Fooocus VS zero123

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) .

Someone with resources will have to train Zero123 [1] with this backbone.

[1] https://zero123.cs.columbia.edu/

This looks a fine-tune of the classic zero123 (https://github.com/cvlab-columbia/zero123) I’m excited to check out the quality improvements.

Though 3d model synthesis is one use case, I found the less advertised base reprojection model to be more useful for gamedev at the moment. You can generate a multiview spritesheet from an image, and it’s fast enough for synthesis during a gameplay session. I couldn’t get a good quality/time balance to do the same with the 3d models, and the lack of mesh rigging or animation combined with imperfections in a fully 3d model tends to break the suspension of disbelief compared to what players are used to. I’m this will change as the tech develops and we layer more AI on top (automatic animation synthesis is an active research area).

If you’re interested in this you might also want to check out deforum (https://github.com/deforum-art/deforum-stable-diffusion) which provides even more powerful camera controls on top of stable diffusion designed for full scenes rather than single objects.

As described in the stable-dreamfusion repo for the image to 3D using the zero123 model (you can read more about that in their repo here: https://github.com/cvlab-columbia/zero123) I used the 105000 checkpoint of zero123. It took about an hour to go through their initial NeRF generation and cleanup steps to get the model output.

Until then https://github.com/cvlab-columbia/zero123 was kinda okay, but practical results often left to be desired, from the imprecision of the view angles to the at times fanciful re-imaginations of the source object.

For anyone else who tried to download the weights and got Google Drive throwing a quota error at you, they're working on it: https://github.com/cvlab-columbia/zero123/issues/2