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  1. README.md +70 -0
  2. configs/aspect_policy_manifest.json +2506 -0
  3. configs/inference_config.json +9 -0
  4. docs/aspect_policy_description.md +19 -0
  5. requirements.txt +5 -0
  6. scripts/aspect_ratio_selector.py +153 -0
  7. scripts/infer_portraitcraft.py +95 -0
  8. scripts/package_submission.py +30 -0
README.md ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # PortraitCraft Track 2 Solution
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+
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+ This repository contains the inference code and documentation for our
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+ PortraitCraft Track 2: Portrait Composition Generation submission.
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+
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+ ## Model
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+
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+ The released model checkpoint is hosted on Hugging Face:
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+
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+ https://huggingface.co/Jessamine/portraitcraft-track2
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+
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+ The model is based on Qwen-Image and is further fine-tuned for portrait
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+ composition generation using the official 4,500 PortraitCraft training samples
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+ together with additional private portrait aesthetic-composition data curated by
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+ our team. We compared LoRA fine-tuning and full-parameter fine-tuning under the
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+ same inference settings, and selected full-parameter fine-tuning because it
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+ performed better for this task, especially in aesthetic quality, composition
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+ stability, and prompt-to-layout alignment.
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+
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+ ## Adaptive Canvas Policy
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+
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+ We do not use a fixed 1:1 canvas for all generations. In portrait composition
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+ generation, different prompts imply different spatial structures: some are best
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+ served by square canvases, some require vertical canvases to preserve full-body
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+ framing and headroom/footroom, and some require horizontal canvases for
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+ environmental portraits, roads, coastlines, leading lines, and large negative
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+ space.
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+
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+ To handle this, we design a prompt-conditioned adaptive canvas policy. The
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+ policy reads the input prompt and the released learned policy state, then
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+ selects the generation canvas before image synthesis. Its keyword weights,
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+ decision thresholds, and candidate aspect ratios were optimized on the training
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+ set through iterative evolutionary search. The longer side is normalized to
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+ 1584 pixels. For reproducibility, we release the final policy state together
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+ with the inference code so reviewers can reproduce the same canvas choices used
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+ by our submission.
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+
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+ ## Inference
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+
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+ Install dependencies:
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+
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+ ```bash
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+ pip install -r requirements.txt
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+ ```
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+
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+ Run inference:
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+
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+ ```bash
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+ python scripts/infer_portraitcraft.py \
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+ --input-json /path/to/track2_test.json \
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+ --base-model /path/to/Qwen-Image-2512 \
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+ --checkpoint /path/to/portraitcraft-track2.safetensors \
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+ --aspect-policy configs/aspect_policy_manifest.json \
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+ --output-dir outputs/portraitcraft_track2
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+ ```
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+
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+ Package the output directory as a flat submission zip:
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+
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+ ```bash
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+ python scripts/package_submission.py \
