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stable-diffusion-v1-4

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Let's say you want to interpolate between two prompts, with each resulting image "rooted" in the output of the first prompt.

This is what I've found to work for me:

1. Your noise at each interpolation step will be the output of slerp.
2. That noise is added at timestep 0 to the encoded "root" image (using scheduler add_noise function)
3. Start diffusing

That's kind of what the script does. It just interpolates two seeds and then diffuses step 0 to N for the interpolated noise vector.

Suppose I interpolate with a small alpha the noise init2 = init1 * (1.0-alpha) + new_noise * alpha for a second image.
Then it would probably be a waste of time to diffuse init2 for N steps to get an almost identical image as you've got before with init1, when I could diffuse result_latent_vector + alpha * new_noise for only a few steps to find the second image.

Of course it is a good question how much noise you have to add to avoid getting the same result, but to explore how to work with the latent vector I would first need to understand how the latent vector can be reused. As the code uses cond_latents as name for the seed noise, I suppose the seed is the initialization of the latent vector and it should in principle be possible to use the vector as seed for a new diffusion. But in practice it didn't work for me.

The image-to-image script img2img.py essentially does what you're describing, you could adapt it to work with the interpolation script. It takes an image, corrupts it with a lot-or-a-little amount of noise, depending on the strength parameter, and then "resumes" the diffusion process for the corresponding number of steps.

For example, let num_inference_steps=50, strength=0.5:
img2img will:

1. encode your input image (could be the output of previous interpolation step)
2. corrupt it with the corresponding amount of noise you'd expect from step strength*num_inference_steps=25
3. resume diffusion for the remaining 25 steps
   strength=0 means that you're starting from pure noise as usual, strength will give you back the input image.

I had a look into the diffusers StableDiffusionImg2ImgPipeline yesterday but didn't get it to work yet.
For the img2img script in the stable-diffusion repo I first need to get the original code running as I am currently using the diffusers implementation for everything. But maybe I can reuse some parts of the script in the diffusers-based script.

Check out my fork of diffusers - I got image-to-image to work: https://github.com/atarashansky/diffusers

Example usage (be warned, I am not using the safety checker as I found it a little too restrictive):

model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id,...)
pipe = pipe.to("cuda")
prompt = "a fantasy landscape, trending on artstation"
init_image = .... # a PIL.Image object.
grid,images,seeds = pipe.make_grid(
    prompt,
    seed=1234,
    height=512,
    width=512,
    num_rows=3,
    num_columns=3,
    num_inference_steps=59,
    guidance_scale=7.5,
    init_image=init_image, # set this to None to do normal text2image
    strength=0.4 # this is the strength parameter.
)

One more disclaimer, this currently only works with the default scheduler.

One more disclaimer, this currently only works with the default scheduler.

Thank you, that was my problem why I didn't get the image_to_image example class to work in an own script.

This colab might also be useful! It shows how to reuse images of the same seed:
https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb