We lifted 9 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ailab-cvc/seed-x | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| cosine_loss | Ran | ailab-cvc/seed-x/src/models/mllm/seed_x.py pointer only (licence: NONE) · get_code("66a2fe5698649c4c") |
| extract_box | Ran | ailab-cvc/seed-x/src/inference/eval_img2text_seed_x.py pointer only (licence: NOASSERTION) · get_code("c3e1f507b8bc217d") |
| get_transform | Ran | ailab-cvc/seed-x/src/processer/transforms.py pointer only (licence: NOASSERTION) · get_code("bda035480e718551") |
| resize_and_pad_image | Ran | ailab-cvc/seed-x/src/inference/any_res.py pointer only (licence: NOASSERTION) · get_code("4039ff7e52e2d4fb") |
| select_best_resolution | Ran | ailab-cvc/seed-x/src/inference/any_res.py pointer only (licence: NONE) · get_code("3999ff487573f32c") |
| select_best_resolution_v2 | Ran | ailab-cvc/seed-x/src/inference/any_res.py pointer only (licence: NOASSERTION) · get_code("4db1fefd729f4ef0") |
| trainable_params | Ran | ailab-cvc/seed-x/src/train/train_seed_x_sft.py pointer only (licence: NOASSERTION) · get_code("070e1db10f514798") |
| bert_tokenizer | Not yet run | ailab-cvc/seed-x/src/processer/tokenizer.py pointer only (licence: NOASSERTION) · get_code("04c1966e1890614b") |
| extract_box | Not yet run | ailab-cvc/seed-x/src/inference/eval_img2text_seed_x_i.py pointer only (licence: NOASSERTION) · get_code("685a977b9afc9b0a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language. SEED-Data-Edit is composed of three distinct types of data: (1) High-quality editing data produced by an automated pipeline, ensuring a substantial volume of diverse image editing pairs. (2) Real-world scenario data collected from the internet, which captures the intricacies of user intentions for promoting the practical application of image editing in the real world. (3) High-precision multi-turn editing data annotated by humans, which involves multiple rounds of edits for simulating iterative editing processes. The combination of these diverse data sources makes SEED-Data-Edit a comprehensive and versatile dataset for training language-guided image editing model. We fine-tune a pretrained Multimodal Large Language Model (MLLM) that unifies comprehension and generation with SEED-Data-Edit. The instruction tuned model demonstrates promising results, indicating the potential and effectiveness of SEED-Data-Edit in advancing the field of instructional image editing. The datasets are released in https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2405.04007")
get_code_for_paper("2405.04007")
have("2405.04007")
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