stablediffusion-infinity

Outpainting with Stable Diffusion on an infinite canvas

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Quick Overview

Stablediffusion-infinity is an open-source project that extends the capabilities of Stable Diffusion, a popular text-to-image generation model. It aims to provide infinite outpainting functionality, allowing users to expand generated images in any direction without limitations. This project enhances the creative possibilities for artists and designers working with AI-generated imagery.

Pros

Enables unlimited expansion of AI-generated images in any direction
Maintains consistency and coherence across expanded areas
Integrates seamlessly with existing Stable Diffusion workflows
Provides a user-friendly interface for easy image manipulation

Cons

May require significant computational resources for large-scale expansions
Potential for inconsistencies in complex scenes or highly detailed images
Limited documentation and community support compared to mainstream Stable Diffusion
Possible learning curve for users unfamiliar with advanced image generation techniques

Code Examples

# Initialize the Stablediffusion-infinity model
from sd_infinity import SDInfinity

model = SDInfinity.from_pretrained("path/to/model")

# Generate an initial image
initial_image = model.generate("A serene landscape with mountains")

# Expand the image to the right
expanded_image = model.expand(initial_image, direction="right", steps=5)

# Expand an image in multiple directions
from sd_infinity import SDInfinity, ExpansionDirection

model = SDInfinity.from_pretrained("path/to/model")

image = model.generate("A futuristic cityscape")
expanded_image = model.expand_multi(image, [
    (ExpansionDirection.UP, 3),
    (ExpansionDirection.RIGHT, 2),
    (ExpansionDirection.DOWN, 1)
])

# Use custom prompts for different expansion directions
from sd_infinity import SDInfinity, ExpansionDirection

model = SDInfinity.from_pretrained("path/to/model")

image = model.generate("A cozy living room")
expanded_image = model.expand_with_prompts(image, [
    (ExpansionDirection.LEFT, "A hallway with paintings", 2),
    (ExpansionDirection.RIGHT, "Large windows with a garden view", 3)
])

Getting Started

To get started with Stablediffusion-infinity, follow these steps:

Install the library:
```
pip install stablediffusion-infinity
```

Import and initialize the model:

from sd_infinity import SDInfinity

model = SDInfinity.from_pretrained("path/to/model")

Generate an initial image and expand it:

initial_image = model.generate("Your prompt here")
expanded_image = model.expand(initial_image, direction="right", steps=3)
expanded_image.save("expanded_image.png")

For more advanced usage and customization options, refer to the project's documentation and examples in the GitHub repository.

Competitor Comparisons

stable-diffusion-webui

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Stable Diffusion web UI

Pros of stable-diffusion-webui

More extensive features and options for image generation and manipulation
Larger community and more frequent updates
Better support for custom models and extensions

Cons of stable-diffusion-webui

Higher system requirements and potentially slower performance
Steeper learning curve due to numerous options and settings

Code Comparison

stable-diffusion-webui:

def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
    index = position_in_batch + iteration * p.batch_size

    clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
    enable_hr = getattr(p, 'enable_hr', False)
    denoising_strength = getattr(p, 'denoising_strength', None)

stablediffusion-infinity:

def get_model_path(model_name):
    model_path = os.path.join(models_path, model_name)
    if os.path.exists(model_path):
        return model_path
    return None

The code snippets show different functionalities: stable-diffusion-webui focuses on creating detailed information text for generated images, while stablediffusion-infinity deals with model path retrieval. This reflects the more comprehensive approach of stable-diffusion-webui compared to the simpler implementation of stablediffusion-infinity.

stablediffusion

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High-Resolution Image Synthesis with Latent Diffusion Models

Pros of stablediffusion

Official implementation from Stability AI, ensuring authenticity and direct updates
Comprehensive documentation and extensive community support
Broader range of features and functionalities for advanced users

Cons of stablediffusion

Potentially more complex setup and configuration process
May require more computational resources due to its comprehensive nature

Code Comparison

stablediffusion:

from ldm.util import instantiate_from_config
model = load_model_from_config(config, f"{opt.ckpt}")
sampler = DDIMSampler(model)

stablediffusion-infinity:

from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

The stablediffusion repository uses a more low-level approach, allowing for greater customization but requiring more setup. In contrast, stablediffusion-infinity utilizes the Diffusers library, providing a more streamlined and user-friendly interface for quick implementation.

diffusers

25,061

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

Pros of diffusers

Comprehensive library with support for multiple diffusion models and techniques
Extensive documentation and community support
Regular updates and maintenance from Hugging Face team

Cons of diffusers

Steeper learning curve for beginners
May require more setup and configuration for specific use cases

Code comparison

stablediffusion-infinity:

model = create_model('./models/ldm/stable-diffusion-v1/model.ckpt')
sampler = DDIMSampler(model)

diffusers:

from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")

stablediffusion-infinity focuses on a specific implementation of Stable Diffusion, while diffusers provides a more flexible and extensible framework for various diffusion models. diffusers offers a higher-level API, making it easier to use different models and techniques, but may require more understanding of the underlying concepts. stablediffusion-infinity might be more straightforward for users specifically interested in Stable Diffusion, but lacks the broader functionality and community support of diffusers.

