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-    "activation text": "",
-    "description": "*** Its a Tool - LORA ! ***...higher CFG(10-15) images with a plus-lora are not so overexposed or have that cartoon style.same meaning in the other way, low CFG(1-3) images mostly very real but also very boring, so with negativ-lora weight you can \"pimp up\" the moody....in short words let me explain...It seems that this is a kind of CFG slider (but more), for exampleCFG ~ 2 -> loraweight:-0.5 (in this combiniation you get good images)CFG ~ 12 -> loraweight: 0.5 (in this combiniation you get good images)for DPM Sampler...for Euler_a it seems some kind of turboCFG~4Steps ~10loraweight: -0.4 to -0.8maybe its useful for turbo/lcm models to have a smoother cfg/slider ?!?for abstract art -> minus is more real, plus is more abstract/fantasyfor face portraits, lets say you have a prompt:eg\"portrait photo of a 20yo messy blond haired woman. wears a offices suite.\"cfg 2 - hair ist not that messy and borring surroundingcgf 5 - hair is messycgf 12 - the hair are more messy, but somehow overexposed/cartoonon cgf 2: you can use that lora with a weight arround -0.5 to get a more interesting image with very messy hair ;)on cgf 12: you can use it with weight arround 0.5 to get mroe real images.dont blame me its a tool, maybe you can play with it and tell me more ;):*",
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--- a/Lora/sd_xl_dpo_lora_v1.json
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@@ -1,8 +1,7 @@
 {
-    "sha256": "C100EC5708865A649C68912CE0E541FC69CB1973FE6543310B9B81A42E15ADA3",
+    "activation text": "",
+    "sd version": "SDXL",
     "modelId": 242825,
     "modelVersionId": 273996,
-    "activation text": "",
-    "description": "What is DPO?DPO is Direct Preference Optimization, the name given to the process whereby a diffusion model is finetuned based on human-chosen images. Meihua Dang et. al. have trained Stable Diffusion 1.5 and Stable Diffusion XL using this method and the Pick-a-Pic v2 Dataset, which can be found at https://huggingface.co/datasets/yuvalkirstain/pickapic_v2 https://huggingface.co/datasets/yuvalkirstain/pickapic_v2, and wrote a paper about it at https://huggingface.co/papers/2311.12908 https://huggingface.co/papers/2311.12908.What does it Do?The trained DPO models have been observed to produce higher quality images than their untuned counterparts, with a significant emphasis on the adherence of the model to your prompt. These LoRA can bring that prompt adherence to other fine-tuned Stable Diffusion models.Who Trained This?These LoRA are based on the works of Meihua Dang (https://huggingface.co/mhdang https://huggingface.co/mhdang) athttps://huggingface.co/mhdang/dpo-sdxl-text2image-v1 https://huggingface.co/mhdang/dpo-sdxl-text2image-v1 and https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1 https://huggingface.co/mhdang/dpo-sd1.5-text2image-v1, licensed under OpenRail++.How were these LoRA Made?They were created using Kohya SS by extracting them from other OpenRail++ licensed checkpoints on CivitAI and HuggingFace.1.5: https://civitai.com/models/240850/sd15-direct-preference-optimization-dpo https://civitai.com/models/240850/sd15-direct-preference-optimization-dpo extracted from https://huggingface.co/fp16-guy/Stable-Diffusion-v1-5_fp16_cleaned/blob/main/sd_1.5.safetensors https://huggingface.co/fp16-guy/Stable-Diffusion-v1-5_fp16_cleaned/blob/main/sd_1.5.safetensors.XL: https://civitai.com/models/238319/sd-xl-dpo-finetune-direct-preference-optimization https://civitai.com/models/238319/sd-xl-dpo-finetune-direct-preference-optimization extracted from https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensorsThese are also hosted on HuggingFace at https://huggingface.co/benjamin-paine/sd-dpo-offsets/ https://huggingface.co/benjamin-paine/sd-dpo-offsets/",
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 }
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