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Versatile Diffusion: Text, Images and Variations All in One Diffusion Model

Xingqian Xu, Zhangyang Wang, Eric Zhang, Kai Wang, Humphrey Shi

arXiv:2211.08332v4cs.CV

TL;DR

Existing generative approaches commonly required separate models for separate tasks, motivating a unified multimodal alternative. Versatile Diffusion introduces a shared multi-flow diffusion model for text, images, and variations, and reports competitive primary-task performance plus extensions such as style-semantic disentanglement and context blending.

  • Problem

    Generative approaches commonly focused on specific domains or tasks, leaving multimodal generative modeling comparatively scarce.

  • Method

    VD generalizes a single-flow diffusion pipeline into a unified multi-flow model with sharable and swappable modules for text-to-image, image-to-text, and variation tasks.

  • Results

    VD outperforms its baselines on primary tasks and supports extensions including semantic-style disentanglement and dual- or multi-context blending.

  • Takeaways & Limitations

    The multi-flow multimodal framework performs well across primary tasks and applications and provides a step toward diffusion-based universal AI research.

  • Takeaways & Limitations

    VD’s major weakness is text generation, partly because Optimus’s 768-dimensional latent vectors may inadequately represent long text sentences.

Abstract

from arXiv · show

Recent advances in diffusion models have set an impressive milestone in many generation tasks, and trending works such as DALL-E2, Imagen, and Stable Diffusion have attracted great interest. Despite the rapid landscape changes, recent new approaches focus on extensions and performance rather than capacity, thus requiring separate models for separate tasks. In this work, we expand the existing single-flow diffusion pipeline into a multi-task multimodal network, dubbed Versatile Diffusion (VD), that handles multiple flows of text-to-image, image-to-text, and variations in one unified model. The pipeline design of VD instantiates a unified multi-flow diffusion framework, consisting of sharable and swappable layer modules that enable the crossmodal generality beyond images and text. Through extensive experiments, we demonstrate that VD successfully achieves the following: a) VD outperforms the baseline approaches and handles all its base tasks with competitive quality; b) VD enables novel extensions such as disentanglement of style and semantics, dual- and multi-context blending, etc.; c) The success of our multi-flow multimodal framework over images and text may inspire further diffusion-based universal AI research. Our code and models are open-sourced at https://github.com/SHI-Labs/Versatile-Diffusion.

1. Introduction

Multimodal generative research remained limited despite progress in diffusion models. Versatile Diffusion extends single-flow diffusion into one unified model for multiple modalities, tasks, and novel capabilities.

  • Multimodal generative models remained scarce, while earlier generative vision models focused on specific domains and tasks.
  • Diffusion models opened applications spanning unconditional generation, density estimation, super-resolution, and text-to-image generation.
  • Versatile Diffusion uses a multi-flow diffusion framework to solve text, images, and variations within one unified generative model.
  • The framework enables semantic-style disentanglement and dual-context or multi-context blending as derivative capabilities.
  • VD supports text-to-image, image-variation, image-to-text, and text-variation tasks, with experiments reporting stronger scores and quality than baselines.

2. Related Works

Related work established multimodal representation learning and diffusion-based generation across several tasks. VD builds on latent and context encoders while extending these ideas toward unified multimodal generation.

  • Early multimodal learning combined information across audio, video, vision, and language, including fused representations and zero-shot semantic mappings.
  • Multimodal generative tasks jointly involve representation learning and synthesis, often using representation networks trained with contrastive loss.
  • VD adopts VAEs for latent representations and CLIP for context embeddings, connecting to domain transfer and joint representation learning.
  • Diffusion models differ from GAN-based and flow-based models by optimizing likelihood-related objectives through backward diffusion rather than adversarial training or exact inversion.
  • Recent text-to-image systems combined language or text encoders with conditional diffusion and cascaded or hierarchical generation structures.

