Fooocus VS can-ai-code

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) .

You can run a Q4 quant of a 70B model in about 40GB of RAM (+context). You're single user (batch size 1, bs=1) inference speed will be basically memory bottlenecked, so on a dual channel dedicated box you'd expect somewhere about 1 token/s. That's inference, prefill/prompt generation will take even longer (as your chat history grows) on CPU. So falls into the realm of technically possible, but not for real world use.

If you're looking specifically for CodeLlama 70B, Artificial Analysis https://artificialanalysis.ai/models/codellama-instruct-70b/... lists Perplexity, Together.ai, Deep Infra, and Fireworks as potential hosts, with Together.ai and Deepinfra at about $0.9/1M tokens, with about 30 tokens/s and about 300ms latency (time to first token).

For those looking for local coding models in specifically. I keep a list of LLM coding evals here: https://llm-tracker.info/evals/Code-Evaluation

On the EvalPlus Leaderboard, there about about 10 open models that rank higher than CodeLlama 70B, all smaller models: https://evalplus.github.io/leaderboard.html

A few other evals (worth cross-referencing to counter contamination, overfitting):

* CRUXEval Leaderboard https://crux-eval.github.io/leaderboard.html

* CanAiCode Leaderboard https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

* Big Code Models Leaderboard https://huggingface.co/spaces/bigcode/bigcode-models-leaderb...

From the various leaderboards, deepseek-ai/deepseek-coder-33b-instruct still looks like the best performing open model (it has a very liberal ethical license), followed by ise-uiuc/Magicoder-S-DS-6.7B (a deepseek-coder-6.7b-base fine tune). The former can be run as a Q4 quant on a single 24GB GPU (a used 3090 should run you about $700 atm), and the latter, if it works for you will run 4X faster and fit on even cheaper/weaker GPUs.

There's always recent developments, but two worth pointing out:

OpenCodeInterpreter - a new system that uses execution feedback and outperforms ChatGPT4 Code Interpreter that is fine-tuned off of the DeepSeek code models: https://opencodeinterpreter.github.io/

StarCoder2-15B just dropped and also looks competitive. Announcement and relevant links: https://huggingface.co/blog/starcoder2

This is a completely fair, but open question. Not to be a typical HN user, but when you say SOTA local, the question is really what benchmarks do you really care about in order to evaluate. Size, operability, complexity, explainability etc.

Working out what copilot models perform best has been a deep exercise for myself and has really made me evaluate my own coding style on what I find important and things I look out for when investigating models and evaluating interview candidates.

I think three benchmarks & leaderboards most go to are:

https://huggingface.co/spaces/bigcode/bigcode-models-leaderb... - which is the most understood, broad language capability leaderboad that relies on well understood evaluations and benchmarks.

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul... - Also comprehensive, but primarily assesses Python and JavaScript.

https://evalplus.github.io/leaderboard.html - which I think is a better take on comparing models you intend to run locally as you can evaluate performance, operability and size in one visualisation.

Best of luck and I would love to know which models & benchmarks you choose and why.

Here is a leader board of some models

https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

Don't know how biased this leaderboard is, but I guess you could just give some of them a try and see for yourself.

- Can AI Code? - https://huggingface.co/spaces/mike-ravkine/can-ai-code-results

Check out https://github.com/the-crypt-keeper/can-ai-code for some ideas. I'd love to see more shootouts like this. Especially if they were spread out among a few different languages.

Very cool, this looks like a combination of chatbot-ui and llama-cpp-python? A similar project I've been using is https://github.com/serge-chat/serge. Nous-Hermes-Llama2-13b is my daily driver and scores high on coding evaluations (https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...).

I have several sets of quant comparisons posted on my HF spaces, the caveat is my prompts are all "English to code": https://huggingface.co/spaces/mike-ravkine/can-ai-code-compa...

The dropdown at the top selects which comparison: Falcon compares GGML, Vicuna compares bits and bytes. I have some more comparisons planned, feel free to open an issue if you'd like to see something specific: https://github.com/the-crypt-keeper/can-ai-code

Yeah it seemed suspiciously high for HumanEval and it only ranks 14th for JS and 7th for Python on other benchmarks now: https://huggingface.co/spaces/mike-ravkine/can-ai-code-resul...

WizardCoder is a bit of a problem since it's not llama 1/2 based but is its own 15B model and as such the support for it in anything practical is near nonexistent. WizardLM v1.2 looks like it may be worth checking out.

There are at least 4 different types of quants floating around HF (bitsandbytes, GGML, GPTQ and AWQ) so I dont know if a "GGML" column makes sense vs a more abstract way of linking quants to their base models. I am doing this and its fucking awful: https://github.com/the-crypt-keeper/can-ai-code/blob/main/models/models.yaml

Ah I meant this one but I see now it's WIP.