Pros of stable-diffusion

• More advanced and widely adopted in the AI image generation community
• Offers pre-trained models and easier integration with various applications
• Extensive documentation and community support

Cons of stable-diffusion

• Larger model size and higher computational requirements
• More complex architecture, potentially harder for beginners to understand and modify

Code Comparison

diffusion:

def p_sample(self, x, t, clip_denoised=True):
    out = self.p_mean_variance(x, t, clip_denoised=clip_denoised)
    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
    e_t = self.model.apply_model(x, t, c)
    if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
        e_t = e_t
    else:
        e_t = e_t * unconditional_guidance_scale + (1. - unconditional_guidance_scale) * self.model.apply_model(x, t, unconditional_conditioning)

Pros of guided-diffusion

• More comprehensive documentation and examples
• Supports a wider range of diffusion models and techniques
• Actively maintained with regular updates and contributions

Cons of guided-diffusion

• Higher computational requirements due to more complex models
• Steeper learning curve for beginners
• Less focus on specific image generation tasks

Code Comparison

guided-diffusion:

def create_model(
    image_size,
    num_channels,
    num_res_blocks,
    channel_mult="",
    learn_sigma=False,
    class_cond=False,
    use_checkpoint=False,
    attention_resolutions="16",
    num_heads=1,
    num_head_channels=-1,
    num_heads_upsample=-1,
    use_scale_shift_norm=False,
    dropout=0,
    resblock_updown=False,
    use_fp16=False,
    use_new_attention_order=False,
):
    # ... (implementation details)

diffusion:

def create_model(
    image_size,
    num_channels,
    num_res_blocks,
    channel_mult="",
    learn_sigma=False,
    class_cond=False,
    use_checkpoint=False,
    attention_resolutions="16",
    num_heads=1,
    num_head_channels=-1,
    num_heads_upsample=-1,
    use_scale_shift_norm=False,
    dropout=0,
):
    # ... (implementation details)

The code comparison shows that guided-diffusion offers more parameters and options for model creation, allowing for greater flexibility and customization.

Pros of ddim

• Implements Denoising Diffusion Implicit Models (DDIM), offering faster sampling
• Provides a more comprehensive set of pre-trained models
• Includes additional utilities for image manipulation and processing

Cons of ddim

• Less active development and maintenance compared to diffusion
• Fewer options for customizing the diffusion process
• Limited documentation on advanced usage and model fine-tuning

Code Comparison

diffusion:

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

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,
    )
    noise = th.randn_like(x)
    nonzero_mask = (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
    sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
    return {"sample": sample, "pred_xstart": out["pred_xstart"]}

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 maintenance, with regular updates and improvements

Cons of denoising-diffusion-pytorch

• Higher complexity, which may be overwhelming for beginners or those seeking a simpler implementation
• Potentially slower execution due to additional features and abstractions

Code Comparison

diffusion:

def p_sample(self, x, t, clip_denoised=True):
    t_batch = torch.full((x.shape[0],), t, device=x.device, dtype=torch.long)
    noise_level = extract(self.sqrt_alphas_cumprod, t_batch, x.shape)
    x_recon = self.predict_start_from_noise(x, t=t_batch, noise=self.denoise_fn(x, t_batch))
    if clip_denoised:
        x_recon.clamp_(-1., 1.)

denoising-diffusion-pytorch:

@torch.no_grad()
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)

Pros of k-diffusion

• More flexible and customizable implementation
• Supports a wider range of sampling methods
• Better documentation and examples for usage

Cons of k-diffusion

• Less optimized for performance in some cases
• May require more setup and configuration
• Potentially steeper learning curve for beginners

Code Comparison

k-diffusion:

model = diffusion.get_model(config)
x = torch.randn(1, 3, 64, 64)
samples = diffusion.sample(model, x, steps=1000)

diffusion:

model = create_model(config)
noise = torch.randn(1, 3, 64, 64)
samples = p_sample_loop(model, noise, num_timesteps=1000)

Both repositories provide implementations of diffusion models, but k-diffusion offers more flexibility and customization options, while diffusion may be simpler to use out of the box. The code comparison shows that k-diffusion uses a more modular approach, separating the model creation and sampling steps, while diffusion combines these steps in a single function call.