Comment by fishfasell
10 hours ago
The capabilities of local LLM text-to-image is honestly pretty damn impressive. IMO, I think local image generation is currently ahead of local code generation. I can get an image in seconds locally with the quality being way higher than what I'd expect from a local model. However with coding it's much slower and much less impressive. I'm sure there's a reason for this and I'm not an AI expert so I'll let the smarter folks tell me why, but that's just been my observation thus far.
I've played with diffusion models on and off since the first release of Stable Diffusion - just for amusement, without a particular goal.
Recently, I've been helping a friend's wife with some basic vector images for her sewing hobby (she has what is essentially a CNC sewing machine) and have been super-impressed with FLUX.1-Kontext, which I've been running on my Macbook Pro with mflux. Its ability to (for example) take a photo of a human or an animal and return a line drawing which is recognisably them (rather than just a generic similarish image as I've experienced with other models) is excellent.
It's an older model now, but (AIUI) has the text-handling features baked in, and in my various testing is very reliable at giving me the outputs that I want, without the randomness I've experienced previously. It's big and relatively slow (~3 mins per 512x512 image edit on my M1 Max Mac) but excellent to work with. It's also very straightforward to set up, without the harness complexity of e.g. comfyui.
How are you converting the bitmaps into vector images?
This is currently the weakest part of the workflow. I'm tracing the output images with VTracer [0] and then finishing them manually.
Through a combination of careful initial prompting (e.g. requesting line drawings, requesting limited colours ["posterization" works] if you need colour at all, requesting background removal) and then using the settings in VTracer to limit the colours used for the resulting SVG to the minimum needed, you can end up with an SVG that doesn't take too long to clean up in Inkscape. It usually takes a few minutes of deletions and path unions to produce something usable.
Diffusion models outputting SVGs would obviously be very interesting for this, but this is probably a small niche compared to everything that diffusion models can do with raster images, and I've not found anything usable online so far. LLMs (understandably, given their core technology) are still pretty bad at creating SVG art.
[0] https://github.com/visioncortex/vtracer
is the cnc sewing machine an off the shelf model or something DIY? I'd love to hear more
Off the shelf - it’s a Brother. It prints via a proprietary file format (.PES) but there’s an extension for Inkscape that supports creation and export.
Remember that quality output is a necessary but insufficient property of a generative model.
Prompt-adherence is really hit-or-miss—especially if one lacks the visual vocabulary. Likewise with coding, I find junior devs don't think to prompt re: respecting this-or-that interface, or refactoring to point-free style, etc.
So, as others have said, the artist knows better.
I mean I’m sure it’s the reverse for an artist. They would be less impressed with the image and more impressed with the code quality
The point I think is interesting is that this is just 7B. The current SOTA 7B LLMs are barely usable for quite simple coding.
Text is in a sense way harder to do than images because of radical nonlocality. A word at the start of one paragraph can directly influence the meaning of a word five paragraphs away. Whereas images typically represent the real world, or at least a spatial domain, which gives you a lot of structure 'for free'. If you are drawing a human, you can make a reasonable guess where their hands go in relation to their face. If someone hands you the first half of an essay, finishing it is not trivial.
To generalize, LLMs are great at what you are not skilled at.
That's how they're sold, isn't it?
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That's a fair statement, I agree. I'm quite an abysmal artist so I could be a victim of my own bias here