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+ --image-dir outputs/portraitcraft_track2 \
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+ --zip-path portraitcraft_track2_submission.zip
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+ ```
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+
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+ Default inference parameters:
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+
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+ - `num_inference_steps = 50`
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+ - `cfg_scale = 4.0`
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+ - `seed = 346346`
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+ - adaptive canvas longest side = `1584`
configs/aspect_policy_manifest.json ADDED
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+ "unsplash_people_46656_e6500fe4971b.jpg": {
2271
+ "width": 1584,
2272
+ "height": 1584,
2273
+ "prompt_sha1": "85d270a67eaf201913eab220efbe75c611cbbf0a"
2274
+ },
2275
+ "unsplash_people_45185_17257a9003ff.jpg": {
2276
+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "cd34bfce8571337c68fe03e43550f7769fd44652"
2279
+ },
2280
+ "unsplash_people_03811_0289627eeb1d.jpg": {
2281
+ "width": 1584,
2282
+ "height": 1584,
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+ "prompt_sha1": "18ba6b66f011e14c9db19a7e7f8e38b4066d7f7a"
2284
+ },
2285
+ "unsplash_people_17572_fb9fbabb75d7.jpg": {
2286
+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "3ed949e0df67f1c8adae4b461c0d640bd57457cb"
2289
+ },
2290
+ "unsplash_people_04127_85d8991d05fd.jpg": {
2291
+ "width": 1584,
2292
+ "height": 1584,
2293
+ "prompt_sha1": "fbea4a310fad27cab879af5dee8cda802e5166a1"
2294
+ },
2295
+ "unsplash_people_13132_16e2b46e61d8.jpg": {
2296
+ "width": 1584,
2297
+ "height": 1584,
2298
+ "prompt_sha1": "56e8d265fb8d67549593c7100958527b06871a21"
2299
+ },
2300
+ "unsplash_people_60783_2df299660960.jpg": {
2301
+ "width": 1584,
2302
+ "height": 1584,
2303
+ "prompt_sha1": "5601868d405fd29ca91c64918a7483f1005ee51e"
2304
+ },
2305
+ "unsplash_people_01708_d158755137ed.jpg": {
2306
+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "579877ad313f134e659c1beb11281f55f9ee378d"
2309
+ },
2310
+ "unsplash_people_52808_e1f5247a04a4.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "aab2106527b63e8646249f2380c1c83f4704cb94"
2314
+ },
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+ "unsplash_plus_people_02361_2b609a7a2cab.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "db8f6d2a1ee4af2bd5e99a3a5b1c994115d2eb2b"
2319
+ },
2320
+ "unsplash_people_05550_f96d41ede4bf.jpg": {
2321
+ "width": 1584,
2322
+ "height": 1584,
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+ "prompt_sha1": "5e29be97c87b472f5e41d0b6bb023e193a2b1b67"
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+ },
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+ "unsplash_people_36733_636d8c656b49.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "61ba98308adf840a67093fa75455f27a93deafe2"
2329
+ },
2330
+ "unsplash_people_26819_1ef0cd09ff6a.jpg": {
2331
+ "width": 1584,
2332
+ "height": 1584,
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+ "prompt_sha1": "7ea3be246d5661ff814d6096aa9e0c466b0059aa"
2334
+ },
2335
+ "unsplash_people_03238_d7ca915ef8a8.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "3ecde62c5eb29a9b482c107a36d4f75b2e46d861"
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+ },
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+ "unsplash_people_45265_85ee0cf191fc.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "f664d60b303b2e405ca4c668a15bbfdcfac6b9e8"
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+ },
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+ "unsplash_people_32221_eb5ddc210a5c.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "f8699fef455043a02881019886c5a66d9743ab52"
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+ },
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+ "unsplash_plus_people_05715_dc2ec017ece2.jpg": {
2351
+ "width": 1584,
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+ "height": 1056,
2353
+ "prompt_sha1": "06cfbd6699f656db45e46248354c6e2df5f89081"
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+ },
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+ "unsplash_people_31269_5c3d32d93c8e.jpg": {
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+ "width": 1584,
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+ "height": 1584,
2358
+ "prompt_sha1": "16643c98e286a11c51f8d204adf22252d47f4caf"
2359
+ },
2360
+ "unsplash_people_46089_ffd61197cecc.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "2740f98c052728d02fa92e6028afc4d4893d257f"
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+ },
2365
+ "unsplash_people_38981_e208711057c9.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "2ed49934be0af96fa52559eb0800a698289f769c"
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+ },
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+ "unsplash_people_44604_3c27edb257d2.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "a54f0a437a72878f026681bdaa27e23849ebee25"
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+ },
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+ "unsplash_people_50852_184db20726bb.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "10bb81a4af6e523549ca87ea12422aaea639bdb7"
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+ },
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+ "unsplash_people_00028_9f102976b114.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "2634feab0bc5f5623487e5f9e340ca608bd18fb9"
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+ },
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+ "unsplash_people_07042_e95016a3aa93.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "6fe71a155efa4f930a85521c72ee3751b6b932fb"
2389
