TD-Paint: Faster Diffusion Inpainting Through Time-Aware Pixel Conditioning
Authors: Tsiry Mayet
Pourya Shamsolmoali
Simon Bernard
Eric Granger
Romain HÉRAULT
Clement Chatelain
Venue: ICLR 2025 Poster (2025)
Keywords: Diffusion model
Inpainting
Primary area: generative models

Abstract: Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of the input condition during the initial sampling steps. As a result, the sampling trajectory deviates from the data manifold, requiring complex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion-based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TD-Paint outperforms state-of-the-art diffusion models while maintaining lower complexity.

Published as a conference paper at ICLR 2025
TD-PAINT: FASTER DIFFUSION INPAINTING
THROUGH TIME- PIXEL CONDITIONING
Tsiry Mayet
Pourya Shamsolmoali
INSA Rouen Normandie, LITIS UR 4108, F-76000 Rouen, France
University of York, United Kingdom East China Normal University, China
Simon Bernard
Eric Granger
Université Rouen Normandie, LITIS UR 4108, F-76000 Rouen, France
LIVIA, Dept. of Systems Engineering, ETS Montreal, Canada
Romain Hérault
Clément Chatelain
Université Caen Normandie, CNRS, GREYC UMR6072, F-14000, Caen, France
INSA Rouen Normandie, LITIS UR 4108, F-76000 Rouen, France
ABSTRACT
Diffusion models have emerged highly effective techniques for inpainting, how- ever, they remain constrained by slow sampling rates, While recent advances have enhanced generation quality, they have also increased sampling time, thereby lim- iting scalability in real-world applications. We investigate the generative sam- pling process of diffusion- make minimal use of the input condition during the initial sampling steps. As result, the sampling trajectory deviates from the data manifold, requiring com- plex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion- based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TDD-aint out- performs state-of-the-art diffusion models while maintaining lower complexity. Github code: https://github.. http:/ghhwhboooo/aaaaggnn//////a
inpainting models and observe that these models
INTRODUCTION
Given an image and a binary mask, image inpainting aims to generate the missing region while preserving the semantics of the visible region. This task is challenging because the generated content must not only be coherent with the existing parts of the image but also appear realistic. Additionally, the generation process should be stochastic model must also address variations in mask shape and size. Generalizing to unseen masks during training and accurately filling large missing regions further complicates the task.
produce diverse outputs. An effective inpainting
Diffusion models have shown significant success generative models (Dhariwal & Nichol, 2021 Rombach et al.) 2022), by approximating the distribution real images through a fixed Markov chain that transforms Gaussian noise into the real image distribution. During training, forward diffusion process gradually adds noise to an image, and the model trained to reverse this process, learning denoise and recover the original image distribution. During generation, the backward diffusion iteratively removes noise from an initial Gaussian noise image. The trained model predicts and removes noise at each step, gradually refining the image until photorealistic result is achieved.

