# ⚡️ Sana: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer

This guide covers training and inference for Sana text-to-image models. ## Hardware Requirements | Task | VRAM | |------|------| | Inference (0.6B) | 9GB | | Inference (1.6B) | 12GB | | Inference (4-bit) | < 8GB | | Training | 32GB | !!! Note All tests are done on A100 GPUs. Different GPU versions may vary. ______________________________________________________________________ ## Inference ### Using Diffusers (Recommended) ```bash pip install git+https://github.com/huggingface/diffusers ``` ```python import torch from diffusers import SanaPipeline pipe = SanaPipeline.from_pretrained( "Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers", torch_dtype=torch.bfloat16, ) pipe.to("cuda") pipe.vae.to(torch.bfloat16) pipe.text_encoder.to(torch.bfloat16) prompt = 'a cyberpunk cat with a neon sign that says "Sana"' image = pipe( prompt=prompt, height=1024, width=1024, guidance_scale=4.5, num_inference_steps=20, generator=torch.Generator(device="cuda").manual_seed(42), )[0] image[0].save("sana.png") ``` ### Using SanaPAGPipeline ```python import torch from diffusers import SanaPAGPipeline pipe = SanaPAGPipeline.from_pretrained( "Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers", torch_dtype=torch.bfloat16, pag_applied_layers="transformer_blocks.8", ) pipe.to("cuda") pipe.text_encoder.to(torch.bfloat16) pipe.vae.to(torch.bfloat16) image = pipe( prompt='a cyberpunk cat with a neon sign that says "Sana"', guidance_scale=5.0, pag_scale=2.0, num_inference_steps=20, generator=torch.Generator(device="cuda").manual_seed(42), )[0] image[0].save('sana.png') ``` ### Using Native Pipeline ```python import torch from app.sana_pipeline import SanaPipeline from torchvision.utils import save_image device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") generator = torch.Generator(device=device).manual_seed(42) sana = SanaPipeline("configs/sana1-5_config/1024ms/Sana_1600M_1024px_allqknorm_bf16_lr2e5.yaml") sana.from_pretrained("hf://Efficient-Large-Model/SANA1.5_1.6B_1024px/checkpoints/SANA1.5_1.6B_1024px.pth") image = sana( prompt='a cyberpunk cat with a neon sign that says "Sana"', height=1024, width=1024, guidance_scale=4.5, pag_guidance_scale=1.0, num_inference_steps=20, generator=generator, ) save_image(image, 'output/sana.png', nrow=1, normalize=True, value_range=(-1, 1)) ``` ### Gradio Demo ```bash DEMO_PORT=15432 \ python app/app_sana.py \ --share \ --config=configs/sana_config/1024ms/Sana_1600M_img1024.yaml \ --model_path=hf://Efficient-Large-Model/Sana_1600M_1024px_BF16/checkpoints/Sana_1600M_1024px_BF16.pth \ --image_size=1024 ``` ### Batch Inference ```bash # Run samples in a txt file python scripts/inference.py \ --config=configs/sana_config/1024ms/Sana_1600M_img1024.yaml \ --model_path=hf://Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth \ --txt_file=asset/samples/samples_mini.txt # Run samples in a json file python scripts/inference.py \ --config=configs/sana_config/1024ms/Sana_1600M_img1024.yaml \ --model_path=hf://Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth \ --json_file=asset/samples/samples_mini.json ``` ______________________________________________________________________ ## Training ### Data Preparation Prepare image-text pairs in the following format: ``` asset/example_data ├── AAA.txt ├── AAA.png ├── BCC.txt ├── BCC.png ├── CCC.txt └── CCC.png ``` ### Train from Scratch ```bash # Train Sana 0.6B with 512x512 resolution bash train_scripts/train.sh \ configs/sana_config/512ms/Sana_600M_img512.yaml \ --data.data_dir="[asset/example_data]" \ --data.type=SanaImgDataset \ --model.multi_scale=false \ --train.train_batch_size=32 ``` ### Fine-tuning ```bash # Fine-tune Sana 1.6B with 1024x1024 resolution bash train_scripts/train.sh \ configs/sana_config/1024ms/Sana_1600M_img1024.yaml \ --data.data_dir="[asset/example_data]" \ --data.type=SanaImgDataset \ --model.load_from=hf://Efficient-Large-Model/Sana_1600M_1024px/checkpoints/Sana_1600M_1024px.pth \ --model.multi_scale=false \ --train.train_batch_size=8 ``` ### Multi-Scale WebDataset Convert data to WebDataset format: ```bash python tools/convert_scripts/convert_ImgDataset_to_WebDatasetMS_format.py ``` Then train: ```bash bash train_scripts/train.sh \ configs/sana_config/512ms/Sana_600M_img512.yaml \ --data.data_dir="[asset/example_data_tar]" \ --data.type=SanaWebDatasetMS \ --model.multi_scale=true \ --train.train_batch_size=32 ``` ### Training with FSDP ```bash # Download toy dataset huggingface-cli download Efficient-Large-Model/toy_data --repo-type dataset --local-dir ./data/toy_data # DDP training bash train_scripts/train.sh \ configs/sana1-5_config/1024ms/Sana_1600M_1024px_allqknorm_bf16_lr2e5.yaml \ --data.data_dir="[data/toy_data]" \ --data.type=SanaWebDatasetMS \ --model.multi_scale=true \ --data.load_vae_feat=true \ --train.train_batch_size=2 # FSDP training bash train_scripts/train.sh \ configs/sana1-5_config/1024ms/Sana_1600M_1024px_AdamW_fsdp.yaml \ --data.data_dir="[data/toy_data]" \ --data.type=SanaWebDatasetMS \ --model.multi_scale=true \ --data.load_vae_feat=true \ --train.use_fsdp=true \ --train.train_batch_size=2 ``` ______________________________________________________________________ ## Related - [Model Zoo](model_zoo.md) - All available models - [4-bit Sana](4bit_sana.md) - Memory-efficient inference - [LoRA & DreamBooth](sana_lora_dreambooth.md) - Fine-tuning methods ______________________________________________________________________ ## Citation ```bibtex @misc{xie2024sana, title={Sana: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer}, author={Enze Xie and Junsong Chen and Junyu Chen and Han Cai and Haotian Tang and Yujun Lin and Zhekai Zhang and Muyang Li and Ligeng Zhu and Yao Lu and Song Han}, year={2024}, eprint={2410.10629}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2410.10629}, } @misc{xie2025sana, title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer}, author={Xie, Enze and Chen, Junsong and Zhao, Yuyang and Yu, Jincheng and Zhu, Ligeng and Lin, Yujun and Zhang, Zhekai and Li, Muyang and Chen, Junyu and Cai, Han and others}, year={2025}, eprint={2501.18427}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2501.18427}, } ```