Instructions to use yang1232009/DC-ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yang1232009/DC-ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("yang1232009/DC-ControlNet", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +1 -1
- diffusion_pytorch_model.safetensors +3 -0
config.json
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{
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"_class_name": "UNet2DCondition_IntraElement_Controller",
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"_diffusers_version": "0.31.0",
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"_name_or_path": "
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"act_fn": "silu",
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"addition_embed_type": "text_time",
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"addition_embed_type_num_heads": 64,
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{
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"_class_name": "UNet2DCondition_IntraElement_Controller",
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"_diffusers_version": "0.31.0",
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"_name_or_path": "/group/40063/hongjiyang/code/opensource/ICCV25/code/DC-Control/sdxl-intra_element_controller-continue-continue/checkpoint-30000",
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"act_fn": "silu",
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"addition_embed_type": "text_time",
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"addition_embed_type_num_heads": 64,
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diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:f27c3c0e6f1aea509a0532e0c98195b6b4b65477941a233d58e071486a860699
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size 5671025064
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