Instructions to use hf-internal-testing/tiny-IFPipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-IFPipeline with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-IFPipeline", 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
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "DDPMScheduler", | |
| "_diffusers_version": "0.39.0.dev0", | |
| "beta_end": 0.02, | |
| "beta_schedule": "squaredcos_cap_v2", | |
| "beta_start": 0.0001, | |
| "clip_sample": true, | |
| "clip_sample_range": 1.0, | |
| "dynamic_thresholding_ratio": 0.95, | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "epsilon", | |
| "rescale_betas_zero_snr": false, | |
| "sample_max_value": 1.0, | |
| "steps_offset": 0, | |
| "thresholding": true, | |
| "timestep_spacing": "leading", | |
| "trained_betas": null, | |
| "variance_type": "learned_range" | |
| } | |