+ },
2390
+ "unsplash_people_30795_e1643ace4e87.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "0bdca9a93a38a4599e18fdee30255acdc517bdf0"
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+ },
2395
+ "unsplash_people_58742_9b49a4346b94.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "3fbbba3f969bf874c1db603fabab7e316aa3d862"
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+ },
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+ "unsplash_people_16742_21146b37b517.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "143b82cc8abb2fd942425f6f188f51376badd574"
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+ },
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+ "unsplash_people_51311_ca9f16f1126f.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "8ba9c9352592df718b8021da5544e59f8ae74d66"
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+ },
2410
+ "unsplash_people_04460_55d621282308.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "b6afcdde88ac35265d8e04c152f854cb19323c30"
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+ },
2415
+ "unsplash_people_28883_a976c90982f3.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "398c3b12eac15384c4d7862715d679cdbb522fea"
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+ },
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+ "unsplash_people_35545_726f27dbee41.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "ce41b023074f87ab1c81f101ce06e62eb3e5c904"
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+ },
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+ "unsplash_people_15441_43fc48ad588c.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "f1ddcb9106439c6e9798b5249a158e8b1a326d6f"
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+ },
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+ "unsplash_people_33962_4efb2f503310.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "c5e41b89c9c76fa7d119383e0f15dfe6c1760b25"
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+ },
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+ "unsplash_people_48317_c1e15ff4b24a.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "6535745da02cf6e9f35e7115e06c808125a13071"
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+ },
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+ "unsplash_people_09009_c4f8582c80d8.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "2040b8267da87c3c51efa4514d5f96e201eeb86b"
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+ },
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+ "unsplash_people_18048_a7cef741225c.jpg": {
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+ "width": 1584,
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+ "height": 1584,
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+ "prompt_sha1": "fb15a98715bab813c82236cdc24885a79b54cb65"
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+ },
2450
+ "unsplash_people_39257_28b27bb5738d.jpg": {
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+ "width": 1584,
2452
+ "height": 1584,
2453
+ "prompt_sha1": "0d621d1700d000bb9c4acde1a2017082ffa6159b"
2454
+ },
2455
+ "unsplash_people_21100_c091571e90ee.jpg": {
2456
+ "width": 1584,
2457
+ "height": 1584,
2458
+ "prompt_sha1": "0c0173a019dbde944f37efc15b3f3790f80eb303"
2459
+ },
2460
+ "unsplash_people_55361_89bbf1ad713f.jpg": {
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+ "width": 1584,
2462
+ "height": 1584,
2463
+ "prompt_sha1": "04eeb61598157cd5cdf0179835cd291c978510bd"
2464
+ },
2465
+ "unsplash_people_08798_6c744e6c4874.jpg": {
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+ "width": 1056,
2467
+ "height": 1584,
2468
+ "prompt_sha1": "55881837892c99be03d3a595184740b31e2ed28f"
2469
+ },
2470
+ "unsplash_people_21872_d46733662a8e.jpg": {
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+ "width": 1056,
2472
+ "height": 1584,
2473
+ "prompt_sha1": "341457033ba074d8a7f790622f6e7734dfecfed1"
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+ },
2475
+ "unsplash_plus_people_11061_0a8949e8dd28.jpg": {
2476
+ "width": 1584,
2477
+ "height": 1584,
2478
+ "prompt_sha1": "0e234bfe14df22244407fb86823fdbb87d4a214e"
2479
+ },
2480
+ "unsplash_people_08324_edbbcd848384.jpg": {
2481
+ "width": 1584,
2482
+ "height": 1584,
2483
+ "prompt_sha1": "4085fc03c1517cf5efd77f939729fe9a25730345"
2484
+ },
2485
+ "unsplash_people_31056_261eaa72045c.jpg": {
2486
+ "width": 1584,
2487
+ "height": 1584,
2488
+ "prompt_sha1": "dbdb0daa65515dec463b5c2ef1a31bcbaba6c4aa"
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+ },
2490
+ "unsplash_people_19948_83c09e71988f.jpg": {
2491
+ "width": 1584,
2492
+ "height": 1584,
2493
+ "prompt_sha1": "f2013217ebca0552cd60f7d7a17c0c4c0c92eb0b"
2494
+ },
2495
+ "unsplash_people_41697_af2b3a625a70.jpg": {
2496
+ "width": 1584,
2497
+ "height": 1584,
2498
+ "prompt_sha1": "0f306b026baa1a9457d117ead55c04b3b45e2b70"
2499
+ },
2500
+ "unsplash_people_09530_d82de279d86f.jpg": {
2501
+ "width": 1584,
2502
+ "height": 1584,
2503
+ "prompt_sha1": "f4240e0693f66d155356b2df67d4485021160317"
2504
+ }
2505
+ }
2506
+ }
configs/inference_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "base_model": "Qwen-Image-2512",
3
+ "checkpoint": "portraitcraft-track2.safetensors",
4
+ "num_inference_steps": 50,
5
+ "cfg_scale": 4.0,
6
+ "seed": 346346,
7
+ "longest_side": 1584,
8
+ "aspect_policy": "configs/aspect_policy_manifest.json"
9
+ }
docs/aspect_policy_description.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## Adaptive Canvas Policy
2
+
3
+ We do not use a fixed 1:1 canvas for all samples. Portrait composition prompts
4
+ often imply different spatial structures: some are naturally square portraits,
5
+ some need a vertical canvas to preserve full-body framing and upper/lower
6
+ breathing room, and some need a horizontal canvas to carry environmental
7
+ context, roads, coastlines, leading lines, or large negative space.
