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limingcv
/
reward_controlnet

Diffusers
Safetensors
Model card Files Files and versions
xet
Community

Instructions to use limingcv/reward_controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use limingcv/reward_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("limingcv/reward_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
reward_controlnet / checkpoints
13 GB
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  • 3 contributors
History: 8 commits
limingcv's picture
limingcv
Upload folder using huggingface_hub
cf5068c verified almost 2 years ago
  • ade20k_reward-model-FCN-R101-d8
    Upload folder using huggingface_hub almost 2 years ago
  • ade20k_reward-model-UperNet-R50
    Upload folder using huggingface_hub almost 2 years ago
  • canny
    init demo about 2 years ago
  • cocostuff
    Upload folder using huggingface_hub almost 2 years ago
  • depth
    init demo about 2 years ago
  • hed
    init demo about 2 years ago
  • lineart
    init demo about 2 years ago
  • seg
    remove wrong ckpt about 2 years ago