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H-角色相关/体态引导/hyperfusion_279k_64dim-LoCon-v6.civitai.info

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-{
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-    "updatedAt": "2023-08-13T19:53:28.848Z",
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-    "description": "<p>Added ~60k more images</p><p>Lots more image classifiers trained, and existing classifiers improved</p><p>Training Notes:</p><ul><li><p>~300k images from Danbooru, r-34, e621 etc...</p></li><li><p>LR 2e-4</p></li><li><p>TE_LR 5e-5</p></li><li><p>batch 4</p></li><li><p>GA 32</p></li><li><p>dim 65 (resized kohya lora, with sv_ratio=10, max dim 64)</p></li><li><p>conv_dim 32</p></li><li><p>alpha 32</p></li><li><p>conv_alpha 32</p></li><li><p>scheduler: cosine_with_restarts, 33 restarts</p></li><li><p>base model Av3</p></li><li><p>No --vae</p></li><li><p>flip aug</p></li><li><p>clip skip 2</p></li><li><p>225 token length</p></li><li><p>bucketing at 1024 (I should have stuck with max_bucket_size=768 for reduced training time)</p></li><li><p>tag drop chance 0.1</p></li><li><p>network_dropout 0.33</p></li><li><p>scale_weight_norms 1</p></li><li><p>tag shuffling</p></li><li><p>50,000 unique tags, but about 500 frequently occurring ones (see attached training data for tag list)</p></li><li><p>about 14 days training time (slower than last because of bucket size choice)</p></li></ul>",
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-    "model": {
-        "name": "hyperfusion 280k images",
-        "type": "LORA",
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-        "description": "<p>This LoRA model was trained on <s>100k</s> 280k images of normal to hyper sized anime characters. It focus mainly on breasts/ass/belly/thighs but is trained on over 50,000 unique tags.</p><p></p><p>Note: Might try SDXL next, but no further plans on SD1x at the moment. Time for a break from training<br /></p><h2 id=\"heading-760\">v6 Changelog 2023/08/13</h2><ul><li><p>Increased dataset size by ~60k images</p></li><li><p>less overcooked, oversaturated look. I think this was helped by higher network_dropout=0.33</p></li><li><p>Trained further than v5. You need less prompt weight now to get the same results</p></li><li><p>Better control over body shape with topheavy/bottomheavy/bellyheavy tags</p></li><li><p>New classifiers for: (see tag docs for details)</p><ul><li><p>body_shape / body_weight / motion_lines</p><ul><li><p>should be better at a range of skinny to obese sizes thanks to better labeling</p></li></ul></li></ul></li><li><p>Improved existing classifiers for better tag accuracy</p></li><li><p>More clothing related tags like, topwear, bottomwear, clothed, nipple outline</p></li><li><p>Kohya lora resize with sv_ratio=10 to reduce file size by half with very little concept loss</p></li></ul><p></p><p></p><h2 id=\"heading-21\">v5 ChangeLog</h2><ul><li><p>Doubled the dataset size to 200k+</p></li><li><p>Trained with Kohya's LoCon LoRA, but does not require any additional extensions. &lt;lora:model_name:1&gt; is all you need</p><ul><li><p>There is a bug in A1111 version 1.0 and 1.1 that prevents Kohya's LoCon LoRA from loading. If you are on this version, you can either upgrade to 1.2.x or install the LoRA extension.</p></li></ul></li><li><p>v4 focused on body size, this one focuses more on body shapes. See tag docs for examples. ex :round belly, chubby belly, sagging belly, hanging belly, breasts..., body... etc...</p></li><li><p>Better at belly related tags overall (because a good part of the new data was belly stuff)</p></li><li><p>Better tag accuracy for sizes and shapes, thanks to more accurate image classifiers. Still plenty of room for improvement</p></li><li><p>v5 is trained a bit further than v4 so you may need to reduce the strength for some prompts. It's usually fine at :1 for anime type models, but should be lowered when mixing LoRAs</p></li></ul><h3 id=\"heading-22\"><br />Tag Info (You definitely want to read the tag docs, see :Training Data)</h3><p><br />Because hyperfusion is a conglomeration of multiple tagging schemes, I've included a tag guide \"tag-data\" in the training data download. It will describe the way the tags work (similar to Danbooru tags) and which tags the model knows best. <br />But for the most part you can use a majority of tags from Danbooru, r-43, e621, related to breasts/ass/belly/thighs/nipples/body_shape.</p><p></p><p>The best method I have found for tag exploration is going to danbooru/r-34 and copying the tags from any image you like, and use them as a base. Because there are just too many tags trained into this model to test them all.<br /></p><h3 id=\"heading-23\">Tips</h3><ul><li><p>If you are not getting the results you expect from a tag, find other similar tags and include those as well. I've found that this model tends to spread its knowledge of a tag around to other related tags. So including more will increase your chances of getting what you want.</p></li><li><p>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.</p></li><li><p>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.</p></li><li><p>...more tips in tag docs</p></li></ul><p></p><h3 id=\"heading-24\">Extra</h3><p><br />This model took me months of failures and plenty of lessons learned (hence v4)! If LoCon LoRA picks up in popularity, I may retrain it in the near future. I would eventually like to train a few more image classifiers to improve certain tags, but all future dreams for now.</p><p>As usual I have no intention of monetizing any of my models. Enjoy the thickness!</p><p></p><h3 id=\"heading-25\">Training Hurdles</h3><p><strong>-Tagging-</strong></p><p>The key to tagging a 200k 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, etc..., and let those do the heavy lifting. Finally convert any similar tags into one single tag as described in the tag docs.