Instructions to use Kashg7474/stable-diffusion-1.5_io32_amdgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kashg7474/stable-diffusion-1.5_io32_amdgpu with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kashg7474/stable-diffusion-1.5_io32_amdgpu", 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
Download vae_encoder/model.onnx from Kashg7474/stable-diffusion-1.5_io32_amdgpu: direct link, hf CLI and curl.
- Browser
- Download file 68.4 MB
-
https://huggingface.co/Kashg7474/stable-diffusion-1.5_io32_amdgpu/resolve/main/vae_encoder/model.onnx
- Command line
-
hf download hf://Kashg7474/stable-diffusion-1.5_io32_amdgpu/vae_encoder/model.onnx
-
curl -L -o model.onnx https://huggingface.co/Kashg7474/stable-diffusion-1.5_io32_amdgpu/resolve/main/vae_encoder/model.onnx
68.4 MB
- Xet hash:
- 707ab0f32cfbfc26d3f5297877fd05742104d61e60289b4edd3801d6a7160dd1
- Size of remote file:
- 68.4 MB
- SHA256:
- 1a21051b05342daf05d7f58c9c98d60262cc177c38587e8bffc541d40fcc63ef
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