ahmedheakl/rand-mobile

Mobile-O — soup3-targets

A weight-space model soup of three Mobile-O v2 checkpoints, each of which already met a different benchmark target. It clears three of the four tracked benchmarks at a single checkpoint and a single guidance setting, which no individual checkpoint in this project does.

Built with no additional training — it is a uniform 1/3 average of three existing checkpoints that share one SFT init.

Runnable code: https://github.com/ahmedheakl/mobileov2-minicpm

Benchmarks

Recommended setting: cfg 1.5, 12 DPM-Solver++ steps.

benchmark	value	target	status
GenEval	0.90242	≥ 0.90	met
ImageReward (MJHQ-30K)	0.9571	≥ 0.90	met
GEdit (EN, local Qwen2.5-VL-72B judge)	6.740	≥ 6.7	met
DPG-Bench	82.190	≥ 85	not met
FID (MJHQ-30K)	14.36	≤ 8	not met
ImgEdit (local judge)	not measured	≥ 3.5	—

Three of six met is the most of any checkpoint in this project — every other configuration measured tops out at two, including the previous best (v2mcptf-grpo-500 @ cfg 3.0: GenEval 0.9284, DPG 81.73, FID 15.40, ImageReward 0.9250, ImgEdit 3.020, GEdit 6.620).

At cfg 3.0, 12 steps the same checkpoint scores GenEval 0.91876, ImageReward 1.0831, GEdit 6.750, DPG 83.228 — wider margins on the three met targets, but it costs human-image quality measurably (see Caveats), so cfg 1.5 is the recommended setting.

FID is the other clear miss. It is not a regression specific to this soup — RL-tuned checkpoints in this project sit in the 13–18 range while the FID-optimised ones (5.9–7.3) give up 0.2+ GenEval. ImgEdit was never run on this checkpoint; its members score 3.0–3.1, below the 3.5 target.

DPG-Bench was not reached by anything in this project. The best DPG measured across 121 settings is 84.612 (a different checkpoint at cfg 7.5 + interval guidance, whose GenEval then falls to 0.880), and the guidance response peaks and declines rather than continuing to climb.

What is in this repo

Only the trained head: the SANA DiT and the diffusion connector, 602 tensors (model.dit.* 548, model.diffusion_connector.* 54). The vision-language model is frozen during training and is not included here.

To run this you also need:

• openbmb/MiniCPM-V-4_6 — the frozen VLM text/image encoder
• Efficient-Large-Model/Sana_600M_512px_diffusers — for the DC-AE (f32, 32-channel, 16×16 latent at 512px) and the scheduler config

Connector type is mcptf and it fuses 1 VLM layer. Note that a vlm_num_layers field elsewhere in this project's configs can read 4; the weights are authoritative — derive it from fusion.layer_weights.shape[0].

1024x1024

This repo also carries upsampler_1024/head_ema.safetensors (426 MB, 106M parameters): a trained decoder head that produces 1024px output from the same 512px latent. The DiT is not involved and is not modified -- it keeps its 16x16 latent and runs the same steps. The frozen DC-AE decoder emits its last hidden features (128 channels at 512px, the tensor that normally feeds conv_out) and the head maps those to 1024px RGB, anchored on a bicubic x2 of the ordinary 512px decode. The VAE stays frozen and must stay bf16 (fp16 underflows the DC-AE decoder).

Cost: +3 ms/image at batch 8 on one RTX PRO 6000 (459 -> 462 ms). Rendering the same prompt and seed at both sizes and downscaling the 1024 result back to 512 differs from the native 512px output by a mean of 1.0/255 -- the same image, decoded better, not more diffusion detail.

Selected over the alternative (a SwinIR latent upsampler applied 16x16 -> 32x32 before the frozen decode) on a reconstruction eval: FID 1.76 vs 2.077, OCR 47.9 vs 40.

git clone https://github.com/ahmedheakl/mobileov2-minicpm && cd mobileov2-minicpm
pip install -r requirements.txt
python infer.py --size 1024 --prompt "a woman holding a ceramic mug" --out out/big.png

Inference settings used for every number above

scheduler	DPM-Solver++, solver_order=2, flow_shift=3
steps	12
guidance	cfg 1.5
null condition	the empty prompt through the same VLM+connector path (not a zero vector)
resolution	512×512

For editing, the null condition is the instruction rather than the empty prompt.

12 steps is deliberate, not a shortcut: on this model sample quality for human subjects peaks around 8–16 steps and declines by 20–40, so 12 steps is both better and ~40% cheaper than the 20-step default. Alignment (GenEval/DPG) and GEdit are unchanged at 12 vs 20 steps.

Provenance

Uniform 1/3 soup of:

member	what it contributed
ta-dpg-s1.0	GenEval 0.9293
qual-imagereward @ step 500	ImageReward 0.9165
joint-grpo-refl-500 @ step 500	GEdit 6.710

All three are fine-tuned from the same init (Mobile-O-0.5B-SFT-minicpm-v2mcptf-mixed), which is what makes averaging them valid. The soup beats all three members on DPG, ImageReward and GEdit simultaneously.

Caveats

• DPG-Bench is not met (82.190 vs 85).
• It does not improve human-image quality. On a paired human-quality benchmark it is neutral versus the baseline (+0.018, not significant). At cfg 3.0 it is significantly worse for people (−0.147, t = −2.68), which is why cfg 1.5 is recommended despite cfg 3.0's better target numbers.
• GEdit here is scored by a local Qwen2.5-VL-72B judge, not the GPT-4o leaderboard scale; the two are not comparable.
• The head alone is not a runnable model — see What is in this repo.