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Invoke and Comfy are both incredibly complicated tools, and I've recently been put off by both communities.
The r/StableDiffusion folks will go as far as calling those complaining about comfy "PEBKACs", which is a term I hadn't seen since the 00's.
Neither tool is really appropriate for beginners, and frankly they're not even fun to use for extended periods as they're constantly breaking.
If all you're trying to do is generate images, it's not worth installing a universe of broken Python and 100 GB of weights on your machine.
It honestly feels like 2000's-era Linux hobbyists proclaiming the "Year of Linux on the desktop" when interacting with these communities. Their tools are busted, but lots of folks will blame you, the user.
I'm not going to argue that they're not complicated tools - Invoke and Comfy are both actively used to push the boundaries of what can be achieved in professional generative media.
But I'd be curious to hear what issues you ran into with Invoke, and where you found issues with the community -- Or, are you generalizing across SD reddit?
We try to do a pretty good job keeping things welcome, and are nearing 100 videos of educational content to help folks learn how to use GenAI. Open to feedback on where we can promote more accessibility.
The best I've found is using asdf and venv for each of these python ML projects. Install the python version they assume, create and source a virtual env, install their dependencies, then activate the venv's when you boot up a terminal to run them again.
That still doesn't solve the other half of the issues that Python has with dependency management, but this method at least keeps me sane about it.
edit: and also not sure how I'd even start incorporating this into Invoke/Comfy... I've been using the latter but I'm not that comfy with it.
Yes, these are largely prototyping tools. I'm not fond of Invoke's "professional" pitch in particular, this workflow would feel alien for any actual creative. However AI CGI is more like 3D CGI in the sense that you have to get technical and understand what you're doing. There are a bit more competent tools in this busted-tool world if you want to get things done, e.g.: https://github.com/Acly/krita-ai-diffusion . It still a nerdy tool and has way more friction than necessary, with all this python hell (it uses Comfy as a backend, although it's somewhat automated). It's far from being as easy as Adobe's Firefly.
Invoke is actually one of the easier tools to use - providing the ability to download model checkpoints in place, relatively easy inpainting, etc.
ComfyUI is definitely for the bleeding edge.
Forge is a good middle ground. With the huge number of tutorials available on YouTube (Olivio's channel is good for novices), I don't think it's that particularly difficult for beginners to grok, but it does require some patience and follow through.
If all you care about is generating some generic looking images for your blog and don't want any flexibility, you can always pay for a subscription to Midjourney.
I'm the CEO at Invoke (i.e., the creator of this piece) -- we're one of the longest running projects for open-source image generation, originating from the initial explosion around Stable Diffusion's release.
We've always focused more heavily on professionals/artists, and are happy to have (finally) been able to get some clarity on these points as we've pushed the USCO to respect where/how human creativity factors into GenAI usage.
You can learn more about us invoke.com (and download the local studio at invoke.com/downloads)
Hey all. I'm the CEO of Invoke - appreciate everyone who has mentioned us in the thread.
To OP -- We work with professional artists regularly, and I'm seeing things pick up as more begin to understand the potential for creative control. Artists mainly want to be afforded creative flexibility and control, and need an interface that feels natural for their workflow.
Invoke is OSS, we release continued training/education on a weekly basis (free, on YT) and we'll be releasing a simplified installer soon.
The Invoke team released regional guidance using IP Adapter a few months ago, which can use color palettes + style transfer mode, along with text prompts and controlnets.
Would take a look at that for some inspiration -- The UI is Apache 2.0 and used by professional artists. I'd be curious how you think it performs relative to the workflow you've developed.
You're spot on that researchers don't always build the UI that end-users want to use. Always love to see people thinking about the creatives. Good work!
Some perspectives from someone working in the image space.
These tests don't feel practical - That is, they seem intended to collapse the model, not demonstrate "in the wild" performance.
The assumption is that all content is black or white - AI or not AI - and that you treat all content as equally worth retraining on.
