We lifted 10 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| rinongal/StyleGAN-nada | canonical | 4 of 10 |
| Function | Status | Where it lives |
|---|---|---|
| convert_conv | Ran | rinongal/StyleGAN-nada/convert_weight.py code served (permissive licence) · get_code("7fe245a7216ae67b") |
| convert_modconv | Ran | rinongal/StyleGAN-nada/convert_weight.py code served (permissive licence) · get_code("1c7cd5abc48bcf75") |
| convert_torgb | Ran | rinongal/StyleGAN-nada/convert_weight.py code served (permissive licence) · get_code("96b279b9ef818d94") |
| make_kernel | Ran | rinongal/StyleGAN-nada/ZSSGAN/model/sg2_model.py code served (permissive licence) · get_code("6f65e378a4313f87") |
| ask_yes_no | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/dnnlib/util.py code served (permissive licence) · get_code("9d31d2c4cd16bb2d") |
| duplicate_latent | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/generate_videos.py code served (permissive licence) · get_code("323317029c8b57cb") |
| format_time | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/dnnlib/util.py code served (permissive licence) · get_code("053fc534bc6bb989") |
| format_time_brief | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/dnnlib/util.py code served (permissive licence) · get_code("77f4aa0649e7f404") |
| interpolate_forward_backward | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/generate_videos.py code served (permissive licence) · get_code("0929a327abb07197") |
| project_code | Not yet run | rinongal/StyleGAN-nada/ZSSGAN/generate_videos.py code served (permissive licence) · get_code("53b619216cd18d2b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be trained "blindly"? Leveraging the semantic power of large scale Contrastive-Language-Image-Pre-training (CLIP) models, we present a text-driven method that allows shifting a generative model to new domains, without having to collect even a single image. We show that through natural language prompts and a few minutes of training, our method can adapt a generator across a multitude of domains characterized by diverse styles and shapes. Notably, many of these modifications would be difficult or outright impossible to reach with existing methods. We conduct an extensive set of experiments and comparisons across a wide range of domains. These demonstrate the effectiveness of our approach and show that our shifted models maintain the latent-space properties that make generative models appealing for downstream tasks.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2108.00946")
get_code_for_paper("2108.00946")
have("2108.00946")
Connect an agent — have() is free.