Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use choicow/sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use choicow/sample with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("choicow/sample", dtype=torch.bfloat16, device_map="cuda") prompt = "Generate an image of sks person matching this pose: There is sks person in the image who is performing conditioning exercise, resistance training." image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| { | |
| "_class_name": "PNDMScheduler", | |
| "_diffusers_version": "0.34.0.dev0", | |
| "beta_end": 0.012, | |
| "beta_schedule": "scaled_linear", | |
| "beta_start": 0.00085, | |
| "clip_sample": false, | |
| "num_train_timesteps": 1000, | |
| "prediction_type": "epsilon", | |
| "set_alpha_to_one": false, | |
| "skip_prk_steps": true, | |
| "steps_offset": 1, | |
| "timestep_spacing": "leading", | |
| "trained_betas": null | |
| } | |