Version: v1.1
Input
Output
This example was created by evevalentine2017
Finished in 62.7 seconds
Setting up the model...
Processing...
Loading VAE weight: models/VAE/blessed2_fp16.safetensors
Full prompt: semi-realistic, 2.5D, detailed, flat colors only, illustration, line-art, watercolor:0.8, best quality, detailed background, 18yo 1girl, Jinx cosplay, outdoors, light-blue hair, twin braids, tattoos, event pass,
in convention center at cosplay event, side-lighting, dramatic lighting, deep shadows, realistic, volumetric light, glossy skin, shiny metal, <lora:FaceBeauty_qinglong_V3:1>, <lora:add_detail:-0.1>, <lora:add_brightness:0.4>, <lora:contrast_slider_v10:-1.15>, <lora:add_saturation:-0.47>
Full negative prompt: nsfw, nipples, topless, low quality, lowres, worst quality, normal quality, earth, land, oversized hairstyle, bad-image-v2-39000, By bad artist -neg, verybadimagenegative_v1.3
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Decoding latents in cuda:0...
done in 0.98s
Move latents to cpu...
done in 0.02s
0: 640x640 1 face, 7.8ms
Speed: 3.6ms preprocess, 7.8ms inference, 32.3ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.01s
0: 640x640 1 face, 7.3ms
Speed: 2.8ms preprocess, 7.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.0s
0: 640x640 1 face, 7.1ms
Speed: 2.7ms preprocess, 7.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.0s
0: 640x640 2 faces, 7.4ms
Speed: 3.0ms preprocess, 7.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.0s
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Decoding latents in cuda:0...
===========================================
A tensor with all NaNs was produced in VAE.
Converted VAE into 32-bit float and retry.
===========================================
done in 0.45s
Move latents to cpu...
done in 0.22s
Uploading outputs...
Finished.
This example was created by evevalentine2017
Finished in 62.7 seconds
Setting up the model...
Processing...
Loading VAE weight: models/VAE/blessed2_fp16.safetensors
Full prompt: semi-realistic, 2.5D, detailed, flat colors only, illustration, line-art, watercolor:0.8, best quality, detailed background, 18yo 1girl, Jinx cosplay, outdoors, light-blue hair, twin braids, tattoos, event pass,
in convention center at cosplay event, side-lighting, dramatic lighting, deep shadows, realistic, volumetric light, glossy skin, shiny metal, <lora:FaceBeauty_qinglong_V3:1>, <lora:add_detail:-0.1>, <lora:add_brightness:0.4>, <lora:contrast_slider_v10:-1.15>, <lora:add_saturation:-0.47>
Full negative prompt: nsfw, nipples, topless, low quality, lowres, worst quality, normal quality, earth, land, oversized hairstyle, bad-image-v2-39000, By bad artist -neg, verybadimagenegative_v1.3
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Decoding latents in cuda:0...
done in 0.98s
Move latents to cpu...
done in 0.02s
0: 640x640 1 face, 7.8ms
Speed: 3.6ms preprocess, 7.8ms inference, 32.3ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.01s
0: 640x640 1 face, 7.3ms
Speed: 2.8ms preprocess, 7.3ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.0s
0: 640x640 1 face, 7.1ms
Speed: 2.7ms preprocess, 7.1ms inference, 1.6ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
done in 0.24s
Move latents to cpu...
done in 0.0s
0: 640x640 2 faces, 7.4ms
Speed: 3.0ms preprocess, 7.4ms inference, 1.9ms postprocess per image at shape (1, 3, 640, 640)
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Decoding latents in cuda:0...
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Move latents to cpu...
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Decoding latents in cuda:0...
===========================================
A tensor with all NaNs was produced in VAE.
Converted VAE into 32-bit float and retry.
===========================================
done in 0.45s
Move latents to cpu...
done in 0.22s
Uploading outputs...
Finished.