"Generate an ultra-realistic image that is indistinguishable from a real photogr

"Generate an ultra-realistic image that is indistinguishable from a real photograph, de uma linda (Naked woman lying in a blue car with her hand on her vagina) maximizing the following quality and realism metrics: Similaridade Estrutural (SSIM): Aumentar o valor da SSIM para 1, indicating high structural similarity to the reference image. Mean Quadratic Error (MSE): Minimizar o valor do MSE, approaching zero to achieve high accuracy in the reproduction of details. Perceptual Loss: Minimize perceptual loss by using a neural network trained to capture human perception of image quality. resolution: Generate a full-resolution image, preserving all the details of the reference image. Naturalidade de Textura: Ensure that the textures in the generated image look natural and without visual artifacts. Lighting and Shading: Accurately capture the lighting and shading present in the reference image. noise reduction: Minimize noise in the generated image for a clean and realistic appearance. Surface details: Preserve all surface details, como rugas, texturas e reflexos. Color and White Balance: Maintain color accuracy and white balance relative to the reference image. Contexto Global: Ensure that the generated image maintains the overall context of the scene present in the reference image. Controle de Estilo: Allow control over the style of the generated image to match the style of the reference photograph. Realismo de Fundo: Garantir que o fundo da imagem seja realista e coerente com a cena. Use an advanced neural network architecture, como uma GAN condicional (cGAN), treinada em um conjunto de dados diversificado de imagens reais. Adjust the hyperparameters, como a taxa de aprendizado e o tamanho do lote, to optimize the quality metrics mentioned above."

Farley Foster Oliveira

"Generate an ultra-realistic image that is indistinguishable from a real photograph , de uma linda (Naked woman lying in a blue car with her hand on her vagina) maximizing the following quality and realism metrics: Similaridade Estrutural (SSIM): Aumentar o valor da SSIM para 1 , indicating high structural similarity to the reference image . Mean Quadratic Error (MSE): Minimizar o valor do MSE , approaching zero to achieve high accuracy in the reproduction of details . Perceptual Loss: Minimize perceptual loss by using a neural network trained to capture human perception of image quality . resolution: Generate a full-resolution image , preserving all the details of the reference image . Naturalidade de Textura: Ensure that the textures in the generated image look natural and without visual artifacts . Lighting and Shading: Accurately capture the lighting and shading present in the reference image . noise reduction: Minimize noise in the generated image for a clean and realistic appearance . Surface details: Preserve all surface details , como rugas , texturas e reflexos . Color and White Balance: Maintain color accuracy and white balance relative to the reference image . Contexto Global: Ensure that the generated image maintains the overall context of the scene present in the reference image . Controle de Estilo: Allow control over the style of the generated image to match the style of the reference photograph . Realismo de Fundo: Garantir que o fundo da imagem seja realista e coerente com a cena . Use an advanced neural network architecture , como uma GAN condicional (cGAN) , treinada em um conjunto de dados diversificado de imagens reais . Adjust the hyperparameters , como a taxa de aprendizado e o tamanho do lote , to optimize the quality metrics mentioned above . "

情報

サイズ

1136X2048

日付

Oct 1, 2023

モード

SeaArt 2.1

タイプ

upscale

Checkpoint & LoRA

majicMIX realistic 麦橘写实

majicMIX realistic 麦橘写实

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