</p><p>If only there there were a r-34 or e621 style tagger available.</p><p></p><p>I used this to train my image classifiers<br /><a target=\"_blank\" rel=\"ugc\" href=\"https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification\">https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification</a></p><p></p><p><strong>-Poor Results-</strong></p><p>For 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 to specify 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.</p><p></p><p><strong>-Tag Bleeding-</strong></p><p>An 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 \"huge ass\", vs \"size 200 breasts\". This way they don't share the same \"huge\" size tag.</p><p>I 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.</p><p>The 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.</p><p></p><p><strong>-Testing-</strong></p><p>In 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 origin model.</p><p></p><p><strong>-Software/Hardware-</strong></p><p>The training was all done on a 3090 in an Ubuntu docker instance. The software was Kohya's trainer using the LoRA network and lots of patience.</p>",
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-                "prompt": "masterpiece, best quality, solo, self-leaning, (gigantic breasts:1.2), (covered nipples:0.6), 1girl, topheavy, beach, bikini top, from front, strap gap, breasts touching floor, immobile <lora:hyperfusion_279k_64dim-LoCon-v6:1>",
-                "sampler": "DPM++ 2S a",
-                "cfgScale": 10,
-                "clipSkip": 2,
-                "resources": [
-                    {
-                        "hash": "0fc198c490",
-                        "name": "AbyssOrangeMix2_hard",
-                        "type": "model"
-                    }
-                ],
-                "Model hash": "0fc198c490",
-                "Hires upscale": "2",
-                "Hires upscaler": "Lanczos",
-                "negativePrompt": "low quality, monochrome, bad art, bad anatomy, asymmetrical, censored, bad art, glasses, disfigured, no arms, extra arms, extra fingers, multiple arms, more than two legs, blur, blurry deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands, text (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) (text), (((bad anatomy))), (((extra legs))), (((extra arms))), ((ugly face)), (disfigured), ((out of frame)), (((poo art))), ((bad hands)), (bad art), ((deformed)),(mutated) loose fitting cropped face, bad composition, two heads, long neck, missing limbs, deformed arms, deformed hands, extra fingers, (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), fat, (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, (text) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands ((ditigal ilustration)), ((art)) obese, (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored",
-                "Face restoration": "GFPGAN",
-                "Denoising strength": "0.25"
-            }
-        },
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/47ecb55d-bd23-43e7-b1d9-aea3b49e99cb/width=450/2015068.jpeg",
-            "nsfw": "X",
-            "width": 576,
-            "height": 768,
-            "hash": "UNJbQp~pc:9G*HE1M{xHE1I.jG-;~C$+IUR*",
-            "type": "image",
-            "metadata": {
-                "hash": "UNJbQp~pc:9G*HE1M{xHE1I.jG-;~C$+IUR*",
-                "size": 595438,
-                "width": 576,
-                "height": 768
-            },
-            "meta": {
-                "Eta": "0.69",
-                "ENSD": "72",
-                "Size": "576x768",
-                "seed": 3296561998,
-                "Model": "AbyssOrangeMix2_hard",
-                "steps": 25,
-                "hashes": {
-                    "model": "0fc198c490"
-                },
-                "prompt": "masterpiece, best quality, highres, 1girl, (slimthick:1.1), solo female, cowboy shot, solo focus, dolphin shorts, (bubble butt, from front, ass visible from front:1.2), from side (gigantic ass, skinny:1.6), cel shading, soft shadows, (pregnant, big belly, sagging:1.3), (huge ass), grey hair, medium hair, amethyst, business suit, green background, temple <lora:hyperfusion_279k_64dim-LoCon-v6:1>",
-                "skinny": "1.6), cel shading, soft shadows, (pregnant, big belly",
-                "Version": "1.5.1",
-                "sagging": "1.3), (__my-body__), __hair-color__, __hair-length__, __eye-color__, __clothing__, __background__",
-                "sampler": "DPM++ 2S a",
-                "cfgScale": 10,
-                "clipSkip": 2,
-                "resources": [
-                    {
-                        "hash": "0fc198c490",
-                        "name": "AbyssOrangeMix2_hard",
-                        "type": "model"
-                    }
-                ],
-                "(slimthick": "1.1), solo female, cowboy shot, solo focus, dolphin shorts, (bubble butt, from front",
-                "Model hash": "0fc198c490",
-                "\"bad-artist": "2d356134903e",
-                "boring_e621": "8b580f3de3e1",
-                "easynegative": "c74b4e810b03",
-                "negativePrompt": "3d, lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality), normal quality, jpeg artifacts, signature, watermark, username, blurry, monochrome, (young, loli:1.2) bad-artist, bad-image-v2-39000, boring_e621, easynegative, ng_deepnegative_v1_75t",
-                "Wildcard prompt": "\"masterpiece, best quality, highres, 1girl",
-                "Face restoration": "GFPGAN",
-                "bad-image-v2-39000": "5b9281d7c6ad",
-                "ass visible from front": "1.2), from side (gigantic ass",
-                "ng_deepnegative_v1_75t": "54e7e4826d53\"",
-                "hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "1>\"",
-                "\"hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "8304be6cfe2a\""
-            }
-        },
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/c1cadd88-6468-4b0b-9d81-0df7db8f9748/width=450/2015069.jpeg",
-            "nsfw": "None",
-            "width": 640,
-            "height": 768,
-            "hash": "UXJ?q1$m^S9r~Ds:oMo2$+bFnlR%n%s;R%j?",
-            "type": "image",
-            "metadata": {
-                "hash": "UXJ?q1$m^S9r~Ds:oMo2$+bFnlR%n%s;R%j?",
-                "size": 621068,
-                "width": 640,
-                "height": 768
-            },
-            "meta": {
-                "Eta": "0.69",
-                "ENSD": "69",
-                "Size": "640x768",
-                "seed": 158187655,
-                "Model": "AbyssOrangeMix2_hard",
-                "steps": 20,
-                "hashes": {
-                    "model": "0fc198c490"
-                },
-                "prompt": "masterpiece, best quality, highres, 1girl, solo, cel shading, soft shadows, (skinny:1.2), (chubby belly, innie navel, huge ass, ass biger than head:1.05), black hair, very long hair, pink, Bodysuit, purple background, hangar <lora:hyperfusion_279k_64dim-LoCon-v6:1>",
-                "Version": "1.5.1",
-                "sampler": "DPM++ 2S a",
-                "cfgScale": 10,
-                "clipSkip": 2,
-                "resources": [
-                    {
-                        "hash": "0fc198c490",
-                        "name": "AbyssOrangeMix2_hard",
-                        "type": "model"
-                    }
-                ],
-                "Model hash": "0fc198c490",
-                "negativePrompt": "3d, film grain, motion lines, low quality, monochrome, bad art, bad anatomy, asymmetrical, censored, bad art, glasses, disfigured, no arms, extra arms, extra fingers, multiple arms, more than two legs, blur, blurry deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands, text (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) (text), (((bad anatomy))), (((extra legs))), (((extra arms))), ((ugly face)), (disfigured), ((out of frame)), (((poo art))), ((bad hands)), (bad art), ((deformed)),(mutated) loose fitting cropped face, bad composition, two heads, long neck, missing limbs, deformed arms, deformed hands, extra fingers, (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, (text) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands ((ditigal ilustration)), ((art)) (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored, english text, japanese text, (pixel art), motion lines, (young:1.2), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, body out of frame, out of frame, poorly drawn, deformed, disproportionate, blurry, ugly, extra nipples, third nipple, asymmetrical, multiple heads, clothed, genshin",
-                "Face restoration": "GFPGAN",
-                "\"hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000006-": "1acc646cbd2e\""
-            }
-        },
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/3642e7ee-8a54-4625-a70f-8fad40fb999b/width=450/2015070.jpeg",
-            "nsfw": "Mature",
-            "width": 512,
-            "height": 768,
-            "hash": "UVIORx~W?GE1%#bcaKoM~qM{-;s:_3tQVsV@",
-            "type": "image",
-            "metadata": {
-                "hash": "UVIORx~W?GE1%#bcaKoM~qM{-;s:_3tQVsV@",
-                "size": 441212,
-                "width": 512,
-                "height": 768
-            },
-            "meta": {
-                "Eta": "0.69",
-                "ENSD": "72",
-                "Size": "512x768",
-                "seed": 3262062305,
-                "Model": "AbyssOrangeMix2_hard",
-                "steps": 25,
-                "hashes": {
-                    "model": "0fc198c490"
-                },
-                "prompt": "masterpiece, best quality, highres, 1girl, skinny, solo female, cowboy shot, solo focus, looknig back, dolphin shorts, (bubble butt), from side (huge ass,skinny:1.7), cel shading, soft shadows, (huge belly), aqua hair, very long hair, red, Dress, drama layer background, cooling tower <lora:hyperfusion_279k_64dim-LoCon-v6:1.1>",
-                "skinny": "1.7), cel shading, soft shadows, (__my-body__), __hair-color__, __hair-length__, __eye-color__, __clothing__, __background__",
-                "Version": "1.5.1",
-                "sampler": "DPM++ 2S a",
-                "cfgScale": 10,
-                "clipSkip": 2,
-                "resources": [
-                    {
-                        "hash": "0fc198c490",
-                        "name": "AbyssOrangeMix2_hard",
-                        "type": "model"
-                    }
-                ],
-                "Model hash": "0fc198c490",
-                "\"bad-artist": "2d356134903e",
-                "boring_e621": "8b580f3de3e1",
-                "easynegative": "c74b4e810b03",
-                "negativePrompt": "3d, lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality), normal quality, jpeg artifacts, signature, watermark, username, blurry, monochrome, (young, loli:1.1) bad-artist, bad-image-v2-39000, boring_e621, easynegative, ng_deepnegative_v1_75t",
-                "Wildcard prompt": "\"masterpiece, best quality, highres, 1girl, skinny, solo female, cowboy shot, solo focus, looknig back, dolphin shorts, (bubble butt), from side (huge ass",
-                "Face restoration": "GFPGAN",
-                "bad-image-v2-39000": "5b9281d7c6ad",
-                "ng_deepnegative_v1_75t": "54e7e4826d53\"",
