Instructions to use colt12/maxcushion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use colt12/maxcushion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("colt12/maxcushion", 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 model_index.json from colt12/maxcushion: direct link, hf CLI and curl.
- Browser
- Download file 481 Bytes
-
https://huggingface.co/colt12/maxcushion/resolve/main/model_index.json
- Command line
-
hf download hf://colt12/maxcushion/model_index.json
-
curl -L -o model_index.json https://huggingface.co/colt12/maxcushion/resolve/main/model_index.json
481 Bytes
| { | |
| "_class_name": "StableDiffusionXLPipeline", | |
| "_diffusers_version": "0.26.0", | |
| "image_processor": [null, null], | |
| "safety_checker": [null, null], | |
| "scheduler": [null, null], | |
| "text_encoder": ["text_encoder", "CLIPTextModel"], | |
| "text_encoder_2": ["text_encoder_2", "CLIPTextModelWithProjection"], | |
| "tokenizer": ["tokenizer", "CLIPTokenizer"], | |
| "tokenizer_2": ["tokenizer_2", "CLIPTokenizer"], | |
| "unet": ["unet", "UNet2DConditionModel"], | |
| "vae": ["vae", "AutoencoderKL"] | |
| } |