Body: <p>For this question, consider the <a href="https://arxiv.org/pdf/2112.10752.pdf" rel="nofollow noreferrer">stable diffusion model</a>.</p>
  <p>For a given text embedding, Stable Diffusion can generate diverse images. In this context, 'diversity' refers to the variation among the images generated, meaning that several images with the same semantic content (given by the text embedding) can be produced.</p>
  <p>What factors contribute to the diversity of images generated from a given text embedding in the model? I understand that the random noise vector is a primary contributor to this diversity. What factors contribute to the diversity of images generated from a given text embedding in the model, apart from the random noise vector? I am interested in understanding the inputs that lead to this diversity.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:29:39.840Z
Comments.Text: @NeilSlater So, are you saying that the 'nosie vector' is the only input that leads to diversity in images generated? Then where does the diversity emerge? Is it in some other hyperparatemeters? If yes,a re they related to neural network or some other?
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:31:11.283Z
Comments.Text: Are you asking about parameters other than the text prompt. E.g. choice of sampler, cfg scale etc? Or can those be considered fixed, like the prompt. Also are you asking about *gamut* of possible outputs related to what the different noise values produce, or about the image varying at all?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:40:47.370Z
Comments.Text: @NeilSlater  I am investigating the factors that contribute to image diversity in the Stable Diffusion model beyond the random noise vector, assuming other settings like the sampler are fixed. My focus is on understanding which additional inputs or some other model related values can influence the diversity of the generated images from the same text embedding under a constant configuration.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:44:17.797Z
Comments.Text: I'm still not understanding what you are getting at with "diversity". Do you mean just that the image is *different* - i.e. the pixels are different colours, or are you referring to the limits of what differences can be realised (e.g. if you ran the same settings with random noise a million times with the prompt "cat", would one of the cats be surfing, or doing something else unusual that typically you would need to prompt for)?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:46:42.567Z
Comments.Text: @NeilSlater Diversity here is generation from all the possible images that can be obtained for a fixed text embedding in constant configuration. As you mentioned 'gamut of possible outputs' in fixed configuration.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:51:29.733Z
Comments.Text: Still not quite there. Are you interested in that it changes at all, or in the practical limits of what those changes can be? Are you asking if there are other factors beyond noise and settings. Or are you asking "can a cat prompt generate a dog sometimes"?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:53:45.963Z
Comments.Text: @NeilSlater I am interested in those factors, like random noise, that help in generating different images from a fixed text embedding in a fixed configuration.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:54:26.777Z
Comments.Text: OK. Got there. There are none. You can fix the seed and see this, the same image is generated every time (there are *some* caveats, because different GPUs may behave differently). If that's not what you mean, please explain?
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-04-25T07:56:16.877Z
Comments.Text: Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/152791/discussion-between-hanugm-and-neil-slater).
Comments.UserDisplayName: hanugm
Tags: <diffusion-models><noise><text-to-image-generation>
Title: What are all the inputs that support diversity of images in text to image generation?
UserDisplayName: hanugm
ThreadText: # What are all the inputs that support diversity of images in text to image generation?
  
  (Asked by: username_0 on 2024-04-25)
  
  For this question, consider the [stable diffusion model](https://arxiv.org/pdf/2112.10752.pdf).
  
  For a given text embedding, Stable Diffusion can generate diverse images. In this context, 'diversity' refers to the variation among the images generated, meaning that several images with the same semantic content (given by the text embedding) can be produced.
  
  What factors contribute to the diversity of images generated from a given text embedding in the model? I understand that the random noise vector is a primary contributor to this diversity. What factors contribute to the diversity of images generated from a given text embedding in the model, apart from the random noise vector? I am interested in understanding the inputs that lead to this diversity.
  
  **Tags:** diffusion-models, noise, text-to-image-generation
  
  > **Comments:**
  > **username_0**: @username_1 So, are you saying that the 'nosie vector' is the only input that leads to diversity in images generated? Then where does the diversity emerge? Is it in some other hyperparatemeters? If yes,a re they related to neural network or some other?  
  > **username_1**: Are you asking about parameters other than the text prompt. E.g. choice of sampler, cfg scale etc? Or can those be considered fixed, like the prompt. Also are you asking about *gamut* of possible outputs related to what the different noise values produce, or about the image varying at all?  
  > **username_0**: @username_1  I am investigating the factors that contribute to image diversity in the Stable Diffusion model beyond the random noise vector, assuming other settings like the sampler are fixed. My focus is on understanding which additional inputs or some other model related values can influence the diversity of the generated images from the same text embedding under a constant configuration.  
  > **username_1**: I'm still not understanding what you are getting at with "diversity". Do you mean just that the image is *different* - i.e. the pixels are different colours, or are you referring to the limits of what differences can be realised (e.g. if you ran the same settings with random noise a million times with the prompt "cat", would one of the cats be surfing, or doing something else unusual that typically you would need to prompt for)?  
  > **username_0**: @username_1 Diversity here is generation from all the possible images that can be obtained for a fixed text embedding in constant configuration. As you mentioned 'gamut of possible outputs' in fixed configuration.  
  > **username_1**: Still not quite there. Are you interested in that it changes at all, or in the practical limits of what those changes can be? Are you asking if there are other factors beyond noise and settings. Or are you asking "can a cat prompt generate a dog sometimes"?  
  > **username_0**: @username_1 I am interested in those factors, like random noise, that help in generating different images from a fixed text embedding in a fixed configuration.  
  > **username_1**: OK. Got there. There are none. You can fix the seed and see this, the same image is generated every time (there are *some* caveats, because different GPUs may behave differently). If that's not what you mean, please explain?  
  > **username_0**: Let us [continue this discussion in chat](https://chat.stackexchange.com/rooms/152791/discussion-between-username_0-and-username_1).  
  
  ---
  
  ## 0 answers
  
  