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LoRA de usuario

SD

Clothing +/- Adjuster Complement

#Naturaleza
Clothing +/- Adjuster Complement - AI Model cover image
Subido en
4 oct 2024, 18:43
Usos
253
Reseñas
Sin reseñas
Palabras clave

nature breast,

Permisos
  • Permitir la generación y el intercambio de imágenes
  • Permitir que los usuarios descarguen este modelo
  • Usos comerciales

Descripción

This is a functional LoRA for adjusting the amount of clothing on the drawn objects. By adjusting the LoRA weight from -1.0 to 1.0, a gradual reduction of the clothing on the drawn objects can be achieved. Step 1: Select N groups of comparison images of different characters in State A and State B. Put them into the State A image training set and the State B image training set, respectively. Please ensure that the file names of the two images of State A and State B for the same character are the same.

Step 2: Add txt labels to the State A training set, with each image receiving only one unique word label that distinguishes different characters. For example, if there are 10 characters, assign each character a label from jinitaimei1 to jinitaimei10. Then copy and paste all labels from the State A training set into the State B image training set.

Step 3: Choose a base model C with a similar style to the training set. Use the State A image training set for Lora training until the model overfits.

Step 4: Merge the overfitted LoRA model obtained from training into the base model C at a ratio of 1.0 (Update: After further testing, it will be better to choose same to strength). Then, use the State B image training set for Lora training based on the new base model. This training process does not necessarily need to be trained until severe overfitting occurs; you can choose the LoRA process file for AI drawing tests, as long as the transition from State A to State B can be achieved by adjusting the weight. (Update: 400~800 steps for single pic)

Step 5: If the LoRA training involves complex scenes, overfitting may occur at high weights. There are two suggested improvements: one is to perform layer-by-layer adjustment of LoRA, reducing the weights of layers unrelated to the A/B state switch; the other is to compress the dimensions of LoRA, such as from 64 to 4.

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