Text-to-Image
Diffusers
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Instructions to use JawadC/mimolette with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JawadC/mimolette with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JawadC/mimolette") prompt = "A piece of MIMOLETTE cheese sits on a wooden board in warm sunlight." image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-500/optimizer.bin from JawadC/mimolette: direct link, hf CLI and curl.
- Browser
- Download file 47.4 MB
-
https://huggingface.co/JawadC/mimolette/resolve/main/checkpoint-500/optimizer.bin
- Command line
-
hf download hf://JawadC/mimolette/checkpoint-500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/JawadC/mimolette/resolve/main/checkpoint-500/optimizer.bin
47.4 MB
- Xet hash:
- a8b63b527561edd7579a28ecae6086279183e1e7fd5183a606f1d5bc05325639
- Size of remote file:
- 47.4 MB
- SHA256:
- 4df98ecf7d730485bd668d25b643ddb9396bd4f515c2ec5fcef0f90399274c84
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