Pros of stable-diffusion

• More advanced and feature-rich, capable of generating high-quality images
• Actively maintained with regular updates and improvements
• Extensive documentation and community support

Cons of stable-diffusion

• Higher computational requirements and more complex setup
• Steeper learning curve for beginners
• Larger codebase, which may be overwhelming for some users

Code Comparison

Diffusion-Models-pytorch:

def forward(self, x, t):
    t = t.unsqueeze(-1).type(torch.float)
    t = self.time_mlp(t)
    
    h = self.init_conv(x)
    for block in self.down_blocks:
        h = block(h, t)

stable-diffusion:

def forward(self, x, t, context=None):
    t_emb = timestep_embedding(t, self.model_channels, repeat_only=False)
    emb = self.time_embed(t_emb)

    h = x.type(self.dtype)
    for module in self.input_blocks:
        h = module(h, emb, context)

Both repositories implement diffusion models, but stable-diffusion offers a more sophisticated approach with additional features and flexibility. While Diffusion-Models-pytorch is simpler and easier to understand, stable-diffusion provides a more powerful and production-ready solution for image generation tasks.

Pros of denoising-diffusion-pytorch

• More comprehensive implementation with additional features like attention mechanisms and advanced scheduling
• Better code organization with modular architecture
• Includes support for various image sizes and flexible configuration options

Cons of denoising-diffusion-pytorch

• Higher complexity, which may be challenging for beginners
• Requires more computational resources due to advanced features
• Less focused on educational purposes compared to Diffusion-Models-pytorch

Code Comparison

Diffusion-Models-pytorch:

def forward(self, x, t):
    t = t.unsqueeze(-1).type(torch.float)
    return self.linear(x)

denoising-diffusion-pytorch:

def forward(self, x, time):
    t = self.time_mlp(time)
    h = self.init_conv(x)
    for block1, block2, attn, downsample in self.downs:
        h = block1(h, t)
        h = block2(h, t)
        h = attn(h)
        if downsample:
            h = downsample(h)
    return h

The code comparison shows that denoising-diffusion-pytorch has a more complex forward pass with additional components like time embedding, attention, and downsampling, while Diffusion-Models-pytorch has a simpler linear transformation.

Pros of guided-diffusion

• More advanced and feature-rich implementation of diffusion models
• Includes classifier guidance and other advanced techniques
• Better documentation and code organization

Cons of guided-diffusion

• More complex and potentially harder to understand for beginners
• Requires more computational resources due to its advanced features

Code Comparison

Diffusion-Models-pytorch:

def p_sample_loop(self, x, continous=False):
    device = self.betas.device
    sample = x.to(device)
    if not continous:
        for i in reversed(range(0, self.n_T)):
            sample = self.p_sample(sample, torch.full((1,), i, device=device, dtype=torch.long))
    else:
        for i in reversed(range(0, self.n_T)):
            sample = self.p_sample(sample, torch.full((1,), i, device=device, dtype=torch.long))
            if i % self.log_every_t == 0 or i == self.n_T-1:
                yield sample
    return sample

guided-diffusion:

def p_sample_loop(
    self,
    model,
    shape,
    noise=None,
    clip_denoised=True,
    denoised_fn=None,
    cond_fn=None,
    model_kwargs=None,
    device=None,
    progress=False,
):
    final = None
    for sample in self.p_sample_loop_progressive(
        model,
        shape,
        noise=noise,
        clip_denoised=clip_denoised,
        denoised_fn=denoised_fn,
        cond_fn=cond_fn,
        model_kwargs=model_kwargs,
        device=device,
        progress=progress,
    ):
        final = sample
    return final["sample"]

Pros of diffusion

• More comprehensive implementation with additional features like sampling and evaluation
• Better documentation and code organization
• Supports multiple architectures and datasets

Cons of diffusion

• More complex codebase, potentially harder for beginners to understand
• Less focused on educational purposes compared to Diffusion-Models-pytorch

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,
    )

Diffusion-Models-pytorch:

def p_sample(self, model, x, t, t_index):
    betas_t = extract(self.betas, t, x.shape)
    sqrt_one_minus_alphas_cumprod_t = extract(
        self.sqrt_one_minus_alphas_cumprod, t, x.shape
    )
    sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)

The diffusion repository offers a more flexible implementation with additional parameters and options, while Diffusion-Models-pytorch provides a simpler, more straightforward approach. This reflects the overall differences in complexity and focus between the two projects.

Pros of ddim

• More comprehensive implementation of diffusion models, including DDIM sampling
• Better documentation and examples for various use cases
• Supports both unconditional and conditional generation

Cons of ddim

• Less beginner-friendly, with a steeper learning curve
• Requires more computational resources due to its complexity

Code Comparison

ddim:

def p_sample_ddim(self, x, t, clip_denoised=True, condition_x=None):
    out = self.p_mean_variance(x, t, clip_denoised, condition_x)
    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

Diffusion-Models-pytorch:

def p_sample(self, x, t, t_index):
    betas_t = extract(self.betas, t, x.shape)
    sqrt_one_minus_alphas_cumprod_t = extract(
        self.sqrt_one_minus_alphas_cumprod, t, x.shape
    )
    sqrt_recip_alphas_t = extract(self.sqrt_recip_alphas, t, x.shape)
    
    model_mean = sqrt_recip_alphas_t * (
        x - betas_t * self.model(x, t) / sqrt_one_minus_alphas_cumprod_t
    )
    
    if t_index == 0:
        return model_mean
    else:
        posterior_variance_t = extract(self.posterior_variance, t, x.shape)
        noise = torch.randn_like(x)
        return model_mean + torch.sqrt(posterior_variance_t) * noise