Fooocus VS PixArt-alpha

But we can come to a pretty realistic (although not as accurate) conclusion if we put our minds to it. I chose Fooocus for this example, which is the most straightforward (and I believe popular) stable diffusion GUI out there. Let's start simple:

# Install the JuiceFS client. !curl -sSL https://d.juicefs.com/install | sh - # Mount the JuiceFS file system. !juicefs mount rediss://:[email protected]/1 myjfs -d # Create the directory structure for Fooocus models in JuiceFS. !mkdir -p myjfs/models/{checkpoints,loras,embeddings,vae_approx,upscale_models,inpaint,controlnet,clip_vision} # Clone the Fooocus repository. !git clone https://github.com/lllyasviel/Fooocus.git

That looks very impressive unless the demo is cherrypicked, would be great if this could be implemented into a frontend like Fooocus https://github.com/lllyasviel/Fooocus

You might be thinking of Fooocus: https://github.com/lllyasviel/Fooocus

The Stable Diffusion web interface that got a lot of people's attention originally was Automatic1111: https://github.com/AUTOMATIC1111/stable-diffusion-webui

Fooocus is definitely more beginner friendly. It does a lot of the prompt engineering for you. Automatic1111 has a ton of plugins, most notably ControlNet which gives you fine grained control over the images, but there is a learning curve.

I just installed this last night on my laptop:

https://github.com/lllyasviel/Fooocus

Highly recommend:

>"Looking up from the deck of golden gate bridge at the towers and metal work, the towers rise and arch back in an ominous and foreboding manner. more artistic, like an alphonse mucha propaganda poster - slightly fish-eye feeling" -- https://i.imgur.com/vyNg79f.jpg

the local UI and 1.27.0.0.1 - https://i.imgur.com/wRwghuN.jpg

Why not use the Google Colab notebook while it's still a free option at: https://github.com/lllyasviel/Fooocus It's not bad. I've been using the Colab Fooocus notebook and A1111 on Sage with the 4 free hours of GPU time. The Colab has the Juggernaut model preloaded, but I combined some code from another notebook to add other models and loras.

i'm trying to test the abilities of image generators and the risks that can comes with it. and i'm looking for an image generator that work locally and has no limits. i used the Fooocus project from github and the juggernut model and it's capable of generating nude pictures but not fully nude pictures. and it doesn't work will with bloody scenes. any recommendation for a better model

I recently saw this video from Fireship and I started wondering about licensing of SD models and frameworks. Fireship shows Fooocus and advertises it as a cool solution. What I started wondering about is, Fooocus downloads a couple of models: Juggernaut XL, some control nets, some loras. What licensing is tied to all of this? One I am most insterested in is JuggernautXL, on civitai it's listed as having CreativeML Open RAIL++-M license but in the description there's remark: For business inquires, commercial licensing, custom models, and consultation contact me under [email protected] There's a lot of separate parts going on in AI frameworks and it's a bit unclear to understand if I can use this commercialy.

For running locally w/o a lot of "hazzle" i would recommend using https://github.com/lllyasviel/Fooocus and the "Sticker" style, which is available within the UI (Advanced tab). If you need lot of text directly in you images, you may have difficulties with SD and other AI models. In this case LoRas could help. Example: https://civitai.com/posts/880523 (check details for used prompt) In this case i used the following LoRa (a LoRa is kind of specialized submodel for style, concept or person) .

Yes, it's mostly a new training technique (that is impressive: "PIXART-α only takes 10.8% of Stable Diffusion v1.5's training time"). I'm not really sure if it really improves the image quality over SDXL, but it may a bit: https://pixart-alpha.github.io

I know she does not want to listen, but if you ever get into an argument with her again, point out to her that there are models that are build on photos and artwork that have given permission explicitly to allow training for A.I., and that these models are very capable: https://github.com/PixArt-alpha/PixArt-alpha

Isn't this the same technique that PIXART-α is already using? Pixart has already achieved state of the art in image generation with a fraction of the training cost and data using transformers

The source code license is AGPL-3.0 license. Perfect for these kinds of models: https://github.com/PixArt-alpha/PixArt-alpha

> But it's all been built on top of a base model trained by Stability AI at a cost of $600k (or at least, it would have cost that at AWS GPU prices).

The LAION dataset was notoriously bad, and the training process wasn't optimal by any measure, though. The costs of training are rapidly falling due to various optimizations.

Take a look at Pixart-alpha [0]. They claim SDXL-comparable performance for just $26k in training from scratch, with just 600M parameters in the unet and 25M pictures in the training set. Supposedly they achieved this due to the high quality training set tagged by a third-party model. The weights got leaked recently and the claim looks beleivable.

[0] https://pixart-alpha.github.io/

I wonder if you could use this to auto-label training data as well - similar to how PIXART-α got better results from less training data by auto-labeling it with an image captioning model.

Unfortunately, the GitHub link they provide returns a 404 Not Found. I tried going to the organization's GitHub page, then the repo's, but there wasn't anything there other than some HTML. The link to their Hugging Face page doesn't have the model, either. So I guess they aren't ready to share either yet, if they ever will. The pictures they posted on this page https://pixart-alpha.github.io/ look quite good, though.