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sophialan/
pg-map-sd15

Name: pg-map-sd15
Availability: InStock

diffusers

text-to-image

stable-diffusion

inference-time-alignment

preference-optimization

pg-map

2606.22958

mit

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PG-MAP for Stable Diffusion 1.5

Custom diffusers pipeline for PG-MAP (Preference-Guided Adaptive MAP) on SD 1.5. Per-step joint optimization of conditioning $c$ and latent $z_t$ via a trajectory-level Gibbs-MAP / proximal energy objective, optionally guided by a frozen preference reward (PickScore by default).

Preprint: arXiv:2606.22958. See github.com/sophialanlan/PG-MAP for code, full configs, and reproduction scripts.

Install

bash

pip install pgmap-align
# or
pip install git+https://github.com/sophialanlan/PG-MAP

Usage

python

from diffusers import DiffusionPipeline
from pgmap import sd15_defaults, FrozenRewardModel
import torch

pipe = DiffusionPipeline.from_pretrained(
    "stable-diffusion-v1-5/stable-diffusion-v1-5",
    custom_pipeline="sophialan/pg-map-sd15",
    torch_dtype=torch.float16,
    safety_checker=None,
).to("cuda")

cfg = sd15_defaults()                                       # paper defaults
reward = FrozenRewardModel("pickscore", device="cuda")

image = pipe(
    "a phoenix rising from ashes, vivid orange and red feathers",
    pg_map_config=cfg,
    reward_model=reward,
).images[0]

Passing pg_map_config=None falls through to the vanilla StableDiffusionPipeline, so the class is a strict superset of the parent.

Method overview

Per denoising step $t$, PG-MAP solves the proximal MAP problem:

$$\mathcal{J}t(c, z_t) = -\tfrac{1}{2\beta{t|s}}|r_t(c,z_t)|^2 - \tfrac{1}{2\sigma_c^2}|c-\mu_t|^2 - \tfrac{1}{2\sigma_z(t)^2}|z_t-z_t^{\text{ddim}}|^2 + \lambda,Q(\hat x_0(z_t,c), y)$$

with $K$ inner gradient-ascent steps and a schedule-adaptive trust region $\sigma_z(t)=\gamma\sqrt{1-\bar\alpha_t}$.

Paper headline (SD 1.5, PartiPrompts $n=1632$, seed 123)

Method	PickScore	HPS	Aesthetic	CLIP
PG-MAP (default)	56.8%	52.8%	54.0%	50.6%
Tuned-CFG + PG-MAP	53.6%	66.0%	60.2%	56.0%

Win-rate vs. same-seed static baseline.

Citation

bibtex

@misc{sun2026pgmap,
  title={{PG-MAP}: Joint {MAP} Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models},
  author={Sun, Ruolan and Polak, Pawel},
  year={2026},
  eprint={2606.22958},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2606.22958}
}

License

MIT (see LICENSE). Pretrained weights remain under their original licenses.