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DeepFloyd IF: open-source text-to-image model

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Re: DeepFloyd IF: open-source text-to-image model

#5
Has anyone tried the Scott Alexander AI bet prompts?

1. A stained glass picture of a woman in a library with a raven on her shoulder with a key in its mouth

2. An oil painting of a man in a factory looking at a cat wearing a top hat

3. A digital art picture of a child riding a llama with a bell on its tail through a desert

4. A 3D render of an astronaut in space holding a fox wearing lipstick

5. Pixel art of a farmer in a cathedral holding a red basketball

Re: DeepFloyd IF: open-source text-to-image model

#6

16GB VRAM minimum is a bit steep. Sadly excludes my 3080 which is annoying because I'd like something better than Stable Diffusion locally.

If you don't mind the power consumption I noticed that older nvidia P6000's (24GB) are pretty cheap on ebay! My 16GB P5000 is pretty handy for this stuff.

Re: DeepFloyd IF: open-source text-to-image model

#9

16GB VRAM minimum is a bit steep. Sadly excludes my 3080 which is annoying because I'd like something better than Stable Diffusion locally.

There's a note which suggests you might be able to get by on lower. My 3060 struggles with SD on the defaults, but works fine with float16.

There are multiple ways to speed up the inference time and lower the memory consumption even more with diffusers. To do so, please have a look at the Diffusers docs:

        Optimizing for inference time [1]
        Optimizing for low memory during inference [2]

[1] https://huggingface.co/docs/diffusers/api/pipelines/if#optim...

[2] https://huggingface.co/docs/diffusers/api/pipelines/if#optim...

Re: DeepFloyd IF: open-source text-to-image model

#10
post #7

So this one can create perfect text in images? If true, that’s insane

LDM-400M was already able to generate text (predecessor of Stable Diffusion), thanks to the fact that every token in the text encoder (trained from scratch) was available in the attention layer.
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