Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use NadaGh/working with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NadaGh/working with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NadaGh/working", dtype=torch.bfloat16, device_map="cuda") prompt = "tst chair" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download diffusers/docs/source/en/api/schedulers/lcm.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/schedulers/lcm.md
- Command line
-
hf download hf://NadaGh/working/diffusers/docs/source/en/api/schedulers/lcm.md
-
curl -L -o lcm.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/schedulers/lcm.md
1.09 kB
Latent Consistency Model Multistep Scheduler
Overview
Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao.
This scheduler should be able to generate good samples from [LatentConsistencyModelPipeline] in 1-8 steps.
LCMScheduler
[[autodoc]] LCMScheduler