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Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion

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arxiv 2310.03502 v1 pith:6XXDE33V submitted 2023-10-05 cs.CV

Anton Razzhigaev , Arseniy Shakhmatov , Anastasia Maltseva , Vladimir Arkhipkin , Igor Pavlov , Ilya Ryabov , Angelina Kuts , Alexander Panchenko
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Andrey Kuznetsov Denis Dimitrov
This is my paper
classification cs.CV
keywords imagegenerationmodelmodelsdiffusionlatentpriortext-to-image
verification ladder T0 review T1 audit T2 compute T3 formal
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Text-to-image generation is a significant domain in modern computer vision and has achieved substantial improvements through the evolution of generative architectures. Among these, there are diffusion-based models that have demonstrated essential quality enhancements. These models are generally split into two categories: pixel-level and latent-level approaches. We present Kandinsky1, a novel exploration of latent diffusion architecture, combining the principles of the image prior models with latent diffusion techniques. The image prior model is trained separately to map text embeddings to image embeddings of CLIP. Another distinct feature of the proposed model is the modified MoVQ implementation, which serves as the image autoencoder component. Overall, the designed model contains 3.3B parameters. We also deployed a user-friendly demo system that supports diverse generative modes such as text-to-image generation, image fusion, text and image fusion, image variations generation, and text-guided inpainting/outpainting. Additionally, we released the source code and checkpoints for the Kandinsky models. Experimental evaluations demonstrate a FID score of 8.03 on the COCO-30K dataset, marking our model as the top open-source performer in terms of measurable image generation quality.

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Cited by 11 Pith papers

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    MA-GIG uses VAE latent space to align Integrated Gradients paths with the data manifold for more faithful feature attributions in deep neural networks.

  2. Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors

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    Graph-PiT adds graph priors and a hierarchical GNN to part-based image synthesis to enforce relational constraints and improve structural coherence over vanilla PiT.

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    CSF is the first black-box method to attribute fine-tuned text-to-image models to original lineages via compositional semantic probes and Bayesian decisions across multiple model families.

  4. Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification

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    A hybrid diffusion/inpainting pipeline grows a 656-image dermatology set into 266k synthetic images, lifting DDI malignancy classification to 90.9% and improving skin-tone fairness metrics.

  5. Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification

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    A hybrid generative framework expands scarce dermatology data over 400× and reports 90.9% malignancy classification accuracy with improved fairness on the DDI benchmark.

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    MA-GIG improves Integrated Gradients by performing path integration in the latent space of a pre-trained VAE so that decoded points remain closer to the learned data manifold and reduce off-manifold gradient noise.

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  11. FBI: Learning Dexterous In-hand Manipulation with Dynamic Visuotactile Shortcut Policy

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