Instructions to use hf-internal-testing/tiny-AceStepPipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-AceStepPipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-AceStepPipeline", 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
Download condition_encoder/diffusion_pytorch_model.safetensors from hf-internal-testing/tiny-AceStepPipeline: direct link, hf CLI and curl.
- Browser
- Download file 644 kB
-
https://huggingface.co/hf-internal-testing/tiny-AceStepPipeline/resolve/main/condition_encoder/diffusion_pytorch_model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-AceStepPipeline/condition_encoder/diffusion_pytorch_model.safetensors
-
curl -L -o diffusion_pytorch_model.safetensors https://huggingface.co/hf-internal-testing/tiny-AceStepPipeline/resolve/main/condition_encoder/diffusion_pytorch_model.safetensors
644 kB
- Xet hash:
- 1b69602fc5f19690c20ee074773b116e85b65a3edc3dcfc50ad26c03c503c9fc
- Size of remote file:
- 644 kB
- SHA256:
- d9c0f57b259bd121d54daa1569b7b7322308aa30e088b0cefa20f3f505256633
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.