Instructions to use wangfuyun/PCM_Weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wangfuyun/PCM_Weights with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wangfuyun/PCM_Weights", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
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README.md
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Note:
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[[paper](https://huggingface.co/papers/2405.18407)] [[arXiv](https://arxiv.org/abs/2405.18407)] [[code](https://github.com/G-U-N/Phased-Consistency-Model)] [[project page](https://g-u-n.github.io/projects/pcm)]
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- The normalCFG LoRAs are more sensitive to the prompts. Set proper positive and negative prompts for better quality.
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- Just find the normalCFG with 4-step is not working very well. Trying to solve the issue.
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[[paper](https://huggingface.co/papers/2405.18407)] [[arXiv](https://arxiv.org/abs/2405.18407)] [[code](https://github.com/G-U-N/Phased-Consistency-Model)] [[project page](https://g-u-n.github.io/projects/pcm)]
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