PaperlayerView exact revision on arXiv

Source-linked AI summary

GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

Ming Tao, Bing-Kun Bao, Hao Tang, Changsheng Xu

arXiv:2301.12959v1cs.CVcs.AI

TL;DR

Large autoregressive and diffusion text-to-image models achieve strong synthesis but require substantial data and parameters and generate slowly, while GANs can struggle with complex scenes. GALIP integrates pretrained CLIP into its discriminator and generator, achieving comparable performance with far less data and trainable capacity and approximately 120× faster synthesis. The method’s scope remains limited by its smaller pretraining resources and CLIP text encoder.

  • Problem

    Large autoregressive and diffusion models require tremendous training data and parameters, generate slowly, and offer difficult-to-control visual synthesis, while text-to-image GANs can have unsatisfactory quality on complex scenes.

  • Method

    GALIP integrates pretrained CLIP into a CLIP-based discriminator and CLIP-empowered generator using CLIP features, bridge features, and predicted prompts.

  • Results

    GALIP achieves comparable performance to large pretrained models with significantly smaller computational costs and approximately ∼120× faster synthesis speed.

  • Takeaways & Limitations

    Integrating an understanding model into a generative model can support efficient text-to-image synthesis while retaining high-quality complex-image generation.

  • Takeaways & Limitations

    GALIP’s smaller model and pretraining dataset limit its synthesis ability for imaginary images, and replacing CLIP’s text encoder with T5 may improve performance.

Abstract

from arXiv · show

Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are difficult to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about 3% training data and 6% learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves 120 times faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of our GALIP. Code is available at https://github.com/tobran/GALIP.

1. Introduction

GALIP integrates pretrained CLIP into both a discriminator and generator to address the quality, efficiency, speed, and controllability limitations of text-to-image synthesis. It combines CLIP-based scene understanding and domain generalization with GAN generation, achieving comparable synthesis ability at substantially lower computational cost and much higher speed.

  • Motivation: Large autoregressive and diffusion models require extensive training data and parameters, generate more slowly than GANs, and offer less controllable visual synthesis.GANs provide faster generation and smoother latent spaces, but existing text-to-image GANs can struggle with complex-scene quality and unstable training.
  • Method: GALIP integrates pretrained CLIP into both the discriminator and generator for text-to-image synthesis.The CLIP-based discriminator assesses generated image quality, while the CLIP-empowered generator uses text features and predicted CLIP visual features.
  • Method: The CLIP-based discriminator uses a frozen CLIP-ViT and learnable Mate-D to extract visual features and assess complex generated images more accurately.The frozen encoder preserves CLIP’s scene-understanding knowledge while Mate-D performs adversarial discrimination.
  • Method: The CLIP-empowered generator induces visual concepts from text through bridge features and prompts, avoiding additional labeled bridge-domain data.Its design uses CLIP’s domain generalization ability to map generated features toward shared visual concepts.
  • Results: ∼120× faster synthesis speed and comparable synthesis ability are achieved with significantly smaller trainable parameters and training data.The introduction reports comparable performance to large pretrained models at substantially lower computational cost.
  • Results: GALIP is presented as an efficient, fast, controllable model for synthesizing high-quality complex images.The architecture combines a CLIP-based discriminator and CLIP-empowered generator for this goal.

2. Related Work

Prior text-to-image work includes conditional and multi-stage GANs, one-stage high-resolution GANs, and large pretrained autoregressive and diffusion models. These approaches differ in generation architecture, scale, and use of text-image or latent representations.

  • Text-to-Image GANs: Early text-to-image GANs introduced conditional generation, while Stack-GAN, AttnGAN, and DM-GAN used multiple generators and discriminators for higher-resolution synthesis.These methods developed progressively more structured GAN pipelines for text-conditioned image generation.
  • Text-to-Image GANs: DF-GAN simplified text-to-image generation with a one-stage framework for high-resolution synthesis.It is described as a simpler alternative to stacked generator-discriminator designs.
  • Text-to-Image GANs: LAFITE introduced CLIP text-image contrastive loss for text-to-image training and reported large improvements on CC3M.Its approach uses CLIP during GAN training rather than integrating CLIP into both generator and discriminator components.
  • Text-to-Image Large Models: Large text-to-image models use autoregressive token generation or diffusion, with methods including DALL-E, Parti, LDM, Stable Diffusion, and Imagen.These systems represent images as discrete tokens, latent variables, or diffusion processes and vary in their text-conditioning components.

