SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Our work might lead to common negative social impacts of generation models for computer vision. One concern is the proliferation of fake or manipulated images, leading to a crisis of trust and
credibility. As these models become more sophisticated, it turns increasingly difficult to discern between real and generated images, undermining the integrity of visual evidence. This can have significant implications in journalism, forensics, and other fields that heavily rely on accurate visual representation. Image generation models can also be misused for harmful purposes such as creating realistic but false identities, deepfake pornography, or even propaganda and disinformation campaigns. These applications can lead to privacy violations, cyberbullying, defamation, and manipulation of public opinion. Therefore, it is crucial to address the potential negative social impacts of image generation models and implement ethical guidelines and safeguards to mitigate harmful effects.
D More Extrapolation Results
Figure 11: Extrapolation Results. Patch Diffusion could generate beyond the boundary by extrapolating the learned coordinate manifold. For each pair of images, the left panel is the reference image in resolution  $256 \times 256$  and it is fixed in the center during the reverse process of Patch Diffusion, while the right panel shows the generated sample in resolution  $512 \times 512$ , where the out-of-boundary region is regenerated. Note our model is trained only on  $256 \times 256$  images.
Figure 12: Randomly generated images from Latent Patch Diffusion (EDM-ADM backbone) trained on ImageNet-  $256\times 256$  . Left images are generated without CFG, while the right images are generated with CFG.