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--- |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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library_name: diffusers |
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license: cc-by-nc-4.0 |
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license_name: flux-1-dev-non-commercial-license |
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md |
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pipeline_tag: image-to-image |
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tags: |
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- ControlNet |
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- super-resolution |
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- upscaler |
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--- |
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# ⚡ Flux.1-dev: Upscaler ControlNet ⚡ |
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This is [Flux.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) ControlNet for low resolution images developped by Jasper research team. |
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<p align="center"> |
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<img style="width:700px;" src="examples/showcase.jpg"> |
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</p> |
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# How to use |
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This model can be used directly with the `diffusers` library |
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```python |
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import torch |
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from diffusers.utils import load_image |
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from diffusers import FluxControlNetModel |
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from diffusers.pipelines import FluxControlNetPipeline |
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# Load pipeline |
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controlnet = FluxControlNetModel.from_pretrained( |
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"jasperai/Flux.1-dev-Controlnet-Upscaler", |
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torch_dtype=torch.bfloat16 |
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) |
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pipe = FluxControlNetPipeline.from_pretrained( |
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"black-forest-labs/FLUX.1-dev", |
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controlnet=controlnet, |
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torch_dtype=torch.bfloat16 |
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) |
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pipe.to("cuda") |
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# Load a control image |
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control_image = load_image( |
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"https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler/resolve/main/examples/input.jpg" |
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) |
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w, h = control_image.size |
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# Upscale x4 |
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# This can be set to any arbitrary target resolution |
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control_image = control_image.resize((w * 4, h * 4)) |
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image = pipe( |
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prompt="", |
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control_image=control_image, |
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controlnet_conditioning_scale=0.6, |
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num_inference_steps=28, |
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guidance_scale=3.5, |
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height=control_image.size[1], |
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width=control_image.size[0] |
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).images[0] |
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image |
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``` |
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<p align="center"> |
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<img style="width:500px;" src="examples/output.jpg"> |
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</p> |
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# Training |
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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] |
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[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. |
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# Licence |
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This model is released under the the Creative Commons BY-NC license. |
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Note that if used with Flux.1-dev, the model under the Flux.1-dev model also applies to this model. |