-                "hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "1.1>\"",
-                "\"hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "8304be6cfe2a\""
-            }
-        },
-        {
-            "url": "https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f10a5e2e-ca40-4ce6-84cb-c118d2b56752/width=450/2015071.jpeg",
-            "nsfw": "None",
-            "width": 512,
-            "height": 768,
-            "hash": "UNIrNaR5yG_1A2M{NgW=;%V?t1xu9FRkRjNG",
-            "type": "image",
-            "metadata": {
-                "hash": "UNIrNaR5yG_1A2M{NgW=;%V?t1xu9FRkRjNG",
-                "size": 554220,
-                "width": 512,
-                "height": 768
-            },
-            "meta": {
-                "ENSD": "72",
-                "Size": "512x768",
-                "seed": 2770233901,
-                "Model": "AbyssOrangeMix2_hard",
-                "steps": 28,
-                "hashes": {
-                    "model": "0fc198c490"
-                },
-                "prompt": "(masterpiece, best quality, highres, absurdres, ultra detailed:1.1), (goblin, goblin girl:1.1), shortstack, slimthick, (dark areola), green body, short hair, white hair, embarrassed, shy, skin fang, open mouth, wide (hips, thick thighs,:1.4) fat_hips, fat_thighs, swimsuit, gigantic ass, (bottomheavy:1.4), from front, ass visible from front, navel, thigh gap, medium breasts best shadow, best rendering, neon, beach, boulders, ocean, wet, solo, 1girl, pregnant, hyperpreg, straw hat, (huge hat:1.1), floppy ears, sand, (outline, cel shading:1.2), angry, annoyed, (from side:1.1) <lora:hyperfusion_279k_64dim-LoCon-v6:1>",
-                "sampler": "DPM++ 2M Karras",
-                "cfgScale": 10,
-                "clipSkip": 2,
-                "resources": [
-                    {
-                        "hash": "0fc198c490",
-                        "name": "AbyssOrangeMix2_hard",
-                        "type": "model"
-                    }
-                ],
-                "Model hash": "0fc198c490",
-                "negativePrompt": "low quality, monochrome, bad art, bad anatomy, asymmetrical, censored, bad art, glasses, disfigured, no arms, extra arms, extra fingers, multiple arms, more than two legs, blur, blurry deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands, text (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) (text), (((bad anatomy))), (((extra legs))), (((extra arms))), ((ugly face)), (disfigured), ((out of frame)), (((poo art))), ((bad hands)), (bad art), ((deformed)),(mutated) loose fitting cropped face, bad composition, two heads, long neck, missing limbs, deformed arms, deformed hands, extra fingers, (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, (text) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands ((ditigal ilustration)), ((art)) (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored, english text, japanese text, (pixel art), motion lines",
-                "Face restoration": "GFPGAN"
-            }
-        }
-    ],
-    "downloadUrl": "https://civitai.com/api/download/models/140123"
+{
+    "id": 140123,
+    "modelId": 16928,
+    "name": "v6",
+    "createdAt": "2023-08-13T19:34:31.335Z",
+    "updatedAt": "2024-02-21T15:18:30.413Z",
+    "status": "Published",
+    "publishedAt": "2023-11-07T01:53:45.369Z",
+    "trainedWords": [
+        "<size> breasts",
+        "<size>ass",
+        "<size> belly",
+        "topheavy",
+        "bottomheavy",
+        "bellyheavy",
+        "the rest are under \"Training Images\""
+    ],
+    "trainingStatus": null,
+    "trainingDetails": null,
+    "baseModel": "SD 1.4",
+    "baseModelType": null,
+    "earlyAccessEndsAt": null,
+    "earlyAccessConfig": {},
+    "description": "<p>Added ~60k more images</p><p>Lots more image classifiers trained, and existing classifiers improved</p><p>Training Notes:</p><ul><li><p>~300k images from Danbooru, r-34, e621 etc...</p></li><li><p>LR 2e-4</p></li><li><p>TE_LR 5e-5</p></li><li><p>batch 4</p></li><li><p>GA 32</p></li><li><p>dim 64 (resized with kohya, with sv_ratio=10, and max dim 64)</p></li><li><p>conv_dim 32</p></li><li><p>alpha 32</p></li><li><p>conv_alpha 32 (I meant to set this to conv_dim/2, but ohh well)</p></li><li><p>scheduler: cosine_with_restarts, 33 restarts</p></li><li><p>base model NAI</p></li><li><p>No --vae</p></li><li><p>flip aug</p></li><li><p>clip skip 2</p></li><li><p>225 token length</p></li><li><p>bucketing at 1024 max 1024 (I should have stuck with max_bucket_size=768 for reduced training time)</p></li><li><p>tag drop chance 0.1</p></li><li><p>network_dropout 0.33</p></li><li><p>scale_weight_norms 1 (either this or dropout resulted in less blown out results, which allowed for longer training at higher LR)</p></li><li><p>tag shuffling</p></li><li><p>50,000 unique tags, but about 500 frequently occurring ones (see attached training data for tag list)</p></li><li><p>about 14 days training time (slower than last because of bucket size choice)</p></li></ul>",
+    "uploadType": "Created",
+    "air": "urn:air:sd1:lora:civitai:16928@140123",
+    "stats": {
+        "downloadCount": 10981,
+        "ratingCount": 338,
+        "rating": 5,
+        "thumbsUpCount": 648
+    },
+    "model": {
+        "name": "hyperfusion LoRA 550k images",
+        "type": "LORA",
+        "nsfw": true,
+        "poi": false,
