We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| sen-mao/StyleDiffusion | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| str2bool | Ran | sen-mao/StyleDiffusion/Jittor/stylediffusion_csv.py pointer only (licence: NONE) · get_code("b4991c55e7f549d1") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A significant research effort is focused on exploiting the amazing capacities of pretrained diffusion models for the editing of images.They either finetune the model, or invert the image in the latent space of the pretrained model. However, they suffer from two problems: (1) Unsatisfying results for selected regions and unexpected changes in non-selected regions.(2) They require careful text prompt editing where the prompt should include all visual objects in the input image.To address this, we propose two improvements: (1) Only optimizing the input of the value linear network in the cross-attention layers is sufficiently powerful to reconstruct a real image. (2) We propose attention regularization to preserve the object-like attention maps after reconstruction and editing, enabling us to obtain accurate style editing without invoking significant structural changes. We further improve the editing technique that is used for the unconditional branch of classifier-free guidance as used by P2P. Extensive experimental prompt-editing results on a variety of images demonstrate qualitatively and quantitatively that our method has superior editing capabilities compared to existing and concurrent works. See our accompanying code in Stylediffusion: \url{https://github.com/sen-mao/StyleDiffusion}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.15649")
get_code_for_paper("2303.15649")
have("2303.15649")
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