Instructions to use austin-k-wang/SanaSprint1.6B-RWTD-GenEval with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use austin-k-wang/SanaSprint1.6B-RWTD-GenEval with PEFT:
Task type is invalid.
- Sana
How to use austin-k-wang/SanaSprint1.6B-RWTD-GenEval 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://austin-k-wang/SanaSprint1.6B-RWTD-GenEval") 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
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README.md
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@@ -20,6 +20,8 @@ This repository contains a Reward-Weighted Transport Distillation (RWTD) LoRA
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adapter for
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[SANA-Sprint 1.6B 1024px](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_1.6B_1024px).
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Only the adapter is included. The base model, text encoder, and VAE are
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downloaded separately from their respective repositories.
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adapter for
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[SANA-Sprint 1.6B 1024px](https://huggingface.co/Efficient-Large-Model/Sana_Sprint_1.6B_1024px).
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This adapter is obtained by post-training SANA Sprint 1.6B with the method presented in the paper
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[Aligning One-Step Generative Models with Reward-Weighted Transport Distillation](https://arxiv.org/pdf/2609.30840).
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Only the adapter is included. The base model, text encoder, and VAE are
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downloaded separately from their respective repositories.
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