I review the state-of-the-art photorealistic and open-source text-to-image model DeepFloyd IR. I conclude that it is similar to Google’s Imagen but better. Opensource is always better.
The benchmark score is also better than Imagen. A zero-shot FID score of 6.66 on the COCO dataset.
The Official Introduction Message follows:
We introduce DeepFloyd IF, a novel state-of-the-art open-source text-to-image model with a high degree of photorealism and language understanding. DeepFloyd IF is a modular composed of a frozen text encoder and three cascaded pixel diffusion modules: a base model that generates 64×64 px image based on text prompt and two super-resolution models, each designed to generate images of increasing resolution: 256×256 px and 1024×1024 px. All stages of the model utilize a frozen text encoder based on the T5 transformer to extract text embeddings, which are then fed into a UNet architecture enhanced with cross-attention and attention pooling. The result is a highly efficient model that outperforms current state-of-the-art models, achieving a zero-shot FID score of 6.66 on the COCO dataset. Our work underscores the potential of larger UNet architectures in the first stage of cascaded diffusion models and depicts a promising future for text-to-image synthesis.
#LLaMA #stabilityai #T5 #imagen #deeplearning #transformer #Opensource #open-source #LLM #AI #artificialintelligence #agi #samaltman #yanlecun #largelanguagemodels #autogtp #openai #chatgtp #openai #openaichat
#davinciresolve
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