It offers no room for assumptions around data augmentation, human-guided quality discrimination, or anything else that might alter the set of outputs to mitigate the "poison"
As someone also working in the imaging space, ai generated data is useful solong as it's used carefully.
Specifically, we're implementing AI culled training sets which contain some generated data that then gets reviewed manually for a few specific things, then pushed into our normal training workflows. This makes for a huge speedup versus 100% manual culling and the metrics don't lie, the models continue to improve steadily.
There may be a point where they're poisoned and will collapse, but I haven't seen it yet.
This is exactly right. Model collapse does not exist in practice. In fact, LLMs trained on newer web scrapes have increased capabilities thanks to the generated output in their training data.
For example, "base" pretrained models trained on scrapes which include generated outputs can 0-shot instruction follow and score higher on reasoning benchmarks.
Intentionally produced synthetic training data takes this a step further. For SoTA LLMs the majority of, or all of, their training data is generated. Phi-2 and Claude 3 for example.
Granted, one could argue that this only happened because the API version of Claude doesn't appear to use a system prompt. If that's the case, then the LLM lacks any identity otherwise defined by the initial system prompt, and thus, kind of makes one up.
Nonetheless, point remains, it's kind of interesting to see that in the years since the launch of ChatGPT we're already seeing a tangible impact on publicly available training data. LLMs "know" what ChatGPT is, and may even claim to be it.
that is the meat the article tries to cook. the impacts so far aren’t all that negative.
but time flows like a river, and the more shit that gets into it…
poison does not need to be immediately fatal to be fatal. some take a frighteningly long time to work. by the time you know what’s happening, not only is it too late, you have already suffered too much.
does this sound like anything more than a scary story to tell around campfires? not yet.
Claude 3 does use publically available data. Not everything is synthetically generated. Look at the section for training data in the below link. It has an quote from the paper which states that it uses a mix of public data, data from labelers and synthetic data
I can't find a link to the actual clause paper to verify the above link but a few other places mention the same thing about the training data. We don't know if this improved performance is because of synthetic data or something else. I'm guessing even antropic might not be knowing this too.
Wouldn’t reinforcement learning just weigh any nonsense data very low and then spammy garbage doesn’t really affect the model in the end much ? If the model and human experts can’t tell the difference then it’s probably pretty good AI generated data
Why would you limit a model to be like a brain in a vat? Instead let the model out so people use it, then use the chat logs to fine-tune. A chat room is a kind of environment, there is a human, maybe some tools. The LLM text will generate feedback and right there is a learning signal.
Even without a human, if a LLM has access to code execution it can practice solving coding tasks with runtime feedback. There are many ways a LLM could obtain useful learning signals. After all, we got all our knowledge from the environment as well, in the end there is no other source for knowledge and skills.
Dude what? That’s a pretty absurd claim. Most generally available models specifically curate their inputs for the express purpose of avoiding AI garbage induced collapse. It’s literally on their cited reasons for avoiding ai generated data as inputs.
This is the part that I don't really understand. Isn't this basically an evolutionary algorithm, where the fitness function is "whatever people like the most" (or at least enough to post it online)?
People rarely generate 10 pieces of content with AI and then share all 10 with the world. They usually only share the best ones. This naturally filters for better output.
Are they saying that evolutionary algorithms don't work?
> Use the model to generate some AI output. Then use that output to train a new instance of the model and use the resulting output to train a third version, and so forth. With each iteration, errors build atop one another. The 10th model, prompted to write about historical English architecture, spews out gibberish about jackrabbits.
That this happens doesn't surprise me, but I'd love to see a curve of how each organic vs machine content mixe ratio results in model collapse over N generations.
There is a ton you can do to help SOTA AI remain open.
Join the community building the tools - Help with UI/UX, documentation, keeping up with the latest, and evangelizing whatever method the team building it has devised to keep it sustained.
Being part of the community itself is more valuable than you realize.
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