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00063-3972587558.webp ADDED
00138-3421449950.webp ADDED
M0nstrcrz_SDXL_v1_32.safetensors ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:3f2ec7cea2bdb281b74ae46e5a5a4725f278db3142f2ad50736c21f7430ffc51
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+ size 228459308
README.md CHANGED
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ base_model: stabilityai/stable-diffusion-xl-base-1.0
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+ tags:
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+ - stable-diffusion
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+ - stable-diffusion-xl
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+ - stable-diffusion-xl-diffusers
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+ - stable-diffusion-diffusers
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+ - text-to-image
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+ - diffusers
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+ - lora
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+ widget:
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+ - text: m0nstrcrz, pumpkin man, driving an orange car, purple clouds, moon, foggy, hazy (cfg = 8, seed = 3421449950)
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+ - text: m0nstrcrz, driving, yellow car, Frankenstein, zombies, bats, red moon, fog, purple clouds (cfg = 8, seed = 3972587558)
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+ inference: true
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+ language:
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+ - en
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  ---
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+
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+ # monster-cars-sdxl-lora
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+
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+ ![](00138-3421449950.webp)
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+
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+ LoRA for SDXL 1.0 Base for generating Monster + Car images. The LoRA is in a `safetensors` format for use in diffusers or in UIs such as A1111.
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+
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+ ## How to use
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+ In A1111, specify the LoRA in the prompt along with a weight \<lora:M0nstrcrz_SDXL_v1_32:1\>, then use the trigger keyword. Further example images with A1111 prompts at (https://civitai.com/models/152956/monster-cars-sdxl)
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+
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+ Example diffusers prompt which you can run in the inference to the right: 'm0nstrcrz, pumpkin man, driving an orange car, purple clouds, moon, foggy, hazy (cfg = 8, seed = 3421449950)'
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+
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+ ## Recommended Weight:
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+ 1.0 (lowering the LoRA weight in A1111 may produce less monstery results)
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+
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+ ## Trigger:
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+ m0nstrcrz
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+
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+ ## Helper:
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+ In general you can throw in characters and cars in different scenes.
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+
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+ ## Notes:
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+ The LoRA seems to work well with other base SDXL models, but I didn't spend much time playing with this. If producing images at 512x512, this model is known as Abstractio 3000, i.e., the images are so awful that it's hilarious.
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+
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+ ## Methodology:
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+ This model was trained on onlyimages at 1024x1024. No regularization images were used. 30 epochs with 14580 overall steps.
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+
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+ ![](00063-3972587558.webp)
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+
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+