wildcard_stable_diffusion.ipynb

Kernel: Unknown Kernel

Wildcard Stable Diffusion

Following examples from the GitHub repositories ( https://github.com/jtkelm2/stable-diffusion-webui-1/blob/master/scripts/wildcards.py) and (https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts#wildcards) , this implementation allows users to add "wildcards" (denoted by wildcard) to prompts as placeholders for randomly sampled values from either a dictionary or a .txt file.

For instance, given a prompt like animal sitting on a object wearing a clothing, possible values for animal, object, and clothing can be specified in corresponding .txt files or combined in a dictionary like {"animal":["dog", "cat", "mouse"]}.

The pipeline functions similarly to the StableDiffusionPipeline, with the call method taking in wildcard_files (list of file paths for wildcard replacement), wildcard_option_dict (dictionary with wildcard keys and list of possible replacements), and num_prompt_samples (number of prompts to sample, uniformly sampling wildcards).

For example, creating animal.txt with dog, cat, mouse and object.txt with chair, sofa, bench would provide the necessary values for the wildcards in the prompt. This script was contributed by Shyam Sudhakaran and the notebook byParagEkbote.

pip install diffusers torch

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Note: you may need to restart the kernel to use updated packages.

# Create the object.txt file
with open("object.txt", "w") as f:
    f.write("chair\n")
    f.write("sofa\n")
    f.write("bench\n")

# Create the animal.txt file
with open("animal.txt", "w") as f:
    f.write("cat\n")
    f.write("dog\n")
    f.write("mouse\n")

from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="wildcard_stable_diffusion",
    torch_dtype=torch.float32,
)
prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
out = pipe(
    prompt,
    wildcard_option_dict={
        "clothing":["hat", "shirt", "scarf", "beret"]
    },
    wildcard_files=["object.txt", "animal.txt"],
    num_prompt_samples=1
)
torch.cuda.empty_cache()
out.images[0].save("image.png")