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øT-1
-f-1
kn
GT
mask
27
mask
RePaint
(Ours) TD-Paint
Comparison noisy-condition models (e-g., RePaint) and TD-Paint generation pro- cesses. Reverse denoising steps (po (1 are depicted in purple lines forward noising steps ((+))) are shown in green lines - Here, 2t represents the input to the diffusion process at step f, mask denotes the conditional area (shown in white) and the region to be generated (shown in black), and ê; represents the model's 's prediction at diffusion step t. (left) RePaint (Lugmayr et al.) 2022) generation process. RePaint applies cycle of reverse and forward diffusion steps. It can be observed that the intermediate steps of the generation process lack consistency, changing from a man with dark hair f due to the synchronization process, where the initially predicted images 2n are not well aligned with clean
Figure 1:
with blond hair at d to woman at LT+k- These changes occur
the given condition. (right) TD-Paint generation process. In comparison, TD-Paint condition from the beginning of the inpainting process, resulting in faster and more stable process. Note how the intermediate TD-Paint steps are consistent from one another.
Methods proposed in the literature have investigated using the standard diffusion model (Lugmayr et al. 2022 Chung et al. (022) for inpainting by combining the noisy condition with the current generation each step. This technique has limitations. While the textures match, creates disharmony between the conditioning and generation parts. This disharmony comes from the fact that, during the early steps of the generation process, the condition contains a lot of noise that the model cannot leverage. Therefore, the generation moves away from the intended semantics and produces unsatisfactory results. An illustration of such inpainting can be found in Figure Iefft).
To address this limitation, RePaint (Lugmayr et al. 2022) introduced resampling mechanism that repeats the diffusion steps multiple times, enabling the model to synchronize condition and gener- ation better. Although RePaint produces highly faithful images, this resampling significantly slows the generation process. Indeed, RePaint requires approximately 5k steps to generate a single image, increasing time complexity. An illustration of RePaint's generative process is shown in Figure (left). Other approaches introduce additional constraints at each diffusion step(Chung et al.) 2022] Li al. 2023). For example, (Chung et 2022 integrates correction mechanism that encour- ages the diffusion path to remain close to the data manifold. This is achieved by minimizing the reconstruction error of the known image region relative the unknown region.
In contrast to models that use noisy Condition(Lugmayr et al., 2022 Chung et al. 2022). our Time-aware Diffusion Paint (TD-Paint) approach integrates the currently generated sample with clean condition. Our method uses semantic information from the beginning of the generation process, resulting more efficient and cost-effective approach. To achieve this, TD-Paint uses time conditioning derived from the standard formulation of diffusion models. Instead of using a single scalar for the entire image, TD-Paint assigns a unique value to each pixel. The known image region is assigned smaller t, indicating lower noise levels. In contrast, the region to be generated assigned t value proportional to the current step in the generation process. An illustration of TD-Paint's generative process is provided in Figure [ right).
Our main contributions are summarized as follows:
We propose novel noise modeling paradigm for diffusion models that allows for integrating vary-
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ing noise levels into the input of diffusion models for the known and unknown regions. TD-Paint exploits time conditioning in diffusion models to achieve faster and higher-quality generation.
An extensive set of experiments on the challenging CelebA-HQ. ImageNetI K and Places2 datasets demonstrates that our TD>-Paint, not only outperforms state-of-the-an diffusion- models but also surpasses other inpainting methods, including those based on CNNs and transformers. Addi- tionally, the results indicate TD-Paint is a more cost-effective solution for diffusion models.
RELATED WORKS
Inpainting aims to fill in a missing part of the image realistically. Traditional inpainting methods try combine techniques to propagate texture and structural information onto the missing parts (Criminisi et al. 2004). Some algorithms (Grossauer) 2004) use large image datasets and assume that the possible semantic space for missing regions is limited. In recent years, deep learning models for inpainting have made impressive progress using two types of generative models, VAEs (Kingma) & WWlling 2013 and GANs (Goodfellow et al. 2020 Mirza & Osindero 2014).
(a) Single-Stage Inpainting: Most single-stage methods use the context encoding setting intro- duced by Pathak et al. (Pathak al., 2016). with an encoder-decoder setup. A reconstruction loss (L2) ensures global structure consistency, while an adversarial loss ensures the reconstruction is realistic. Global consistency is an important consideration. CNNs are limited by a receptive field that grows slowly with network layers. Many layers are needed for information to travel from one side of the image to the other. Dilated convolution (Yu & Koltun 2015) has been used by (lizuka eet al. 2017) to increase the receptive field. Partial convolution (Liu et al. 2018) uses mask infor- mation to attend only the visible regions. The pyramid context encoder (Zeng et al., 2019) learns an affinity map between regions in a pyramidal fashion. Fourier convolution (Suvorov et al. 2022) aims to provide a global receptive field to both the inpainting network and the loss function. Fast Fourier Convolutions have an image-wing wide receptive field, which helps with large missing areas. Mask-Aware Transformer (MAT) (Li et al. (2022) is a transformer-based architecture that allows the processing of high-resolution images. A customized transformer block considers only valid tokens, and style manipulation module updates convolution weights with noise to produce diverse outputs.
(b) Progressive Image Inpainting: These methods seek address global consistency by using coarse-to -o---to---eeee multi-stage approaches. Multiple generations are possible for a large missing region in single-stage training. Some may have a large pixel-to-pixel distance from the original ground truth, which can be misleading when training models with pixel-wise distance losses. To address this issue, Yun et al. (Yu et al., 2018) proposed two-stage generative approach. The first stage produces coarse output optimized with L11 loss incorporating spatial discounting, while the second stage refines the output further using both global and local critics. Gated convolution (Dauphin et al., 2017) has been used in coarse-to-fine network to learn valid pixels (Yu et al. 2019).
(c) Prior Knowledge Inpainting: These methods leverage and mine information from generative models. Prior Guided GAN (PGG) (Suvorov et al.) 2022) uses the latent space of pre-trained GAN and learns to map masked images to this space using an encoder. A masked image can be mapped to latent code during inference, and the generator can produce corresponding inpainted image. Deep Generative Prior (Pan et al.) 2021) relaxes the frozen generator assumption of GAN inversion methods and proposes progressively refining each layer. PSP (Richardson al. 2021] uses StyleGAN (Karras et al. 2019) latent space to encode an image into its latent space. Inpainting is formulated as domain translation task performed in the latent space of StyleGAN, removing adversarial components from the training process.
(d) Diffusion Model Inpainting: While GANs have recently shown impressive results, most ap- plications are limited to generating a specific domain. In contrast, Diffusion models have gained traction for image generation; Denoising Diffusion Probabilistic Model (DDPM) (Ho et al., 2020) and Denoising Diffusion Implicit Models (DDIM) (Song et al., 2020a) can generate very diverse and high-quality images. Some unconditional diffusion models have shown the ability to perform zero-shot inpainting (Sohl-Dickstein et al., 2015 Song et al., 2020b) but provide only qualitative results. The Pixel Spread Model (PSM) (Li et al. 2023) uses a decoupled probabilistic model that combines the efficiency of GAN optimization with the prediction traceability of diffusion models. Latent Diffusion Models (LDM) (Rombach et al., 2022) decouple two tasks: image processing and compression, and the denoising process is learned in latent space instead of pixel space. Inpainting
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is performed by encoding the masked input image, downsampling the inpainting mask, and con- catenating them as additional conditions to the denoising model. Designing an image and mask conditional diffusion model requires a special architecture accept additional input for inpainting, as done in (Rombach et al. 2022 et et al. 2023), and often treats inpainting as a domain translation task. In contrast, TD-Paain does not require any architecture modification by directly combining the masked input and current generation with different noise levels. Differential Diffusion (Levin & Fried (023) introduces new approach to soft-inpainting, where both the generated region and the conditional input are modified to ensure coherence across the entire image. This method uses LDM along with a strength map to focus on different image regions during each diffusion step. However, Differential Diffusion operates
