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Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive license to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/2205.11487

Summary

Most text-to-image systems learn their text encoders from paired image-caption data, which may limit how well they handle complex or compositional prompts. The authors present Imagen, a Google Research system that passes embeddings from a frozen, text-only language model (T5-XXL) to a cascade of diffusion models. A base generator works at 64×64, and two text-conditioned super-resolution stages raise the output to 1024×1024. Their central finding is that enlarging the language encoder improves fidelity and prompt alignment much more than enlarging the image diffusion network. They propose dynamic thresholding, which allows strong classifier-free guidance without oversaturated, unnatural outputs, and a leaner, faster-converging Efficient U-Net. Imagen reaches a zero-shot COCO FID of 7.27, and human judges rate its alignment as comparable to real COCO images. The authors also introduce DrawBench, a structured prompt suite for evaluating text-to-image models, on which raters prefer Imagen over competing systems. The work shows that general-purpose language models can serve as the semantic core of image synthesis. Because of concerns about training data and bias, the model was not released publicly.

Key points

  • Zero-shot FID-30K on COCO is 7.27. This beats GLIDE (12.4), DALL-E 2 (10.4) and even Make-A-Scene (7.6), which was trained on COCO.
  • The authors tested BERT, T5 and CLIP as frozen text encoders. T5-XXL and CLIP perform similarly on MS-COCO, but on DrawBench humans prefer T5-XXL for both alignment and fidelity.
  • Dynamic thresholding works at each sampling step. It sets a percentile-based bound s on the predicted image, clips the values to [−s, s] and rescales them, which keeps pixels from saturating under large guidance weights.
  • DrawBench probes compositionality, cardinality, spatial relations, rare words, long prompts and implausible scenes. It was used to compare Imagen with VQ-GAN+CLIP, Latent Diffusion Models, GLIDE and DALL-E 2.
  • The training data comes from several image–English alt-text datasets, including LAION-400M. The authors report weaker results on images of people and social biases, such as a skew toward lighter skin tones and Western gender stereotypes for professions.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

351 papers of our library cite it (of the 140673 whose bibliographies we read); 9 of them have a page here, 342 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2205.11487 · arXiv 2205.11487

@misc{saharia2022photorealistic,
  title = {{Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding}},
  author = {Saharia, Chitwan and Chan, William and Saxena, Saurabh and Li, Lala and Whang, Jay and Denton, Emily and Ghasemipour, Seyed Kamyar Seyed and Ayan, Burcu Karagol and Mahdavi, S. Sara and Lopes, Rapha Gontijo and Salimans, Tim and Ho, Jonathan and Fleet, David J and Norouzi, Mohammad},
  year = {2022},
  doi = {10.48550/arxiv.2205.11487},
  eprint = {2205.11487},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2205.11487}
}

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