+        "description": "<p>This LoRA was trained on 550k 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.</p><p></p><p><strong>**If you're wondering where the v8 LoRA is, read the </strong><a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/articles/3320\"><strong>Changelog Article</strong></a><strong>.**</strong></p><p></p><p>Also a back up <a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/thojm/HyperFusion\">HuggingFace link</a> for these models</p><p></p><p><br /><strong>Recommendations</strong>: <br /><strong>negative (depends on base model, use what works for you)</strong>: <code>lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality), normal quality, jpeg artifacts, signature, watermark, username, blurry, monochrome, [3d], comic, (sketch), transparent background, artist name</code> <br /><strong>cfg</strong>: 9-12<br /><strong>resolution</strong>: 768 to 1024, but prefers to be closer to 768<br /><strong>clip skip</strong>: 2</p><p><strong>sampler</strong>: With v7+ try to avoid Karras samplers. Training with --zero_terninal_snr makes karras samplers problematic, but they still work somewhat.</p><p></p><h2 id=\"heading-760\"><a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/articles/3320\">Changelog Article Link</a></h2><h3 id=\"heading-22\"><br />Tag Info (You definitely want to read the tag docs, see :Training Data)</h3><p><br />Because hyperfusion is a conglomeration of multiple tagging schemes, I've included a tag guide in the training data download section. It will describe the way the tags work (similar to Danbooru tags), which tags the model knows best, and all my custom labeled tags. <br />For the most part you can use a majority of tags from Danbooru, Gelbooru, r-34, e621, related to breasts/ass/belly/thighs/nipples/body_shape.</p><p></p><p>The best method I have found for tag exploration is going to one of the booru sites above and copying the tags from any image you like, and use them as a base. Because there are just too many tags trained into this model to test them all.<br /></p><h3 id=\"heading-23\">Tips</h3><ul><li><p>If you are not getting the results you expect from a tag, find other similar tags and include those as well. I've found that this model tends to spread its knowledge of a tag around to other related tags. So including more will increase your chances of getting what you want.</p></li><li><p>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.</p></li><li><p>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.</p></li><li><p>...more tips in tag docs</p></li></ul><p></p><h3 id=\"heading-24\">Extra</h3><p><br />This 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.</p><p>As usual, I have no intention of monetizing any of my models. Enjoy the thickness!</p><p></p><p></p><hr /><p></p><h3 id=\"heading-25\">Training Hurdles</h3><p><strong>-Tagging-</strong></p><p>The key to tagging a large dataset is to automate it all. I started with the wd-tagger (or similar danbooru tagger) to append some common tags on top of the original tags.  Eventually I added an e621 tagger too, but I generally only tag with a limited set of tags and not the entire tag list (some tags are not accurate enough). Then I trained a handful of image classifiers like breast size, breasts shape, innie/outie navel, directionality, motion lines, and about 20 others..., and let those tag for me.  They not only improve on existing tags, but add completely new concepts to the dataset.  <s>Finally I converted similar tags into one single tag as described in the tag docs</s> (I stopped doing this now.  With 3m images it really doesn't matter as much).  </p><p>Basically any time I find its hard to prompt for a specific thing, I throw together a new classifier, and so far the only ones that don't work well are ones that try to classify small details in the image, like signatures.</p><p>Starting in v9 I will be including ~5% captions along side the tags. These captions are generated with CogVLM.</p><p></p><p>I used this to train my image classifiers<br /><a target=\"_blank\" rel=\"ugc\" href=\"https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification\">https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification</a></p><p>Ideally, 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.</p><p></p><p></p><p><strong>-Poor Results-</strong></p><p>For 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.</p><p><strong>2023/08/13</strong> 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.</p><p></p><p><strong>-Tag Bleeding-</strong></p><p>An 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.</p><p><strong>2024/03/15</strong> After training some larger models, I find bleeding is less of an issue.  It seems the answer is more data.</p><p></p><p><strong>-Testing-</strong></p><p>In 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. </p><p>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 (obvious, but often overlooked).</p><p><strong>2023/08/13</strong> 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. Unfortunately I still don't have a good way to test this other than comparing prompts to older models.  I've tried training without the text encoder many times, but the results are always sub par or too slow to train. Concepts that are foreign to the base model, are so much better understood with TE enabled in training.</p><p></p><p></p><p><strong>-Software/Hardware-</strong></p><p>The training was all done on a 3090 on Ubuntu. The software used is Kohya's trainer, since it currently has the most options to choose from.</p>",
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+                "ng_deepnegative_v1_75t": "54e7e4826d53\"",
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+            "hasMeta": true,
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+        },
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+                "Model": "AbyssOrangeMix2_hard",
+                "steps": 20,
+                "hashes": {
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+                "sampler": "DPM++ 2S a",
