Pros of InvokeAI

• More comprehensive UI with a web interface for easier use
• Supports multiple AI models and pipelines
• Active development with frequent updates and new features

Cons of InvokeAI

• Larger codebase, potentially more complex to set up and customize
• May have higher system requirements due to additional features

Code Comparison

k-diffusion:

def sample(model, x, sigmas, extra_args=None, callback=None):
    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable_progress):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        d = to_d(x, sigmas[i], denoised)
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
        dt = sigmas[i + 1] - sigmas[i]
        if sigmas[i + 1] == 0:
            # Euler method
            x = x + d * dt
        else:
            # Heun's method
            x_2 = x + d * dt
            denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
            d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
            d_prime = (d + d_2) / 2
            x = x + d_prime * dt
    return x

InvokeAI:

def sample(self, noise, prompt, strength=1.0, steps=50, seed=None):
    """Generate an image from a text prompt."""
    if seed is not None:
        torch.manual_seed(seed)
    
    # Prepare latent variables
    latents = self.prepare_latents(noise, strength)
    
    # Text encoding
    text_embeddings = self.get_text_embeddings(prompt)
    
    # Sampling loop
    for i in range(steps):
        latents = self.diffusion_step(latents, text_embeddings, i, steps)
    
    # Decode latents to image
    image = self.vae.decode(latents)
    return image

Pros of stablediffusion

• More comprehensive and feature-rich, offering a complete text-to-image generation pipeline
• Backed by a larger organization (Stability AI), potentially leading to better long-term support and updates
• Includes pre-trained models and a user-friendly interface for easier adoption

Cons of stablediffusion

• Larger codebase and more dependencies, which may increase complexity and setup time
• Potentially slower inference speed due to the full pipeline implementation
• May require more computational resources for training and inference

Code Comparison

k-diffusion:

model = diffusion.DiffusionModel(...)
x = torch.randn(...)
samples = diffusion.sample(model, x, steps=20)

stablediffusion:

pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

Summary

While k-diffusion focuses on providing a flexible and efficient implementation of diffusion models, stablediffusion offers a more complete solution for text-to-image generation. k-diffusion may be preferred for research and customization, while stablediffusion is better suited for production-ready applications and ease of use.

Pros of diffusers

• Extensive library with support for multiple diffusion models and pipelines
• Well-documented and actively maintained by the Hugging Face team
• Seamless integration with other Hugging Face libraries and ecosystem

Cons of diffusers

• Larger codebase and potentially steeper learning curve
• May have more dependencies and overhead for simple use cases

Code Comparison

k-diffusion:

import k_diffusion as K

sampler = K.sampling.sample_lms
x = sampler(model, x, sigmas, extra_args={'cond': cond})

diffusers:

from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
image = pipe(prompt="a photo of an astronaut riding a horse on mars").images[0]

Summary

k-diffusion is a lightweight and flexible library focused on diffusion models, while diffusers offers a more comprehensive suite of tools and models within the broader Hugging Face ecosystem. k-diffusion may be preferred for custom implementations and research, while diffusers excels in providing ready-to-use pipelines and integration with other NLP tasks.

Pros of stable-diffusion-webui

• User-friendly web interface for easy interaction
• Extensive features including inpainting, outpainting, and various sampling methods
• Active community with frequent updates and extensions

Cons of stable-diffusion-webui

• Larger codebase, potentially more complex to modify or extend
• May have higher system requirements due to additional features

Code Comparison

k-diffusion:

def sample(model, x, sigmas, extra_args=None, callback=None):
    """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1):
        denoised = model(x, sigmas[i] * s_in, **extra_args)
        d = (x - denoised) / sigmas[i]
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
        dt = sigmas[i + 1] - sigmas[i]
        x = x + d * dt
    return x

stable-diffusion-webui:

def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
    """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
    extra_args = {} if extra_args is None else extra_args
    s_in = x.new_ones([x.shape[0]])
    for i in trange(len(sigmas) - 1, disable=disable):
        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
        eps = torch.randn_like(x) * s_noise
        sigma_hat = sigmas[i] * (gamma + 1)
        if gamma > 0:
            x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
        denoised = model(x, sigma_hat * s_in, **extra_args)
        d = (x - denoised) / sigma_hat
        dt = sigmas[i + 1] - sigma_hat
        x = x + d * dt
        if callback is not None:
            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
    return x