Instructions to use eugenebokhan/saved_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use eugenebokhan/saved_model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("eugenebokhan/saved_model") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-300/optimizer.bin from eugenebokhan/saved_model: direct link, hf CLI and curl.
- Browser
- Download file 9.03 MB
-
https://huggingface.co/eugenebokhan/saved_model/resolve/main/checkpoint-300/optimizer.bin
- Command line
-
hf download hf://eugenebokhan/saved_model/checkpoint-300/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/eugenebokhan/saved_model/resolve/main/checkpoint-300/optimizer.bin
9.03 MB
- Xet hash:
- 71cfab4a01df1a27b29dbc915bcc76fb74a9ecc2d8bba0ed5ad8b8fda6b1c35a
- Size of remote file:
- 9.03 MB
- SHA256:
- c0f797badfdf9e0fb48f9e3f865b1ea22ba2aef99e8dfa04724603389acb3e01
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