This paper presents instruct-imagen, a model that tackles heterogeneous image
generation tasks and generalizes across unseen tasks. We introduce *multi-modal
instruction* for image generation, a task representation articulating a range
of generation intents with precision. It uses natural language to amalgamate
disparate modalities (e.g., text, edge, style, subject, etc.), such that
abundant generation intents can be standardized in a uniform format.
We then build instruct-imagen by fine-tuning a pre-trained text-to-image
diffusion model with a two-stage framework. First, we adapt the model using the
retrieval-augmented training, to enhance model’s capabilities to ground its
generation on external multimodal context. Subsequently, we fine-tune the
adapted model on diverse image generation tasks that requires vision-language
understanding (e.g., subject-driven generation, etc.), each paired with a
multi-modal instruction encapsulating the task’s essence. Human evaluation on
various image generation datasets reveals that instruct-imagen matches or
surpasses prior task-specific models in-domain and demonstrates promising
generalization to unseen and more complex tasks.
The concept of instruct-imagen presented in this paper is a significant step forward in the field of image generation. It addresses the challenge of heterogeneous image generation tasks by introducing multi-modal instruction, which allows for a more precise representation of generation intents.
One of the key strengths of instruct-imagen is its ability to amalgamate disparate modalities, such as text, edge, style, and subject, using natural language. This enables the model to generate a wide range of image types while maintaining a standardized format for expressing generation intents. This multi-disciplinary approach, combining the fields of natural language processing and computer vision, is crucial for achieving the desired level of precision in image generation tasks.
The model itself is built through a two-stage framework. In the first stage, the pre-trained text-to-image diffusion model is adapted using retrieval-augmented training. This adaptation enhances the model’s ability to ground its generation on external multimodal context, further improving its performance. In the second stage, the adapted model is fine-tuned on diverse image generation tasks that require vision-language understanding, each paired with a multi-modal instruction.
The evaluation results of instruct-imagen are highly encouraging. The model not only matches or surpasses prior task-specific models in the same domain but also demonstrates promising generalization to unseen and more complex tasks. This indicates that instruct-imagen has the potential to be a highly versatile and robust image generation model.
Furthermore, instruct-imagen exemplifies the importance of human evaluation in assessing the performance of AI models. By conducting human evaluations on various image generation datasets, the researchers were able to gain valuable insights into the capabilities and limitations of instruct-imagen. This is especially crucial given the diverse nature of image generation tasks and the subjective nature of evaluating image quality.
In conclusion, instruct-imagen presents a compelling approach to tackling heterogeneous image generation tasks. Its multi-modal instruction and two-stage framework demonstrate the effectiveness of combining natural language processing and computer vision techniques. The promising results in both in-domain and unseen tasks highlight the potential impact of this model in various real-world applications.
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