This is <a target="_blank" rel="ugc" href="https://huggingface.co/black-forest-labs/FLUX.1-dev">Flux.1-dev</a> ControlNet for low resolution images developed by Jasper research team.<img src="https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler/resolve/main/examples/showcase.jpg" /><h1 id="how-to-use-o6waj927g">How to use</h1>This model can be used directly with the <code>diffusers</code> library<pre><code>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
</code></pre><img src="https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler/resolve/main/examples/output.jpg" /><h1 id="training-asfuf3q6m">Training</h1>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.<h1 id="licence-t4cimxd5p">Licence</h1>This model falls under the <a target="_blank" rel="ugc" href="https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md">Flux.1-dev model licence</a>.

jasperai / Flux.1-dev-Controlnet-Upscaler

jasperaiFlux1Dev_v10.safetensors

v1.0

<h3 id="controlnetflux-(cnflux)-a-collection-of-controlnet-models-for-flux.1-dev-dbc3x7t3x">ControlNetFlux (CNFlux) - A collection of Controlnet models for Flux.1 [dev]</h3>This collection strives to create a convenient download location of all currently available Controlnet models for Flux. Please do read the version info for model specific instructions and further resources. If you are the author of one of these models and don't want it to appear here, please contact me to sort this out. Support at the moment is limited to ComfyUI and Forge as far as i know.NEW: windsingai-openposeA ComfyUI workflow for InstantX's union model can be found <a target="_blank" rel="ugc" href="https://www.reddit.com/r/StableDiffusion/comments/1euz2a9/union_flux_controlnet_running_on_comfyui_workflow/">here</a>. The necessary nodes <a target="_blank" rel="ugc" href="https://github.com/EeroHeikkinen/ComfyUI-eesahesNodes">here</a>.A huge thanks to all the authors, devs and contributors including but not limited to: Automatic1111, Black-Forest-Labs, comfyanonymous, dashtoon, eesahe, Illasviel, InstantX, kadirnar, lucataco, PromeAI, Shakker-Labs, windsingai, <a target="_blank" rel="ugc" href="https://civarchive.com/user/xlabs_ai">XLabs-AI</a>

ControlNetFlux (CNFlux)

Upscale (Tile?) model released by Jasper AI on huggingface<a target="_blank" rel="ugc" href="https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler">https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler</a>

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