Wuerstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models (2306.00637v2)

Abstract: We introduce W\"urstchen, a novel architecture for text-to-image synthesis that combines competitive performance with unprecedented cost-effectiveness for large-scale text-to-image diffusion models. A key contribution of our work is to develop a latent diffusion technique in which we learn a detailed but extremely compact semantic image representation used to guide the diffusion process. This highly compressed representation of an image provides much more detailed guidance compared to latent representations of language and this significantly reduces the computational requirements to achieve state-of-the-art results. Our approach also improves the quality of text-conditioned image generation based on our user preference study. The training requirements of our approach consists of 24,602 A100-GPU hours - compared to Stable Diffusion 2.1's 200,000 GPU hours. Our approach also requires less training data to achieve these results. Furthermore, our compact latent representations allows us to perform inference over twice as fast, slashing the usual costs and carbon footprint of a state-of-the-art (SOTA) diffusion model significantly, without compromising the end performance. In a broader comparison against SOTA models our approach is substantially more efficient and compares favorably in terms of image quality. We believe that this work motivates more emphasis on the prioritization of both performance and computational accessibility.

• The paper introduces a three-stage architecture that leverages a compressed latent space to achieve efficient text-to-image synthesis.
• The paper reduces GPU training time by approximately 8x compared to larger models, drastically cutting computational costs.
• The paper validates its design using automated metrics and human studies, demonstrating competitive image quality on benchmarks like COCO.

Overview of "Würstchen: An Efficient Architecture for Large-Scale Text-to-Image Diffusion Models"

The paper presents Würstchen, an architecture tailored for efficient and effective text-to-image synthesis leveraging large-scale diffusion models. Through a series of innovative steps, the authors propose a latent diffusion model architecture that minimizes computational demands while maintaining competitive image generation quality.

Key Contributions and Methodology

Würstchen's efficient architecture hinges on a three-stage process, each stage bringing distinct contributions to the task of text-conditioned image synthesis.

1. Efficient Compression Techniques: At the core of Würstchen's efficiency is a significant reduction in data dimensionality. The authors introduce a high-compression latent space that serves as a detailed semantic representation of the image, beneficial for guiding the image synthesis process. By operating in such a compressed latent space, Würstchen reduces the computational load without a proportional loss in output quality.
2. Three-Stage Architecture: The architecture comprises three interdependent stages:
   • Stage A (Encoder/Decoder): This involves a VQGAN encoder creating a compressed representation, which is pivotal for reducing input dimensionality to manageable levels.
   • Stage B (Intermediate Latent Space): Utilizes the semantic compression guidance from the preceding stage to decode the image into a higher-dimensional latent space.
   • Stage C (Semantic Compression): Responsible for generating the compressed latents that guide Stage B's diffusion model using a text-conditional approach.
3. Reduced Training and Inference Costs: The use of compressed latent spaces significantly cuts down on GPU hours required for training, approximately 8x less than models like Stable Diffusion 2.1, underscoring a reduction from 200,000 GPU hours down to roughly 24,602. Furthermore, inference times are significantly reduced, enhancing the model's accessibility and utility in practice.
4. Evaluation and Comparisons: Würstchen compares favorably against other state-of-the-art models using various automated metrics like FID and PickScore, as well as human preference studies. Its performance on datasets such as COCO and localized narratives demonstrates its high quality in generating images that align well with textual descriptions despite substantially lower computational requirements.

Implications and Future Directions

Würstchen's contributions highlight the importance of efficient model design in AI research, particularly as models scale further in complexity and capacity. The ability to maintain competitive performance while reducing computational overhead has implications for scalability and environmental sustainability in AI. By pushing the limits of model efficiency, Würstchen aligns with the ongoing discourse on responsible AI, emphasizing the need for models that are not only powerful but also accessible and sustainable.

Future research could focus on refining the compression techniques further, exploring alternative architectural paradigms, or extending the approach to other modalities beyond text-to-image synthesis. Moreover, investigating the impacts of these efficient design choices in broader practical applications can inform both industrial and academic pursuits, potentially influencing the development of various AI-driven creative tools.

In summary, Würstchen presents a thoughtfully designed approach to text-to-image synthesis. By integrating innovative compression strategies and a multi-stage model architecture, it delivers impressive results in both computational efficiency and image quality, charting a course for future research in efficient large-scale AI deployments.