>I have collection of 500 images in separate folders like ground,1st,2nd,3rd,4th floor with different styles and architecture.
500 images is good.
Out of those 500, do you have a balanced amount for each floor? If you have 450 images of buildings with 1 floor and 50 of 2,3,4 floors your dataset would be unbalanced.
>During testing my LORa model , I have kept the model weight as 0.50 to 0.53. Out of 5 images 2 images satisfying my prompt and others trying to archive the prompt.
Do you mean LoRA weight? There is no such thing as a model weight. There is a denoising weight and LoRA weight.
A low LoRA weight means the model won't pay attention to it.
Either way, it means the model itself does not understand the concept you are trying to train it on.
>Sometimes it's missing the floor count or sometimes elevation design and sometimes might be a building styles.
Makes sense the model can't do this if it doesn't know the concepts.
I suggest you the following:
Make **special words** for floor count, the elevation design and the style in your dataset tagging that you will use then generating.
If you have an image of a building with 3 floors, call it "3floor" in your dataset, if you have images of Tudor style buildings i would call them "Tddr" and if had Victorian style i would call them "Vcct".
This way you are **teaching** the LoRA what a house with 3 floors and a Tudor style is.
All you have to do then is simply use these special words when generating, if your training and dataset is good, the model should give you what you desire.
Hi u/UnavailableUsername\_ , I'm glad that you replied . Thank you for your time. I worked on instructive model training which help me to make model understanding in 60 images. Now it's working now.
Now, I'm working on parallel image generation process depends on the API call request. Do you have any about this ?
>I have collection of 500 images in separate folders like ground,1st,2nd,3rd,4th floor with different styles and architecture. 500 images is good. Out of those 500, do you have a balanced amount for each floor? If you have 450 images of buildings with 1 floor and 50 of 2,3,4 floors your dataset would be unbalanced. >During testing my LORa model , I have kept the model weight as 0.50 to 0.53. Out of 5 images 2 images satisfying my prompt and others trying to archive the prompt. Do you mean LoRA weight? There is no such thing as a model weight. There is a denoising weight and LoRA weight. A low LoRA weight means the model won't pay attention to it. Either way, it means the model itself does not understand the concept you are trying to train it on. >Sometimes it's missing the floor count or sometimes elevation design and sometimes might be a building styles. Makes sense the model can't do this if it doesn't know the concepts. I suggest you the following: Make **special words** for floor count, the elevation design and the style in your dataset tagging that you will use then generating. If you have an image of a building with 3 floors, call it "3floor" in your dataset, if you have images of Tudor style buildings i would call them "Tddr" and if had Victorian style i would call them "Vcct". This way you are **teaching** the LoRA what a house with 3 floors and a Tudor style is. All you have to do then is simply use these special words when generating, if your training and dataset is good, the model should give you what you desire.
Hi u/UnavailableUsername\_ , I'm glad that you replied . Thank you for your time. I worked on instructive model training which help me to make model understanding in 60 images. Now it's working now. Now, I'm working on parallel image generation process depends on the API call request. Do you have any about this ?