1st prompt: https://i.postimg.cc/T3nZ9bQy/1st.png
2nd prompt: https://i.postimg.cc/XNFm3dSs/2nd.png
3rd prompt: https://i.postimg.cc/c1bCyqWR/3rd.png
This is using some of the popular prompts you can find on sites like prompthero that show amazing examples.
It’s been serious expectation vs. reality disappointment for me and so I just pay the MidJourney or DALL-E fees.
But yes SD can be a bit of a pain to use. Think of it like this. SD = Linux, Midjourney = Windows/MacOS. SD is more powerful and user controllable but that also means it has a steeper learning curve.
You can finetune it on your own material, or choose one of the hundreds of public finetuned models. You can guide it in a precise manner with a sketch or by extracting a pose from a photo using controlnets or any other method. You can influence the colors. You can explicitly separate prompt parts so the tokens don't leak into each other. You can use it as a photobashing tool with a plugin to popular image editing software. Things like ComfyUI enable extremely complicated pipelines as well. etc etc etc
Honestly? Probably YouTube tutorials.
I'm going to sound like an entitled whiny old guy shouting at clouds, but - what the hell; with all the knowledge being either locked and churned on Discord, or released in form of YouTube videos with no transcript and extremely low content density - how is anyone with a job supposed to keep up with this? Or is that a new form of gatekeeping - if you can't afford to burn a lot of time and attention as if in some kind of Proof of Work scheme, you're not allowed to play with the newest toys?
I mean, Discord I can sort of get - chit-chatting and shitposting is easier than writing articles or maintaining wikis, and it kind of grows organically from there. But YouTube? Surely making a video takes 10-100x the effort and cost, compared to writing an article with some screenshots, while also being 10x more costly to consume (in terms of wasted time and strained attention). How does that even work?
I can't see how covering this on YouTube, instead of (vs. in addition to) writing text + some screenshots and diagrams, makes any kind of sense.
This is where video demonstrations come in handy. Since many concepts are novel, it's uncommon to find anyone who deeply understands them, but it's very easy to find people who have picked up on some tricks of the interfaces, which they're happy to click through. I think gradio/automatic1111 makes learning harder than it needs to be by hiding what it's doing behind its UI, while eg- comfyui has a higher initial learning curve but provides a more representational view of process and pipelines.
This is the level we're generally working at - first or second party to the authors of the research papers illustrating implementations of concepts, struggling with the Gradio interface, things going straight from commit to production.
It's way less frustrating to follow all of the authors in the citations of the projects you're interested in than wasting your attention sorting through blogspam, SEO, and YT trash just to find out they don't really understand anything, either.
Best just to dive in if you're interested IMO. Otherwise you'll get lost in all the new jargon and ideas. Great place to start is the A1111 repo, lot of community resources available and batteries included.
For all the promise of control and customization SD boasts, Midjourney beats it hands down in sheer quality. There's a reason like 99% of ai art comic creators stick to Midjourney despite the control handicap.
If you want the power, it’s there. But nearly bone stock SD in auto1111 is going to get to any of these examples easily.
Show me the civitai equivalent for MJ or Dalle2. It doesn’t exist.
Ok...? Read what i wrote carefully. Your 6 sliders won't produce better images than midjourney for your prompt on the base SD model.
Neither of the existing models gives actually passable production-quality results, be it MJ or SD or whatever else. It will be quite some time until they get out of the uncanny valley.
> There's a reason like 99% of ai art comic creators stick to Midjourney
They aren't. MJ is mostly used by people without experience, think a journalist who needs a picture for an article. Which is great and it's what makes them good money.
As a matter of fact (I work with artists), for all the surface-visible hate AI art gets in the artist community, many actual artists are using it more and more to automate certain mundane parts of their job to save time, and this is not MJ or Dall-E.
Opposite of what ? OP posts results from a tuned model.
>For all the promise of control and customization SD boasts, Midjourney beats it hands down in sheer quality.
The results are comparable, but MJ in this comment https://news.ycombinator.com/item?id=36409043 hallucinates more (look at the roofs in the second picture). And it cannot be fixed, maybe except for an upscale making it a bit more coherent. Until MJ obtains better tooling (which it might in the next iteration), it won't be as powerful. I'm not even starting on complex compositions, which it simply cannot do.
>OP posts results from a tuned model.
Yes, which is the first step you should do with SD, as it's a much smaller and less capable model.
And vice versa, which is the exciting part to me - only a matter of time!
If you’re ok with basic aesthetics it’ll work but if you want something a bit less cringe or that will stand out in marketing it won’t cut it.
Default Midjourney is one thing and that’s mid…
Also I see nothing wrong with using different models for different purposes.
In a nutshell:
1. Use a good checkpoint. Vanilla stable diffusion is relatively bad. There are plenty of good ones on civitai. Here's mine: https://civitai.com/models/94176
2. Use a good negative prompt with good textual inversions. (e.g. "ng_deepnegative_v1_75t", "verybadimagenegative_v1.3", etc.; you can download those from civitai too) Even if you have a good checkpoint this is essential to get good results.
3. Use a better sampling method instead of the default one. (e.g. I like to use "DPM++ SDE Karras")
There are more tricks to get even better output (e.g. controlnet is amazing), but these are the basics.
I learned this mostly by experimenting + browsing civitai and seeing what works + googling as I go + watching a few tutorials on YouTube (e.g. inpainting or controlnet can be tricky as there are a lot of options and it's not really obvious how/when to use them, so it's nice to actually watch someone else use them effectively).
I don't really have any particular place I could recommend to discuss this stuff, but I suppose /r/StableDiffusion/ on Reddit is decent.
I'd like to have a go at making one myself targeted towards single objects (be it car,spaceship, dinner plate, apple, octopus, etc). Most checkpoints are very heavily leaning towards people and portraits.
People generally suggest 30+ images. I’ve found - at least with people - the more the better. My wife’s model is trained on ~80 images of her.
We could just as easily say "hosting your own email can be set up in a few minutes if you know what you're doing". I could do that, but I couldn't get local SD to generate comparable images if my life depended on it.
screenshot of the options interface: https://stash.cass.xyz/drawthings-1687292611.png
Here, I've uploaded it to civitai: https://civitai.com/models/94176
There are plenty of other good models too though.
1. Start with a good base model(s) from which to train from.
2. Have a lot of diverse images.
3. Ideally train for only one epoch. (Having a lot of images helps here.)
4. If you get bad results lower the learning rate and try again.
5. After training try to mix your finetuned model with the original one, in steps of 10%, generate X/Y plot of it, pick the best result.
6. Repeat this process as long as you're getting an improvement.
For training I mostly used scripts from here: https://github.com/bmaltais/kohya_ss
The main problem here is that essentially during inference you're using a bag of tricks to make the output better (e.g. good negative embeddings), but when training you don't. (And I'm not entirely sure how you'd actually integrate those into the training process; might be possible, but I didn't want to spend too much time on it.) So your fine tuning as-is might improve the output of the model when no tricks are used, but it can also regress it when the tricks are used. Which I why I did the "mix and pick the best one" step.
But, again, I'm not an expert at this and just did this for fun. Ultimately there might be better ways to do it.
3. Train for only 1 epoch - interesting, any known rationale here?
5. I just read somewhere else that someone got good results from mixing their custom model with the original (60/40 in their case) - good to hear some more anecdotes that this is pretty effective. Especially the further training after merging, sounds promising!
I've also been using kohya_ss for training LoRAs so great to hear it works for you for models as well. On your point about the inference tricks, definitely noted but I did notice that you can feed some params (# of samples, negative embeddings, etc) to the sample images generated during training (check the textarea placeholder text). Still not going to have all usual the tricks but it'll get you a little closer.