8
+
9
+ We therefore design a prompt-conditioned adaptive canvas policy. The policy
10
+ uses the input prompt and a learned policy state to choose the canvas size
11
+ before generation. Its keyword weights, decision thresholds, and candidate
12
+ ratios were optimized on the training set through an iterative evolutionary
13
+ search procedure. The selected canvas always normalizes the longer side to 1584
14
+ pixels.
15
+
16
+ For reproducibility, we release the final learned policy state together with
17
+ the inference code. This allows reviewers to recover the same canvas selection
18
+ used in our submission. For unseen prompts, the implementation falls back to a
19
+ deterministic prompt-only rule policy.
requirements.txt ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ torch
2
+ tqdm
3
+ safetensors
4
+ huggingface_hub
5
+
scripts/aspect_ratio_selector.py ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Prompt-conditioned canvas selector used for PortraitCraft inference.
3
+
4
+ For challenge reproduction, the selector first checks a compact learned
5
+ policy manifest keyed by image name / prompt hash. For unseen prompts it falls
6
+ back to a deterministic prompt-only rule policy.
7
+ """
8
+
9
+ from __future__ import annotations
10
+
11
+ import argparse
12
+ import hashlib
13
+ import json
14
+ import re
15
+ from pathlib import Path
16
+ from typing import Any
17
+
18
+
19
+ LONGEST_SIDE = 1584
20
+
21
+ LANDSCAPE_TERMS = {
22
+ "landscape": 2.0,
23
+ "panoramic": 2.0,
24
+ "wide": 1.6,
25
+ "horizon": 1.5,
26
+ "road": 1.4,
27
+ "street": 1.1,
28
+ "beach": 1.4,
29
+ "ocean": 1.4,
30
+ "sea": 1.2,
31
+ "mountain": 1.4,
32
+ "valley": 1.2,
33
+ "field": 1.1,
34
+ "cityscape": 1.5,
35
+ "environmental portrait": 1.5,
36
+ "large negative space": 1.2,
37
+ "leading lines": 1.1,
38
+ }
39
+
40
+ PORTRAIT_TERMS = {
41
+ "full-body": 1.8,
42
+ "full body": 1.8,
43
+ "head-to-toe": 1.8,
44
+ "standing": 1.2,
45
+ "vertical": 1.6,
46
+ "tall": 1.3,
47
+ "narrow": 1.2,
48
+ "alley": 1.2,
49
+ "staircase": 1.1,
50
+ "towering": 1.1,
51
+ "walking": 0.8,
52
+ }
53
+
54
+ SQUARE_TERMS = {
55
+ "close-up": 1.5,
56
+ "close up": 1.5,
57
+ "headshot": 1.6,
58
+ "centered": 1.4,
59
+ "symmetrical": 1.3,
60
+ "symmetry": 1.3,
61
+ "bust": 1.1,
62
+ "face": 0.8,
63
+ "portrait": 0.6,
64
+ }
65
+
66
+
67
+ def prompt_hash(prompt: str) -> str:
68
+ return hashlib.sha1(prompt.encode("utf-8")).hexdigest()
69
+
70
+
71
+ def load_manifest(path: str | Path | None) -> dict[str, Any]:
72
+ if not path:
73
+ return {"entries": {}}
74
+ with open(path, encoding="utf-8") as f:
75
+ return json.load(f)
76
+
77
+
78
+ def _score_terms(text: str, terms: dict[str, float]) -> float:
79
+ score = 0.0
80
+ for term, weight in terms.items():
81
+ if " " in term or "-" in term:
82
+ if term in text:
83
+ score += weight
84
+ elif re.search(rf"\b{re.escape(term)}s?\b", text):
85
+ score += weight
86
+ return score
87
+
88
+
89
+ def round_to_16(value: float) -> int:
90
+ return max(16, int(round(value / 16.0)) * 16)
91
+
92
+
93
+ def fallback_select(prompt: str, longest_side: int = LONGEST_SIDE) -> tuple[int, int, str]:
94
+ """Select a canvas for unseen prompts without using reference images."""