noisy conditional inputs. The Manifold Constrained Gradient (MCG) (Chung al., 2022) adds a correction term to ensure each sample step remains close to the data manifold, allowing for more stable inpainting. RePaint (Lugmayr et al.) (022) which aligns with our approach, combining the noisy conditional region with the current generation, where the diffusion model iteratively updates missing pixels using the surrounding context. We observe that during the early inpainting step of RePaint, the condition is dominated by noise and does not contain any semantic information. This causes the model prediction to deviate from the target manifold (see Figure [left). A resampling mechanism is needed to synchronize the condition and generation re- gions, allowing semantically corrected images to be produced at the cost of significantly increasing computation time.
Instead of degrading the condition to the same level as the generation, we propose keeping it clean and conditioning the missing part on the known pixels from the beginning of the generation process (see Figure rright). Although it requires fine-tuning the model, our approach provides much faster sampling by avoiding resampling (Lugmayr et al. 2022 or additional constraints (Chung et al.) p.22]. We use temporal information to model the detail in the image. The condition, containing clean data, is assigned low noise level while the generation region starts with maximum noise that gradually decreases during the process. This approach allows for direct combination of the clean and generated regions in the same space without changing the model architecture.
3 BACKGROUND ON INPAINTING DIFFUSION MODELS
Diffusion Models: Diffusion models learn data distribution from a training dataset by inverting a noise process. During training. the forward diffusion process transforms data point 2g into Gaussian noise 2т (0, I) in T steps by creating series of latent variables Ti,.,, JT using:
q< Nax / 3, B,1),
where 3r represents the predefined variance schedule. Given a = 1 3r, ā = IT a, and N/ I), Fe at step can be marginalized from øy using the reparametrization trick follows:
It Vāgzo + 11-acc.
The reverse denoising process po pa( (x,,, allows generating from the data distribution by first sampling from IT (O, I) and iteratively reducing the noise in the sequence IT, .... Wo- T-.. The model eo ( t) is trained to predict the added noise to produce the sample at time step t. The model is trained using mean square error (MSE):
L Ee ll (Vo.oo + v1 - -,,, c1l2. c
RePaint Methodology: Given an image æ and binary mask m, the goal of inpainting to generate the missing region r specified by @ (1 m) conditioned on the known region x specified by a O m. For this, RePaint combines noisy version of the condition wg @ m with the previous output of the generation process:
I _ ~ N (Vārza, (1 ā. T.)
(4)
9 ~ N (na (I,, 1), Eo Ex(,,, ()) 4
(5)
Ft-1 - ④ m + E ② (1 - m))
(6)
RePaint uses the known pixels noisy condition to guide the generation of the unknown pixels. In the first inpainting step, inputs contain a high noise level and limited information about the condition. This leads to samples that deviate from the intended semanties of the condition, often resulting in
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artifacts. To address this, RePaint introduces resampling mechanism that harmonizes the two semantics by applying forward diffusion on the output тt-1 back to 2t+j- The denoising and re- noising process involves executing the same diffusion step multiple times during the generation phase, sacrificing computational efficiency to achieve higher image quality.
4 THE PROPOSED TD-PAINT METHOD
In TDDPaint, the model is conditioned on known and unknown regions using the time step t, already present in diffusion models. Instead of using _ za is combined directly with 2; without forward diffusion. This results in faster diffusion process without changing the model architecture.
4.1 NOISE MODELING FOR FAST INPAINTING
The objective of TD-Paint is to enable the diffusion model to discern the infor- mation content of each input region. This allows the model to differentiate between conditioned regions and those needing to be painted. By maintaining the known regions free of additional noise, TD-Paint preserves the maximum amount of information in these areas. Using the time step t allows for smooth and continuous modeling of information content, Regions from the known part of the im- age given a t value close to zero, indicating minimal noise. On the other hand, regions that need to be inpainted start with a value close to T, which progressively decreases during the generation process. To do so, ilarly, other variables are accommodated to perform a pixel-wise diffusion process. With one t per pixel, ar (resp. āg 3r) becomes a, (resp. ā,, (r)). This innovative modification can be integrated into most diffusion model training pipelines. Figure 2 illlsstrates the intermediate images used for training. During training, each input image pixel Jz,y receives an amount of noise controlled by тi The forward diffusion process is then applied to on a pixel-wise basis, as illustrated in Figure and can be formulated as:
Training Process:
(0, .... T} is transformed from scalar tensor E {o,-... T) T) n Sim-
Iy Va, + vI a
in which ~ (O,,), -~ ~ Quain and @uain is a training strategy to sample different noise per pixel. The diffusion network then predicts e, using less noisy regions reconstruct more noisy regions by optimizing the loss:
L= cx 1)
(
The final component of TD-Paint training is the strategy used for @main A random patch size and proportion of patches are sampled to define a condition. Based on this sampled proportion, the input image is then divided into known regions ιh and unknown regions x (with corresponding t 0 and to t). The possible patch sizes are defined as powers of two, i..., 2' N, 2 < w. up to the maximum size of the image. The fraction of pixels designated as the known region is represented by a real value within the interval [0, 1]. ensuring that least one patch remains in the unknown region. For example, illustrated in Figure 2 for patch size of 128, 25% of the pixels are assigned to the known region.
Generation Process: Inpainting an image with TD-Paint involves sampling time / for the condi- tioning region Ω and a time for the inpainted region x using Φ. Unless otherwise specified, we set tthh for the condition and of t for the region to inpaint for training and generation. For generation, the known region @ is merged with the current unknown region 2f, and one reverse step can be expressed as: x, - x @1 m) + x
2 N (NO Eo (z(
(10)
This approach allows the of input-known regions from the beginning of the diffusion process. Consequently, we can eliminate RePaint's 's resampling mechanism. resulting in faster inpainting. The general algorithm for inpainting an image with an arbitrary e function is given by Algorithm
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30
2 Zo Fi
2t-1
171
Next Iteration
Figure 2: Illustration of the TD-Paint patch- wise training procedure. Each region of the input zo receives different level of noise con- trolled by т. The network uses less noisy re- gions reconstruct more noisy regions.
Figure 3: The conditional generation proce- dure. TD-Paint modifies the standard denoising process to condition the diffusion model on the known region without noise while gradually de- noising the generated region.
4.2 TIME-AWARE DIFFUSION ARCHITECTURE
Diffusion models using U-Net architectures (Ronneberger et al. 2015) can incorporate
Algorithm PDintint Generation Process.
Require: _º (()) condition
time map т without requiring architectural modifications. This adaptation achieved by applying pixel-wise time conditioning to each pixel in the feature maps.
Require: condition mask
Require: 6e giving the condition noise level for
the known and unknown regions
1: FT ~ (0,
Classical time conditioning first uses position embedding layer obtain a time embedding E Rd from the time step f:
2: for = T, T,.., do
3:
<~ I0,
4: ~~ NO ift > 1, clse & =
(11) where L denotes linear layers, is the sigmoid function, and
7 = L [E(t) (L(((())))
5: x Vaga2" + V
6:
T
о (1 m) + 0:
2
④ m
sinusoidal timestep embed-
7:
Er x oi
① (1 - m) + x
m
ding. Additionally, each layer of the U-Net, the vector 7 is used to perform time condition- ing with scale-shift normalization and can be written as:
be
8: Ft-1
va,
I-
vT-a
B.
oo (IT,T) +
0+2
9: end for
Ri. GGN(h)) (. (1 Tscate (T)) + Tshin (
10:
return 250
(12)
where h E x the current features for layer GN is a group normalization layer, while
Lycale and Lytan are linear layers that change the dimension of ry from d to q, We apply the pixel-wise time conditioning across each spatial dimension h by using Tig instead of follows:
Fij = L(E(D(T) oLL(E(LEE)) (((((((())ss))))
(13)
1 (N))), x (1 Lucale (T) Lain
(14)
where I Rdxhxx is the embedding of time T, and D is downscaling operation function that rescales 7 to hy x w
RESULTS AND DISCUSSION
This section empirically shows that TD-Paint: (a) produces high-quality inpainting with clean con- dition on various mask sizes and shapes, on par or better than other inpainting models; (b) provides more efficient sampling, making faster than other diffusion- based models without requiring ded- icated architecture; (c) generates diverse, high-quality images. Details on the training masks and their corresponding ablation study are presented in Appendix A and additional qualitative results are provided in Appendix [ In Appendix C TD-Paint is compared with state-of-the -ftt----t-- mask and mmnge-conditioned inpainting models: the CNN-based LaMa (Suvorov al. 2022) and transformer-
based MAT (Li et al. 2022).
We use bilinear interpolation, but min-pool other techniques could be used with similar effect.