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+                "clipSkip": 2,
+                "resources": [
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+                        "name": "AbyssOrangeMix2_hard",
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+                "negativePrompt": "3d, film grain, motion lines, low quality, monochrome, bad art, bad anatomy, asymmetrical, censored, bad art, glasses, disfigured, no arms, extra arms, extra fingers, multiple arms, more than two legs, blur, blurry deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands, text (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) (text), (((bad anatomy))), (((extra legs))), (((extra arms))), ((ugly face)), (disfigured), ((out of frame)), (((poo art))), ((bad hands)), (bad art), ((deformed)),(mutated) loose fitting cropped face, bad composition, two heads, long neck, missing limbs, deformed arms, deformed hands, extra fingers, (((bad anatomy))), (((extra legs))), (((extra arms))), ((poorly_drawn_face)), ((poorly drawn hands)), ((poorly drawn feet)), (disfigured), ((out of frame)), (((long neck))), (big ears), (((poo art))), ((((tiling)))), ((bad hands)), (bad art), ((deformed)),(mutated) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, (text) deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands ((ditigal ilustration)), ((art)) (ugly:1.3), (duplicate:1.3), (morbid), (mutilated), out of frame, extra fingers, mutated hands, (poorly drawn hands), (poorly drawn face), (mutation:1.3), (deformed:1.3), (amputee:1.3), blurry, bad anatomy, bad proportions, (extra limbs), cloned face, (disfigured:1.3), gross proportions, (malformed limbs), (missing arms), (missing legs), (extra arms), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3), lowres, text, error, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, black and white, monochrome, censored, english text, japanese text, (pixel art), motion lines, (young:1.2), lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, body out of frame, out of frame, poorly drawn, deformed, disproportionate, blurry, ugly, extra nipples, third nipple, asymmetrical, multiple heads, clothed, genshin",
+                "Face restoration": "GFPGAN",
+                "\"hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000006-": "1acc646cbd2e\""
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+            "hasMeta": true,
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+            "height": 768,
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+                "hashes": {
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+                "skinny": "1.7), cel shading, soft shadows, (__my-body__), __hair-color__, __hair-length__, __eye-color__, __clothing__, __background__",
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+                "\"bad-artist": "2d356134903e",
+                "boring_e621": "8b580f3de3e1",
+                "easynegative": "c74b4e810b03",
+                "negativePrompt": "3d, lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality), normal quality, jpeg artifacts, signature, watermark, username, blurry, monochrome, (young, loli:1.1) bad-artist, bad-image-v2-39000, boring_e621, easynegative, ng_deepnegative_v1_75t",
+                "Wildcard prompt": "\"masterpiece, best quality, highres, 1girl, skinny, solo female, cowboy shot, solo focus, looknig back, dolphin shorts, (bubble butt), from side (huge ass",
+                "Face restoration": "GFPGAN",
+                "bad-image-v2-39000": "5b9281d7c6ad",
+                "ng_deepnegative_v1_75t": "54e7e4826d53\"",
+                "hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "1.1>\"",
+                "\"hyperfusion_279k_v13-lr2e-4-4b-32ga-64dim-flip-te5e-5-tkn225-c_dim32-a32-t_drp0.1-drp0.33-1024bucket-cosine_with_restarts-wgt_norm1-n-locon-000005-": "8304be6cfe2a\""
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+                "Face restoration": "GFPGAN"
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+            "onSite": false
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+    "downloadUrl": "https://civitai.com/api/download/models/140123",
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 }

H-角色相关/体态引导/hyperfusion_550k_128dim-LoCon_extracted-v7.civitai.info

@@ -1,496 +1,588 @@
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-        "<size> belly",
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-        "bottomheavy",
-        "bellyheavy",
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+    "modelId": 16928,
+    "name": "v7-extracted",
+    "createdAt": "2023-11-03T00:17:31.822Z",
+    "updatedAt": "2024-04-05T02:37:18.507Z",
+    "status": "Published",
+    "publishedAt": "2023-11-07T01:53:45.369Z",
+    "trainedWords": [
+        "<size> breasts",
+        "<size> ass ",
+        "<size> belly",
+        "topheavy",
+        "bottomheavy",
+        "bellyheavy",
+        "the rest are under \"Training Images\""
+    ],
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+    "trainingDetails": null,
+    "baseModel": "SD 1.4",
+    "baseModelType": null,
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+    "description": "<p>This LoCon LoRA was extracted from the <a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/models/186849\">hyperfusion-v7 finetune</a> instead of trained on its own. It does seem to be more consistent than v6.</p><p>Because it is an extracted LoRA I had to keep the dim at a larger size to preserve its concept knowledge, sorry for the larger than usual file size. <br />*I am limited by the technology of my time*</p><p>Training details are under the finetune link above.</p>",
