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@@ -94,3 +94,4 @@ LCM-2.5D/dreamshaper_8LCM.preview.png filter=lfs diff=lfs merge=lfs -text
 inpainting/dreamshaper_8Inpainting.preview.png filter=lfs diff=lfs merge=lfs -text
 LCM-2D/aamAnyloraAnimeMixAnime_v1LCM.preview.png filter=lfs diff=lfs merge=lfs -text
 SDXL[[:space:]]Turbo-2D/aamXLAnimeMix_v10HalfturboEulera.preview.png filter=lfs diff=lfs merge=lfs -text
+2D/hyperfusionFinetune1_v8.preview.png filter=lfs diff=lfs merge=lfs -text

2D/bubbleHentai_v30.civitai.info

@@ -0,0 +1,698 @@
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+    "modelId": 213243,
+    "name": "v3.0",
+    "createdAt": "2024-02-10T04:32:44.902Z",
+    "updatedAt": "2024-02-10T04:40:23.416Z",
+    "status": "Published",
+    "publishedAt": "2024-02-10T04:40:23.415Z",
+    "trainedWords": [],
+    "trainingStatus": null,
+    "trainingDetails": null,
+    "baseModel": "SD 1.5",
+    "baseModelType": "Standard",
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+    "description": null,
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+        "name": "Bubble Hentai",
+        "type": "Checkpoint",
+        "nsfw": true,
+        "poi": false,
+        "description": "<!--\nV2: I was able to pull out more detail in my testing , particularly in hair, and with the VAE I use (color101) some more vibrant color. I still recommend using \"medium breasts\" or \"large breasts\" in the positive prompt to avoid 'chibi' nudity.\n\nMy aim here was to land somewhere between cute and sexy, chibi and hentai, in a modern/polished version of the hentai style of the 90s used in series like Lunatic Night, Rei Rei, and F3, that were drawn in an older, more rounded, what I think of as a \"bubbly\" style.\n\nI think it came out pretty well with a solid hentai capability, though it occasionally does still lean farther toward chibi than I would prefer, that's what future versions are for.\n\nPreview Images were made with some variation of these settings:\nSampler: I mostly use Euler, DPMs work fine too\nSteps: 20-40\nhires steps: ~20 (I use a mix of hires fix and Ultimate SD upscale)\ndenoise: .3-.5\nClip Skip: 2 (1 also works fine, but I prefer 2)\n\nusual positive prompt: masterpiece, best quality, etc.\nnegative prompt: worst quality, normal quality, low quality\n\nI would also recommend a breast tag, medium/large/huge/etc. as the style leans small anyway. chibi in the negative may help some with that as well.\n\nBy request, I've uploaded to tensor.art as well here for users who prefer their on-site generation.\n-->",
+        "tags": [
+            "anime",
+            "sexy",
+            "style",
+            "cute",
+            "hentai"
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+        "allowNoCredit": true,
+        "allowCommercialUse": "Rent",
+        "allowDerivatives": true,
+        "allowDifferentLicense": false
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+                "Hires upscale": "2",
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+    "modelId": 186849,
+    "name": "v8",
+    "createdAt": "2024-02-09T18:38:37.528Z",
+    "updatedAt": "2024-02-09T20:07:34.619Z",
+    "status": "Published",
+    "publishedAt": "2024-02-09T20:07:34.617Z",
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+    "trainingDetails": null,
+    "baseModel": "SD 1.5",
+    "baseModelType": "Standard",
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+    "description": "<!--\nIncreased image count to 1.4 million. \n\nIncluded artist tags for better styling choices, search for \"(artist)\" in the tags csv.\n\nThis version was trained on SD 1.5, so there is no NovelAI influence in this checkpoint unlike previous versions.\n\nMore image classifiers trained, and existing classifiers improved (list of classified tags under Training Data section)\n\nTraining Notes:\n\n* ~1401k images\n\n* LR 3e-6\n\n* TE_LR 2e-6\n\n* batch 8\n\n* GA 32\n\n* default Adam optimizer\n\n* scheduler: linear\n\n* base model SD1.5\n\n* No custom VAE, and none needed for inference unless you prefer one\n\n* flip aug\n\n* clip skip 2\n\n* 375 token length\n\n* bucketing at 768 max 1024\n\n* bucket resolution steps 32 for more buckets\n\n* tag drop chance 0.1\n\n* tag shuffling\n\n* --min_snr_gamma 4\n\n* --ip_noise_gamma 0.02 (lower than v7)\n\n* --zero_terninal_snr\n\n* custom code to drop out 75% of tags 5% of the time to hopefully improve short tag length results\n\n* about 70 days training time (pray for my GPU)\n-->",
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+        "rating": 0
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+        "name": "hyperfusion finetune 1.4m images",
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+        "poi": false,
