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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## Important Usage Guidance
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1. Use DDIM or Euler instead of LCM for sampling! When using DDIM, set timestep_spacing="trailing".
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2. The name of each LoRA weights indicates how many inference steps they should be applied.
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## Important Usage Guidance
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1. Use DDIM or Euler instead of LCM for sampling! When using DDIM, set timestep_spacing="trailing", clip_sample = False and set_alpha_to_one = False.
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2. The name of each LoRA weights indicates how many inference steps they should be applied.
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