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README.md

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+---
+base_model:
+- black-forest-labs/FLUX.1-dev
+library_name: diffusers
+license: other
+license_name: flux-1-dev-non-commercial-license
+license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
+pipeline_tag: image-to-image
+inference: False
+tags:
+- ControlNet
+- super-resolution
+- upscaler
+---
+# ⚡ Flux.1-dev: Upscaler ControlNet ⚡
+
+This is [Flux.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) ControlNet for low resolution images developed by Jasper research team.
+
+<p align="center">
+   <img style="width:700px;" src="examples/showcase.jpg">
+</p>
+
+# How to use
+This model can be used directly with the `diffusers` library
+
+```python
+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
+```
+
+<p align="center">
+   <img style="width:500px;" src="examples/output.jpg">
+</p>
+
+
+# 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](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).

config.json

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+{
+  "_class_name": "FluxControlNetModel",
+  "_diffusers_version": "0.31.0.dev0",
+  "_name_or_path": "/data/checkpoints/flux_controlnet_hf/controlnet_upscaling//",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    16,
+    56,
+    56
+  ],
+  "guidance_embeds": true,
+  "in_channels": 64,
+  "joint_attention_dim": 4096,
+  "num_attention_heads": 24,
+  "num_layers": 5,
+  "num_mode": null,
+  "num_single_layers": 0,
+  "patch_size": 1,
+  "pooled_projection_dim": 768
+}

diffusion_pytorch_model.safetensors

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+size 3583232168