Initial release

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+
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+
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NOTICE

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+FrameLite
+Inference code and task-specific language LoRA and vision merger weights.
+
+FrameLite uses Qwen/Qwen3.5-0.8B as its pretrained backbone. The backbone
+weights, tokenizer, processor, and model configuration are downloaded
+separately from Qwen and are not redistributed in this model repository.
+The upstream model is released by the Qwen team under the Apache License,
+Version 2.0.
+Upstream: https://huggingface.co/Qwen/Qwen3.5-0.8B
+Upstream license: https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/LICENSE
+
+FrameLite's trained language LoRA and vision merger parameters are distributed
+in framelite.safetensors. The language LoRA is not merged into the backbone.
+The inference code is available at https://github.com/JianzeLi-114/FrameLite.
+
+Runtime dependencies, including PyTorch, Transformers, PEFT, Pillow, and
+Safetensors, retain their respective licenses. They are installed separately.

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.5-0.8B
+base_model_relation: finetune
+language:
+  - en
+tags:
+  - image-cropping
+  - aesthetic-image-cropping
+  - computer-vision
+  - vision-language-model
+---
+
+<div align="center">
+
+# FrameLite: Decision-Oriented Aesthetic Cropping<br>with a Compact Vision–Language Model
+
+**Jianze Li**<sup>1</sup>, **Kai Liu**<sup>1</sup>, **Ziqing Zhang**<sup>1</sup>, **Weihang Zhang**<sup>2</sup>,<br>
+**Yong Guo**<sup>1</sup>, **Linghe Kong**<sup>1</sup>, **Yulun Zhang**<sup>1,†</sup>
+
+<sup>1</sup> Shanghai Jiao Tong University<br>
+<sup>2</sup> Institute of Media Technology and Experience Design, Huawei<br>
+<sup>†</sup> Corresponding author
+
+[**Code**](https://github.com/JianzeLi-114/FrameLite) · [**Paper**](#) · [**Supplementary Material**](https://github.com/JianzeLi-114/FrameLite/releases/download/v1.0/FrameLite_supplementary.pdf)
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-4f6d7a?style=flat-square)](https://huggingface.co/mon3tr/FrameLite/blob/main/LICENSE)
+[![Weights](https://img.shields.io/badge/Weights-224_MB-697d6c?style=flat-square)](https://huggingface.co/mon3tr/FrameLite/tree/main)
+
+</div>
+
+## Abstract
+
+> Aesthetic image cropping improves composition by balancing subjects, context, and image boundaries. Large vision–language models bring semantic knowledge to this task, but their scale limits lightweight photography assistance. We propose **FrameLite**, a compact cropping model built on Qwen3.5-0.8B. Our approach is motivated by the observation that a small model can generate better crops than its first answer suggests. We develop three-stage training to turn this potential into stronger direct predictions. Spatial and composition training establishes localization and framing preferences. Quality-Guided Online Supervision (QOS) then learns the highest-quality complete answers among the current policy's generated candidates. Greedy-Referenced Optimization (GRO) further refines the policy using positive and negative quality differences relative to its current greedy answer. Both online stages use annotation-derived feedback, while inference returns one coordinate answer without search or an external scorer. Experiments show that FrameLite outperforms the compared large-VLM croppers across public benchmarks with a substantially smaller backbone. Controlled studies confirm that selecting high-quality training answers improves the final model beyond online sampling alone.
+
+## Method
+
+<p align="center">
+  <img src="assets/method_overview.png" width="100%" alt="FrameLite's three-stage training overview">
+</p>
+
+1. **Stage I — Spatial and composition training.** Learn spatial localization, composition preferences, and complete reference-frame prediction.
+2. **Stage II — Quality-Guided Online Supervision (QOS).** Generate greedy and sampled answers, evaluate complete-frame quality, and imitate the best answer.
+3. **Stage III — Greedy-Referenced Optimization (GRO).** Refine fresh samples using positive and negative quality differences relative to the current greedy answer.
+
+At inference, FrameLite directly predicts one normalized crop through greedy decoding. No candidate search, external scorer, retrieval database, or external API is needed.
+
+## Model
+
+| Model | Backbone | Weights |
+|:---|:---|:---|
+| FrameLite | Qwen3.5-0.8B | [224 MB](https://huggingface.co/mon3tr/FrameLite/resolve/main/framelite.safetensors) |
+
+The FrameLite checkpoint contains the learned language LoRA and vision merger.
+Download [Qwen3.5-0.8B](https://huggingface.co/Qwen/Qwen3.5-0.8B) separately and