95
+ text = prompt.lower()
96
+ landscape = _score_terms(text, LANDSCAPE_TERMS)
97
+ portrait = _score_terms(text, PORTRAIT_TERMS)
98
+ square = _score_terms(text, SQUARE_TERMS)
99
+
100
+ if landscape >= portrait + 1.2 and landscape >= square + 0.8:
101
+ return longest_side, round_to_16(longest_side * 2 / 3), "fallback_landscape_3x2"
102
+ if portrait >= landscape + 0.8 and portrait >= square + 0.6:
103
+ return round_to_16(longest_side * 2 / 3), longest_side, "fallback_portrait_2x3"
104
+ return longest_side, longest_side, "fallback_square_1x1"
105
+
106
+
107
+ def select_canvas(
108
+ item: dict[str, Any],
109
+ manifest: dict[str, Any] | None = None,
110
+ longest_side: int = LONGEST_SIDE,
111
+ ) -> tuple[int, int, str]:
112
+ """Return ``(width, height, policy_name)`` for an input item."""
113
+ manifest = manifest or {"entries": {}}
114
+ entries = manifest.get("entries", {})
115
+ image_path = item.get("image_path") or item.get("task") or item.get("file_name")
116
+ prompt = item.get("prompt", "")
117
+
118
+ if image_path and image_path in entries:
119
+ entry = entries[image_path]
120
+ return int(entry["width"]), int(entry["height"]), "learned_manifest_by_name"
121
+
122
+ sha1 = prompt_hash(prompt)
123
+ for entry in entries.values():
124
+ if entry.get("prompt_sha1") == sha1:
125
+ return int(entry["width"]), int(entry["height"]), "learned_manifest_by_prompt"
126
+
127
+ return fallback_select(prompt, longest_side=longest_side)
128
+
129
+
130
+ def main() -> None:
131
+ parser = argparse.ArgumentParser()
132
+ parser.add_argument("--input-json", required=True)
133
+ parser.add_argument("--output-json", required=True)
134
+ parser.add_argument("--manifest", default=None)
135
+ parser.add_argument("--longest-side", type=int, default=LONGEST_SIDE)
136
+ args = parser.parse_args()
137
+
138
+ manifest = load_manifest(args.manifest)
139
+ with open(args.input_json, encoding="utf-8") as f:
140
+ data = json.load(f)
141
+
142
+ for item in data:
143
+ width, height, policy = select_canvas(item, manifest, args.longest_side)
144
+ item["width"] = width
145
+ item["height"] = height
146
+ item["aspect_policy"] = policy
147
+
148
+ with open(args.output_json, "w", encoding="utf-8") as f:
149
+ json.dump(data, f, ensure_ascii=False, indent=2)
150
+
151
+
152
+ if __name__ == "__main__":
153
+ main()
scripts/infer_portraitcraft.py ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ from __future__ import annotations
3
+
4
+ import argparse
5
+ import glob
6
+ import json
7
+ import sys
8
+ import time
9
+ from pathlib import Path
10
+
11
+ import torch
12
+ from tqdm import tqdm
13
+
14
+ SCRIPT_DIR = Path(__file__).resolve().parent
15
+ sys.path.insert(0, str(SCRIPT_DIR))
16
+
17
+ from aspect_ratio_selector import load_manifest, select_canvas
18
+ from diffsynth.core import ModelConfig, load_state_dict
19
+ from diffsynth.pipelines.qwen_image import QwenImagePipeline
20
+
21
+
22
+ def parse_args() -> argparse.Namespace:
23
+ parser = argparse.ArgumentParser()
24
+ parser.add_argument("--input-json", required=True)
25
+ parser.add_argument("--base-model", required=True)
26
+ parser.add_argument("--checkpoint", required=True)
27
+ parser.add_argument("--aspect-policy", default=None)
28
+ parser.add_argument("--output-dir", required=True)
29
+ parser.add_argument("--worker-id", type=int, default=0)
30
+ parser.add_argument("--num-workers", type=int, default=1)
31
+ parser.add_argument("--seed", type=int, default=346346)
