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XujuntaoFighting/GC-DPO-diagnostic-ckpts

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GC-ST-DPO diagnostic checkpoints

Best-step UNet checkpoints for the GC-ST-DPO paper experiments (gradient-attributed spatio-temporal-token credit assignment for diffusion preference optimization). All models trained on Pick-a-Pic v2, seed=0, eff-batch 1024 x 500 steps, beta=2000.

Layout

path	backbone	init	note
`sd15/gc_dpo_full/`	SD-1.5	JaydenLu666/SD1.5-SFT	main method, best step 300
`sd15/mask_dpo/`	SD-1.5	SFT	spatial-mask-only ablation, step 400
`sd15/diff_dpo/`	SD-1.5	SFT	Diffusion-DPO reproduction, step 400
`sd15/gc_dpo_full_random_w/`	SD-1.5	SFT	falsification control (shuffled W), step 500
`sdxl/gc_dpo_full/`	SDXL	base (bf16)	main method SDXL, step 500
`sdxl/mask_dpo/`	SDXL	base (bf16)	ablation SDXL, step 100
`sdxl/sft_winners/`	SDXL	base (bf16)	SFT-on-winners baseline (Wallace `--sft`)

Each directory contains a bare UNet2DConditionModel (config.json + diffusion_pytorch_model.safetensors, fp16). Load by overriding the UNet of the matching base pipeline (runwayml/stable-diffusion-v1-5 / stabilityai/stable-diffusion-xl-base-1.0).

Best-step was selected on held-out Pick-a-Pic validation prompts (500) by equal-weight mean rank of PickScore + HPS-v2.

Code + eval pipelines: https://github.com/JuntaoXu/GCPO