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 |
|---|---|---|
| OPPO-Mente-Lab/GlyphDraw | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| image_grid | Ran | this paper's copy was not recorded; identical code first harvested from thomas-wyh/fuseanypart pointer only · get_code("d2b9faa80b2570e4") |
| resize_and_concatenate | Ran | OPPO-Mente-Lab/GlyphDraw/test_en.py code served (permissive licence) · get_code("2ae403a85a1be544") |
| save_out_hook | Ran | OPPO-Mente-Lab/GlyphDraw/test_en.py code served (permissive licence) · get_code("90bbe02d7e8f4340") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent breakthroughs in the field of language-guided image generation have yielded impressive achievements, enabling the creation of high-quality and diverse images based on user instructions.Although the synthesis performance is fascinating, one significant limitation of current image generation models is their insufficient ability to generate text coherently within images, particularly for complex glyph structures like Chinese characters. To address this problem, we introduce GlyphDraw, a general learning framework aiming to endow image generation models with the capacity to generate images coherently embedded with text for any specific language.We first sophisticatedly design the image-text dataset's construction strategy, then build our model specifically on a diffusion-based image generator and carefully modify the network structure to allow the model to learn drawing language characters with the help of glyph and position information.Furthermore, we maintain the model's open-domain image synthesis capability by preventing catastrophic forgetting by using parameter-efficient fine-tuning techniques.Extensive qualitative and quantitative experiments demonstrate that our method not only produces accurate language characters as in prompts, but also seamlessly blends the generated text into the background.Please refer to our \href{https://1073521013.github.io/glyph-draw.github.io/}{project page}. \end{abstract}
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.17870")
get_code_for_paper("2303.17870")
have("2303.17870")
Connect an agent — have() is free.