YODA: You Only Diffuse Areas. An Area-Masked Diffusion Approach For Image Super-Resolution
Authors: Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, Andreas Dengel
Abstract: This work introduces “You Only Diffuse Areas” (YODA), a novel method for partial diffusion in Single-Image Super-Resolution (SISR). The core idea is to utilize diffusion selectively on spatial regions based on attention maps derived from the low-resolution image and the current time step in the diffusion process. This time-dependent targeting enables a more effective conversion to high-resolution outputs by focusing on areas that benefit the most from the iterative refinement process, i.e., detail-rich objects. We empirically validate YODA by extending leading diffusion-based SISR methods SR3 and SRDiff. Our experiments demonstrate new state-of-the-art performance gains in face and general SR across PSNR, SSIM, and LPIPS metrics. A notable finding is YODA’s stabilization effect on training by reducing color shifts, especially when induced by small batch sizes, potentially contributing to resource-constrained scenarios. The proposed spatial and temporal adaptive diffusion mechanism opens promising research directions, including developing enhanced attention map extraction techniques and optimizing inference latency based on sparser diffusion.
What, Why and How
Here is a summary of the key points from this paper:
What: This paper proposes a new method called “You Only Diffuse Areas” (YODA) to improve image super-resolution using diffusion models.
Why: Standard diffusion models apply the same amount of diffusion to the entire image during training and sampling. But not all image regions require the same level of diffusion for super-resolution. YODA selectively applies diffusion to important regions using attention maps, enabling more efficient and higher quality super-resolution.
How:
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YODA uses time-dependent masking to determine which regions to diffuse based on an attention map and the current time step. This focuses diffusion on detail-rich areas.
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Attention maps are generated using the self-supervised method DINO. Combining multiple attention heads works best.
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The training loss and sampling process are modified to incorporate selective diffusion based on the masks.
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YODA is evaluated by extending SR3 for faces and SRDiff for general images. It improves results over both baseline methods across PSNR, SSIM and LPIPS.
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An additional benefit is YODA stabilizes training for SR3 with small batch sizes, preventing color shifts. This enables training on limited hardware.
In summary, YODA selectively applies diffusion to important image regions over time using attention maps. This improves efficiency, quality and training stability for diffusion-based super-resolution models.
Main Contributions
Here are the main contributions of this paper:
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Proposes YODA, a novel attention-guided and time-dependent diffusion approach for image super-resolution. This selectively applies diffusion to important spatial regions over time.
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Analyzes different techniques to derive attention maps and finds that using DINO with a ResNet-50 backbone and combining attention heads works best.
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Modifies the training loss and sampling process of diffusion models to incorporate selective diffusion based on time-dependent masking.
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Validates YODA by extending leading diffusion models SR3 and SRDiff, demonstrating improved performance for face and general super-resolution over baseline methods.
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Shows YODA has a stabilization effect during training of SR3 with small batch sizes, preventing color shifts induced by limited hardware.
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Opens promising research directions such as developing enhanced attention map extraction methods and optimizing inference latency through sparser diffusion steps.
In summary, the main contributions are proposing the YODA approach for selective diffusion, integrating it effectively into training and sampling of diffusion models, and demonstrating improved performance and training stability through experimental validation.
Method Section
Here is a summary of the methodology section from the paper:
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Introduces the concept of time-dependent masking to selectively apply diffusion. An attention mask indicates the importance of each spatial location. Higher mask values lead to more diffusion steps applied in that region over time.
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A lower bound hyperparameter is used to ensure a minimum number of diffusion steps at each location. The time-dependent mask is formulated based on the attention mask and time step.
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The training loss is modified to apply diffusion only to regions selected by the time-dependent mask at each step.
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The sampling process is adapted to handle masked and non-masked regions correctly across time steps. Regions needing refinement are diffused to get a super-resolved (SR) image. Other areas remain as low-resolution (LR) image sampled from the original LR input.
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The SR and LR regions are then combined to reconstruct a complete image for the next iteration. This retains the necessary noise level in non-diffused areas.
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Over time, the SR prediction regions expand progressively based on the masks, enabling gradual enhancement of the full image.
In summary, the methodology introduces time-dependent masking based on attention maps, and adapts the training objective and sampling process of diffusion models to incorporate selective diffusion to important regions. This provides a general framework to apply YODA to any existing diffusion model.
High-Level Pseudo Code
Here is high-level pseudocode for the key components of YODA:
# Attention Map Extraction
attention_map = DINO(lr_image)
# Time-Dependent Mask
mask = get_mask(attention_map, time_step, lower_bound)
# Training
for t in timesteps:
masked_noise = mask * (noise - model(lr, noise, t))
loss = L1_loss(masked_noise)
update_model(loss)
# Sampling
for t in reversed(timesteps):
# Identify diffused regions
diffused = mask * sample
# Split into SR and LR regions
sr_region = diffuse_model(diffused)
lr_region = sample_lr(lr_image)
# Reconstruct full image
sample = mask * sr_region + (1-mask) * lr_region
# Final HR Image
hr_image = sampleIn summary, the key steps are:
- Extract attention maps from LR image
- Generate time-dependent masks
- Modify training loss for selective diffusion
- Split image into SR and LR regions during sampling
- Reconstruct full image and repeat process over time
The selective diffusion through masking at each time step allows concentrating on important image regions and efficiently improving quality.
Detailed Pseudo Code
Here is more detailed pseudocode to implement the key components of YODA:
# Hyperparameters
T = 1000 # Total timesteps
alphas = [schedule of noise levels]
# Attention Map Extraction
attention_map = DINO(lr_image)
attention_map = combine_heads(attention_map)
# Time-Dependent Mask
lower_bound = 0.05
def get_mask(attention_map, t):
mask = np.zeros(attention_map.shape)
for i in range(H):
for j in range(W):
if t * (attention_map[i,j] + lower_bound) >= t:
mask[i,j] = 1
return mask
# Training
for t in range(1, T+1):
# Get mask
mask = get_mask(attention_map, t)
# Sample noise
noise = sqrt(alphas[t]) * lr_image + sqrt(1-alphas[t]) * epsilon
# Calculate loss
masked_noise = mask * (noise - model(lr, noise, t))
loss = L1_loss(masked_noise)
# Update model
update_model(loss)
# Sampling
for t in reversed(range(0, T+1)):
# Get mask
mask = get_mask(attention_map, t)
# Identify diffused regions
diffused = mask * sample
# Denoise diffused regions
sr_region = model(lr, diffused, t) + sqrt(1-alphas[t]) * epsilon
# Sample LR regions
lr_region = sqrt(alphas[t]) * lr_image + sqrt(1-alphas[t]) * epsilon
# Reconstruct full image
sample = mask * sr_region + (1-mask) * lr_region
# Final HR Image
hr_image = sampleThis implements the key steps of YODA - extracting attention maps, creating time-dependent masks, modifying the training objective for selective diffusion, and adapting the sampling process to handle masked and non-masked regions.