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/edm_euler.md from NadaGh/working: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/schedulers/edm_euler.md
- Command line
-
hf download hf://NadaGh/working/diffusers/docs/source/en/api/schedulers/edm_euler.md
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curl -L -o edm_euler.md https://huggingface.co/NadaGh/working/resolve/main/diffusers/docs/source/en/api/schedulers/edm_euler.md
1.28 kB
EDMEulerScheduler
The Karras formulation of the Euler scheduler (Algorithm 2) from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson.
EDMEulerScheduler
[[autodoc]] EDMEulerScheduler
EDMEulerSchedulerOutput
[[autodoc]] schedulers.scheduling_edm_euler.EDMEulerSchedulerOutput