File size: 1,382 Bytes
aef6fd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | # EdgeDiffusion - Distilled
A pruned and distilled Stable Diffusion 1.5 UNet (647.2M params, ~25% smaller than original 858.5M).
## Pipeline
1. **Iterative Pruning**: 4 rounds of ~7% Taylor-importance pruning (858.5M → 647.2M)
2. **Knowledge Distillation**: 15K steps with Realistic Vision v5.1 as teacher (feature + noise MSE loss)
## Files
| File | Description |
|------|-------------|
| `pruned_unet.safetensors` | Pruned + distilled UNet weights |
| `pruned_unet.config.json` | Model config (contains `model_config` for rebuilding) |
| `pruned_rebuild.py` | Script to rebuild the pruned UNet architecture |
## Usage
```python
# 1. Download all 3 files to the same directory, then:
from pruned_rebuild import create_unet_from_safetensors
from diffusers import StableDiffusionPipeline
import torch
# 2. Rebuild the pruned UNet
unet = create_unet_from_safetensors(
"pruned_unet.safetensors",
"pruned_unet.config.json"
)
# 3. Load into a standard SD 1.5 pipeline
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
unet=unet,
torch_dtype=torch.float16,
safety_checker=None,
)
pipe = pipe.to("cuda")
# 4. Generate
image = pipe("a beautiful sunset over mountains", num_inference_steps=30).images[0]
image.save("output.png")
```
## Requirements
```
pip install diffusers transformers safetensors torch accelerate
```
|