xlmrblng15-1300.safetensors

SafeTensor

marblingtixl_v10_trainingData.zip

82 MB · Other

<marbling-xl>

31 Jan 2024 v2.0 isn’t a better version of MarblingTIXL. Just different. v1.0 still works fine. With the changes in kohya it turns out the way I made v1 of this TI no longer works, or at least doesn’t produce anything very useful. Thanks to for letting me know about this. I still think there’s great potential in SDXL embeddings so I’ve done a fresh kohya_ss install (v22.6.0 at time of writing) and worked my way through various parameters/settings until I found a combo that makes a close relative of the original MarblingTIXL. In case anyone’s interested in SDXL TIs (and I know there are at least 2 of you out there!), I’ve included my training data and kohya_ss config JSON. Possibly some notes as well if I can think of something useful. On the upside, this TI trained faster... on the downside it’s not as consistent as the older TI. Or maybe I haven’t played with it enough. Who can tell out here on the bleeding edge?! If anyone’s got any questions, observations, opinions or wisdom to share please stick a comment below. There’s doesn’t seem to be much hard info out there about how to create TI styles at the moment... I’ve read/watched lots of contradictory viewpoints. It can be done though, and I think there’s scope for better TIs than I’ve managed so far. Competition for LoRAs? Nope, not really - LoRAs add something to a checkpoint whereas TIs leverage what’s already in the checkpoint. If I’ve understood it right, TIs let you reach areas in a checkpoint’s possibility space that it would be difficult to reach consistently. So TIs and LoRAs are different things for different purposes... that you can use together. So everyone is happy :-) There are technical papers around (covering what a TI is, how you should train one, stuff about text encoders, etc) but I’m usually out my depth by half-way through the first page :-( As far as I can tell, kohya_ss is only training the first TE (text encoder) in SDXL. That’s the one from SD v1.x that should work in auto1111 SDXL generation but doesn’t. (Some people have reported that SD v1.x TIs do work in Comfy, but the experience seems to be variable.) As far as I can tell the second TE isn’t being trained in kohya_ss (it’s the one from SD v2.x). Or maybe it’s a duplicate of TE1? I had a go with OneTrainer (which has options for both TEs) but didn’t have any success with the few runs I tried, so I’m sticking with kohya_ss for now. For reference, I’m using an RTX-3060 with 12GB on a reasonable PC. Current kohya_ss runs just overflow the 12GB (+ another 6GB if I’m generating samples) so it’s heavier on resources than doing a LoRA. I thought TIs would need less (or the same) resources so I’m a bit surprised. Perhaps there’s no perceived need to optimise for TIs? Yet :-) The TI here was trained on: sd_xl_base_1.0_0.9vae.safetensors The showcase images were generated using: crystalClearXL_ccxl.safetensors [0b76532e03] i.e. a TI trained on vanilla base should work with other checkpoints. Images are generated in a1111 v1.7.0 and I’ve used Hires.fix but no other adjusters. The additional gallery below shows without/with pairs so you can see how the TI affects some selected prompts. Label “xlmrblnh36-500” means without, label “xlmrblng36-500” means with. I’ve done it that way to keep the two prompts as similar as possible. If you’re interested, the Training Data zip contains the all the saved TIs at 25-step (*4 gradient accumulation = 100 normal steps) intervals. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - NOTE: There is a problem with SDXL in the current version of automatic1111’s webui (v1.6.0). If you use a refiner checkpoint, webui forgets all your embeddings until you load a different checkpoint and then reload your original checkpoint (or restart webui). I have raised the issue with the developers: and it has been confirmed as a bug. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ***SUMMARY*** This embedding will apply a surreal/fantasy aesthetic inspired by vintage marbled paper patterns. The effect varies from low to extreme depending on how “close” your prompt already is to this aesthetic. The training for this TI did not include any artist works or tags. Copy the generation data from one of the showcase images and adjust it to taste, or start with a prompt like this that should give a decent result with any seed: award-winning Art Nouveau xlmrblng15-1300, analog realistic colour photo of a Japanese mermaid sitting on a rock in the midst of crashing waves, very detailed checkpoint: crystalClearXL_ccxl.safetensors [0b76532e03] sampler: DPM++ 2M Karras steps: 40 CFG: 7 height=width=1024 and then vary the terms as you please. Try to keep between 3 and 5 words before “xlmrblng15-1300”. The simplest prompts worth trying are this sort of thing: cybernetic nun, xlmrblng15-1300 fantasy winter landscape, xlmrblng15-1300 but generally you’ll need more words to get interesting results. After lots of experimentation I found I was getting my best results with prompts between 30 and 45 tokens, with no negative prompts. I have provided some before/after image pairs in the extra galleries below. xlmrblnh15 = without this TI xlmrblng15 = with this TI As you’ll see, this TI does more than simply adding marbled paper patterns :-) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ***MORE DETAIL & TRAINING INFO*** This is a TI (textual inversion) embedding that adjusts your image generations by adding marbled paper patterns, or adjusting things towards marbled paper patterns depending on your prompts. Because of the way the SDXL system works, the effect with longer/complex prompts will often be structural rather than simplistic. It’s the SDXL successor of my MarblingTI for SD v1.5: Because of all the changes in SDXL I had quite a lot of false starts (20+), but I think this new TI is more useful than the old one... at least for the surreal/illustrative stuf

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