Papers › FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization
Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu
Generating images from natural language instructions is an intriguing yet highly challenging task. We approach text-to-image generation by combining the power of the retrained CLIP representation with an off-the-shelf image generator (GANs), optimizing in the latent space of GAN to find images that achieve maximum CLIP score with the given input text. Compared to traditional methods that train generative models from text to image starting from scratch, the CLIP+GAN approach is training-free, zero shot and can be easily customized with different generators. However, optimizing CLIP score in the GAN space casts a highly challenging optimization problem and off-the-shelf optimizers such as Adam fail to yield satisfying results. In this work, we propose a FuseDream pipeline, which improves the CLIP+GAN approach with three key techniques: 1) an AugCLIP score which robustifies the CLIP objective by introducing random augmentation on image. 2) a novel initialization and over-parameterization strategy for optimization which allows us to efficiently navigate the non-convex landscape in GAN space. 3) a composed generation technique which, by leveraging a novel bi-level optimization formulation, can compose multiple images to extend the GAN space and overcome the data-bias. When promoted by different input text, FuseDream can generate high-quality images with varying objects, backgrounds, artistic styles, even novel counterfactual concepts that do not appear in the training data of the GAN we use. Quantitatively, the images generated by FuseDream yield top-level Inception score and FID score on MS COCO dataset, without additional architecture design or training. Our code is publicly available at \url{https://github.com/gnobitab/FuseDream}.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (few-shot, k=5) | FID | 21.16 | #45 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (few-shot, k=5) | Inception score | 34.26 | #45 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (k=5, 256) | FID | 21.16 | #46 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (k=5, 256) | Inception score | 34.26 | #46 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (k=10, 256) | FID | 21.89 | #47 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | FuseDream (k=10, 256) | Inception score | 34.67 | #47 of 69 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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