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README.md

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+# EdgeDiffusion — Distilled Final (647M params)
+
+A pruned and distilled Stable Diffusion 1.5 UNet, reduced from **858M → 647M parameters** (24.6% reduction) while preserving generation quality.
+
+## Model Details
+
+| | Value |
+|---|---|
+| Base model | Stable Diffusion v1.5 |
+| Pruning method | Iterative structured Taylor pruning (4 rounds × ~7%) |
+| Distillation teacher | DreamShaper v8 |
+| Distillation steps | 40K constant LR (1e-5) + 10K cosine decay (1e-5 → 1e-6) |
+| Loss | L_out (noise MSE) + 0.1 × L_feat (feature MSE) |
+| Parameters | 647.2M (vs 858.5M baseline) |
+| Dataset | 20K images (DiffusionDB 10K + COCO 2017 10K) |
+
+## Usage
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline
+from pruned_rebuild import create_unet_from_safetensors
+
+# Rebuild the pruned UNet
+unet = create_unet_from_safetensors(
+    "pruned_unet.safetensors",
+    "pruned_unet.config.json"
+)
+unet = unet.to(dtype=torch.float16, device="cuda")
+
+# Load into SD1.5 pipeline
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    unet=unet,
+    torch_dtype=torch.float16,
+    safety_checker=None,
+    requires_safety_checker=False,
+).to("cuda")
+
+image = pipe("a beautiful landscape, 4k", num_inference_steps=30).images[0]
+image.save("output.png")
+```
+
+## Files
+
+- `pruned_unet.safetensors` — Pruned + distilled UNet weights
+- `pruned_unet.config.json` — UNet architecture config (channel dimensions)
+- `pruned_rebuild.py` — Script to rebuild the pruned UNet from safetensors
+
+## Pipeline
+
+1. **Iterative Structured Pruning**: 4 rounds of Taylor importance-based channel pruning (~7% per round)
+2. **Sensitivity-Guided**: Latent Divergence (LD) sensitivity analysis to protect critical blocks
+3. **Knowledge Distillation**: BK-SDM style distillation with DreamShaper v8 teacher
+4. **Fine-grained Recovery**: Final 10K steps with cosine LR decay for quality refinement