<p></p><p></p><hr /><p>If anyone wants to collaborate on further training who has the resources, please contact me. I have had a great deal of success in improving prompt adherence and I suspect this can be massively grown with a solid community effort.</p><hr /><p>Carefully examine the weights used to know how to mess with this LoRa. Think of it like adjusting the fooocu on a lens that you are looking through. Every prompt and Checkpoint combination will have different needs, but ultimately, most of them can be dialed in such that adherence begins to work within a certain delimiter where it wasn't working previously.</p><p></p><p>I suppose I will have to do a video on the "Why" behind this soon as my adhd and time constraints make writing it, as I want to, beyond my current capacity. But a video, I probably can do, although it will be... chaotic.</p><hr /><p></p><hr /><p></p><hr /><p>This model is based on work I did on my "Unsettling" Lora. It uses some of the images generated there, along with subsequent images made using, again, the LoRa progeny of those, as well as the techniques I experimented with.</p><p></p><p>Basically, the goal of this LoRa is to "semantically shift" SDXL such that terms that have a set meaning are entirely changed in an internally consistent manner. I used a technique to do this partially in the Unsettling LoRa, although it was overtrained, and became intrigued by the idea that "good" prompts remain "good," albeit on a different axis, even if the internal understanding of them "shifts" within a given model. In other words: a unique and interesting prompt can create unique and interesting images in multiple new and unique themes if you play with the brain of the model in a directed way.</p><p></p><p>How did I do this?</p><p></p><p>I found areas of overtraining within SDXL and targeted them. Mona Lisa, Pillars of Creation, etc, and I redirected them to new images. As I suspected, this had ripple effects in the way the entire model perceives the concepts connected to the images modified, and these effects are quite substantial.</p><p></p><p>I hope you all enjoy!</p>
Public
SDXL 1.0
<p>This is the more fleshed out version, however there was a single abberant image in the dataset which occasionally can make it obsessed with nuclear apocalypse, and as such I included 0.4(a) as well.<br /><br />Additionally, its my personal philosophy that, if you have multiple loras that go after the same thing, then using as many as possible at low weights is more likely to "average out" overfitting, as opposed to trying to get rid of it by cleverness. Sometimes, two gentle hands gives better results than one strong one.</p>
https://civitai.com/api/download/models/485909
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2024-05-04T20:02:10.221Z
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Astigmatism 0.4b
2024-05-04T20:36:28.102Z
Public
SDXL 1.0
<p>An overall improvement, again, without specifically targeting adherence. All of the improvements in adherence are the direct result of improvements targeting overtraining and redirecting those overtrained areas semantically.</p>
https://civitai.com/api/download/models/485806
https://civitai.com/api/download/models/485806
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Astigmatism 0.4a
2024-05-04T18:23:22.182Z
Public
SDXL 1.0
<p>Slightly expanded the base with some unintended consequences. However, it is overall far less overtrained.<br /><br />In any case, I recommend using, especially at the start when "dialing in" at a weak setting.</p>
https://civitai.com/api/download/models/483315
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2024-05-03T15:11:33.141Z
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Astigmatism +0.3
2024-05-03T15:12:35.660Z
Public
SDXL 1.0
<p>So this is meant to be used inverted. Positive use is also possible with interesting results, mostly in that it will often create even greater prompt adherence, at the cost of potential creativity.<br /><br />In general, we're still at a pretty primitive stage: both of these 0.2 are trained with less than 60 images. Yet, their capabilities for generating interesting imagery with very little overtraining makes them quite interesting, in my opinion, and I plan to continue to grow out the process to see if we can hit a "generalization" point with diminishing returns.<br /><br />In any case, using this as a negative broadly improves the creative diversity of many prompts. It does somewhat decrease prompt adherence, at least it seems likely to me, but the increased diversity directly relates to its effectiveness in combatting overtraining issues within the model, and as such I suspect it has improved composition, in general, despite its "alternative" adherence.<br /><br />Note that, depending on the model, you may need to use very odd CFGs, especially if combined with Astigmatism 0.2 postivie.</p>