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+        "name": "hyperfusion LoRA 550k images",
+        "type": "LORA",
+        "nsfw": true,
+        "poi": false,
+        "description": "<p>This LoRA was trained on 550k 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.</p><p></p><p><strong>**If you're wondering where the v8 LoRA is, read the </strong><a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/articles/3320\"><strong>Changelog Article</strong></a><strong>.**</strong></p><p></p><p>Also a back up <a target=\"_blank\" rel=\"ugc\" href=\"https://huggingface.co/thojm/HyperFusion\">HuggingFace link</a> for these models</p><p></p><p><br /><strong>Recommendations</strong>: <br /><strong>negative (depends on base model, use what works for you)</strong>: <code>lowres, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, (worst quality, low quality), normal quality, jpeg artifacts, signature, watermark, username, blurry, monochrome, [3d], comic, (sketch), transparent background, artist name</code> <br /><strong>cfg</strong>: 9-12<br /><strong>resolution</strong>: 768 to 1024, but prefers to be closer to 768<br /><strong>clip skip</strong>: 2</p><p><strong>sampler</strong>: With v7+ try to avoid Karras samplers. Training with --zero_terninal_snr makes karras samplers problematic, but they still work somewhat.</p><p></p><h2 id=\"heading-760\"><a target=\"_blank\" rel=\"ugc\" href=\"https://civitai.com/articles/3320\">Changelog Article Link</a></h2><h3 id=\"heading-22\"><br />Tag Info (You definitely want to read the tag docs, see :Training Data)</h3><p><br />Because hyperfusion is a conglomeration of multiple tagging schemes, I've included a tag guide in the training data download section. It will describe the way the tags work (similar to Danbooru tags), which tags the model knows best, and all my custom labeled tags. <br />For the most part you can use a majority of tags from Danbooru, Gelbooru, r-34, e621, related to breasts/ass/belly/thighs/nipples/body_shape.</p><p></p><p>The best method I have found for tag exploration is going to one of the booru sites above and copying the tags from any image you like, and use them as a base. Because there are just too many tags trained into this model to test them all.<br /></p><h3 id=\"heading-23\">Tips</h3><ul><li><p>If you are not getting the results you expect from a tag, find other similar tags and include those as well. I've found that this model tends to spread its knowledge of a tag around to other related tags. So including more will increase your chances of getting what you want.</p></li><li><p>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.</p></li><li><p>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.</p></li><li><p>...more tips in tag docs</p></li></ul><p></p><h3 id=\"heading-24\">Extra</h3><p><br />This 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.</p><p>As usual, I have no intention of monetizing any of my models. Enjoy the thickness!</p><p></p><p></p><hr /><p></p><h3 id=\"heading-25\">Training Hurdles</h3><p><strong>-Tagging-</strong></p><p>The key to tagging a large dataset is to automate it all. I started with the wd-tagger (or similar danbooru tagger) to append some common tags on top of the original tags.  Eventually I added an e621 tagger too, but I generally only tag with a limited set of tags and not the entire tag list (some tags are not accurate enough). Then I trained a handful of image classifiers like breast size, breasts shape, innie/outie navel, directionality, motion lines, and about 20 others..., and let those tag for me.  They not only improve on existing tags, but add completely new concepts to the dataset.  <s>Finally I converted similar tags into one single tag as described in the tag docs</s> (I stopped doing this now.  With 3m images it really doesn't matter as much).  </p><p>Basically any time I find its hard to prompt for a specific thing, I throw together a new classifier, and so far the only ones that don't work well are ones that try to classify small details in the image, like signatures.</p><p>Starting in v9 I will be including ~5% captions along side the tags. These captions are generated with CogVLM.</p><p></p><p>I used this to train my image classifiers<br /><a target=\"_blank\" rel=\"ugc\" href=\"https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification\">https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification</a></p><p>Ideally, 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.</p><p></p><p></p><p><strong>-Poor Results-</strong></p><p>For 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.</p><p><strong>2023/08/13</strong> 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.</p><p></p><p><strong>-Tag Bleeding-</strong></p><p>An 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.</p><p><strong>2024/03/15</strong> After training some larger models, I find bleeding is less of an issue.  It seems the answer is more data.</p><p></p><p><strong>-Testing-</strong></p><p>In 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. </p><p>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 (obvious, but often overlooked).</p><p><strong>2023/08/13</strong> 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. Unfortunately I still don't have a good way to test this other than comparing prompts to older models.  I've tried training without the text encoder many times, but the results are always sub par or too slow to train. Concepts that are foreign to the base model, are so much better understood with TE enabled in training.</p><p></p><p></p><p><strong>-Software/Hardware-</strong></p><p>The training was all done on a 3090 on Ubuntu. The software used is Kohya's trainer, since it currently has the most options to choose from.</p>",
