Instructions to use vladmandic/Anima-1.0-Base-sdnq-svd-dynamic-uint4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vladmandic/Anima-1.0-Base-sdnq-svd-dynamic-uint4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vladmandic/Anima-1.0-Base-sdnq-svd-dynamic-uint4", 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
A Diffusers snippet?
#1
by tintwotin - opened
Could you share a Diffusers snippet indicating how to use these weights? Do you load it as a custom pipeline?
Normally, to load SDNQ prequantized model you'd simply install and register sdnq and then load as usual.
pip install sdnq
from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
pipe = diffusers.xxxxPipeline.from_pretrained(repo_id)
for Anima, SD.Next has a custom pipeline as diffusers does not implement it fully.
Pipeline code is included here in pipeline.py