Flux.1 Dev Controlnet Upscaler

🚀 ⚡ Flux.1-dev: 超分辨率ControlNet ⚡

这是由Jasper研究团队开发的适用于低分辨率图像的 Flux.1-dev ControlNet模型。它能够有效处理低分辨率图像，实现超分辨率等功能，为图像生成领域带来新的解决方案。

🚀 快速开始

本模型可直接与 diffusers 库配合使用。

💻 使用示例

基础用法

import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetModel
from diffusers.pipelines import FluxControlNetPipeline

# Load pipeline
controlnet = FluxControlNetModel.from_pretrained(
  "jasperai/Flux.1-dev-Controlnet-Upscaler",
  torch_dtype=torch.bfloat16
)
pipe = FluxControlNetPipeline.from_pretrained(
  "black-forest-labs/FLUX.1-dev",
  controlnet=controlnet,
  torch_dtype=torch.bfloat16
)
pipe.to("cuda")

# Load a control image
control_image = load_image(
  "https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler/resolve/main/examples/input.jpg"
)

w, h = control_image.size

# Upscale x4
control_image = control_image.resize((w * 4, h * 4))

image = pipe(
    prompt="", 
    control_image=control_image,
    controlnet_conditioning_scale=0.6,
    num_inference_steps=28, 
    guidance_scale=3.5,
    height=control_image.size[1],
    width=control_image.size[0]
).images[0]
image

🔧 技术细节

本模型采用了一种合成的复杂数据退化方案进行训练。该方案以一张真实场景图像作为输入，通过结合多种退化方式（如高斯噪声、泊松噪声、图像模糊和JPEG压缩等）对图像进行人工退化，其思路与文献 [1] 类似。

[1] Wang, Xintao, et al. "Real-esrgan: Training real-world blind super-resolution with pure synthetic data." Proceedings of the IEEE/CVF international conference on computer vision. 2021.

📄 许可证

本模型遵循 Flux.1-dev 模型许可证。

📦 模型信息