Hands-on Practical: Comparing Advanced Samplers

You've learned about the theoretical underpinnings of advanced sampling algorithms designed to overcome the speed limitations of basic diffusion methods. Now, let's put theory into practice by implementing and comparing several popular samplers. Our goal is to observe the trade-offs between generation speed (primarily influenced by the Number of Function Evaluations, or NFEs, which correlates with inference steps) and the resulting sample quality.

We will use a pre-trained diffusion model and compare the performance of the standard DDIM sampler against more advanced solvers like DPM-Solver++ and UniPC. This exercise will provide practical insight into choosing the right sampler for your specific needs.

Setup

First, ensure you have the necessary libraries installed. We'll primarily use the Hugging Face diffusers library for its convenient pipeline and scheduler implementations, along with PyTorch.

# pip install diffusers transformers accelerate torch
import torch
from diffusers import DiffusionPipeline, DDIMScheduler, DPMSolverMultistepScheduler, UniPCMultistepScheduler
import time
import matplotlib.pyplot as plt
from PIL import Image
import math

# Check for GPU availability
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")

# Load a pre-trained pipeline (e.g., Stable Diffusion or a smaller model)
# Using a smaller model like runwayml/stable-diffusion-v1-5 with reduced resolution
# or google/ddpm-cifar10-32 might be faster for experimentation.
# Here we use stable-diffusion-v1-5 as an example. Adjust based on your hardware.
model_id = "runwayml/stable-diffusion-v1-5"
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32)
pipeline = pipeline.to(device)

# Define a common prompt for comparison
prompt = "A photo of an astronaut riding a horse on the moon"

Make sure to log in to Hugging Face if necessary (huggingface-cli login) to download models like Stable Diffusion. Adjust the model_id and torch_dtype based on your available hardware and desired model. Using torch.float16 significantly speeds up generation on compatible GPUs.

Defining the Samplers

The diffusers library makes it easy to switch schedulers (samplers). We'll instantiate the ones we want to compare: DDIM, DPM-Solver++, and UniPC.

# Instantiate the schedulers we want to compare
scheduler_ddim = DDIMScheduler.from_config(pipeline.scheduler.config)
scheduler_dpm = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)
scheduler_unipc = UniPCMultistepScheduler.from_config(pipeline.scheduler.config)

# Store them for easy access
samplers = {
    "DDIM": scheduler_ddim,
    "DPM-Solver++": scheduler_dpm,
    "UniPC": scheduler_unipc
}

Running the Comparison

Now, let's write a function to generate images using a given sampler and number of inference steps, measuring the time taken. We'll iterate through different step counts for each sampler.

def generate_and_time(pipe, sampler_name, sampler, prompt_text, num_steps, num_images=1):
    """Generates images using a specified sampler and times the process."""
    pipe.scheduler = sampler  # Set the pipeline's scheduler
    
    start_time = time.time()
    # Use a fixed generator for reproducibility if desired
    generator = torch.Generator(device=device).manual_seed(42) 
    
    images = pipe(
        prompt=prompt_text, 
        num_inference_steps=num_steps, 
        generator=generator,
        num_images_per_prompt=num_images
    ).images
    
    end_time = time.time()
    generation_time = end_time - start_time
    
    print(f"Sampler: {sampler_name}, Steps: {num_steps}, Time: {generation_time:.2f}s")
    
    return images, generation_time

# Define the step counts to test
step_counts = [10, 20, 30, 50] 
num_samples_per_setting = 1 # Generate 1 image per setting for quick visual comparison

results = {} # Store images and times

# Run generation for each sampler and step count
for name, sampler_instance in samplers.items():
    results[name] = {"images": [], "times": [], "steps": []}
    for steps in step_counts:
        # Ensure model is compiled only once if using torch.compile (optional optimization)
        # pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True) # Example
        
        generated_images, gen_time = generate_and_time(
            pipeline, name, sampler_instance, prompt, steps, num_samples_per_setting
        )
        results[name]["images"].extend(generated_images) # Store the first image
        results[name]["times"].append(gen_time)
        results[name]["steps"].append(steps)

Note: The optional torch.compile line can further accelerate generation on newer PyTorch versions and compatible hardware but might add overhead on the first run.

