Instructions to use krasnova/ddim_afhq_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use krasnova/ddim_afhq_64 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("krasnova/ddim_afhq_64", 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
Download scheduler_config.json from krasnova/ddim_afhq_64: direct link, hf CLI and curl.
- Browser
- Download file 434 Bytes
-
https://huggingface.co/krasnova/ddim_afhq_64/resolve/main/scheduler_config.json
- Command line
-
hf download hf://krasnova/ddim_afhq_64/scheduler_config.json
-
curl -L -o scheduler_config.json https://huggingface.co/krasnova/ddim_afhq_64/resolve/main/scheduler_config.json
434 Bytes
| { | |
| "_class_name": "DDIMScheduler", | |
| "_diffusers_version": "0.16.1", | |
| "beta_end": 0.02, | |
| "beta_schedule": "squaredcos_cap_v2", | |
| "beta_start": 0.0001, | |
| "clip_sample": true, | |
| "clip_sample_range": 1.0, | |
| "dynamic_thresholding_ratio": 0.995, | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "epsilon", | |
| "sample_max_value": 1.0, | |
| "set_alpha_to_one": true, | |
| "steps_offset": 0, | |
| "thresholding": false, | |
| "trained_betas": null | |
| } | |