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1000
Por
ICD
Th-Paa
BLD
Toeahhie TDchir
Figure 4: Qualitative results: TD-Paint against the inpainting methods on CelebA-HQ. Zoom in for better details. Additional examples can be found in Appendix D
5400
LBM
Touth TD
MC0
Cahar
BED
Fowehhire
To/Me
Figure 5: Qualitative results: TD-Paint against state-of-the-ar inpainting methods on ImageNetIK. Additional examples can be found in Appendix D
5.1 EXPERIMENTAL METHODOLOGY
We evaluate TD-Paint against sfate-of-the-art diffusion-based inpainting methods in the pixel space: RePaint (Lugmayr al. 2022), which conditions the generative process using noisy inputs and synchronizes them with the output through resampling. MCG (Chung et al. 2022), which adds a correction term to keep the generation closer to the data manifold, and CoPaint (Zhang al. 2023a) that utilizes Tweedie's 's formula for better generation. Recent studies (Chung et al. 2022 Zhang et al., (023a) have shown that CoPaint and MCG are among the best-performing impainting methods. We denote the RePaint-20 model as using 20 resampling steps, while RePaint-1 refers to the model with resampling step matching the number of steps used by TD-Paint. Furthermore, we conduct comparisons with latent diffusion models that have access to the complete context: LDM
Baselines:
(Rombach et al., 2022) and ControlNet (Zhang et al. 2023b). Additionally, we compare against foundation model-based latent diffusion approaches, including Blended Latent Diffusion (BLD)
(Avrahami et al. 2023), Uni-paint (Yang et al. 2023). and PowerPaint (Zhuang et al. 2025).
Implementation Details: Our approach is validated using the CelebA-HQ (Karras al., 2018) dataset, the ImageNetIK (Russakovsky et al. 2015) dataset, and the Places2 dataset (Zhou et al. 2018) at 256x256 resolution. We modify the implementation of (Dhariwal & Nichol (021), main- taining all their hyperparameters. Training on CelebA-HQ is conducted for approximately 150K steps with batch size 64 on 4 A100, for ImageNetIK and Places2 for about 200K steps with batch size 128 on 8 A 1100. For baselines, we utilize existing code and pre-trained models when available. For ImageNetIK, we train LaMa for 1M steps on batch size using their implementation, and MAT for 300K steps on batch size 32 using their implementation. Both LDM and ControlNet are trained using computational resources equivalent
TD-Paint. For LDM, the encoded masked image and the downsampled mask provide additional context during the sampling process. For ControlNet, the encoded masked image alone provides additional context.
Image quality is evaluated using established metrics from the inpainting lit- crature: the Learned Perceptual Image Patch Similarity (Zhang et al. 2018) (LPIPS), the Structural Similarity Index Measure (Wang et al. 2004) (SSIM), and the Kernel Inception Distance (Binkowski et al. 2018) (KID) (using the TorchMetrics (Nicki Skafte Detlefsen et al., 2022) implementation). The number of diffusion steps (NBS) and the mean time to inpaint an image (Runtime) are used to evaluate the computational efficiency of TD-Paint. For evaluation, we use 2,824 images from the CelebA-HQ test set, 5,000 images from ImageNet1 K, and 2,000 images from Places2.
Evaluation Metrics:
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3400
Coifiir
1014
BCD
Proothie
3
MC0
BED
Figure 6: Qualitative results: TD-Paint against state-of-the-art inpainting methods on Places2. Additional examples can be found in Appendix D
5.2 COMPARISON WITH IIFFUSION-BASDD MODELS
Wide and Narrow masks In the standard image inpainting scenario, TD-Paint is compared using Wide and Narrow masks following LaMa (Suvorov et al., 2022) protocol. Tables I and 2 shows that TD-Paint consistently outperforms other diffusion- based models, improving by 20% RePaint-20 LPIPS's on the Wide mask on CelebA-HQ, ImageNetIK and Places2, and by 30% on the Nar- row masks. MCG lacks global consistency, resulting in significant artifacts on Wide masks, as seen in Figures 4 and 5 where it produces eyes of different colors, strange textures, and inpaint- ing artifacts. RePaint produces high-quality images at the cost of significant inference time. Our approach can produce high-quality images while requiring much less processing time. When pro- cessing small masks. LDM occasionally produces minor artifacts, as evident in the top rows of aid These artifacts are more pronounced in ControlNet, which lacks mask information, resulting in inconsistencies between known and unknown regions. Among foundation-base els, BLD demonstrates superior performance across all datasets, while Uni-paint and PowerPaint show inferior results on CelebA-HQ but achieving better performance This performance variation may be attributed to BLD's use of the provided LDM baselines (our early experiments with Stable Diffusion yielded less favorable results), whereas both Uni-paint and PowerPaint utilize Stable Diffusion as their foundation.
mod-
ImageNet K and Places2.
Super-Resolve 2x and Altern. Lines masks In the Super-Resolve 2x setting, every other pixel is removed from the image, while the Altern. Lines setting removes every other line. All pixel- based baselines achieve low LPIPS scores and produce high-quality images for both types of masks (see Figures to 6). In contrast, latent-based models prove inadequate for this task due to their downsampling of inpainting masks, which results in critical information loss. TD-Paint outperforms all considered baselines on CelebA-HQ and comes a close second to CoPaint on ImageNet K and The third-best performing model RePaint. Compared to RePaint, PDit improves LPIPS by 115% in the Super-Resolve 2x setting and by 69% in the Altern Lines setting.
Places2, as shown in Tables [ and 2
Half and Expand masks The Half setting removes the right part of the images, and the Expand setting keeps only the central 64x64 part of a 256 pixel image. Both LPIPS and SSIM metrics are less suitable when significant portion of the image is missing, as they rely on a single ground truth. This penalizes methods that generate realistic images with semantics different from the ground truth (Lugmayr et al. (022). In this case, the KID, which measures the distance between distribu- tions, is more reliable for assessing image quality. Applying Half and Expand masks is particularly challenging, they remove substantial portions of the images. task where an important part of the images is removed. This complexity is shown both visually in Figures [oo6 by the quantitative Our model performs best on both the Half and Expand masks across all
results in Tables [ and 2 datasets (except Tor CelebA-HQ where it comes close to LDM), as measured by the KID, while significantly reducing the inference time. Figure 4 shows that TD-Paint can produce high-quality images in this challenging setting where RePaint lacks global consistency. On ImageNet I and Places2 (see Figures[Jandd
we observe that MCG and CoPaint often mirror the images from the Half mask, resulting in symmetrical outputs. Similar behavior is observed on Expand masks, with significant texture blending. ControlNet exhibits limited generalization capability when handling very large masks, likely due to the absence of mask information, whereas LDM demonstrates robust performance in these scenarios. Our analysis reveals that foundation-basee models yield inferior qualitative results CelebA-HQ and Places2 datasets. However, they show improved performance on ImageNetIK, where class names provide supplementary textual information, which is particu- larly beneficial in scenarios with limited contextual information.