3. Method

VD generalizes diffusion from a single flow to a shared multi-flow framework for crossmodal generation. It combines modality-specific and shared layers, unified task handling, and accumulated multi-flow training.

  • Diffusion basics: The forward process gradually corrupts data with Gaussian noise, while the backward process removes that noise using network-predicted Gaussian means and variances.
  • Diffusion basics: Diffusion training minimizes a variational negative-log-likelihood bound, commonly simplified to a variational weighted l2 loss.
  • Multi-flow framework: VD’s multi-flow framework generates outputs in one modality from contexts in another, extending beyond text-to-image toward image-to-text, variations, and additional modalities.
  • Multi-flow framework: VD activates or mutes global, data, and context layers according to the task’s output and input modalities.
  • Multi-flow framework: A four-flow VD model halves parameters relative to four separate diffusion models through shared layers, while generalized N × M tasks scale as O(max(N, M)) instead of O(N × M).
  • Versatile Diffusion: VD treats image variation as generating semantically similar images from pure noise while relaxing low-level structure, and supports analogous text variations.
  • Versatile Diffusion: The network combines a multi-flow UNet diffuser, VAEs for latent representations, and context encoders for embeddings.
  • Training: Training accumulates variational weighted l2 gradients across flows and manually adjusts data- and context-layer gradient scales.

4. Experiments

VD is evaluated as a unified model across primary multimodal tasks and derived applications, including style–semantic disentanglement and context blending. Across these experiments, it performs competitively or better than task-specific baselines while supporting deeper multimodal context integration.

  • Data and settings: VD is trained in single-flow, dual-flow, and four-flow settings, with the four-flow model serving as the main model for four tasks.The single-flow model targets image variation; dual-flow targets text-to-image and image variation; four-flow adds image-to-text and text variation.
  • Primary-task evaluation: VD captures input-context semantics more accurately in text-to-image and image variation, while producing more creative and detailed image captions than prior works.These are qualitative conclusions from the comparisons in Figure 5.
  • Primary-task evaluation: VD is compared against SDv1.4 for text-to-image, SD-variation for image variation, and BLIP for image-to-text using qualitative and quantitative evaluations.The comparisons include dual-flow and four-flow text-to-image models, three image-variation models, and four-flow VD for image-to-text.
  • Primary-task evaluation: VD outperforms its baselines on the primary tasks and demonstrates that distinct modalities can be analyzed and generated within one unified model.The reported evaluations include FID comparisons, classifier-free guidance analyses, and user studies on 2,000 COCO-Caption samples.
  • Derived applications: VD supports unsupervised style–semantic disentanglement, enabling image styles to be enhanced or reduced from semantics without further supervision.The paper presents this as an extension beyond the primary tasks and contrasts it with earlier GAN studies restricted to aligned domains.
  • Derived applications: VD’s dual-context blender produces more natural-looking results with fewer distortions than shallow model-level mixing of text-to-image and image-variation models.The multi-flow structure merges intermediate features at deeper layer-level or attention-level stages.
  • Derived applications: The multi-context blender accepts optional text, several image contexts, and optional image masks to provide more detailed generation control.Figure 10 illustrates semantic transfer and reconstruction from multiple contexts.

5. Conclusion

The paper presents Versatile Diffusion as a unified model for text, images, and variations, and generalizes multi-flow multimodal diffusion to additional tasks and domains. Experiments show strong performance on primary tasks and derived applications, positioning the framework as a heuristic step toward universal AI research.

  • Conclusion: Versatile Diffusion handles text, images, and variations in one unified model through a generalized multi-flow multimodal framework.The framework is described as extensible to new tasks and domains.
  • Conclusion: Inclusive experiments demonstrate that the multi-flow multimodal diffusion method performs well on both primary tasks and applications.The conclusion summarizes the reported experimental scope without specifying a single metric.
  • Conclusion: The framework may serve as a heuristic step toward universal AI research.This is the paper’s stated broader implication.

