Instructions to use ndtran0101/pisa-sr-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ndtran0101/pisa-sr-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ndtran0101/pisa-sr-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 544 Bytes
735d0c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"source_checkpoint": "pisa_sr.pkl (csslc/PiSA-SR, CVPR 2025)",
"base_model": "stabilityai/stable-diffusion-2-1-base",
"rank_pix": 4,
"rank_sem": 4,
"lora_alpha": {
"pix": 8.0,
"sem": 8.0
},
"n_target_modules_per_branch": 258,
"merge_precision": "fp32 on CPU, cast to fp16 once at the end",
"inference": {
"timestep": 1,
"prompt": "",
"noise": "none (deterministic)",
"formula": "latent_out = z - unet(z, t=1, emb(''))",
"adjustable": "lambda_pix*pred_pix + lambda_sem*(pred_sem - pred_pix)"
}
} |