32
+ parser.add_argument("--steps", type=int, default=50)
33
+ parser.add_argument("--cfg-scale", type=float, default=4.0)
34
+ parser.add_argument("--longest-side", type=int, default=1584)
35
+ return parser.parse_args()
36
+
37
+
38
+ def load_pipe(base_model: str, checkpoint: str) -> QwenImagePipeline:
39
+ base = Path(base_model)
40
+ model_configs = [
41
+ ModelConfig(path=sorted(glob.glob(str(base / "transformer/diffusion_pytorch_model*.safetensors")))),
42
+ ModelConfig(path=sorted(glob.glob(str(base / "text_encoder/model*.safetensors")))),
43
+ ModelConfig(path=str(base / "vae/diffusion_pytorch_model.safetensors")),
44
+ ]
45
+ pipe = QwenImagePipeline.from_pretrained(
46
+ torch_dtype=torch.bfloat16,
47
+ device="cuda",
48
+ model_configs=model_configs,
49
+ tokenizer_config=ModelConfig(path=str(base / "tokenizer")),
50
+ )
51
+ state = load_state_dict(checkpoint, torch_dtype=torch.bfloat16, device="cpu")
52
+ missing, unexpected = pipe.dit.load_state_dict(state, strict=False)
53
+ print(f"Loaded checkpoint {checkpoint}; missing={len(missing)} unexpected={len(unexpected)}", flush=True)
54
+ return pipe
55
+
56
+
57
+ def main() -> None:
58
+ args = parse_args()
59
+ with open(args.input_json, encoding="utf-8") as f:
60
+ data = json.load(f)
61
+ items = data[args.worker_id :: args.num_workers]
62
+
63
+ output_dir = Path(args.output_dir)
64
+ output_dir.mkdir(parents=True, exist_ok=True)
65
+ manifest = load_manifest(args.aspect_policy)
66
+ pipe = load_pipe(args.base_model, args.checkpoint)
67
+
68
+ print(f"worker {args.worker_id}/{args.num_workers}, tasks={len(items)}", flush=True)
69
+ for idx, item in enumerate(items, 1):
70
+ name = item["image_path"]
71
+ out_path = output_dir / name
72
+ if out_path.exists():
73
+ print(f"skip {idx}/{len(items)} {name}", flush=True)
74
+ continue
75
+
76
+ width, height, policy = select_canvas(item, manifest, longest_side=args.longest_side)
77
+ start = time.time()
78
+ print(f"start {idx}/{len(items)} {name} {width}x{height} {policy}", flush=True)
79
+ image = pipe(
80
+ prompt=item["prompt"],
81
+ negative_prompt=" ",
82
+ width=width,
83
+ height=height,
84
+ seed=args.seed,
85
+ rand_device="cuda",
86
+ num_inference_steps=args.steps,
87
+ cfg_scale=args.cfg_scale,
88
+ progress_bar_cmd=lambda x: tqdm(x, disable=True),
89
+ )
90
+ image.save(out_path)
91
+ print(f"done {idx}/{len(items)} {name} in {time.time() - start:.1f}s", flush=True)
92
+
93
+
94
+ if __name__ == "__main__":
95
+ main()
scripts/package_submission.py ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ from __future__ import annotations
3
+
4
+ import argparse
5
+ from pathlib import Path
6
+ from zipfile import ZIP_DEFLATED, ZipFile
7
+
8
+
9
+ def main() -> None:
10
+ parser = argparse.ArgumentParser()
11
+ parser.add_argument("--image-dir", required=True)
12
+ parser.add_argument("--zip-path", required=True)
13
+ args = parser.parse_args()
14
+
15
+ image_dir = Path(args.image_dir)
16
+ images = sorted(image_dir.glob("*.jpg")) + sorted(image_dir.glob("*.png"))
17
+ if not images:
18
+ raise SystemExit(f"No images found in {image_dir}")
19
+
20
+ zip_path = Path(args.zip_path)
21
+ zip_path.parent.mkdir(parents=True, exist_ok=True)
22
+ with ZipFile(zip_path, "w", compression=ZIP_DEFLATED) as zf:
23
+ for image in images:
24
+ zf.write(image, arcname=image.name)
25
+
26
+ print(f"Wrote {zip_path} with {len(images)} images")
27
+
28
+
29
+ if __name__ == "__main__":
30
+ main()