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> Google's Gemini LLM model was not to blame for the image generation weirdness. That would be like blaming DALL-E weirdness on GPT-4. The way I read the Gemin
by espadrine 3y ago
> Google's Gemini LLM model was not to blame for the image generation weirdness.
That would be like blaming DALL-E weirdness on GPT-4.
The way I read the Gemini technical report, it seemed like, unlike GPT-4 vs DALL-E, Gemini was pretrained with multimodal outputs. Is that not the case?
- simonw 3y agoIs that right? I didn't think Gemini was generating images directly, I assumed it was using a separate image generation tool. The paper here https://arxiv.org/pdf/2403.05530.pdf https://arxiv.org/pdf/2403.05530.pdf has a model card for Gemini 1.5 Pro that says: Output(s): Generated text in response to the input (e.g., an answer to the question, a summary of multiple documents, comparing documents/videos).
- espadrine 3y agoHuh, that is true in both the model cards of Gemini 1.5 Pro and Gemini 1.0. That feels like it runs counter to this statement from the Gemini 1.0 technical report[0]: > Gemini models are trained to accommodate textual input interleaved with a wide variety of audio and visual inputs, such as natural images, charts, screenshots, PDFs, and videos, and they can produce text and image outputs [0]: https://arxiv.org/pdf/2312.11805.pdf https://arxiv.org/pdf/2312.11805.pdf
- simonw 3y agoYeah what does that bit about "image outputs" mean I wonder?