blanchon
/

nitro_e_1024

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 ---
 library_name: diffusers
 ---
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 ---
+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)