metadata

library_name: sana
tags:
  - text-to-image
  - Sana
  - 1024px_based_image_size
  - Multi-language
language:
  - en
  - zh
base_model:
  - Efficient-Large-Model/Sana_600M_1024px_diffusers
pipeline_tag: text-to-image

Note

Weakness in Complex Scene Creation: Due to limitation of data, our model has limited capabilities in generating complex scenes, text, and human hands.
Enhancing Capabilities: The model’s performance can be improved by increasing the complexity and length of prompts. Below are some examples of prompts and samples.

Model Description

Developed by: NVIDIA, Sana
Model type: Linear-Diffusion-Transformer-based text-to-image generative model
Model size: 590M parameters
Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width.
License: NSCL v2-custom. Governing Terms: NVIDIA License. Additional Information: Gemma Terms of Use | Google AI for Developers for Gemma-2-2B-IT, Gemma Prohibited Use Policy | Google AI for Developers.
Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders (Gemma2-2B-IT) and one 32x spatial-compressed latent feature encoder (DC-AE).
Resources for more information: Check out our GitHub Repository and the Sana report on arXiv.

Model Sources

For research purposes, we recommend our generative-models Github repository (https://github.com/NVlabs/Sana), which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated. MIT Han-Lab provides free Sana inference.

# pip install git+https://github.com/huggingface/diffusers
# pip install transformer
import torch
from diffusers import SanaPAGPipeline

pipe = SanaPAGPipeline.from_pretrained(
  "kpsss34/SANA600.fp8_Realistic_SFW_V1",
  torch_dtype=torch.float16,
)
pipe.to("cuda")

pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)

prompt = 'A cute 🐼 eating 🎋, ink drawing style'
image = pipe(
    prompt=prompt,
    height=1024,
    width=1024,
    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')