docs: new README, architecture/training/data-pipeline/inference docs, summary, blog post#3
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…efault, memory_format flag - DDIMScheduler: make pred_x0 clamp opt-in via clamp_pred_x0 param (default False) - inference.py: wire --negative prompt through to uncond embedding - SD_ImageGen.py: pass model.unet to ema.apply_shadow() - SD_Train.py/train.py/SD_Train_v2.py: fix latent_dir default (remove double nesting) - SD_Train.py/train.py: add --memory_format CLI flag to gate channels_last (use 'contiguous' for AMD/Apple Silicon)
… summary, blog post - Rewrite README with badges, quick start, repo layout, architecture diagram, training summary, checkpoint loading guide, and citation - Add docs/architecture.md — component deep-dive with UNet parameter breakdown - Add docs/training-loop.md — optimization stack, procedure, hyperparameters - Add docs/data-pipeline.md — full LAION pipeline walkthrough (step 1-6) - Add docs/inference.md — CLI reference, DDIM parameter guide, quality tips - Add scripts/download_checkpoint.py — HF Hub checkpoint downloader - Add tests/test_unet_forward.py — CPU smoke tests for all model components - Add tests/test_ddim_step.py — DDIM variance preservation and determinism - Include rewritten summary.md and blog_post.md
- Generate 2×3 hero collage from 6 best samples - Symlink sample images and architecture diagram into docs/images/ - Folder structure: hero/, samples/, architecture/, training-progress/
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Complete documentation rewrite with hero collage, sample gallery, and checkpoint download script.