More Control for Free! Image Synthesis with Semantic Diffusion Guidance (2112.05744v4)

Abstract: Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image. Recently, denoising diffusion probabilistic models have been shown to generate more realistic imagery than prior methods, and have been successfully demonstrated in unconditional and class-conditional settings. We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or image matching scores, without re-training the diffusion model. We explore CLIP-based language guidance as well as both content and style-based image guidance in a unified framework. Our text-guided synthesis approach can be applied to datasets without associated text annotations. We conduct experiments on FFHQ and LSUN datasets, and show results on fine-grained text-guided image synthesis, synthesis of images related to a style or content reference image, and examples with both textual and image guidance.

Semantic Diffusion Guidance for Controllable Image Synthesis

The paper "More Control for Free! Image Synthesis with Semantic Diffusion Guidance" introduces a novel approach for fine-grained controllable image synthesis using diffusion models. This framework, termed Semantic Diffusion Guidance (SDG), enhances the capabilities of denoising diffusion probabilistic models (DDPM) to allow semantic guidance through language, image, or multimodal inputs. Traditional image synthesis using DDPM has been predominantly unconditional or class-conditional, whereas this work focuses on providing a more nuanced form of control that can extend to datasets lacking explicit image-text pairs.

Main Contributions

1. Unified Framework: The paper presents a unified framework which integrates language, image content, or image style guidance into diffusion models. This integration occurs without the need for retraining the diffusion model, making the approach versatile for various synthesis tasks.
2. Semantic Guidance via CLIP: Guidance in SDG is implemented using gradients of image-language and image matching scores provided by CLIP (Contrastive Language-Image Pre-Training). This method can be applied to text-guided synthesis on datasets without text annotations, leveraging CLIP's ability to learn visual-semantic embeddings without paired data.
3. Image Guidance: Two types of image guidance are proposed:
   • Content Guidance: Ensures that synthesized images preserve semantic features of a reference image.
   • Style Guidance: Focuses on transferring stylistic elements from a reference image.
4. Multi-modal Synthesis: The framework supports simultaneous language and image guidance, merging content from both modalities to generate coherently guided image outputs. This multimodal guidance offers flexibility in creative tasks where text alone might not sufficiently describe the desired output.
5. Self-Supervised Fine-tuning: The authors demonstrate a means of self-supervised fine-tuning of the CLIP image encoder, enabling it to process noised images across diffusion timesteps without needed textual annotations. This adaptation ensures the alignment between noisy and clean image embeddings, a necessity for guiding diffusion processes.

Experimental Results

The experimental validation of SDG is conducted on FFHQ and LSUN datasets. The authors deliver comprehensive quantitative results using metrics such as FID (Fréchet Inception Distance) for image quality, LPIPS (Learned Perceptual Image Patch Similarity) for diversity, and retrieval accuracy to measure consistency with guidance. Compared to baselines like ILVR and StyleGAN+CLIP, SDG demonstrates superior diversity and quality in its generated images.

Implications

The implications of this research extend both theoretically and practically. Theoretically, it challenges existing paradigms of image synthesis by proposing an efficient method for multimodal control without exhaustive paired datasets or high compute retraining demands. Practically, this could democratize content creation in fields such as digital art and entertainment, where nuanced control over image generation is vital.

Future Directions

Future work stemming from this paper could investigate:

• Extending SDG to more diverse datasets and task-specific applications, including video synthesis.
• Exploring adaptive scaling factors for guidance strength, potentially through reinforcement learning approaches.
• Investigating the ethical implications and safeguards for mutable image synthesis, given its potential misuse in fabricated media.

In conclusion, Semantic Diffusion Guidance is a significant contribution to the domain of generative modeling, addressing core limitations in controllable synthesis by cleverly leveraging existing advancements in language-image embeddings and diffusion models. The flexibility and innovation of SDG invite further research into scalable and ethically responsible applications of AI in image synthesis.