Instructions to use bondarchukb/minicooper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bondarchukb/minicooper with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bondarchukb/minicooper", 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
Commit History
-
Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (#2)
- Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`. (04872562f03fa5ec285e62c6134a350fbda4ce0d)3d30e04
-
Upload sample_images with huggingface_hub
c2638fb -
Upload model_index.json with huggingface_hub
5e57382 -
Upload vae with huggingface_hub
d555290 -
Upload unet with huggingface_hub
84f8331 -
Upload tokenizer with huggingface_hub
a417e92 -
Upload text_encoder with huggingface_hub
52ddfc2 -
Upload scheduler with huggingface_hub
d79d8ac -
Upload safety_checker with huggingface_hub
add59a5 -
Upload feature_extractor with huggingface_hub
0308871 -
Upload the concept minicooper embeds and token
7cd3988 -
initial commit
e1f2697