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Table 1: Quantitative results: LPIPS and SSIM evaluation of diffusion models for inpainting on the CelebA-HQ, ImageNet1 and Places2 datasets.
CelebA-HQ
Wide
Norrow
Super-Resntse 2x
Altemn
Limes
Half
Expond
LPIPSL
SSIM
LPIPS_
SSIMT
LPIPSL
SSIM
I.PISL.
SSIMI
LPIPS.
SSIMI
LPIPS}
SSIM
RePaint-1
0.098
0.523
(1.076
0.273
0.688
0,016
0.925
(1238)
0.381
0.568
0.133
RePaint-20
0.067
0.861
0.036
0.906
0.037
0.901
0.016
0.961
0.189
0.645
0.489
0.191
MCG
0.070
0.823
0.045
0.856
0.081
0.829
0.030
0.903
0.173
0.639
0.437
0.250
CoPaint
0.073
0.835
0.044
0.877
0.033
0.899
0.020
0.929
0.185
0.638
0.454
0.210
LDM
0.060
0.863
0.049
0.879
1.233
0.040
0.740
0.126
0.168
0.682
0.432
0.228
ControlNet
0.091
0.834
0.199
0,763
0.593
0.187
0.318
0.362
0.205
0.620
0.552
0.173
B
0.014
0.942
0.017
0,953
0.039
0.208
0.558
0.2244
0.232
0.021
0.450
0,263
Uni-paiot
0.119
0.805
0. 0.169
0.776
0.825
0.072
0.711
0.127
0.282
0.355
0.087
0. 101
PowerPaint
0.111
0.821
0.110
0.837
0.788
0.111
0.567
0.207
0.300
0.548
0.604
0.155
TD-Paint
0.053
0.873
0.028
0.918
0.017
0.939
0.010
0.971
0.170
0.667
0.457
0.212
ImageNetTK
Wide
Narrow
Super
Rexolve 2x
AlGere
Limev
Hay
Expanal
RePaint-1
0.
0.781
0.137
0.784
0.022
0.208
0.245
0.6T9
0.336
0.542
0.050
0.007
RePaint-20 20
0.124
0.820
0.067
0.854
0.169
0.693
0.089
0.816
0.301
0.590
0.676
0.134
0.120
0.780
0.075
0,806
0.182
0.649
0.104
0.708
0,273
0.561
0.634
0. 152
CoPaint
0.137
0.798
0.080
0.835
0.071
0.818
0.040
0.884
0.299
0.578
0.645
0.145
LDM
0.138
0.744
0,7d8
1.101
0.0332
0,687
0.102
0,292
0.538
0.620
0.133
ControlNet
0.171
0.728
0.234
0.672
0.605
0.163
0.340
0.340
0.326
0.523
0.686
0.115
BLD
0.050
0.831
0.054
0,819
0.737
0.134
0.500
0.130
0,300
0.021
0.000
0.172
Uni paint
0.210
0.694
0.263
0.645
0.795
0.076
0.633
0.153
0.365
0.480
0.706
0.082
PowerPuint
0.712
0.182
0,725
0.797
0.097
0.520
0.217
0,356
0.490
0.657
0.100
TD-Paint
0.099
0.830
0.037
0.864
0.136
0.648
0.059
0.847
0.257
0.603
0.597
0.159
Places2
Wide
Norrow
Super-Resobre 2x
Aftemm
Lines
Hulf
Expand
RePaint-1
0.179
0.776
0.152
0.788
0.544
0.332
0.232
D.656
0.347
0.541
0.696
0.080
RePaint 20
0.138
0.816
0.078
0.853
0.155
0.729
0.085
0.841
0.320
0.584
0.688
0.121
MCG
0.131
0.788
0.092
0,809
0,250
0.626
0.115
0.786
0.269
0.577
0.618
0.156
CoPaint
0,133
0.801
0.082
0,812
0.071
0.828
0.037
0.902
0,282
0.581
0.630
0.150
LDM
0.128
0.807
0.112
0,803
1.176
0.022
0.706
0.091
0,283
0.582
0.612
0.142
ControlNet
0.173
0.781
0.2331
0,730
0.614
0.170
0.312
0.392
0.327
0.518
0.670
0.102
BLD
0.035
0.901
0.039
0.888
0.734
0.134
0.600
0.2006
0.335
0.367
0.663
0.173
Uni paint
0.184
0.753
0.219
0,717
0.835
0.071
0.651
0.159
0.344
0.522
0.727
0.082
PowerPuint
0.187
0.774
0.152
0.783
0.797
0.097
0.483
0.258
0.348
0.536
0.653
0.101
Paint
0.112
0.826
0.064
0.885
0.130
0.098
0,080
0.879
0.273
0.594
0.607
0.148
5.3 QUALITY vs. EFFICIENCY
We compare the time efficiency of different dif- fusion approaches working in the pixel space by computing the average time to sample 100 im- ages consecutively on single V100, and the results are reported Table State-of-the-ant art approaches require over 6x longer to sample compared to TD-Paint. The increased time in RePaint is due to the resampling mechanism needed to synchronize condition and genera- tion, while MCG requires additional back- ward pass and more steps to optimize the im- age. In contrast, model reduces inference time by trading fine-tuning costs, enabling faster generation of high-quality images than other diffusion bbased inpainting models.
Table 3: Inpainting speed for different diffusion model in the pixel space.
Ce LPIPS
Runtime
NBS
RePaint-1 RePaint-20
0.076
19.68
250
0.036
189.62
4750
MCG
0.045
184.59
1000
CoPaint
0.044
128.14
1000
TD-Paint 0.028
30.67
250
Input
Sample
Sample
Sample
Sample
Input
Sample
Sample
Sample
Sample 4
Figure 7:
Examples of diverse generations using TD-Paint on the CelebA and ImageNet, with the same input image and different initial noise. Additional examples are available in Appendix D
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Table 2: Quantitative results: KID evaluation of diffusion models for inpainting on the CelebA-
HQ, ImageNet K and Places2 datasets.
HQ
KIDI
Wide
Narrow
Super-Res. 2x
Altern. Lines
Half
Expand
RePaint-1
0.001 62
0.004 23
0.072 77
0.012 71
0.00434
0.023 99
RePaint-20
0.001 15
0.001 38
0.016 07
0.006 15
0.004 18
0.024 15
MCG
0.001 10
0.001 49
0.022
0.004 93
0.001 18
0.01096
CoPaint
0.001
0.00223
0.007
0.003 08
0.00344
0.011
LDM
0.000 10
0.001 85
0.304 01
0.236 88
0.001 01
0.004 96
ControlNet
0.003 14
0.099 10
0.180 62
0.072 80
0.003
0.047 67
BLD
0.00000
0.000
0.13401
10103 56
0.005520
0.025 67
Uni-paint
0.007 68
0.054 62
0.450
0.252 60
0.010
0.144 14
PowerPaint
0.008330
0.01816
03
0.051
0.217
TD-Paint
0.000 0.
0,000 59
0,000
0.000 44
0.007 10
ImageNetlK
Wide
Narrow
Super-Res 2x
Altern. Lines
Half
Expand
RePaint-1
0.001 28
0.001 51
0.11122 27
0.010
0.002
0.004
RePaint-20
0.000
0.00007
0.003
68
0.001
0.005
MCG
0.000
0.000
0.004 23
0.001 28
0.007 41
0.053
CoPaint
0.00457
0.000 34
0.000 39
0.000 05
0.015
0.085 38
LDM
0.00931
0.007 89
0.215 63
0.150 21
0.018
0,076
ControlNet
0.005
0.01442 42
0.063
0.01714
0.00397
0.005
BLD
0.005
0.005
0.043 20
0.004 94
0.010033
0.013 78
Uni-paint
0.007 08
0.022 50
0.064 00
0.092 72
0.006 11
0.010
PowerPuint
0.006
0.008 57
0.157 53
0.055 63
0.006
0.009
TD-Paint
01000
0.000
0.004 72
0.000
0.000 28
0.003 86
Places2
Wide
Narrow
Super-Res. 2x
Altern. Lines
Half
Expand
RePaint-1
0.003
0,00221
0.110 56
0.011 34
0.01452
0,025
RePaint-20
0.000 44
0.000 26
0,004 58
0.001 47
0.006 15
0.032 35
MCG
0.000
0.000 47
0.009 92
0.002 9G
0.004 98
0.01609
CoPaint
0.000
0,000
0.000 76
0.000
0.001
0,02094
LDM
0.000 78
0.001 37
0.237
0.139 34
0.003
0,018
ControlNet
0.002 77
0.009 95
0.126 10
0.023 21
0.008 32
0.018 70
BLD
0.000 25
0.000
0.12399
0.121 02
0.01862
0.033 62
paint
0.001 57
0.009 01
0.241 63
0.165 31
0.00-1 65
0,026 02
PowerPaint
0.018
0.005 83
0.217
0.057 73
0.074
0,251
TD-Paint
0.000
0.000 30
0.005
0.000
0.000 64
0.008
5.4 DIVERSITY OF GENERATED IMAGES
While TD-Paint performs fast and bigh-quality inpainting, we must ask whether this comes at the cost of diversity. To evaluate this, we compute the Diversity Score (Lugmayr et al. 2021 by gen- erating 10 different images for 100 inputs. The quantitative results reported in Table The most diverse model is RePaint-1, 1, which also has a high LPIPS score. In contrast, TD-Paint achieves high Diversity Score across most masks while consistently producing high-quality images (see Figure with lower LPIPS scores.
Table Diversity Score on CelebA-HQ with random generations across 100 images.
CelebA-HQ
Diversity Scoret
Wide
Narrow
Super-Resolve 2x
Altern, Lines
Half
Expand
RePaint-1
23.716
33.355
32,564
30.917
22.682
666
RePaint-20
23.003
28.276
23.296
23.277
23.059
22.618
MCG
17.694
18.084
17.427
17.547
17.690
17.369
CoPaint
23.979
27. 57.
24.840
24.279
23.802
23.211
TD-Paint
22.620
20.402
27.358
29.331
23.459
21.753
6 CONCLUSIONS
