Text-to-Image
Diffusers
Safetensors
StableDiffusionPipeline
stable-diffusion
stable-diffusion-diffusers
Instructions to use stablediffusiontutorials/stable-diffusion-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stablediffusiontutorials/stable-diffusion-v1.5 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("stablediffusiontutorials/stable-diffusion-v1.5", 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
| from clip import CLIP | |
| from encoder import VAE_Encoder | |
| from decoder import VAE_Decoder | |
| from diffusion import Diffusion | |
| import model_converter | |
| def preload_models_from_standard_weights(ckpt_path, device): | |
| state_dict = model_converter.load_from_standard_weights(ckpt_path, device) | |
| encoder = VAE_Encoder().to(device) | |
| encoder.load_state_dict(state_dict['encoder'], strict=True) | |
| decoder = VAE_Decoder().to(device) | |
| decoder.load_state_dict(state_dict['decoder'], strict=True) | |
| diffusion = Diffusion().to(device) | |
| diffusion.load_state_dict(state_dict['diffusion'], strict=True) | |
| clip = CLIP().to(device) | |
| clip.load_state_dict(state_dict['clip'], strict=True) | |
| return { | |
| 'clip': clip, | |
| 'encoder': encoder, | |
| 'decoder': decoder, | |
| 'diffusion': diffusion, | |
| } |