README.md

---
license: mit
library_name: diffusers
tags:
- text-to-image
- diffusion
- nitro-e
- amd
base_model: amd/Nitro-E
---

# Nitro-E 1024px - Diffusers Integration

This is the Nitro-E 1024px text-to-image diffusion model in diffusers format.

## Model Description

Nitro-E is a family of text-to-image diffusion models focused on highly efficient training. With just 304M parameters, Nitro-E is designed to be resource-friendly for both training and inference.

**Key Features:**
- 304M parameters
- Efficient training: 1.5 days on 8x AMD Instinct MI300X GPUs
- High throughput: Optimized samples/second on single MI300X
- Consumer GPU support: Fast per 1024px image on Strix Halo iGPU

## Model Variant

This is the **1024px** variant, optimized for generating 1024x1024 images.

**Note**: This variant uses standard attention (no ASA subsampling).

## Original Model

This model is based on [amd/Nitro-E](https://huggingface.co/amd/Nitro-E) and has been converted to the diffusers format for easier integration and use.

## Usage

```python
import torch
from diffusers import NitroEPipeline

# Load pipeline
pipe = NitroEPipeline.from_pretrained("blanchon/nitro_e_1024", torch_dtype=torch.bfloat16)
pipe = pipe.to("cuda")

# Generate 1024x1024 image
prompt = "A hot air balloon in the shape of a heart grand canyon"
image = pipe(
    prompt=prompt,
    width=1024,
    height=1024,
    num_inference_steps=20,
    guidance_scale=4.5,
).images[0]

image.save("output.png")
```

## Technical Details

### Architecture
- **Type**: E-MMDiT (Efficient Multi-scale Masked Diffusion Transformer)
- **Attention**: Standard attention
- **Text Encoder**: Llama-3.2-1B
- **VAE**: DC-AE-f32c32 from MIT-Han-Lab
- **Scheduler**: Flow Matching with Euler Discrete Scheduler
- **Sample Size**: 32 (latent space)

### Training
- **Dataset**: ~25M images (real + synthetic)
- **Duration**: 1.5 days on 8x AMD Instinct MI300X GPUs
- **Training Details**: See [Nitro-E Technical Report](https://arxiv.org/abs/2510.27135)

## Citation

If you use this model, please cite:

```bibtex
@article{nitro-e-2025,
  title={Nitro-E: Efficient Training of Diffusion Models},
  author={AMD AI Group},
  journal={arXiv preprint arXiv:2510.27135},
  year={2025}
}
```

## License

Copyright (c) 2025 Advanced Micro Devices, Inc. All Rights Reserved.

Licensed under the MIT License. See the [LICENSE](https://mit-license.org/) for details.

## Related Projects

- [Nitro-T](https://github.com/AMD-AGI/Nitro-T): Efficient Training of diffusion models
- [Nitro-1](https://github.com/AMD-AGI/Nitro-1): One-step distillation of diffusion models
- [Original Nitro-E Repository](https://github.com/AMD-AGI/Nitro-E)
- [AMD Nitro-E on HuggingFace](https://huggingface.co/amd/Nitro-E)