In this paper, we introduce Time-aware Diffusion Paint (TD-Paint), method that accelerates in- painting by modeling multiple noise levels through time conditioning in the diffusion process. Un- like other diffusion-bran based models, TD-Paint does not require any special architecture for inpainting, and generates high-quality, diverse images more quickly. This efficiency makes TD-Paint highly practical for real-world applications and usable for resource-constrained devices.
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ACKNOWLEDGMENTS
This work was financially supported by the ANR Labcom LLisa ANR-20-LCVI-0009 We thank CRIANN, who provided with the computation resources necessary for our experiments. This work was performed using HPC resources from GENCI-IDRIS (Grant 2024-AD01 1013862R1) We thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Digital Research Alliance of Canada.
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A ABLATIONS: TRAINING MASK
We examine the contribution of masks during training on the CelebA-HQ dataset using our mask strategy (Ours) described in Section 4.1 LaMa masks (LAMA), and random mix of the two (Ours+LAMA). Unless stated otherwise, all results are reported with Ours+LAMA. Table that using LAMA over Ours reduces the error for every test mask except Super-Resolve 2x and Altern. Lines, which is explained by the more complicated design of LAMA, which produces train masks closer to the Wide and Narrow Ours+LAMA masks still allows for very low Super-Resolve 2x and Altern. Lines errоrs compared to LaMa errors on the same masks in Table Using Ours+LAMA allows the benefits of Ours masks to be retrained while having low Super-Resolyy 2x and Altemn Lines errors.
masks and of more diverse shapes. Notably, using
Table 5: A blation study for the types of training time map. The metrics show how the use of Ours focuses more on very fine inpainting masks. The distribution inpainting masks, such as Wide. Combining the two allows for strong performance across the range of test masks considered.
LAMA masks closer to larger
CelebA-HQ
Wide
Norrow
Super-Resolvv 2x
Albemm
Limes
Half
Expond
TD-Paint
LPIPSI
SSIMI
LPIPS
SSIMI
LPIPS]
SSIM:
LPIPSL
SSIM1
LPIPS.
SSIMI
LPIPS
SSIM
Ours
0.067
0.882
0.032
0.913
0.018
0.912
0.009
0.972
0.174
0.666
0.163
0.214
LAMA
0.053
0.876
0.027
0.920
0.055
0.870
0.044
0.920
0.168
0.085
0.445
0.229
OUsALAAA
0.055
0.873
0.028
0.918
0.017
(1333
0.010
0.971
0.170
0.667
0.457
0.212
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10
TD-Paint Wide
MCG Wide
LPIPS
RePaint Wide
TD-Paint Half
RePaint Half
MCG Half
10
250
1000
2000
3000
4000
4750
Number of steps
Figure 8: Image quality at different time step for 100 images on CelebA-HQ dataset Wide and
Half masks.
109
TD-Paint Super-Resolve
LPIPS
RePaint Resolve 2x
10-1
MCG 2x
250
1000
2000
3000
4000
4750
Number of steps
Figure 9: Image quality at different time step for 100 images on CelebA-HQ dataset for Super-
Resolve 2x mask.
B IMAGE QUALITY AND DIFFUSION STEPS
We compare the LPIPS metrics over the diffusion step for TD-Paint, RePaint and MCG in Figures and | averaged over 100 images from the CelebA-HQ dataset. TD-Paint can produce high-quality images in fraction of the steps required by RePaint because it takes advantage of the state since the early step of the diffusion process.
C COMPARISON WITH CNN- AND TRANSFORMER-BASEE MODELS
We compare TD-Paint with LaMa (CNN-based) and MAT (transformer-basee models in Tables and
Wide and Narrow masks Our approach closely matches the performance of LaMa and MAT in the
Wide setting on CelebA-HQ and even surpasses them in the Narrow setting on CelebA-HQ. as well in both the Wide and Narrow settings on ImageNetIK. As shown in Figure I0] LaMa tends to generate pupils of different sizes when one eye is hidden the Wide and different eye colors in the Narrow settings.
Super-Resobs 2x and Altern. Lines masks Table shows that TD-Paint outperforms the baselines by wide margin. Particularly MAT struggles with this task and often produces images with signif- icant artifacts and blurring (see Figures 1o [2]. contrast, TD-Paint consistently achieves lower LPIPS scores and higher SSIM values, reflecting superior image quality and performance.
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Half and Expand masks On CelebA-HQ. TD-Paint achieves the best results across all datasets,
as indicated by the KID metrics (see Table 7 As shown Figures I0 I2 LaMa generates blurry artifacts in both the Half and Expand settings, whereas produces high-quality images. This behavior of LaMa may be due to overfitting to the training mask distribution, suggested by previous studies (Lugmayr al. 2022
proposed model consistently
D ADDITIONAL QUALITATIVE RESULTS
We provide additional qualitative inpainting results compared to the state-of-the-ar models de- scribed in Section and and Appendix
For CelebA-HO on Wide and Narrow masks in Figure Super-Resolve 2x and Altern Lines masks in Figure 14, Half and Expand in Figure 15
For ImageNetIK on Wide and Narrow masks in Figure 16 masks in Figure 17 Half and Expand in Figure 18
Super-Resoly 2x and Altern. Lines
For Places2 on Wide and Narrow masks in Figure Super-Resolve 2x and Altemn Lines masks in Figure 20 Half and Expand in Figure 21
Additional diversity results CelebA-HQ and ImageNetI can be found in Figure 22 and for
I with different conditional classes in Figure 23 Through class conditioning, TD-Paint
Table 6: Quantitative results: evaluation CNN- and transformer-baat based models for inpainting on the CelebA-HQ, ImageNetIK and Places2 datasets.
CelebA-HQ
Wide
Narrow
Super-Resolse
Albern.
Limes
Half
Expond
LPIPS
SSIM
LPIPS.
SSIM
LPIPS).
SSIM
LPIPS
SSIM1
LPIPS]
SSIM
LPIPS
SSIM
LaMa
0.052
0.033
0.91
0.219
0.002
0.728
0.693
0.410
0.237
MAT
0.055
0.872
0.030
0.911
0.509
0.201
0.251
0.668
0.171
0.668
0.475
0.192
TD-Paint
0.055
0.873
0.028
0.017
0.939
0.010
0.971
0.170
0.087
0.457
0.212
ImageNetIK
Wide
Narrow
Super-Resntre
Altem.
Lines
Half
Expond
LPIPS
SSIMI
LPIPS]
SSIM
LPIPS
SSIM
LPIPS,
SSIM1
LPIPS
SSIMI
LPIPS
SSIM
LaMa
0.107
0.832
0.068
0.853
0.375
0.422
0.271
0.483
0.281
0.618
0.626
0.196
MAT
0.143
0,751
0.095
0.781
0.512
0.257
0.410
0.422
0,308
0.312
0.633
0.114
TD Paint
0.099
0.830
0.057
0.864
0.136
0.648
0.059
0.847
0.257
0.603
0.597
0.159
Places2
Wide
Narrow
Super Resoive
Alten.
Lines
Hal
Expand
L.IPS
SSIM1
[.PPS...
SSIM
L.PPS))
SSIM
I.PIPS
SSTM1
I.PIPS.
SSIM
I.PPS.
SSIM
LaMa
0.106
0.836
0.061
0.864
0.477
0.348
0.1
0.606
0.281
0.625
0.611
0.212
MAT
0.131
0.792
0.079
0.832
0.186
0.658
0.087
0.795
0.284
0.380
0.651
0.144
TD-Paint
0.112
0.826
0.064
0.865
0.130
0.696
0.060
0.879
0.273
0.584
0.607
0.146
Table 7: Quantitative results: KID evaluation of CNN- and transformer-basee models for inpaint- ing on the Cel,bA-HQ, ImageNetIK and Places2 datasets.
CelebA-HQ
KID, Super-Res. 2x
Wide
Narrow
Altern Lines
Half
Expand
LaMa
0.000 96
0.001 64
0.059
0.037
0.006
0.090 38
MAT
0.000 07
0.000 16
0.149 92
0.054 72
0.000 80
0.050 62
TD-Paint
0.000 08
0.000 09
0.000 59
0.000 24
0.000
0.007 10
ImageNetIK
KID Altern. Lines
Wide
Narrow
Super-Res, 2x
Half
Expand
LaMa
0.002 53
0.000
0.066 59
0.023 76
0.006 45
0.072 49
MAT
0.01073 75
0.007 04
0.086 54
0.045
0.022 62
0.095 40
PD-Paint
0.000 01
0.000 10
0.004 72
0.000 41
0.000 28
0.003 86
Places2
KID
Wide
Narrow
Super-Res. 2x
Altern. Lines
Half
Expand
LaMa
0.000 38
0.000
0.096 99
0.013
0.003 78
0.029 88
MAT
0.000 89
0.000 15
0,015 16
0.002 95
0.003 20
0.136 94
TD-Paint
0.000 16
0.000 30
0.005 25
0.000 27
0.000
0.008 36
15

---

Published as a conference paper at ICLR 2025
Alt. Lines.
Wide
Sup. Res. 2x Narrow
Half
Expand
Input
LaMa
MAT
TD-Paint
Input
LaMa
MAT
TD-Paint
Figure 10: Qualitative results: TD-Paint against state-of-the-art inpainting CNN- and transformer- based models on CelebA-HQ. Zoom in for better details. Additional examples can be found in Appendix D
Allem. Lines
Wide
Narrow
Hulf
Super- Resalve 2x
Expand
Input
LaMa
MAT
TD-Paint
Inpul
LaMa
MAT
TD-Paint
Figure II: Qualitative results: TDiPaint against state-of-the-ar inpainting CNN- and transformer- based models ImageNetIK. Additional examples can be found in Appendix D
can generate diverse images based on different target classes. This mechanism allows TD-Paint guiding the inpainting process toward specific semantic categories, resulting more controlled and contextually relevant image completions. This feature is especially useful when the inpainted region must match a particular class, increasing TD-Paint's flexibility and effectiveness across a range of inpainting tasks.
Additional examples inpainting using objuct-focused and eegiof-specific masks are presented in Figures 24 and 25 These examples feature user-drawn masks that naturally follow object bound- aries and regional structures, demonstrating TD-Paint's effectiveness in practical image manipula- tion scenarios. Such realistic mask shapes better reflect how users interact with inpainting tools
16

---

Published as a conference paper at ICLR 2025
Alt. Lines.
Wide
Sup.. Res. 2x Narrow
Half
Expand
Input
LaMa
MAT
TD-Paint
Input
LaMa
MAT
TD-Paint
Figure 12: Qualitative results: TD-Paint against state-of-the-art inpainting CNN- and transformer-
based models on Places2. Additional examples can be found in Appendix D
real-world applications, where selections typically correspond to meaningful objects or regions rather than arbitrary geometric patterns.
Ippun
LaMe
MAT
KePaini 20
MCG
CoPtine
LDW
ControtNer
BLD
Uni gaier
PowerFaint
TD Plint
Figure 13: CelebA-HQ qualitative results
17

---

Published as a conference paper at ICLR 2025
Inpus
LaMa
MAT
Relbire-22
MDG
CoPbini
1DM
CommalNia
BLD
Uni pai-ppann
Powcrfaind
Figure 14: CelebA-HE HQ qualitative results
18

---

Published as a conference paper at ICLR 2025
Inpur
LaMa
MAT
Relhine-20
MDG
CoPain
LDM
CommalNia
BLD
Uni pai-ppans
PowcrPaint
Holy
Figure 15; CelebA-HE qualitative results
19