+        "description": "<!--\nThis checkpoint was trained on 1.4m images of normal to hyper sized anime characters. It focus mainly on breasts/ass/belly/thighs, but now handles more general tag topics as well. It's about 60% anime and 40% furry images as of v8. See change log article below for more version details.\n\n\n\nThe OG hyperfusion LoRA can be found here https://civitai.com/models/16928\nAlso a back up HuggingFace link for these models\n\n\n\nrecommendations for v8:\n\nsampler: Anything that is not a Karras sampler.  Don't use Karras! Training with --zero_terninal_snr makes that sampler problematic.\n\nLora/TI: If you are using LoRA's/TI's trained on NovelAI based models, they might do more harm than good. 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So including more will increase your chances of getting what you want.\n\n* Using the negative \"3d\" does a good job of making the image more anime like if it starts veering too much into a rendered model look.\n\n* Ass related tags have a strong preference for back shots, try a low strength ControlNet pose to correct this, or try one or more of these in the negatives \"ass focus, from behind, looking back\". The new \"ass visible from front\" tag can help too.\n\n* ...more tips in tag docs\n\n\n\nExtra\n\n2023/11/02: Next up I've actually managed to grow the dataset to 1 million images! But training it is going to take a while, so expect a long break between updates like usual. I still want to try this dataset on SDXL, but I think it will take 3-4 months to train at this scale :( So who knows.\n\n\nThis model took me months of failures and plenty of lessons learned (hence v7)! I would eventually like to train a few more image classifiers to improve certain tags, but all future dreams for now.\n\nAs usual, I have no intention of monetizing any of my models. Enjoy the thickness!\n\n\n\n\n\n\n\nTraining Hurdles-Tagging-\n\nThe key to tagging a 500k dataset is to automate it all. Start with the wd-tagger (or similar danbooru tagger) to append some common tags on top of the tags scraped with the images from their source site. Then I trained a handful of image classifiers like breast size, breasts shape, innie/outie navel, directionality, motion lines, etc..., and let those do the tagging for you. Finally convert similar tags into one single tag as described in the tag docs. This helps with low count tags especially.\n\nIf only there there were a r-34 or e621 style tagger available.\n\n\n\nI used this to train my image classifiers\nhttps://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification\n\nIdeally I should train a multi class-per-image classifier like the Danbooru tagger, but for now these single class-per-image classifiers work well enough.\n\n\n\n\n\n-Poor Results-\n\nFor a long time I was plagued with sub par results. I suspected maybe the data was just too low quality, but in the end it just ended up being poorly tagged images. Sites like r-34 tend to have too many tags describing an image like \"big breasts, huge breasts, hyper breasts\" all on the same image. This is not great for a model where you want specific sizes. Using the classifiers I mentioned above I limited each image to a single size tag for each body part, and the results were night and day.\n\n2023/08/13 Coming back to this after even more experience in labeling/training, I still agree with the above statement. As I tag more and more images, the model becomes more reliable with prompts. You can see this clearly with the new bottomheavy, topheavy, bellyheavy tags. It makes it easier to generate specific body types, and helps the model understand what you want from your prompt. I didn't have to add additional images to make these tags work. I just improved tagging.\n\n\n\n-Tag Bleeding-\n\nAn example of tag bleeding is using the tag \"gigantic breasts\", but you end up with everything being gigantic, breasts, ass, thighs. It's been an annoying problem, and the only (partial) solution that I have found is to segment the size tags for each body part. Like using \"huge\", vs \"size 200\" (which is not used). This way they don't share the same \"huge\" size tag.\n\nI trained a model earlier with this tagging scheme and it did work a little better, but there was still some tag bleeding. I think this is because you often have images where the character has both large breasts and a large ass in the same image, so both concepts end up bleeding into each other regardless.\n\nThe reason I chose not to segment the size tags was because it would make prompting more difficult since it deviates too much from the danbooru/r-34 tagging schemes.\n\n\n\nI wonder if tag bleeding is just a result of under training the model?\n\n\n\n-Testing-\n\nIn order to determine if a new model is better than the last, it's important to have some standard prompts that you can compare with. x/y plot is great for this. Just keep in mind that the seeds between models will be totally different, and you likely need to compare dozens of images at a time and not 1 to 1. It's also important to compare new models against the base model output to make sure what you are training is actually having an overall positive effect compared to the original model.\n\n2023/08/13 The hardest part about testing is trying to determine when you have overcooked your text encoder. At some point the models ability to interpret the prompt starts to degrade when training the text encoder at a high enough LR. I've seen it happen with smaller and larger models alike. Maybe I should stop training at some epoc and re-start without TE enabled. I've tried training without the text encoder many times, but the results are always sub par. Concepts that are foreign to the base model, are so much better understood with TE enabled in training.\n\n\n\n\n\n-Software/Hardware-\n\nThe training was all done on a 3090 in an Ubuntu docker instance. The software was Kohya's trainer using the LoRA network with conv_dim and lots of patience.\n-->",
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2D/hyperfusionFinetune1_v8.safetensors

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