Instructions to use dg845/diffusers-cm_edm_imagenet64_ema with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dg845/diffusers-cm_edm_imagenet64_ema with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dg845/diffusers-cm_edm_imagenet64_ema", 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
Commit History
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Upload scheduler_config.json
Fix scheduler (use precondition_type "cm_edm" instead of "edm").291b33e
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Upload scheduler_config.json
Update scheduler for CM EDM ImageNet-64 EMA checkpoint.d85b309
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Update README.md
0bdfc54 -
Upload edm_imagenet64_ema consistency model EDM checkpoint
22f38fc -
initial commit
6877a6f