3. Generative Adversarial CLIPs

GALIP integrates pretrained CLIP into both a discriminator and generator for text-to-image synthesis. Its Mate-D extracts informative CLIP visual features for quality assessment, while Mate-G uses bridge features and predicted prompts to induce visual concepts for image generation.

  • GALIP integrates a CLIP text encoder, a CLIP-based discriminator, and a CLIP-empowered generator within a GAN framework.
  • The CLIP-based discriminator combines a frozen CLIP-ViT with Mate-D to extract informative multilayer visual features and assess complex-image quality more accurately.Mate-D collects features from different CLIP-ViT layers, further extracts visual features, and predicts adversarial quality signals.
  • The CLIP-empowered generator combines a frozen CLIP-ViT with Mate-G, which uses text and noise inputs to generate images from induced visual features.Mate-G contains a Bridge-FP, prompt predictor, frozen CLIP-ViT, and image generator.
  • Bridge Feature Predictor: The Bridge-FP translates sentence and noise vectors into bridge features adjusted to induce meaningful visual concepts from CLIP-ViT.It uses a fully connected layer and fusion blocks to combine text information with intermediate bridge features.
  • Prompt Predictor and Image Generator: The prompt predictor generates text-conditioned prompts appended to CLIP-ViT visual patch embeddings, while omitting the last few layers to preserve final image representations.The image generator combines predicted visual concepts and bridge features, fuses text and noise vectors, upsamples features, and outputs RGB images.
  • Objective Functions: GALIP uses hinge loss and a one-way discriminator to stabilize adversarial training.The formulation defines z as Gaussian noise, e as the sentence vector, G as the CLIP-empowered generator, and D as Mate-D.

4. Experiments

Experiments evaluate GALIP across quantitative, qualitative, controllability, ablation, and limitation settings. GALIP improves reported image-quality metrics, generates complex visual content, supports smooth style interpolation, and remains limited on some imaginary images.

  • Quantitative Evaluation: GALIP decreases FID from 14.58 to 10.08 and improves CLIPSIM from 0.3125 to 0.3164 against LAFITE on CUB.On COCO, GALIP decreases FID from 8.21 to 5.85 against LAFITE.
  • Quantitative Evaluation: 0.04s per image makes GALIP approximately 120× faster than LDM while using much smaller model parameters and data size.GALIP can also run quickly on CPU without additional acceleration settings.
  • Qualitative Evaluation: GALIP produces correct object shapes and clearer fine-grained contents than LAFITE and VQ-Diffusion in the shown CUB and COCO examples.The comparisons include complex scenes with multiple objects and strict visual requirements.
  • Qualitative Evaluation: GALIP generates described objects and visual features that LDM misses in the reported sample comparisons.Examples include ghosts, teddy bears, shining eyes, a blue lighthouse, and a surprised girl.
  • Qualitative Evaluation: Interpolating sentence embeddings produces gradual style changes, making stylization controllable and enabling blended new styles.The authors describe this behavior as inherited from GALIP's smooth latent space.
  • Ablation Study: Adding the CLIP-based discriminator reduces FID from 17.31 to 7.92, while adding the CLIP-empowered generator with Bridge-FP reduces it further to 6.52.The corresponding CLIPSIM values increase from 0.2996 to 0.3221 and then to 0.3301.
  • Limitations: GALIP's smaller model size and pretraining dataset limit its ability to synthesize imaginary images.The authors suggest larger pretraining data and model size may improve performance.

5. Conclusion

The paper presents GALIP, which integrates CLIP-ViT into a generative model through a CLIP-based discriminator and CLIP-empowered generator. Its results support higher-quality complex image synthesis and suggest commonalities between visual understanding and generation models.

  • Conclusion: GALIP integrates CLIP-ViT into both a CLIP-based discriminator and a CLIP-empowered generator for text-to-image synthesis.The discriminator and generator exploit CLIP's complex scene understanding and domain generalization abilities.
  • Conclusion: GALIP achieves significant improvements on challenging datasets and synthesizes higher-quality complex images than previous models.The authors frame the integration of an understanding model into a generative model as potentially useful for building a general large model.
Loading 2301.12959v1…