Jessamine/portraitcraft-track2

PortraitCraft Track 2 Solution

This repository contains the inference code and documentation for our PortraitCraft Track 2: Portrait Composition Generation submission.

Model

The released model checkpoint is hosted on Hugging Face:

https://huggingface.co/Jessamine/portraitcraft-track2

The model is based on Qwen-Image and is further fine-tuned for portrait composition generation using the official 4,500 PortraitCraft training samples together with additional private portrait aesthetic-composition data curated by our team. We compared LoRA fine-tuning and full-parameter fine-tuning under the same inference settings, and selected full-parameter fine-tuning because it performed better for this task, especially in aesthetic quality, composition stability, and prompt-to-layout alignment.

Adaptive Canvas Policy

We do not use a fixed 1:1 canvas for all generations. In portrait composition generation, different prompts imply different spatial structures: some are best served by square canvases, some require vertical canvases to preserve full-body framing and headroom/footroom, and some require horizontal canvases for environmental portraits, roads, coastlines, leading lines, and large negative space.

To handle this, we design a prompt-conditioned adaptive canvas policy. The policy reads the input prompt and the released learned policy state, then selects the generation canvas before image synthesis. Its keyword weights, decision thresholds, and candidate aspect ratios were optimized on the training set through iterative evolutionary search. The longer side is normalized to 1584 pixels. For reproducibility, we release the final policy state together with the inference code so reviewers can reproduce the same canvas choices used by our submission.

Inference

Install dependencies:

pip install -r requirements.txt

Run inference:

python scripts/infer_portraitcraft.py \
  --input-json /path/to/track2_test.json \
  --base-model /path/to/Qwen-Image-2512 \
  --checkpoint /path/to/portraitcraft-track2.safetensors \
  --aspect-policy configs/aspect_policy_manifest.json \
  --output-dir outputs/portraitcraft_track2

Package the output directory as a flat submission zip:

python scripts/package_submission.py \
  --image-dir outputs/portraitcraft_track2 \
  --zip-path portraitcraft_track2_submission.zip

Default inference parameters:

• num_inference_steps = 50
• cfg_scale = 4.0
• seed = 346346
• adaptive canvas longest side = 1584