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It's already supported in automatic1111 (see recent updates), and someone in the community will convert it to the automatic1111 format within minutes/hours after it's released on huggingface.
whats the memory usage of sdxl ?
Midjourney is still going to be hard to beat imo. Comparing SD to MJ is a little unfair considering their applications and flexibility, but I do really enjoy the "out of the box" experience that comes with MJ.
Different use case. I can run SDXL 1.0 offline from my home. I can’t do this with Midjourney. A closed source model that doesn’t have the limitation of running on consumer level GPUs will have certain advantages.
Midjourney is still going to be hard to beat imo. Comparing SD to MJ is a little unfair considering their applications and flexibility, but I do really enjoy the "out of the box" experience that comes with MJ.
Earlier quoted context omitted.
Different use case. I can run SDXL 1.0 offline from my home. I can’t do this with Midjourney. A closed source model that doesn’t have the limitation of running on consumer level GPUs will have certain advantages.
What type of setup do you have at home? What type of GPU? MJ completes a pretty high quality photo in about a minute. Does SD compare?
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Diffusion is more parameter-efficient and you quickly saturate the target fidelity, especially with some refiner cascade. It's a solved problem. You do not need more than maybe 4B total. Images are far more redundant than text. In fact, most interesting papers since Imagen show that you get more mileage out of scaling the text encoder part, which is, of course, a Transformer. This is what drives accuracy, text render…
> Seeing as you can throw out diffusion altogether and synthesize images with transformers [3] That’s actually how this whole party got started. DALL-E (the first one) was a transformer model trained on image tokens from an early VAE (and text tokens ofc). Researchers from CompVis developed VQGAN in response. OpenAI showed improved fidelity with guided diffusion over ImageNet (classes) and subsequently DALLE2 using p…
Is this still true in 2023? Sure, back in the dark ages it seemed like a 860M model is just about the limit for a regular consumer, but I don't see why we wouldn't be able to use quantized encoders; and even 30B LLMs run okay on Macbooks now.
Midjourney is still going to be hard to beat imo. Comparing SD to MJ is a little unfair considering their applications and flexibility, but I do really enjoy the "out of the box" experience that comes with MJ.
Midjourney is destroyed by the ecosystem around stable diffusion, especially all the features and extensions in automatic1111. It’s not even close
A couple of accordion pictures do look passable at a distance.
Another test: how well does it do at drawing a woman waving a flag?
One thing that strikes me is that it generates four images at a time, but there is little variety. It's a similar looking woman wearing a similar color and style of clothing, a similar street, and a large American flag. (In one case drawn wrong.) I guess if you want variety you have to specify it yourself?
AI models seem to be getting ever better in resolution and at portraits.
Earlier quoted context omitted.
> Seeing as you can throw out diffusion altogether and synthesize images with transformers [3] That’s actually how this whole party got started. DALL-E (the first one) was a transformer model trained on image tokens from an early VAE (and text tokens ofc). Researchers from CompVis developed VQGAN in response. OpenAI showed improved fidelity with guided diffusion over ImageNet (classes) and subsequently DALLE2 using p…
> The same goes for using T5-XXL Is this still true in 2023? Sure, back in the dark ages it seemed like a 860M model is just about the limit for a regular consumer, but I don't see why we wouldn't be able to use quantized encoders; and even 30B LLMs run okay on Macbooks now.