We lifted 5 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| TencentARC/PhotoMaker | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| FeedForward | Ran | TencentARC/PhotoMaker/photomaker/resampler.py pointer only (licence: NONE) · get_code("5105747c2711b1cb") |
| masked_mean | Ran | TencentARC/PhotoMaker/photomaker/resampler.py pointer only (licence: NONE) · get_code("230588e8f1237737") |
| rescale_noise_cfg | Ran | TencentARC/PhotoMaker/photomaker/pipeline_controlnet.py pointer only (licence: NONE) · get_code("bea2d776a332f2b0") |
| reshape_tensor | Ran | TencentARC/PhotoMaker/photomaker/resampler.py pointer only (licence: NONE) · get_code("4cb2e2a2ca0bec9f") |
| retrieve_timesteps | Ran | TencentARC/PhotoMaker/photomaker/pipeline_controlnet.py pointer only (licence: NONE) · get_code("22b1f260da28f6a5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent advances in text-to-image generation have made remarkable progress in synthesizing realistic human photos conditioned on given text prompts. However, existing personalized generation methods cannot simultaneously satisfy the requirements of high efficiency, promising identity (ID) fidelity, and flexible text controllability. In this work, we introduce PhotoMaker, an efficient personalized text-to-image generation method, which mainly encodes an arbitrary number of input ID images into a stack ID embedding for preserving ID information. Such an embedding, serving as a unified ID representation, can not only encapsulate the characteristics of the same input ID comprehensively, but also accommodate the characteristics of different IDs for subsequent integration. This paves the way for more intriguing and practically valuable applications. Besides, to drive the training of our PhotoMaker, we propose an ID-oriented data construction pipeline to assemble the training data. Under the nourishment of the dataset constructed through the proposed pipeline, our PhotoMaker demonstrates better ID preservation ability than test-time fine-tuning based methods, yet provides significant speed improvements, high-quality generation results, strong generalization capabilities, and a wide range of applications. Our project page is available at https://photo-maker.github.io/
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2312.04461")
get_code_for_paper("2312.04461")
have("2312.04461")
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