blanchon
/

nitro_e_512_lite

@@ -1,198 +1,116 @@
 ---
 library_name: diffusers
 ---
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 ---
+license: mit
 library_name: diffusers
+tags:
+- text-to-image
+- diffusion
+- nitro-e
+- amd
+- dc-ae-lite
+base_model: amd/Nitro-E
 ---
+# Nitro-E 512px Lite - Fast Decoding Variant
+This is the Nitro-E 512px text-to-image diffusion model with **DC-AE-Lite** for faster image decoding.
+## Key Features
+- 🚀 **1.8× Faster Decoding**: Uses DC-AE-Lite instead of standard DC-AE
+- 🎯 **Same Quality**: Similar reconstruction quality to standard DC-AE
+- ⚡ **Drop-in Compatible**: Uses the same Nitro-E transformer weights
+- 💾 **Memory Efficient**: Smaller decoder footprint
+## Performance Comparison
+| VAE Variant | Decoding Speed | Quality |
+|-------------|---------------|---------|
+| DC-AE (Standard) | 1.0× | Reference |
+| **DC-AE-Lite** | **1.8×** | Similar |
+This makes Nitro-E even faster for real-time applications!
+## Model Details
+- **Transformer**: Nitro-E 512px (304M parameters)
+- **VAE**: DC-AE-Lite-f32c32 (faster decoder)
+- **Text Encoder**: Llama-3.2-1B
+- **Scheduler**: Flow Matching with Euler Discrete
+- **Attention**: Alternating Subregion Attention (ASA)
+## Usage
+```python
+import torch
+from diffusers import NitroEPipeline
+# Load the lite variant
+pipe = NitroEPipeline.from_pretrained(
+    "blanchon/nitro_e_512_lite",
+    torch_dtype=torch.bfloat16
+)
+pipe.to("cuda")
+# Generate image (1.8x faster decoding!)
+prompt = "A hot air balloon in the shape of a heart grand canyon"
+image = pipe(
+    prompt=prompt,
+    width=512,
+    height=512,
+    num_inference_steps=20,
+    guidance_scale=4.5,
+).images[0]
+image.save("output.png")
+```
+## When to Use This Variant
+**Use DC-AE-Lite (this model) when:**
+- You need faster inference
+- Running real-time applications
+- Batch processing many images
+- Decoding is your bottleneck
+**Use standard DC-AE when:**
+- You need absolute best reconstruction quality
+- Decoding speed is not critical
+## Technical Details
+### Architecture
+- **Type**: E-MMDiT (Efficient Multi-scale Masked Diffusion Transformer)
+- **Attention**: Alternating Subregion Attention (ASA)
+- **Text Encoder**: Llama-3.2-1B
+- **VAE**: DC-AE-Lite-f32c32 (1.8× faster decoding)
+- **Scheduler**: Flow Matching with Euler Discrete Scheduler
+- **Latent Size**: 16×16 for 512×512 images
+### Recommended Settings
+- **Steps**: 20 (good quality/speed trade-off)
+- **Guidance Scale**: 4.5 (balanced)
+- **Resolution**: 512×512 (optimized)
+## Citation
+```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.
+## Related Models
+- [Nitro-E 512px (Standard DC-AE)](https://huggingface.co/blanchon/nitro_e_512)
+- [Nitro-E 1024px](https://huggingface.co/blanchon/nitro_e_1024)
+- [Original AMD Nitro-E](https://huggingface.co/amd/Nitro-E)
+- [DC-AE-Lite VAE](https://huggingface.co/dc-ai/dc-ae-lite-f32c32-diffusers)