https://civitai.com/api/download/models/482152
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Astigmatism -0.2
2024-05-02T23:58:48.432Z
Public
SDXL 1.0
<p>This one completely changes the data set and moves from the initial experiment to a more directed effect. So, for example, whereas "a mona lisa Kudrow" would just produce images of the Mona Lisa (which is overtrained in base SDXL), with this LoRa, you actually get Lisa Kudrow/Mona Lisa hybrid. It's worth noting that this is emergent behavior ie I did NOT train against a mona lisa prompt.<br /><br />In fact, I trained against 5 prompts, focusing on the MOST overtrained images I know of, and then trying to associate them with images that actually, semantically, and diversly, represent the meaning of that overtrained phrase. The side effect is that the model appears to, in many cases, be broadly more intelligent.</p>
https://civitai.com/api/download/models/475225
https://civitai.com/api/download/models/475225
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2024-04-29T14:01:55.598Z
Model
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Astigmatism 0.2
2024-04-29T14:18:28.725Z
Public
SDXL 1.0
<p>This is a version of my "That Special Face" LoRa that has been further refined using the techniques outlined here, while retaining flexibility. Trigger it with basically any art or art style of some heaviness, and/or reference to face.</p>
https://civitai.com/api/download/models/397407
https://civitai.com/api/download/models/397407
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SafeTensor
That_Special_Face_2-000003.safetensors
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Success
2024-03-17T21:32:54.269Z
Model
Success
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That Special Face
2024-03-17T21:36:00.375Z
Public
SDXL 1.0
<p>This is my first attempt at using my method of diversifying overtrained areas in the core SDXL model to try to coax out more "intelligence" in the model.</p><p>This obviously is challenging to do with LoRas, and a checkpoint fine-tune would be a LOT more effective, but mostly I'm just testing out the idea and seeing what can happen with this.</p><p><br />This first LoRa is just 45 images, and the correction targets only 10 specific overtrained areas, with each prompt getting a few images to diversify its output:</p><p>Pillars of Creation</p><p>The Birth of Venus</p><p>Flaming June</p><p>Irises in Monet's Garden</p><p>A Sunday Afternoon on the Island of La Grande Jatte</p><p>Girl with a Pearl Earring</p><p>The Mona Lisa</p><p>Abbey Road</p><p>Liberty Leading the People</p><p>The Scream</p><p>Note that this one wasn't semantically perfect, or rather, the correction wasn't as strict as I'd like to apply in the future if I were to actually see what I can do with this method in terms of improving prompt understanding. It's my thought that these overtrained images directly affect the semantic understanding of the model, which can be partially corrected for.</p><p>In this case, the images included tried to keep elements of the semantic meaning of the titles above, but in some cases this may not have been as strict or obvious as I'd like in order to guide the model the way I want to.</p>
https://civitai.com/api/download/models/395266
https://civitai.com/api/download/models/395266
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SafeTensor
Semantic_Shift_-_Targeted_Treatment-000002.safetensors
No Pickle imports
Success
2024-03-16T16:41:00.919Z
Model
Success
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Astigmatic Correction 0.1
2024-03-16T16:47:43.073Z
Public
SDXL 1.0
<p>This is the same model as the 2.0 version, but baked for an epoch longer. All 8 epochs past this one are way overfitted. This one is also overfitted in some particular scenarios, but it makes it a bit fun and exciting, as there are some unexpected "demons" waiting inside that can pop out arbitrarily and with powerful and unique effect on the overall composition.</p>
https://civitai.com/api/download/models/388803
https://civitai.com/api/download/models/388803
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SafeTensor
Unsettling_2.0-000002.safetensors
No Pickle imports
Success
2024-03-12T21:20:54.730Z
Model
Success
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Unsettling 2.1 - Baked
2024-03-12T21:35:33.957Z
Public
SDXL 1.0
https://civitai.com/api/download/models/388388
https://civitai.com/api/download/models/388388
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SafeTensor
Unsettling_2.0-000001.safetensors
No Pickle imports
Success
2024-03-12T16:45:57.291Z
Model
Success
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Unsettling 2.0
2024-03-12T16:57:31.429Z
Semantic Shift
style