The goal of “image design and generation” is to generate an image based on a broad concept provided by the user. This input IDEA may include reference images, such as “the dog looks like the one in the image,” or instructional instructions that further define the design’s intended application, such as “a logo for the Idea2Img system.” Humans can utilize text-to-image (T2I) models to create a picture based on a thorough description of an imagined image (IDEA). Users must manually explore several options until they find the one that best describes the problem (the T2I prompt).
In light of the impressive capabilities of large multimodal models (LMMs), the researchers investigate whether or not we can train systems based on LMMs to acquire the same iterative self-refinement ability, freeing people from the laborious task of translating concepts into visuals. When venturing into the unknown or tackling difficult tasks, humans have the innate propensity to continually enhance their methods. Natural language processing tasks like acronym generation, sentiment retrieval, text-based environment exploration, etc., can be better addressed with the help of self-refinement, as shown by large language model (LLM) agent systems. Challenges in enhancing, grading, and verifying multimodal contents, such as many interleaved image-text sequences, arise when we move from text-only activities to multimodal settings.
Self-exploration enables an LMM framework to automatically learn to address a wide range of real-world challenges, such as using a graphical user interface (GUI) to interact with a digital device, traversing the unknown with an embodied agent, playing a digital game, and so on. Researchers from Microsoft Azure study the multimodal capacity for iterative self-refinement by focusing on “image design and generation” as the job to investigate. To this purpose, they present Idea2Img, a self-refinancing multimodal framework for the development and design of images automatically. An LMM, GPT-4V(vision), interacts with a T2I model in Idea2Img to investigate the model’s application and identify a useful T2I cue. Both the analysis of the T2I model’s return signal (i.e., draft images) and the creation of the subsequent round’s inquiries (i.e., text T2I prompts) will be handled by the LMM.
T2I prompt generation, draft image selection, and feedback reflection all contribute to the multimodal iterative self-refinement capability. To be more specific, GPT-4V performs the following steps:
- Prompt generation: GPT-4V generates N text prompts that correspond to the input multimodal user IDEA, conditioned on the previous text feedback and refinement history
- Draft image selection: GPT-4V carefully compares N draft images for the same IDEA and selects the most promising one
- Feedback reflection: GPT-4V analyzes the discrepancy between the draft image and the IDEA. Then, GPT-4V gives feedback on what went wrong, why it went wrong, and how the T2I prompts could be improved.
In addition, Idea2Img has a built-in memory module that keeps track of your exploration history for each prompt kind (picture, text, and feedback). For automated image creation and generation, the Idea2Img framework repeatedly cycles between these three GPT-4V-based processes. As an improved picture design and creation helper, Idea2Img is a useful tool for users. By accepting design directions instead of a thorough picture description, accommodating the multimodal IDEA input, and producing images with higher semantic and visual quality, Idea2Img stands out from T2I models.
The team reviewed some sample cases of picture creation and design. For instance, Idea2Img may process IDEA with arbitrarily interleaved picture-text sequences, include the visual design and intended usage description into IDEA, and extract arbitrary visual information from the input image. Based on these updated features and use cases, they created a 104-sample evaluation IDEA set with complex questions that humans might get wrong the first time. The team employs Idea2Img and various T2I models to conduct user preference studies. Improvements in user preference scores across many image-generating models, such as +26.9% with SDXL, demonstrate Idea2Img’s efficacy in this area.
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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.
