Instructions to use opencli/sana-0.6b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use opencli/sana-0.6b-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("opencli/sana-0.6b-4bit", 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
Refresh 512/1024 artifacts after duplicate-weight dedup in quantization pipeline
Browse files- published-artifacts.json +1 -2
- variants/sana-0.6b-4bit-1024/manifest.json +0 -1
- variants/sana-0.6b-4bit-1024/quantization.json +0 -0
- variants/sana-0.6b-4bit-1024/text_encoder/model-00001-of-00002.safetensors +2 -2
- variants/sana-0.6b-4bit-1024/text_encoder/model-00002-of-00002.safetensors +2 -2
- variants/sana-0.6b-4bit-1024/text_encoder/quantization.json +0 -0
- variants/sana-0.6b-4bit-1024/transformer/diffusion_pytorch_model.safetensors +2 -2
- variants/sana-0.6b-4bit-1024/transformer/quantization.json +0 -0
- variants/sana-0.6b-4bit-512/manifest.json +0 -1
- variants/sana-0.6b-4bit-512/quantization.json +0 -0
- variants/sana-0.6b-4bit-512/text_encoder/model-00001-of-00002.safetensors +2 -2
- variants/sana-0.6b-4bit-512/text_encoder/model-00002-of-00002.safetensors +2 -2
- variants/sana-0.6b-4bit-512/text_encoder/quantization.json +0 -0
- variants/sana-0.6b-4bit-512/transformer/diffusion_pytorch_model.safetensors +2 -2
- variants/sana-0.6b-4bit-512/transformer/quantization.json +0 -0
published-artifacts.json
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"repo_id" : "opencli\/sana-0.6b-4bit",
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