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
Consistency Decoder
Consistency decoder can be used to decode the latents from the denoising UNet in the [StableDiffusionPipeline]. This decoder was introduced in the DALL-E 3 technical report.
The original codebase can be found at openai/consistencydecoder.
Inference is only supported for 2 iterations as of now.
The pipeline could not have been contributed without the help of madebyollin and mrsteyk from this issue.
ConsistencyDecoderVAE
[[autodoc]] ConsistencyDecoderVAE - all - decode