torch_dtype=dtype).to(device)
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# 加载主 pipeline,并指定使用较小的 VAE 进行快速潜在向量生成
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pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-Krea-dev", torch_dtype=dtype, vae=taef1).to(device)
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if device == "cuda":
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torch.cuda.empty_cache()
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print(f"✅ 模型加载完成,使用设备: {device}")
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# --- 图像生成 ---
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print(f"🚀 开始为提示生成图像: '{args.prompt}'")
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randomize = args.seed is None
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seed_value = args.seed if not randomize else 42 # 如果指定了种子则使用,否则 generate_image 会随机生成
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generated_image, used_seed = generate_image(
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pipe=pipe,
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good_vae=good_vae,
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prompt=args.prompt,
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seed=seed_value,
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randomize_seed=randomize,
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width=args.width,
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height=args.height,
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num_inference_steps=args.steps
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)
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# --- 保存图像 ---
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output_dir = "output"
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os.makedirs(output_dir, exist_ok=True)
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# 清理提示词以用作文件名
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safe_prompt = re.sub(r'[^\w\s-]', '', args.prompt).strip()
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safe_prompt = re.sub(r'[-\s]+', '_', safe_prompt)
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filename = f"{safe_prompt[:50]}_{used_seed}.png"
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filepath = os.path.join(output_dir, filename)
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print(f"💾 正在保存图像到: {filepath}")
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generated_image.save(filepath)
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print("🎉 完成!")
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if __name__ == "__main__":
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main()
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