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--- |
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license: apache-2.0 |
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pipeline_tag: text-to-image |
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--- |
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# AMD Nitro-AR |
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<img src="images/fig4.png" width="100%" alt="Nitro-AR" /> |
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## Introduction |
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Nitro-AR is a compact masked autoregressive (AR) model for high-quality text-to-image generation. With just ~0.3B parameters, Nitro-AR matches the performance of diffusion-based counterparts while significantly reducing inference latency. By combining continuous token prediction with efficient sampling strategies, it achieves diffusion-level image quality with ultra-low latency. On a single AMD Radeon™ RX 7900 XT GPU, Nitro-AR can generate a 512×512 image in as little as 0.23 seconds (standard) or 74ms (optimized). The release consists of: |
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* **Nitro-AR-512px-GAN.safetensors**: A masked autoregressive model with a diffusion MLP head, optimized via adversarial fine-tuning for few-step diffusion head sampling. |
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* **Nitro-AR-512px-Joint-GAN.safetensors**: A high-performance variant using **Joint Sampling** (transformer-based head) and adversarial fine-tuning, enabling single-step AR generation with superior consistency. |
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⚡️ **[Open-source code](https://github.com/AMD-AGI/Nitro-E/tree/main/Nitro-AR)!** |
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## Details |
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**Model architecture:** Nitro-AR is built on the **E-MMDiT** (Efficient Multimodal Diffusion Transformer) backbone, adapted to a masked modeling autoregressive framework. Unlike traditional AR models that predict discrete tokens, Nitro-AR employs a **diffusion prediction head** to sample continuous tokens, ensuring compatibility with VAE models. |
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To achieve high efficiency and quality, we introduce several key optimizations: |
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* **Joint Sampling:** Replaces the standard MLP head with a small transformer head to model token dependencies, significantly improving coherence in few-step generation. |
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* **Global Token:** Enhances diversity by using a global token as the initial state for masked tokens. |
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* **Adversarial Fine-Tuning:** Treats the diffusion prediction head as a generator trained against a discriminator, compressing the diffusion process to just 3–6 denoising steps. |
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**Dataset:** Our models were trained on a dataset of ~25M images consisting of both real and synthetic data sources that are openly available on the internet. We make use of the following datasets for training: Segment-Anything-1B, JourneyDB, DiffusionDB and DataComp as prompt of the generated data. |
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**Training cost:** The models were trained using **AMD Instinct™ MI325X GPUs**. The efficient architecture allows for rapid convergence comparable to the Nitro-E family. |
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## Quickstart |
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### Environment |
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When running on AMD Instinct™ GPUs, it is recommended to use the [public PyTorch ROCm images](https://hub.docker.com/r/rocm/pytorch-training/) for optimized performance. |
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```bash |
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pip install diffusers==0.32.2 transformers==4.49.0 accelerate==1.7.0 wandb torchmetrics pycocotools torchmetrics[image] mosaicml-streaming==0.11.0 beautifulsoup4 tabulate timm==0.9.1 pyarrow einops omegaconf sentencepiece==0.2.0 pandas==2.2.3 alive-progress ftfy peft safetensors |
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``` |
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### Model Inference |
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Download the checkpoints (`Nitro-AR-512px-GAN.safetensors` and `Nitro-AR-512px-Joint-GAN.safetensors`) from [Hugging Face](https://huggingface.co/amd/Nitro-AR) and place them in the `ckpts/` directory. |
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We support two optimized model variants: `gan` and `joint_gan`. |
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```python |
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from core.tools.inference_pipe import init_pipe |
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import torch |
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device = torch.device('cuda') |
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dtype = torch.bfloat16 |
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# Initialize pipeline for 'gan' model (Adversarial Fine-tuned, 3 steps) |
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pipe = init_pipe(device, dtype, model_type='gan') |
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# Run inference |
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prompt = "a photo of a dog, with a white background" |
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output = pipe(prompt=prompt) |
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output.images[0].save("output_gan.png") |
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# Initialize pipeline for 'joint_gan' model (Joint Sampling + Adversarial Fine-tuned, 6 steps) |
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pipe_joint = init_pipe(device, dtype, model_type='joint_gan') |
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# Run inference |
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output_joint = pipe_joint(prompt=prompt) |
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output_joint.images[0].save("output_joint_gan.png") |
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``` |
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## License |
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Copyright (C) 2026 Advanced Micro Devices, Inc. All Rights Reserved. |
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This project is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). |