Appendices

The disentanglement application manipulates CLIP image-context embeddings to control semantic and stylistic aspects of image variation. Principal-component analysis provides the control directions, while later components contribute little and removing the global feature can harm structure.

  • Disentanglement method: The disentanglement application uses VD’s image-variation flow to perform controllable image variation by manipulating CLIP image-context embeddings.The embeddings guide diffusion through cross-attention and have shape 257×768.
  • Disentanglement method: Principal-component manipulation separates semantic-focused and style-focused image-variation results by removing or retaining major components.Removing one or two major components emphasizes semantics, while retaining only 10 or 2 emphasizes style.
  • Practical observations: Principal components after order 50 have little effect on results, allowing the disentanglement PCA to compute only the first 50 components.This is presented as a practical speedup for the manipulation procedure.
  • Practical observations: The global CLIP feature mainly controls object-location semantics; removing it can negatively affect image structure but may help some art-generation cases.The paper distinguishes this global feature from local patch features.

A.2. Dual-context Blender

The dual-context blender generates images guided by one image and one text prompt, using mixing strategies that combine their diffusion processes. Attention-level mixing best resolves conflicts between contexts and supports smooth transitions across mixing rates.

  • Dual-context blending: The dual-context blender generates images from one image context and one text prompt by combining diffusion models or their outputs.VD supports attention-level, layer-level, and model-level mixing strategies for merging the two contexts.
  • Mixing strategies: Attention-level mixing produces the best results, smoothly transitioning between conflicting image and text contexts as the mixing rate increases.In the car and double-decker bus example, attention-level mixing avoids the distortions seen with layer-level mixing.
  • Mixing strategies: Layer-level mixing slightly underperforms attention-level mixing, while model-level mixing performs worst among the three methods.The model-level comparison uses SDv1.4 and SD-variation as baseline models.
  • Multi-context extension: The multi-context blender extends dual-context blending to multiple images, optional text, per-image scales, and optional masks.Multiple image embeddings are concatenated along the sequence dimension without changing the dual-context mixing strategies.
  • Multi-context extension: Image scales weight image-context embeddings, while masks provide finer control over how reference-image content guides generation.The method modifies the CLIP network because replacing masked content with zeros can introduce confusing black patches.

A.4. Editable I2T2I

I2T2I edits images by converting them to text, modifying the representation, and converting the result back to an image. The approach avoids object masks but can lose content or fail when generated descriptions are inadequate.

  • I2T2I approach: I2T2I edits an image through image-to-text conversion, text editing, and text-to-image generation.The implemented solution modifies latent text vectors using negative prompts for removal and positive prompts for addition.
  • I2T2I approach: I2T2I requires no object masks because it is designed to locate and substitute objects according to prompt instructions.Its outputs need not match the input images pixel by pixel because the process distills semantics and creates content.
  • Limitations: I2T2I quality depends on both image-to-text and text-to-image performance, so failure in either stage produces unsatisfactory results.The approach is therefore constrained by both sub-procedures in its sequential pipeline.
  • Limitations: Directly editing generated text can be infeasible because the text may not contain the descriptions users want to modify.The method addresses this issue by modifying latent text vectors instead of editing text directly.
  • Limitations: I2T2I may alter more reference-image content than users want because image-to-text distills information while text-to-image creates content.This flexibility differs from demands to preserve more content from the reference image.

B. Image-Variation Analysis and Beyond

The image-variation analysis studies classifier-free guidance choices for conditioning generation. The two options trade off stronger reference-content emphasis against possible color and structure distortions.

  • Guidance formulation: Classifier-free guidance combines unconditional and conditional generator outputs using an unconditional guidance scale.The final output is y = yu + (yc − yu) * s, where yu and yc are the unconditional and conditional outputs.
  • Guidance options: For image variation, one unconditional-guidance option uses CLIP embeddings of empty images with all zeros.This is contrasted with the alternative guidance choice discussed alongside it.
  • Guidance trade-offs: Option (a) emphasizes reference-image content and style, producing more art-focused results with better color contrast.It may also improve disentanglement and dual-context results by sensitively capturing and magnifying input details.
  • Guidance trade-offs: Option (a) can over-react, causing severe color and structure distortions in some outputs.The figure compares cases where each unconditional-guidance type yields better results.

B.2. Image-Variation with ControlNet

VD’s image-variation flow is combined with ControlNet to provide prompt-free controllable image generation. The resulting model uses structural hints from ControlNet while retaining VD’s semantic and style guidance.

  • ControlNet integration: Combining ControlNet with VD’s image-variation flow forms an application for prompt-free controllable image generation.The approach transfers the adaptive-network strategy used with text-to-image models to image variation.
  • ControlNet integration: The ControlNet application uses canny-edge and depth controls without requiring text prompts.A trained VD image-variation model is loaded together with pretrained ControlNets to guide generation.
  • Model adaptation: The ControlNet-specific image-variation model removes positional embeddings from CLIP and is finetuned with a position-agnostic CLIP-PA encoder.The final outputs combine ControlNet’s structural hints with VD’s semantic and style information.
  • Prompt cleaning: Cleaning Laion2B captions makes VD’s text-generation training more robust and easier to converge.The cleaning removes links, HTML, bracketed content, unnecessary symbols, and most quotation marks.

C.2. Alternative Training

VD can be trained with different task orderings: the paper’s progressive image-variation-first schedule and an alternative text-to-image-first schedule both produce similar final performance. The authors present this flexibility as a property worth exploring in generalized multi-flow models.

  • C.2. Alternative Training: VD normally trains progressively from single-flow to dual-flow and then four-flow, beginning with image-variation.The single-flow model is the initial image-variation task before adding further flows.
  • C.2. Alternative Training: VD-Alt instead begins with text-to-image, yet its final results are similar to those of VD.The comparison covers text-to-image and image-variation, with image-variation evaluated under two unconditional-guidance settings.
  • C.2. Alternative Training: The training graphic generalizes these alternatives to M context types and N output types, distinguishing VD’s image-variation path from VD-Alt’s text-to-image path.The current VD and VD-Alt paths are shown as subsets of broader possible training schemes.
  • C.2. Alternative Training: The authors conclude that multiple feasible training rules may exist for multi-flow multimodal diffusion models and encourage further exploration.This conclusion follows from the good performance reported for both VD and VD-Alt.

E. Limitation

VD’s main limitations concern text generation and the quality and domain fit of its text representations and training data. The appendix attributes these issues to constrained latent capacity and imperfect web-scraped text, while the accompanying figures provide additional qualitative results.

  • E. Limitation: VD’s major weakness is text generation, including image-to-text, text-variation, and image-to-text-to-image.The authors link this weakness to limitations in the Optimus VAE’s representation of long text.
  • E. Limitation: Optimus VAE uses 768-dimensional single-position vectors that may inadequately represent long sentences and word order.The authors propose sequence-based latent representations as a possible improvement and note repeated descriptions in generated sentences.
  • E. Limitation: Imperfect text data limited VD’s performance, motivating cleaner or finetuned datasets for improving model accuracy.The training data consisted of web-scraped prompts and captions that required extensive engineering and cleaning.
  • E. Limitation: The pretrained Optimus VAE had difficulty reconstructing Laion2B captions because its training data differed from VD’s long, descriptive web text.The cited domain shift is between normal-sentence corpora used for Optimus and Laion2B data used for VD.
  • E. Limitation: The appendix includes additional qualitative examples for text-to-image, image-variation, and dual-context blending, but these figures do not resolve the stated text-generation limitations.The listed figures document supplementary outputs and training loss curves rather than a new limitation analysis.
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