Instructions to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4", dtype=torch.bfloat16, device_map="cuda") prompt = "a puppy in a pond, yarn art style" image = pipe(prompt).images[0] - Sana
How to use wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4 with Sana:
# Load the model and infer image from text import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image sana = SanaPipeline("configs/sana_config/1024ms/Sana_1600M_img1024.yaml") sana.from_pretrained("hf://wsl448/yarn_art_falqon_sana_block_int8_svd_lr2e-4") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=5.0, pag_guidance_scale=2.0, num_inference_steps=18, ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 6f3229cb2c8b1bfa4bf39d893bfd97ff90fd33434b4b0c84c2b2cfb2723ce500
- Size of remote file:
- 3.13 kB
- SHA256:
- 61d6aa1a77fdeaead8fcc7baad12ea632fc9725eddc8c3c8535f3cc56fb61703
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