3 ms·

SDXL has been outclassed for a while, especially since Flux came out.

aeon_ai 10mo ago
Subjective. Most in creative industries regularly still use SDXL. Once Z-image base comes out and some real tuning can be done, I think it has a chance of replacing it for the function SDXL has
- Scrapemist 10mo ago
  Source?
  - echelon 10mo ago
    Most of the people I know doing local AI prefer SDXL to Flux. Lots of people are still using SDXL, even today. Flux has largely been met with a collective yawn. The only thing Flux had going for it was photorealism and prompt adherence. But the skin and jaws of the humans it generated looked weird, it was difficult to fine tune, and the licensing was weird. Furthermore, Flux never had good aesthetics. It always felt plain. Nobody doing anime or cartoons used Flux. SDXL continues to shine here. People doing photoreal kept using Midjourney.
    - kouteiheika 10mo ago
      > it was difficult to fine tune Yep. It's pretty difficult to fine tune, mostly because it's a distilled model. You can fine tune it a little bit, but it will quickly collapse and start producing garbage, even though fundamentally it should have been an easier architecture to fine-tune compared to SDXL (since it uses the much more modern flow matching paradigm). I think that's probably the reason why we never really got any good anime Flux models (at least not as good as they were for SDXL). You just don't have enough leeway to be able to train the model for long enough to make the model great for a domain it's currently suboptimal for without completely collapsing it.
      - magicalhippo 10mo ago
        > It's pretty difficult to fine tune, mostly because it's a distilled model. What about being distilled makes it harder to fine-tune?
        kouteiheika 10mo ago
        AFAIK a big part of it is that they distilled the guidance into the model. I'm going to simplify all of this a lot so please bear with me, but normally the equation to denoise an image would look something like this: pos = model(latent, positive_prompt_emb) neg = model(latent, negative_prompt_emb) next_latent = latent + dt * (neg + cfg_scale * (pos - neg)) So what this does - you trigger the model once with a negative prompt (which can be empty) to get the "starting point" for the prediction, and then you run the model again with a positive prompt to get the direction in which you want to go, and then you combine them. So, for example, let's assume your positive prompt is "dog", and your negative prompt is empty. So triggering the model with your empty prompt with generate a "neutral" latent, and then you nudge it into the direction of your positive prompt, in the direction of a "dog". And you do this for 20 steps, and you get an image of a dog. Now, for Flux the equation looks like this: next_latent = latent + dt * model(latent, positive_prompt_emb) The guidance here was distilled into the model. It's cheaper to do inference with, but now we can't really train the model too much without destroying this embedded guidance (the model will just forget it and collapse). There's also an issue of training dynamics. We don't know exactly how they trained their models, so it's impossible for us to jerry rig our training runs in a similar way. And if you don't match the original training dynamics when finetuning it also negatively affects the model. So you might ask here - what if we just train the model for a really long time - will it be able to recover? And the answer is - yes, but at this point the most of the original model will essentially be overwritten. People actually did this for Flux Schnell, but you need way more resources to pull it off and the results can be disappointing: https://huggingface.co/lodestones/Chroma https://huggingface.co/lodestones/Chroma
        magicalhippo 10mo ago
        Thanks for the extended reply, very illuminating. So the core issue is how they distilled it, ie that they "baked in the offset" so to speak. I did try Chroma and I was quite disappointed, what I got out looked nowhere near as good as what was advertised. Now I have a better understanding why.
- CuriouslyC 10mo ago
  I don't think that's fair. SDXL is crap at composition. It's really good with LoRAs to stylize/inpaint though.
deleted 10mo ago
[deleted]

Subjective. Most in creative industries regularly still use SDXL. Once Z-image base comes out and some real tuning can be done, I think it has a chance of replacing it for the function SDXL has

Scrapemist 10mo ago
Source?
- echelon 10mo ago
  Most of the people I know doing local AI prefer SDXL to Flux. Lots of people are still using SDXL, even today. Flux has largely been met with a collective yawn. The only thing Flux had going for it was photorealism and prompt adherence. But the skin and jaws of the humans it generated looked weird, it was difficult to fine tune, and the licensing was weird. Furthermore, Flux never had good aesthetics. It always felt plain. Nobody doing anime or cartoons used Flux. SDXL continues to shine here. People doing photoreal kept using Midjourney.
  - kouteiheika 10mo ago
    > it was difficult to fine tune Yep. It's pretty difficult to fine tune, mostly because it's a distilled model. You can fine tune it a little bit, but it will quickly collapse and start producing garbage, even though fundamentally it should have been an easier architecture to fine-tune compared to SDXL (since it uses the much more modern flow matching paradigm). I think that's probably the reason why we never really got any good anime Flux models (at least not as good as they were for SDXL). You just don't have enough leeway to be able to train the model for long enough to make the model great for a domain it's currently suboptimal for without completely collapsing it.
    - magicalhippo 10mo ago
      > It's pretty difficult to fine tune, mostly because it's a distilled model. What about being distilled makes it harder to fine-tune?
      - kouteiheika 10mo ago
        AFAIK a big part of it is that they distilled the guidance into the model. I'm going to simplify all of this a lot so please bear with me, but normally the equation to denoise an image would look something like this: pos = model(latent, positive_prompt_emb) neg = model(latent, negative_prompt_emb) next_latent = latent + dt * (neg + cfg_scale * (pos - neg)) So what this does - you trigger the model once with a negative prompt (which can be empty) to get the "starting point" for the prediction, and then you run the model again with a positive prompt to get the direction in which you want to go, and then you combine them. So, for example, let's assume your positive prompt is "dog", and your negative prompt is empty. So triggering the model with your empty prompt with generate a "neutral" latent, and then you nudge it into the direction of your positive prompt, in the direction of a "dog". And you do this for 20 steps, and you get an image of a dog. Now, for Flux the equation looks like this: next_latent = latent + dt * model(latent, positive_prompt_emb) The guidance here was distilled into the model. It's cheaper to do inference with, but now we can't really train the model too much without destroying this embedded guidance (the model will just forget it and collapse). There's also an issue of training dynamics. We don't know exactly how they trained their models, so it's impossible for us to jerry rig our training runs in a similar way. And if you don't match the original training dynamics when finetuning it also negatively affects the model. So you might ask here - what if we just train the model for a really long time - will it be able to recover? And the answer is - yes, but at this point the most of the original model will essentially be overwritten. People actually did this for Flux Schnell, but you need way more resources to pull it off and the results can be disappointing: https://huggingface.co/lodestones/Chroma https://huggingface.co/lodestones/Chroma
        magicalhippo 10mo ago
        Thanks for the extended reply, very illuminating. So the core issue is how they distilled it, ie that they "baked in the offset" so to speak. I did try Chroma and I was quite disappointed, what I got out looked nowhere near as good as what was advertised. Now I have a better understanding why.
CuriouslyC 10mo ago
I don't think that's fair. SDXL is crap at composition. It's really good with LoRAs to stylize/inpaint though.

deleted 10mo ago

[deleted]