Pros of guided-diffusion

• More comprehensive implementation with additional features like classifier guidance
• Better documentation and code organization
• Supports a wider range of diffusion models and sampling techniques

Cons of guided-diffusion

• Higher computational requirements due to more complex models
• Steeper learning curve for newcomers to diffusion models
• Less focused on specific DDIM implementation

Code Comparison

guided-diffusion:

def p_sample_loop(
    model,
    shape,
    noise=None,
    clip_denoised=True,
    denoised_fn=None,
    model_kwargs=None,
    device=None,
    progress=False,
):
    # ... (implementation details)

ddim:

def p_sample_ddim(model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
    out = p_mean_variance(
        model,
        x,
        t,
        clip_denoised=clip_denoised,
        denoised_fn=denoised_fn,
        model_kwargs=model_kwargs,
    )
    # ... (implementation details)

The guided-diffusion repository offers a more flexible sampling loop with additional parameters, while ddim provides a more focused implementation of the DDIM sampling process. guided-diffusion's approach allows for easier integration of various sampling techniques, but ddim's implementation is more straightforward for those specifically interested in DDIM.

Pros of stable-diffusion

• More advanced and versatile image generation capabilities
• Larger community and active development
• Supports text-to-image generation

Cons of stable-diffusion

• Higher computational requirements
• More complex architecture and implementation
• Larger model size, requiring more storage and memory

Code Comparison

DDIM:

def p_sample(self, x, t, clip_denoised=True):
    out = self.p_mean_variance(x, t)
    noise = torch.randn_like(x)
    nonzero_mask = (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
    return out["mean"] + nonzero_mask * torch.exp(0.5 * out["log_variance"]) * noise

stable-diffusion:

@torch.no_grad()
def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
                  temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
                  unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None):
    b, *_, device = *x.shape, x.device

The code snippets show that stable-diffusion has a more complex sampling function with additional parameters and features compared to DDIM's simpler implementation.

Pros of diffusion

• More comprehensive implementation of diffusion models
• Includes additional features like conditional sampling and various noise schedules
• Better documentation and code organization

Cons of diffusion

• Larger codebase, potentially more complex to understand and modify
• May require more computational resources due to additional features
• Less focused on specific applications compared to ddim

Code Comparison

diffusion:

def p_sample(self, model, x, t, clip_denoised=True, denoised_fn=None, cond_fn=None, model_kwargs=None):
    out = self.p_mean_variance(
        model,
        x,
        t,
        clip_denoised=clip_denoised,
        denoised_fn=denoised_fn,
        model_kwargs=model_kwargs,
    )

ddim:

def p_sample(self, model, x, t, t_index, betas):
    e_t = model(x, t)
    alphas = 1.0 - betas
    alphas_cumprod = torch.cumprod(alphas, dim=0)
    x_recon = self._predict_xstart_from_eps(x, t_index, e_t)

The code snippets show different approaches to sampling in the diffusion process, with diffusion offering more flexibility and options, while ddim focuses on a specific implementation.

Pros of denoising-diffusion-pytorch

• More comprehensive implementation with additional features like conditional generation and image inpainting
• Better documentation and code organization, making it easier for users to understand and modify
• Active development and regular updates, incorporating latest research and improvements

Cons of denoising-diffusion-pytorch

• Higher complexity, which may be overwhelming for beginners or those seeking a simpler implementation
• Potentially slower inference time due to the additional features and flexibility

Code Comparison

ddim:

def p_sample(self, x, t, clip_denoised=True):
    out = self.p_mean_variance(x, t)
    noise = torch.randn_like(x)
    nonzero_mask = (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
    return out["mean"] + nonzero_mask * torch.exp(0.5 * out["log_variance"]) * noise

denoising-diffusion-pytorch:

@torch.no_grad()
def p_sample(self, x, t: int, x_self_cond = None, clip_denoised = True):
    b, *_, device = *x.shape, x.device
    model_mean, _, model_log_variance = self.p_mean_variance(x = x, t = t, x_self_cond = x_self_cond)
    noise = torch.randn_like(x) if t > 0 else 0.
    pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
    return pred_img, x_self_cond

Pros of k-diffusion

• More flexible sampling algorithms, including advanced methods like DPM-Solver
• Better support for conditional generation and guidance techniques
• More active development and community support

Cons of k-diffusion

• Steeper learning curve due to more complex architecture
• Less focus on theoretical foundations compared to DDIM
• May require more computational resources for some advanced sampling methods

Code Comparison

k-diffusion:

model = diffusion.DiffusionModel(...)
x = torch.randn(...)
samples = diffusion.sample(model, x, steps=20, eta=0.0)

DDIM:

model = DDIM(...)
x_T = torch.randn(...)
samples = model.sample(x_T, num_steps=20)

Both repositories provide implementations of diffusion models, but k-diffusion offers a wider range of sampling algorithms and more flexibility in model architecture. DDIM focuses on a specific sampling technique (Denoising Diffusion Implicit Models) and provides a more straightforward implementation. k-diffusion is generally more suitable for advanced users and researchers exploring various diffusion model variants, while DDIM may be preferable for those specifically interested in the DDIM algorithm or seeking a simpler starting point.