We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| donahowe/AutoStudio | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| generate_layout_mask | Ran | donahowe/AutoStudio/model/autostudio.py pointer only (licence: NONE) · get_code("f45c3b88da005339") |
| get_token_map | Ran | donahowe/AutoStudio/model/autostudio.py pointer only (licence: NONE) · get_code("e8c33e5e0c35e67d") |
| scale_boxes_to_fit | Ran | donahowe/AutoStudio/model/autostudio.py pointer only (licence: NONE) · get_code("5dedb8c7dd0b624c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
As cutting-edge Text-to-Image (T2I) generation models already excel at producing remarkable single images, an even more challenging task, i.e., multi-turn interactive image generation begins to attract the attention of related research communities. This task requires models to interact with users over multiple turns to generate a coherent sequence of images. However, since users may switch subjects frequently, current efforts struggle to maintain subject consistency while generating diverse images. To address this issue, we introduce a training-free multi-agent framework called AutoStudio. AutoStudio employs three agents based on large language models (LLMs) to handle interactions, along with a stable diffusion (SD) based agent for generating high-quality images. Specifically, AutoStudio consists of (i) a subject manager to interpret interaction dialogues and manage the context of each subject, (ii) a layout generator to generate fine-grained bounding boxes to control subject locations, (iii) a supervisor to provide suggestions for layout refinements, and (iv) a drawer to complete image generation. Furthermore, we introduce a Parallel-UNet to replace the original UNet in the drawer, which employs two parallel cross-attention modules for exploiting subject-aware features. We also introduce a subject-initialized generation method to better preserve small subjects. Our AutoStudio hereby can generate a sequence of multi-subject images interactively and consistently. Extensive experiments on the public CMIGBench benchmark and human evaluations show that AutoStudio maintains multi-subject consistency across multiple turns well, and it also raises the state-of-the-art performance by 13.65% in average Frechet Inception Distance and 2.83% in average character-character similarity.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.01388")
get_code_for_paper("2406.01388")
have("2406.01388")
Connect an agent — have() is free.