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Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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Model Card

Model card

We introduce Sana, a text-to-image framework that can efficiently generate images up to 4096 × 4096 resolution. Sana can synthesize high-resolution, high-quality images with strong text-image alignment at a remarkably fast speed, deployable on laptop GPU.

Source code is available at https://github.com/NVlabs/Sana.

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: 1648M parameters
—Model resolution: This model is developed to generate 1024px based images with multi-scale heigh and width.
—License: Apache License 2.0. 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).
—Special: This model is fine-tuned from the base model Efficient-Large-Model/Sana_1600M_1024px_BF16 and it supports Emoji, Chinese and English and all mixed prompts.
—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.

—Repository: https://github.com/NVlabs/Sana

🧨 Diffusers

1. How to use `SanaPipeline` with `🧨diffusers`

\[!IMPORTANT\] Make sure to specify pipe.transformer to default torch_dtype and variant according to Model Card. Set pipe.text_encoder to BF16 and pipe.vae to FP32 or BF16. For more info, docs are here.

python

# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPipeline

pipe = SanaPipeline.from_pretrained(
    "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
    variant="bf16",
    torch_dtype=torch.bfloat16,
)
pipe.to("cuda")

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

prompt = 'A cute 🐼 eating 🎋, ink drawing style'
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")

2. How to use `SanaPAGPipeline` with `🧨diffusers`

python

# run `pip install git+https://github.com/huggingface/diffusers` before use Sana in diffusers
import torch
from diffusers import SanaPAGPipeline

pipe = SanaPAGPipeline.from_pretrained(
  "Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers",
  variant="bf16",
  torch_dtype=torch.bfloat16,
  pag_applied_layers="transformer_blocks.8",
)
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')

Uses

Direct Use

The model is intended for research purposes only. Possible research areas and tasks include

—Generation of artworks and use in design and other artistic processes.
—Applications in educational or creative tools.
—Research on generative models.
—Safe deployment of models which have the potential to generate harmful content.

—Probing and understanding the limitations and biases of generative models.

Excluded uses are described below.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

Limitations and Bias

Limitations

—The model does not achieve perfect photorealism
—The model cannot render complex legible text
—fingers, .etc in general may not be generated properly.
—The autoencoding part of the model is lossy.

Bias

While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.