r/StableDiffusion Sep 11 '22 Silver 1 Helpful 1 Burning Cash 1

A better (?) way of doing img2img by finding the noise which reconstructs the original image Img2Img

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u/Aqwis Sep 11 '22 edited Sep 11 '22 Silver

I’ve made quite a few attempts at editing existing pictures with img2img. However, at low strengths the pictures tend to be modified too little, while at high strengths the picture is modified in undesired ways. /u/bloc97 posted here about a better way of doing img2img that would allow for more precise editing of existing pictures – by finding the noise that will cause SD to reconstruct the original image.

I made a quick attempt at reversing the k_euler sampler, and ended up with the code I posted in a reply to the post by bloc97 linked above. I’ve refined the code a bit and posted it on GitHub here:

link to code

If image is a PIL image and model is a LatentDiffusion object, then find_noise_for_image can be called like this:

noise_out = find_noise_for_image(model, image, 'Some prompt that accurately describes the image', steps=50, cond_scale=1.0)

The output noise tensor can then be used for image generation by using it as a “fixed code” (to use a term from the original SD scripts) – in other words, instead of generating a random noise tensor (and possibly adding that noise tensor to an image for img2img), you use the noise tensor generated by find_noise_for_image_model.

This method isn’t perfect – deviate too much from the prompt used when generating the noise tensor, and the generated images are going to start differing from the original image in unexpected ways. Some experimentation with the different parameters and making the prompt precise enough will probably be necessary to get this working. Still, for altering existing images in particular ways I’ve had way more success with this method than with standard img2img. I have yet to combine this with bloc97’s Prompt-to-Prompt Image Editing, but I’m guessing the combination will give even more control.

All suggestions for improvements/fixes are highly appreciated. I still have no idea what the best setting of cond_scale, for example, and in general this is just a hack that I made without reading any of the theory on this topic.

Edit: By the way, the original image used in the example is from here and is the output of one of those old "this person does not exist" networks, I believe. I've tried it on other photos (including of myself :), so this works for "real" pictures as well. The prompt that I used when generating the noise tensor for this was "Photo of a smiling woman with brown hair".


u/Doggettx Sep 11 '22 edited Sep 11 '22

Very cool, definitely gonna have to play with this :)

You're example is missing a few things though, like pil_img_to_torch() the tqdm import and the collect_and_empty() function

I assume it's something like:

def collect_and_empty():


u/Aqwis Sep 12 '22

Sorry, I went and added pil_img_to_torch to the gist now! I removed collect_and_empty a couple of hours ago as it was slowing things down and the VRAM issue mysteriously vanished.


u/rservello Sep 12 '22

Input type (torch.cuda.HalfTensor) and weight type (torch.cuda.FloatTensor) should be the same

thoughts on this error now?


u/Etiennera Sep 12 '22

Did you halve your model to save vram?


u/rservello Sep 12 '22

I did. But I tried at full and get the same error.