InvokeAI

22,785

Pros of InvokeAI

More comprehensive and feature-rich, offering a wide range of AI-powered image generation and manipulation tools
Active development with frequent updates and a larger community of contributors
Provides a user-friendly web interface alongside command-line options

Cons of InvokeAI

Potentially more complex to set up and use for beginners due to its extensive feature set
May require more computational resources to run smoothly compared to stablediffusion-infinity

Code Comparison

InvokeAI:

from invokeai.app.invocations.baseinvocation import BaseInvocation, InvocationContext, InvocationError

class MyCustomInvocation(BaseInvocation):
    def invoke(self, context: InvocationContext):
        # Custom invocation logic here

stablediffusion-infinity:

from modules import scripts

class Script(scripts.Script):
    def run(self, p, *args):
        # Custom script logic here

Both projects allow for custom extensions, but InvokeAI uses a more structured approach with its invocation system, while stablediffusion-infinity relies on a simpler script-based extension mechanism.

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README

stablediffusion-infinity

Outpainting with Stable Diffusion on an infinite canvas.

outpaint

https://user-images.githubusercontent.com/1665437/197244111-51884b3b-dffe-4dcf-a82a-fa5117c79934.mp4

Status

Powered by Stable Diffusion inpainting model, this project now works well. However, the quality of results is still not guaranteed. You may need to do prompt engineering, change the size of the selection, reduce the size of the outpainting region to get better outpainting results.

The project now becomes a web app based on PyScript and Gradio. For Jupyter Notebook version, please check out the ipycanvas branch.

Pull requests are welcome for better UI control, ideas to achieve better results, or any other improvements.

Update: the project add photometric correction to suppress seams, to use this feature, you need to install fpie: pip install fpie (Linux/MacOS only)

Docs

Get Started

Setup for Windows: setup_guide
Setup for Linux: setup_guide
Setup for MacOS: setup_guide
Running with Docker on Windows or Linux with NVIDIA GPU: run_with_docker
Usages: usage

FAQs

The result is a black square:
- False positive rate of safety checker is relatively high, you may disable the safety_checker
- Some GPUs might not work with fp16: python app.py --fp32 --lowvram
What is the init_mode
- init_mode indicates how to fill the empty/masked region, usually patch_match is better than others
Why not use postMessage for iframe interaction
- The iframe and the gradio are in the same origin. For postMessage version, check out gradio-space version

Known issues

The canvas is implemented with NumPy + PyScript (the project was originally implemented with ipycanvas inside a jupyter notebook), which is relatively inefficient compared with pure frontend solutions.
By design, the canvas is infinite. However, the canvas size is finite in practice. Your RAM and browser limit the canvas size. The canvas might crash or behave strangely when zoomed out by a certain scale.
The canvas requires internet: You can deploy and serve PyScript, Pyodide, and other JS/CSS assets with a local HTTP server and modify index.html accordingly.
Photometric correction might not work (taichi does not support the multithreading environment). A dirty hack (quite unreliable) is implemented to move related computation inside a subprocess.
Stable Diffusion inpainting model is much slower when selection size is larger than 512x512

Credit

The code of perlin2d.py is from https://stackoverflow.com/questions/42147776/producing-2d-perlin-noise-with-numpy/42154921#42154921 and is not included in the scope of LICENSE used in this repo.

The submodule glid_3_xl_stable is based on https://github.com/Jack000/glid-3-xl-stable

The submodule PyPatchMatch is based on https://github.com/vacancy/PyPatchMatch

The code of postprocess.py and process.py is modified based on https://github.com/Trinkle23897/Fast-Poisson-Image-Editing

The code of convert_checkpoint.py is modified based on https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py

The submodule sd_grpcserver and handleImageAdjustment() in utils.py are based on https://github.com/hafriedlander/stable-diffusion-grpcserver and https://github.com/parlance-zz/g-diffuser-bot

w2ui.min.js and w2ui.min.css is from https://github.com/vitmalina/w2ui. fabric.min.js is a custom build of https://github.com/fabricjs/fabric.js

interrogate.py is based on https://github.com/pharmapsychotic/clip-interrogator v1, the submodule blip_model is based on https://github.com/salesforce/BLIP

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