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README.md
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README.md
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---
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---
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license: Apache License 2.0
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license: other
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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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#model-type:
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language:
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##如 gpt、phi、llama、chatglm、baichuan 等
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- en
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#- gpt
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library_name: diffusers
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pipeline_tag: text-to-image
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#domain:
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tags:
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##如 nlp、cv、audio、multi-modal
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- Text-to-Image
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#- nlp
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- ControlNet
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- Diffusers
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#language:
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- Flux.1-dev
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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- image-generation
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#- cn
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- Stable Diffusion
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base_model: black-forest-labs/FLUX.1-dev
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义,包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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---
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令,或者ModelScope SDK来下载模型
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SDK下载
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# FLUX.1-dev-ControlNet-Union-Pro-2.0
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```bash
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#安装ModelScope
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This repository contains an unified ControlNet for FLUX.1-dev model released by [Shakker Labs](https://huggingface.co/Shakker-Labs). We provide an [online demo](https://huggingface.co/spaces/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0).
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pip install modelscope
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```
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# Keynotes
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In comparison with [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro),
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- Remove mode embedding, has smaller model size.
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- Improve on canny and pose, better control and aesthetics.
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- Add support for soft edge. Remove support for tile.
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# Model Cards
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- This ControlNet consists of 6 double blocks and 0 single block. Mode embedding is removed.
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- We train the model from scratch for 300k steps using a dataset of 20M high-quality general and human images. We train at 512x512 resolution in BFloat16, batch size = 128, learning rate = 2e-5, the guidance is uniformly sampled from [1, 7]. We set the text drop ratio to 0.20.
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- This model supports multiple control modes, including canny, soft edge, depth, pose, gray. You can use it just as a normal ControlNet.
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- This model can be jointly used with other ControlNets.
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# Showcases
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<table>
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<tr>
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<td><img src="./images/canny.png" alt="canny" style="height:100%"></td>
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</tr>
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<tr>
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<td><img src="./images/softedge.png" alt="softedge" style="height:100%"></td>
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</tr>
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<tr>
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<td><img src="./images/pose.png" alt="pose" style="height:100%"></td>
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</tr>
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<tr>
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<td><img src="./images/depth.png" alt="depth" style="height:100%"></td>
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</tr>
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<tr>
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<td><img src="./images/gray.png" alt="gray" style="height:100%"></td>
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</tr>
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</table>
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# Inference
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```python
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```python
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#SDK模型下载
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import torch
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from modelscope import snapshot_download
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from diffusers.utils import load_image
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model_dir = snapshot_download('LiblibAI/FLUX.1-dev-ControlNet-Union-Pro-2.0')
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from diffusers import FluxControlNetPipeline, FluxControlNetModel
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```
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Git下载
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base_model = 'black-forest-labs/FLUX.1-dev'
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```
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controlnet_model_union = 'Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro-2.0'
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#Git模型下载
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git clone https://www.modelscope.cn/LiblibAI/FLUX.1-dev-ControlNet-Union-Pro-2.0.git
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controlnet = FluxControlNetModel.from_pretrained(controlnet_model_union, torch_dtype=torch.bfloat16)
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pipe = FluxControlNetPipeline.from_pretrained(base_model, controlnet=controlnet, torch_dtype=torch.bfloat16)
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pipe.to("cuda")
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# replace with other conds
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control_image = load_image("./conds/canny.png")
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width, height = control_image.size
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prompt = "A young girl stands gracefully at the edge of a serene beach, her long, flowing hair gently tousled by the sea breeze. She wears a soft, pastel-colored dress that complements the tranquil blues and greens of the coastal scenery. The golden hues of the setting sun cast a warm glow on her face, highlighting her serene expression. The background features a vast, azure ocean with gentle waves lapping at the shore, surrounded by distant cliffs and a clear, cloudless sky. The composition emphasizes the girl's serene presence amidst the natural beauty, with a balanced blend of warm and cool tones."
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image = pipe(
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prompt,
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control_image=control_image,
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width=width,
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height=height,
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controlnet_conditioning_scale=0.7,
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control_guidance_end=0.8,
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num_inference_steps=30,
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guidance_scale=3.5,
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generator=torch.Generator(device="cuda").manual_seed(42),
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).images[0]
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```
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者,我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>,及时完善模型卡片内容。</p>
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# Recommended Parameters
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You can adjust controlnet_conditioning_scale and control_guidance_end for stronger control and better detail preservation. For better stability, we suggest to use multi-conditions.
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- Canny: use cv2.Canny, controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
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- Soft Edge: use [AnylineDetector](https://github.com/huggingface/controlnet_aux), controlnet_conditioning_scale=0.7, control_guidance_end=0.8.
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- Depth: use [depth-anything](https://github.com/DepthAnything/Depth-Anything-V2), controlnet_conditioning_scale=0.8, control_guidance_end=0.8.
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- Pose: use [DWPose](https://github.com/IDEA-Research/DWPose/tree/onnx), controlnet_conditioning_scale=0.9, control_guidance_end=0.65.
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- Gray: use cv2.cvtColor, controlnet_conditioning_scale=0.9, control_guidance_end=0.8.
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# Resources
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- [InstantX/FLUX.1-dev-IP-Adapter](https://huggingface.co/InstantX/FLUX.1-dev-IP-Adapter)
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- [InstantX/FLUX.1-dev-Controlnet-Canny](https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny)
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- [Shakker-Labs/FLUX.1-dev-ControlNet-Depth](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Depth)
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- [Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro](https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro)
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# Acknowledgements
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This model is developed by [Shakker Labs](https://huggingface.co/Shakker-Labs). The original idea is inspired by [xinsir/controlnet-union-sdxl-1.0](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0). All copyright reserved.
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{
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"_class_name": "FluxControlNetModel",
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"_diffusers_version": "0.31.0.dev0",
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"attention_head_dim": 128,
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"axes_dims_rope": [
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16,
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56,
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],
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"guidance_embeds": true,
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"in_channels": 64,
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"joint_attention_dim": 4096,
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"num_attention_heads": 24,
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"num_layers": 6,
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"num_mode": null,
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"num_single_layers": 0,
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"patch_size": 1,
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"pooled_projection_dim": 768
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}
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{"framework": "pytorch", "task": "text-to-image", "allow_remote": true}
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