This is Flux.1-dev ControlNet for low resolution images developed by Jasper research team.

How to use

This model can be used directly with the diffusers library

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

Training

This model was trained with a synthetic complex data degradation scheme taking as input a real-life image and artificially degrading it by combining several degradations such as amongst other image noising (Gaussian, Poisson), image blurring and JPEG compression in a similar spirit as [1]

[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.

Licence

This model falls under the Flux.1-dev model licence.

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