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+                    },
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+                        "hash": "0fc198c490",
+                        "name": "AbyssOrangeMix2_hard",
+                        "type": "model"
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+            },
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+            "hasMeta": true,
+            "onSite": false
+        },
+        {
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+            "nsfwLevel": 4,
+            "width": 768,
+            "height": 512,
+            "hash": "UMK^smXBS_XRLNbtE2Nf00M_-;of4pwcofV?",
+            "type": "image",
+            "metadata": {
+                "hash": "UMK^smXBS_XRLNbtE2Nf00M_-;of4pwcofV?",
+                "size": 505895,
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+                "VAE": "Anything-V3.0-pruned-fp16.vae.pt",
+                "ENSD": "72",
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+                "sampler": "DPM++ 2S a",
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+                "resources": [
+                    {
+                        "name": "hyperfusion_v7_ep12_128dim_64cdim_extracted",
+                        "type": "lora",
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+                    },
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+                        "hash": "0fc198c490",
+                        "name": "AbyssOrangeMix2_hard",
+                        "type": "model"
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+                "Face restoration": "GFPGAN",
+                "\"hyperfusion_v7_ep12_128dim_64cdim_extracted": "eeeb6e525208\""
+            },
+            "availability": "Public",
+            "hasMeta": true,
+            "onSite": false
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+            "type": "image",
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+                "size": 355008,
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+                "Eta": "0.69",
+                "VAE": "Anything-V3.0-pruned-fp16.vae.pt",
+                "ENSD": "72",
+                "Size": "576x768",
+                "seed": 3296561998,
+                "Model": "AbyssOrangeMix2_hard",
+                "steps": 25,
+                "hashes": {
+                    "model": "0fc198c490"
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+                "prompt": "masterpiece, best quality, highres, 1girl, (slimthick:1.1), solo female, cowboy shot, solo focus, dolphin shorts, (bubble butt, from front, ass visible from front:1.2), from side (gigantic ass, skinny:1.6), cel shading, soft shadows, (pregnant, big belly, sagging:1.3), (huge ass), grey hair, medium hair, amethyst, business suit, green background, temple <lora:hyperfusion_v7_ep12_128dim_64cdim_extracted:1.2>",
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+                "sampler": "DPM++ 2S a",
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+                "cfgScale": 10,
+                "clipSkip": 2,
+                "resources": [
+                    {
+                        "name": "hyperfusion_v7_ep12_128dim_64cdim_extracted",
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+                    },
+                    {
+                        "hash": "0fc198c490",
+                        "name": "AbyssOrangeMix2_hard",
+                        "type": "model"
+                    }
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+                "\"hyperfusion_v7_ep12_128dim_64cdim_extracted": "eeeb6e525208\""
+            },
+            "availability": "Public",
+            "hasMeta": true,
+            "onSite": false
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+        {
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+            "nsfwLevel": 2,
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+            "hash": "UVK0?Xxu_4IT~Wt8S1s:%NRjRiR*I.n%nif,",
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+                "VAE": "Anything-V3.0-pruned-fp16.vae.pt",
+                "ENSD": "69",
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+                "steps": 20,
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+                        "name": "hyperfusion_v7_ep12_128dim_64cdim_extracted",
+                        "type": "lora",
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+                    },
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+                        "hash": "0fc198c490",
+                        "name": "AbyssOrangeMix2_hard",
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+                "Face restoration": "GFPGAN",
+                "\"hyperfusion_v7_ep12_128dim_64cdim_extracted": "eeeb6e525208\""
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+            "hasMeta": true,
+            "onSite": false
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+            "hash": "URIh{SrWpM?bALRjERWrz;M{x9jFIUaeM{Rj",
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+                "Version": "1.6.0",
+                "sampler": "DPM++ 2M Karras",
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+                "\"hyperfusion_v7_ep12_128dim_64cdim_extracted": "eeeb6e525208\""
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+                "Version": "1.6.0",
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+                "\"hyperfusion_v7_ep12_128dim_64cdim_extracted": "eeeb6e525208\""
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+    "downloadUrl": "https://civitai.com/api/download/models/210093",
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