Update README.md

README.md

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----
-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
----
+---
+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
+
+language:
+  - en
+library_name: diffusers
+pipeline_tag: text-to-image
+
+tags:
+- Text-to-Image
+- ControlNet
+- Diffusers
+- Flux.1-dev
+- image-generation
+- Stable Diffusion
+base_model: black-forest-labs/FLUX.1-dev
+---
+
+# FLUX.1-dev-ControlNet-Depth
+
+This repository contains a Depth ControlNet for FLUX.1-dev model jointly trained by researchers from [InstantX Team](https://huggingface.co/InstantX) and [Shakker Labs](https://huggingface.co/Shakker-Labs).
+
+# Model Cards
+- The model consists of 4 FluxTransformerBlock and 1 FluxSingleTransformerBlock.
+- This checkpoint is trained on both real and generated image datasets. with 16*A800 for 50K steps. The batch size 16*4=64 with resolution=1024. The learning rate is set to 5e-6.
+- The recommended controlnet_conditioning_scale is 0.3-0.7.
+
+# Showcases
+
+<div class="container">
+<img src="./assets/teaser1.png" width="1024"/>
+</div>
+
+<div class="container">
+<img src="./assets/teaser2.png" width="1024"/>
+</div>
+
+
+<div class="container">
+<img src="./assets/teaser3.png" width="1024"/>
+</div>
+
+
+# Inference
+```python
+import torch
+from diffusers.utils import load_image
+from diffusers import FluxControlNetPipeline, FluxControlNetModel
+
+controlnet_model = "black-forest-labs/FLUX.1-dev"
+base_model = "Shakker-Labs/FLUX.1-dev-ControlNet-Depth"
+
+controlnet = FluxControlNetModel.from_pretrained(controlnet_model, torch_dtype=torch.bfloat16)
+pipe = FluxControlNetPipeline.from_pretrained(
+    base_model, controlnet=controlnet, torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+
+control_image = load_image("https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth/resolve/main/assets/cond1.png")
+prompt = "an old man with white hair"
+
+image = pipe(prompt,
+             control_image=control_image,
+             controlnet_conditioning_scale=0.5,
+             width=control_image.size[0],
+             height=control_image.size[1],
+             num_inference_steps=24,
+             guidance_scale=3.5,
+).images[0]
+```
+
+# Acknowledgements
+This project is sponsored by [Shakker AI](https://www.shakker.ai/). All copyright reserved.