diffusers

Pros of stablediffusion

• More focused on the Stable Diffusion model, providing specialized tools and optimizations
• Offers direct integration with Stability AI's ecosystem and services
• Includes advanced features like textual inversion and custom pipelines

Cons of stablediffusion

• Less versatile compared to diffusers, which supports multiple models and architectures
• May have a steeper learning curve for beginners due to its more specialized nature
• Potentially slower update cycle for new features and models

Code Comparison

stablediffusion:

import torch
from ldm.util import instantiate_from_config
from omegaconf import OmegaConf

config = OmegaConf.load("configs/stable-diffusion/v1-inference.yaml")
model = instantiate_from_config(config.model)

diffusers:

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipeline("A beautiful sunset over the ocean").images[0]

The stablediffusion repository provides lower-level access to the model, while diffusers offers a more user-friendly API with pre-built pipelines. diffusers supports a wider range of models and use cases, making it more versatile for general use, while stablediffusion may be preferred for advanced users focusing specifically on Stable Diffusion.

Pros of stable-diffusion-webui

• User-friendly web interface for easy interaction with Stable Diffusion models
• Extensive features including inpainting, outpainting, and various image processing tools
• Active community with frequent updates and extensions

Cons of stable-diffusion-webui

• Less flexible for integration into custom applications or workflows
• Primarily focused on image generation, with limited support for other diffusion tasks
• Steeper learning curve for developers wanting to modify or extend core functionality

Code Comparison

diffusers:

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipeline("A beautiful sunset over the ocean").images[0]
image.save("sunset.png")

stable-diffusion-webui:

import modules.scripts as scripts
import gradio as gr

class ExampleScript(scripts.Script):
    def title(self):
        return "Example Script"

    def ui(self, is_img2img):
        return [gr.Textbox(label="Prompt")]

    def run(self, p, prompt):
        p.prompt = prompt
        return p

Pros of InvokeAI

• More user-friendly interface with a web UI and CLI options
• Specialized features for image generation, including inpainting and outpainting
• Active community with frequent updates and contributions

Cons of InvokeAI

• Less flexible for general machine learning tasks
• Steeper learning curve for developers new to image generation
• More resource-intensive due to its comprehensive features

Code Comparison

InvokeAI:

from invokeai.app.invocations.baseinvocation import BaseInvocation

class CustomInvocation(BaseInvocation):
    def invoke(self, context):
        # Custom image generation logic here

Diffusers:

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipeline("A beautiful sunset over the ocean").images[0]

InvokeAI focuses on providing a complete image generation solution with a user-friendly interface, while Diffusers offers a more flexible and modular approach for various diffusion models. InvokeAI is better suited for end-users and artists, whereas Diffusers is more adaptable for researchers and developers working on diverse machine learning projects.

Pros of generative-models

• Focuses specifically on Stability AI's models, offering deeper integration and optimization
• Provides more direct access to cutting-edge generative AI research from Stability AI
• Includes specialized tools and utilities tailored for Stability AI's model architectures

Cons of generative-models

• Less extensive documentation and community support compared to diffusers
• Narrower scope, primarily centered around Stability AI's models rather than a broad range of architectures
• May have a steeper learning curve for users not familiar with Stability AI's specific approaches

Code Comparison

diffusers:

from diffusers import StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipeline("A beautiful sunset over the ocean").images[0]

generative-models:

from sgm.inference.api import SamplingPipeline
from sgm.inference.helpers import embed_clip

pipeline = SamplingPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
image = pipeline.text_to_image(embed_clip("A beautiful sunset over the ocean"))