Model reference · open weights

nova-d48w1024-sdxl1024

Image BAAI Text→image 1 build Open weights 1k dl/mo

nova-d48w1024-sdxl1024 is an open-weight image model from BAAI. nova-d48w1024-sdxl1024 (FP16) weighs 7.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • nova-d48w1024-sdxl1024 is a non-quantized autoregressive text-to-image generation model developed by BAAI.
  • It generates 1024x1024 images from text prompts using a Phi-2 text encoder and an SDXL-VAE image tokenizer.
  • The model has 647M parameters, runs in FP16 precision, and is released under the Apache 2.0 license.

Summary of the BAAI/nova-d48w1024-sdxl1024 model card, 2026-10-01

What it is

Released byBAAI
Released2024-12-17
Parameters647M
VRAM7.2 GB for the weights

What it runs on

Memory and cards for nova-d48w1024-sdxl1024 (FP16)

7.2 GBweights, file size
5.6 GBbiggest part
5.0 GBworking memory, one 1024² image
537 MBruntime overhead
CardOne imageMemory
RTX 3060 12 GBtight (encoders offloaded)11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What BAAI says about nova-d48w1024-sdxl1024

Read the model card

Model Details

  • Developed by: BAAI
  • Model type: Non-quantized Autoregressive Text-to-Image Generation Model
  • Model size: 645M
  • Model precision: torch.float16 (FP16)
  • Model resolution: 1024x1024
  • Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Non-quantized Video Autoregressive (NOVA) diffusion model that uses a pretrained text encoder (Phi-2) and one VAE image tokenizer (SDXL-VAE).
  • Model License: Apache 2.0 License
  • Resources for more information: GitHub Repository.

Examples

Using the 🤗's Diffusers library to run NOVA in a simple and efficient manner.

pip install diffusers transformers accelerate
pip install git+ssh://git@github.com/baaivision/NOVA.git

Running the pipeline:

import torch
from diffnext.pipelines import NOVAPipeline

model_id = "BAAI/nova-d48w1024-sdxl1024"
model_args = {"torch_dtype": torch.float16, "trust_remote_code": True}
pipe = NOVAPipeline.from_pretrained(model_id, **model_args)
pipe = pipe.to("cuda")

prompt = "a shiba inu wearing a beret and black turtleneck."
image = pipe(prompt).images[0]

image.save("shiba_inu.jpg")

Uses

Direct Use

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

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

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.

Misuse and Malicious Use

Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:

  • Mis- and disinformation.
  • Representations of egregious violence and gore.
  • Impersonating individuals without their consent.
  • Sexual content without consent of the people who might see it.
  • Sharing of copyrighted or licensed material in violation of its terms of use.
  • Intentionally promoting or propagating discriminatory content or harmful stereotypes.
  • Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
  • Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.

Limitations and Bias

Limitations

  • The autoencoding part of the model is lossy.
  • The model cannot render complex legible text.
  • The model does not achieve perfect photorealism.
  • The fingers, .etc in general may not be generated properly.
  • The model was trained on a subset of the web datasets LAION-5B and COYO-700M, which contains adult, violent and sexual content.

Bias

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

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How image models work

Text promptwhat to makeText encoderunderstands itDiffusion stepsdenoise to pixelsImagePNG / JPEGA diffusion model starts from noise and denoises it, guided by your prompt, into a finished image.

Running it yourself

Run it on a rented GPU

Rent a machine by the hour. How to run this model is on its model card.

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