Qualitative Analysis: Visual Comparison

The most direct way to compare samplers, especially concerning artifacts or detail levels, is visual inspection. Let's arrange the generated images for easy comparison.

def plot_comparison_grid(results_dict, steps_list):
    """Plots a grid of generated images for comparison."""
    num_samplers = len(results_dict)
    num_steps_options = len(steps_list)
    
    fig, axes = plt.subplots(num_samplers, num_steps_options, figsize=(num_steps_options * 3, num_samplers * 3.5))
    fig.suptitle("Sampler Comparison: Quality vs. Steps", fontsize=16)

    for i, (sampler_name, data) in enumerate(results_dict.items()):
        for j, steps in enumerate(steps_list):
            img_index = j # Since we generated 1 image per setting
            if img_index < len(data["images"]):
                ax = axes[i, j]
                ax.imshow(data["images"][img_index])
                ax.set_title(f"{sampler_name}\n{steps} Steps ({data['times'][j]:.1f}s)")
                ax.axis('off')
            else:
                axes[i, j].axis('off') # Handle potential errors if generation failed

    plt.tight_layout(rect=[0, 0.03, 1, 0.95]) # Adjust layout to prevent title overlap
    plt.show()

plot_comparison_grid(results, step_counts)

Examine the output grid carefully:

• Low Step Counts (e.g., 10-20): Do the advanced samplers (DPM-Solver++, UniPC) produce significantly better results than DDIM? Look for coherence, detail, and fewer artifacts. Often, higher-order solvers maintain better quality at fewer steps.
• Higher Step Counts (e.g., 30-50): Do the images converge to a similar quality level? Does any sampler introduce specific artifacts even at higher steps? DDIM might require more steps to reach the quality achieved by others sooner.
• Consistency: Does the sampler produce plausible results consistently, or are there occasional failures even with enough steps?

You will likely observe that DPM-Solver++ and UniPC can generate reasonable images in fewer steps compared to DDIM. However, the optimal number of steps and the subtle differences in output style can vary between models and prompts.

Quantitative Analysis: Speed

We already recorded the generation time. Let's visualize the time taken versus the number of steps for each sampler.

Comparison of generation time for different samplers across varying numbers of inference steps. Note: Actual times depend heavily on hardware, model size, image resolution, and software optimizations. The data shown here is illustrative.

This chart typically shows that generation time scales roughly linearly with the number of steps for most samplers. While the advanced solvers might have slightly different computational overhead per step, their main advantage lies in achieving good quality with fewer steps, thus reducing the overall time. In this illustrative data, DPM-Solver++ and UniPC show slightly lower times for the same step count compared to DDIM, but their primary benefit comes from needing fewer steps (e.g., achieving good quality at 20 steps vs. DDIM needing maybe 30-50).

Discussion and Takeaways

This practical demonstrates the clear benefits of using advanced sampling algorithms like DPM-Solver++ and UniPC.

• Speed vs. Quality Trade-off: Advanced solvers generally allow for faster inference by achieving comparable or better quality than DDIM in significantly fewer steps. For applications requiring near real-time generation, using a solver like DPM-Solver++ or UniPC with a low step count (e.g., 15-25) is often preferred.
• Sampler Choice: The "best" sampler can be task-dependent. While DPM-Solver++ and UniPC are excellent general-purpose choices, DDIM might still be useful for specific reproducibility needs or when its particular smoothing effect is desired. Experimentation is key.
• Hyperparameter Tuning: The number of inference steps is a critical hyperparameter. This practical shows how drastically results change with it. Other parameters, like the guidance scale (CFG), also interact with the sampler and step count, requiring careful tuning for optimal results.

By running these comparisons yourself, potentially with different models or prompts, you gain valuable intuition for selecting and configuring samplers effectively, balancing the demands of generation speed and output fidelity in your own projects. Remember also to consider optimizations covered later, such as quantization or model compilation, which work alongside the choice of sampler to further improve performance.