Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/mk1", 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 diffusers/examples/research_projects/onnxruntime/README.md from d-wang26/mk1: direct link, hf CLI and curl.
- Browser
- Download file 567 Bytes
-
https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/examples/research_projects/onnxruntime/README.md
- Command line
-
hf download hf://d-wang26/mk1/diffusers/examples/research_projects/onnxruntime/README.md
-
curl -L -o README.md https://huggingface.co/d-wang26/mk1/resolve/main/diffusers/examples/research_projects/onnxruntime/README.md
567 Bytes
Diffusers examples with ONNXRuntime optimizations
This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.
This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime.