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.