Kosmos-G: Generating Images in Context with Multimodal Large Language Models (2310.02992v3)

Abstract: Recent advancements in subject-driven image generation have made significant strides. However, current methods still fall short in diverse application scenarios, as they require test-time tuning and cannot accept interleaved multi-image and text input. These limitations keep them far from the ultimate goal of "image as a foreign language in image generation." This paper presents Kosmos-G, a model that leverages the advanced multimodal perception capabilities of Multimodal LLMs (MLLMs) to tackle the aforementioned challenge. Our approach aligns the output space of MLLM with CLIP using the textual modality as an anchor and performs compositional instruction tuning on curated data. Kosmos-G demonstrates an impressive capability of zero-shot subject-driven generation with interleaved multi-image and text input. Notably, the score distillation instruction tuning requires no modifications to the image decoder. This allows for a seamless substitution of CLIP and effortless integration with a myriad of U-Net techniques ranging from fine-grained controls to personalized image decoder variants. We posit Kosmos-G as an initial attempt towards the goal of "image as a foreign language in image generation." The code can be found at https://aka.ms/Kosmos-G

An Analysis of JARVIS: Generating Images with Multimodal LLMs

The paper "JARVIS: Generating Images in Context with Multimodal LLMs" introduces a cutting-edge model aimed at revolutionizing subject-driven image generation using Multimodal LLMs (MLLMs). The model, named JARVIS, addresses the limitations of existing state-of-the-art methods by enabling zero-shot generation from interleaved multi-image and text inputs without requiring test-time tuning. It endeavors to achieve the perception of images analogous to processing a "foreign language," utilizing MLLMs to extend perception capabilities across diverse modalities.

Key Contributions and Methodology

JARVIS presents several novel contributions in the field of multimodal image generation:

1. Multimodal LLMing: The researchers propose a framework where vision and language are perceived uniformly, orchestrated through a Transformer-based MLLM architecture. The model is trained with extensive multimodal corpora incorporating monomodal text data, paired image-caption data, and interleaved multimodal data.
2. Image Decoder Alignment: A unique aspect of JARVIS is aligning the output space of multimodal encoders with the CLIP text encoder space via an AlignerNet. The latter serves as an intermediary to ensure the embeddings produced by JARVIS are compatible with those expected by the image decoding modules of diffusion models.
3. Instruction Tuning through Compositional Generation: By curating specific datasets involving complex multimodal interactions, JARVIS undergoes an instruction tuning phase where it learns compositional tasks. This phase leverages score distillation to transfer knowledge from pre-trained image decoders to JARVIS, upholding semantic fidelity across contexts.

Evaluation and Results

JARVIS exhibits superior performance on DreamBench for single-entity and multi-entity subject-driven image generation tasks, as well as conventional text-to-image challenges on datasets like MS-COCO. The model surpasses several contemporary approaches, maintaining impressive DINO and CLIP scores, thus affirming the semantic alignment and fidelity of generated images relative to inputs.

Specifically noted in quantitative analysis, JARVIS achieves a great balance between subject fidelity and text fidelity in scenarios requiring nuanced and combinatorial input representations. The qualitative assessments demonstrate the model’s proficient handling of complex inputs, re-contextualization, stylization, and multi-entity settings in a zero-shot setting—tasks often challenging for prior models relying heavily on fine-tuning strategies per subject or context.

Implications and Future Directions

JARVIS represents a significant stride forward in expanding the capabilities of LLMs to perceive and generate across complex multimodal inputs seamlessly. Its ability to handle nuanced subject-driven tasks without fine-tuning paves the way for more generalized models capable of diverse creative tasks. The flexibility and seamless integration into existing systems through CLIP replacement suggest broad applicability across industries, including personalized digital content creation and automated visual storytelling.

Moving forward, the research presents opportunities to paper the integration of JARVIS with more advanced U-Net techniques, possibly enhancing personalized and stylized image generation. The alignment strategy and instruction tuning offer a framework for further exploration in optimized embeddings and efficient multimodal interaction in real-world applications.

Overall, JARVIS demonstrates potential as an advanced interface for AI-driven creativity, advocating for continual advancements toward truly unified and adaptable multimodal models, where images and text coexist as complementary informational sources.