+load both with the `FrameLite` API below.
+
+## Quick Start
+
+### Install the inference code
+
+Use **Python 3.10** and a CUDA GPU with BF16 support.
+
+```bash
+git clone https://github.com/JianzeLi-114/FrameLite.git
+cd FrameLite
+
+python3.10 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+python -m pip install torch==2.6.0 torchvision==0.21.0 \
+  --index-url https://download.pytorch.org/whl/cu124
+python -m pip install -r requirements.txt
+```
+
+### Download the weights
+
+Download the FrameLite weights and the Qwen backbone:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="mon3tr/FrameLite",
+    local_dir="weights/framelite",
+    allow_patterns=[
+        "framelite.safetensors",
+        "inference_config.json",
+        "LICENSE",
+        "NOTICE",
+    ],
+)
+
+snapshot_download(
+    repo_id="Qwen/Qwen3.5-0.8B",
+    local_dir="weights/framelite/base",
+)
+```
+
+The two downloads place the FrameLite checkpoint and Qwen backbone under `weights/framelite/`.
+
+### Crop an image
+
+```bash
+python infer.py \
+  --image assets/examples/mountain_reflection/input.jpg \
+  --weights weights/framelite \
+  --output-dir outputs \
+  --device cuda:0
+```
+
+The CLI saves predictions to `outputs/predictions.jsonl` and valid crops to `outputs/crops/`. To process a directory, pass its path to `--image`; add `--recursive` to include subdirectories.
+
+### Python API
+
+```python
+from framelite import FrameLite, crop_image
+
+model = FrameLite.from_pretrained("weights/framelite", device="cuda:0")
+image_path = "path/to/image.jpg"
+result = model.predict(image_path)
+
+if result["valid"]:
+    print(result["bbox_normalized"])  # [x1, y1, x2, y2] in [0, 1]
+    crop_image(image_path, result["bbox_normalized"]).save("crop.png")
+else:
+    print(result["error"])
+```
+
+Coordinates refer to the original image, with the origin at its top-left corner. The model returns a single crop, for example `{"crop":[0.1,0.05,0.9,0.95]}`. This JSON illustrates the output format.
+
+## Evaluation
+
+The following results are reported in the paper. The **same final model** produces one crop per image on all three benchmarks.
+
+| GAICv2 Acc .85 ↑ | GAICv2 Acc .90 ↑ | FLMS IoU ↑ | FLMS BDE ↓ | FCDB IoU ↑ | FCDB BDE ↓ |
+|---:|---:|---:|---:|---:|---:|
+| 0.9140 | 0.8340 | 0.8679 | 0.0297 | 0.7124 | 0.0704 |
+
+Acc denotes GAICv2 Acc<sub>1/5</sub> at the specified IoU threshold. See the [paper](#) for the evaluation protocol and the [supplementary material](https://github.com/JianzeLi-114/FrameLite/releases/download/v1.0/FrameLite_supplementary.pdf) for additional analyses.
+
+## Citation
+
+```bibtex
+@misc{li2026framelite,
+  title={FrameLite: Decision-Oriented Aesthetic Cropping with a Compact Vision--Language Model},
+  author={Li, Jianze and Liu, Kai and Zhang, Ziqing and Zhang, Weihang and Guo, Yong and Kong, Linghe and Zhang, Yulun},
+  year={2026},
+  url={https://github.com/JianzeLi-114/FrameLite}
+}
+```
+
+## License and Acknowledgments
+
+FrameLite task weights and inference code are provided under the [Apache License 2.0](https://huggingface.co/mon3tr/FrameLite/blob/main/LICENSE). The separately downloaded Qwen3.5-0.8B backbone is distributed by Qwen under the [Apache License 2.0](https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/LICENSE); see [NOTICE](https://huggingface.co/mon3tr/FrameLite/blob/main/NOTICE) and the [upstream model card](https://huggingface.co/Qwen/Qwen3.5-0.8B).
+
+We thank the developers of [Qwen](https://github.com/QwenLM), [Transformers](https://github.com/huggingface/transformers), and [PEFT](https://github.com/huggingface/peft).
+
+Correspondence: [Yulun Zhang](mailto:yulun100@gmail.com).

framelite.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1a58395c537c6539557b830f714ebea6ece3d433f7476e043399cba01c41e94b
+size 223576720

inference_config.json

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+{
+  "format": "framelite-inference/v1",
+  "base_model": "base",
+  "adapter_file": "framelite.safetensors",
+  "model": {
+    "dtype": "bfloat16",
+    "attn_implementation": "sdpa"
+  },
+  "lora": {
+    "rank": 64,
+    "alpha": 128,
+    "dropout": 0.05,
+    "target_suffixes": [
+      "q_proj",
+      "k_proj",
+      "v_proj",
+      "o_proj",
+      "in_proj_qkv",
+      "in_proj_z",
+      "in_proj_b",
+      "in_proj_a",
+      "out_proj",
+      "gate_proj",
+      "up_proj",
+      "down_proj"
+    ]
+  },
+  "merger_storage_dtype": "float32",
+  "image_processing": {
+    "min_pixels": 65536,
+    "max_pixels": 589824
+  },
+  "prompt": "Select one aesthetically pleasing crop for this image. Return only the requested short structured output.",
+  "generation": {
+    "max_new_tokens": 32,
+    "do_sample": false,
+    "num_beams": 1,
+    "num_return_sequences": 1
+  }
+}