GPT-4 + Stable-Diffusion = ?: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

TL;DR: Textual content Immediate -> LLM -> Intermediate Illustration (corresponding to a picture structure) -> Secure Diffusion -> Picture.

Latest developments in text-to-image technology with diffusion fashions have yielded outstanding outcomes synthesizing extremely life like and numerous photographs. Nonetheless, regardless of their spectacular capabilities, diffusion fashions, corresponding to Stable Diffusion, typically wrestle to precisely comply with the prompts when spatial or frequent sense reasoning is required.

The next determine lists 4 eventualities during which Secure Diffusion falls quick in producing photographs that precisely correspond to the given prompts, particularly negation, numeracy, and attribute project, spatial relationships. In distinction, our methodology, LLM-grounded Diffusion (LMD), delivers significantly better immediate understanding in text-to-image technology in these eventualities.

Visualizations
Determine 1: LLM-grounded Diffusion enhances the immediate understanding means of text-to-image diffusion fashions.

One doable answer to handle this subject is in fact to collect an enormous multi-modal dataset comprising intricate captions and prepare a big diffusion mannequin with a big language encoder. This method comes with important prices: It’s time-consuming and costly to coach each massive language fashions (LLMs) and diffusion fashions.

Our Resolution

To effectively remedy this drawback with minimal price (i.e., no coaching prices), we as a substitute equip diffusion fashions with enhanced spatial and customary sense reasoning by utilizing off-the-shelf frozen LLMs in a novel two-stage technology course of.

First, we adapt an LLM to be a text-guided structure generator via in-context studying. When supplied with a picture immediate, an LLM outputs a scene structure within the type of bounding bins together with corresponding particular person descriptions. Second, we steer a diffusion mannequin with a novel controller to generate photographs conditioned on the structure. Each levels make the most of frozen pretrained fashions with none LLM or diffusion mannequin parameter optimization. We invite readers to read the paper on arXiv for added particulars.

Text to layout
Determine 2: LMD is a text-to-image generative mannequin with a novel two-stage technology course of: a text-to-layout generator with an LLM + in-context studying and a novel layout-guided steady diffusion. Each levels are training-free.

LMD’s Further Capabilities

Moreover, LMD naturally permits dialog-based multi-round scene specification, enabling further clarifications and subsequent modifications for every immediate. Moreover, LMD is ready to deal with prompts in a language that isn’t well-supported by the underlying diffusion mannequin.

Additional abilities
Determine 3: Incorporating an LLM for immediate understanding, our methodology is ready to carry out dialog-based scene specification and technology from prompts in a language (Chinese language within the instance above) that the underlying diffusion mannequin doesn’t assist.

Given an LLM that helps multi-round dialog (e.g., GPT-3.5 or GPT-4), LMD permits the consumer to supply further info or clarifications to the LLM by querying the LLM after the primary structure technology within the dialog and generate photographs with the up to date structure within the subsequent response from the LLM. For instance, a consumer might request so as to add an object to the scene or change the present objects in location or descriptions (the left half of Determine 3).

Moreover, by giving an instance of a non-English immediate with a structure and background description in English throughout in-context studying, LMD accepts inputs of non-English prompts and can generate layouts, with descriptions of bins and the background in English for subsequent layout-to-image technology. As proven in the correct half of Determine 3, this enables technology from prompts in a language that the underlying diffusion fashions don’t assist.

Visualizations

We validate the prevalence of our design by evaluating it with the bottom diffusion mannequin (SD 2.1) that LMD makes use of below the hood. We invite readers to our work for extra analysis and comparisons.

Main Visualizations
Determine 4: LMD outperforms the bottom diffusion mannequin in precisely producing photographs in response to prompts that necessitate each language and spatial reasoning. LMD additionally allows counterfactual text-to-image technology that the bottom diffusion mannequin is just not capable of generate (the final row).

For extra particulars about LLM-grounded Diffusion (LMD), visit our website and read the paper on arXiv.

BibTex

If LLM-grounded Diffusion evokes your work, please cite it with:

@article{lian2023llmgrounded,
    title={LLM-grounded Diffusion: Enhancing Immediate Understanding of Textual content-to-Picture Diffusion Fashions with Giant Language Fashions},
    creator={Lian, Lengthy and Li, Boyi and Yala, Adam and Darrell, Trevor},
    journal={arXiv preprint arXiv:2305.13655},
    yr={2023}
}