You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Dear Mr Shi:
Nice to meet you! I just read your code and am very interested. Now I have a question and need your help:
In my case, I have a target image and a black-box online image-generative service, which is also based on a diffusion model and takes both an image and a text prompt as input, then outputs the generated image.
Now, my goal is to find the optimal unknown input image and text prompt to make the generated output image most similar to my target image using cosine and SSIM similarity.
I wonder if DDIM inversion or null-text inversion is suitable for this black-box case or if they are just for white-box open-source cases. Could you please give me some advice on how to achieve this goal? (Besides a gradient descent method, which needs too many iterations.)
Thank you very much!
The text was updated successfully, but these errors were encountered:
Dear Mr Shi:
Nice to meet you! I just read your code and am very interested. Now I have a question and need your help:
In my case, I have a target image and a black-box online image-generative service, which is also based on a diffusion model and takes both an image and a text prompt as input, then outputs the generated image.
Now, my goal is to find the optimal unknown input image and text prompt to make the generated output image most similar to my target image using cosine and SSIM similarity.
I wonder if DDIM inversion or null-text inversion is suitable for this black-box case or if they are just for white-box open-source cases. Could you please give me some advice on how to achieve this goal? (Besides a gradient descent method, which needs too many iterations.)
Thank you very much!
The text was updated successfully, but these errors were encountered: