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/models/consistency_decoder_vae.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/models/consistency_decoder_vae.md
- Command line
-
hf download hf://NadaGh/working/diffusers/docs/source/en/api/models/consistency_decoder_vae.md
-
curl -L -o consistency_decoder_vae.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/models/consistency_decoder_vae.md
1.33 kB
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