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README.md

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+# 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
+```