---

Published as a conference paper at ICLR 2025
LaMs
MAT
RePaint-30
MCG
Culbini
LDM
CantnolNa
BLB
Uniogaine
PoscerPaint
Enn
Figure 16: ImageNet1K qualitative results
20

---

Published as a conference paper at ICLR 2025
Inpur
LaMi
MAT
Relsire-20
MDG
CoPaini
LDM
CommalNea
BLDD
Uni Uni-paine
PowerPaid
Alben Lave
Figure 17: ImageNet qualitative results
21

---

Published as a conference paper at ICLR 2025
Inpux
LaMi
MAT
Relbine-20
MDG
Colaint
LDM
CommalNa
BLD
Uni paisa
PowcrPaint
ABC AR
Figure 18: ImageNetIK qualitative results
22

---

Published as a conference paper at ICLR 2025
Impo
LaMe
MAT
RePaint-30
MCG
Culbini
EDM
CantrolNia
BLB
Uniepaina
PomserPaint
WD-
Figure 19: Places2 qualitative results
23

---

Published as a conference paper at ICLR 2025
Inpus
LaMa
MAT
Relhine-22
MDO
CoPbint
LDM
Commellea
BLD
Uni pai-paan
Powcerhin
Abbrn
Figure 20: Places2 qualitative results
24

---

Published as a conference paper at ICLR 2025
Inpas
LaMi
MAT
RePbire-20
MDG
CoPain
LDM
ComnalNia
BLD
Uni paint
Powceraint
nto moroum
Figure 21: Places2 qualitative results
25

---

Published as a conference paper at ICLR 2025
CY
Input
Sample
Sample
Sample 3
Sample 4
Figure 22: Example of divers generation using TD-Paint on CelebA-HQ and ImageNet K using the same input image and different initial noise.
26

---

Published as a conference paper at ICLR 2025
plarreer
Lavser catione
degg wete
aligen
plo
2D2
SEREE
punget
1052
UoW 206k, low VaL wroker
Inleppod
Figure 23: ImageNetIK TD-Paint diversity qualitative results using different class conditioning, For a line, TD-Paint is prompted with the same input image and mask but, with different classes.
27

---

Published as a conference paper at ICLR 2025
Figure 24: Demonstration of TD-paint application on the ImageNet1 K dataset. The figure shows user-drawn masks highlighting specific regions objects, followed by four generated image varia- tions for each mask.
28

---

Published as a conference paper at ICLR 2025
ouun
OBLE
OSUR
OSU
WH WA
Figure 25: Demonstration of TD-paint application on the Places2 dataset. The figure shows user- drawn masks highlighting specific regions or objects, followed by four generated image variations for each mask.
29


Reviews:
[{"review_id": "RilkS6eizo", "reviewer": "Reviewer_XQUX", "summary": "This paper proposes a way to perform inpainting by leveraging pre-trained pixel diffusion model. The authors fine-tune a diffusion model such that instead of inputting a single scalar that represents the tilmestep of the noise in the diffusion process, the model inputs a map, representing per-pixel tilmesteps. Then, at inference, this allows them to always input the fully denoised image in the known regions while iteratively denoising the masked areas. Unlike the main baseline, RePaint, this process is much faster, as it does not require multiple diffusion passes to synchronize the known and unknown regions.", "questions": "How can the proposed method be used to scale up to higher resolution images? For instance, is there a straightforward way to extend to LDMs, as some of the more recent inpainting approaches have been doing?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The proposed approach is quite simple but at the same time intuitive and effective. The idea of varying the diffusion at a pixel level is a promising one, both for novel applications (such as inpainting) as well as for a more general approach to accelerating diffusion inference.", "weaknesses": "My main concern has to do with lack of comparison to or discussion of some more recent and relevant SOTA diffusion-based inpainting approaches. For instance, LatentPaint [Corneanu et al., WACV 2024] also a propose an approach that requires fine-tuning with a fast inference pass (compared to RePaint). Additionally, \"Blended Latent Diffusion\" [Avrahami et al., SIGGRAPH 2023] and \"Uni-paint\" [Yang et al., MM 2023] leverage latent diffusion models for an inpainting task (while the former does not explicitly target unconditional inapinting, it ca be used for that purpose).  It would be very helpful to see comparisons to some of these recent works as well as a high-level discussion of their differences in order to evaluate the proposed method.\n\nOn a more minor note, the presentation and exposition could be improved. Figure 1, which illustrates the proposed approach in contrast to RePaint, is somewhat difficult to follow and includes notation that is not defined until much later. In contrast, the idea behind RePaint is explained multiple times in various sections throughout the paper and could be truncated.\n\nI would also encourage the authors to cite and discuss \"Differential Diffusion: Giving Each Pixel Its Strength\" [Levin and Fried, 2023]. Given that this work does not appear in a peer-reviewed publication, I am not considering it in my rating, but given its close relevance to the topic, it would be good to mention.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wNDiZKKUBe", "reviewer": "Reviewer_8fx5", "summary": "The paper presents a novel method for speeding up diffusion-based inpainting. It changes the noised known region condition in the RePaint paper into a clean one for condition. To support this change, it introduces a trained diffusion model that supports a spatial-varying timestep condition to mark the known and missing part of the image. This new design also allows the method to avoid the repeated sampling process in RePaint, greatly increasing the speed. Quantitative results also demonstrate the proposed method performs on par or better with existing methods.", "questions": "1. Training Inference Gap: The training is done on patch-wise known regions. But inference of the known region doesn’t necessarily be axis-aligned patches. How do you solve this gap? Will training on randomly shaped known regions further boost the model’s performance?\n2. I wonder how well the proposed method performs when adapted to larger diffusion models like stable diffusion.\n3. The ControlNet-Inpaint model also provides fast and high-quality diffusion-based inpainting. How does the proposed method perform compared to that model?\n\nI would raise my score if these questions were answered.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Training the diffusion model to directly use clean known parts as the diffusion condition, avoiding complex modifications to the diffusion process. This approach significantly speeds up the method compared to existing approaches, enhancing efficiency without compromising quality.\n2. Introduction of a novel spatial-varying timestep condition for injecting the known region mask. This innovative technique allows for more precise control over the inpainting process, resulting in improved output quality.\n3. Comprehensive testing on multiple diverse datasets, demonstrating superior performance (for both quality and diversity) across various scenarios. The extensive evaluation provides strong evidence for the method's robustness and effectiveness in different contexts.", "weaknesses": "The paper is in general good, I only have concerns on its generality: \n\nSince the proposed method requires training and is not trained with data besides CelebA-HQ, ImageNet1K, and Places2, it probably cannot generalize to open-world images like LaMa.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IwxDoCvr9r", "reviewer": "Reviewer_DSku", "summary": "The authors mainly proposed to use the same T2I architecture to do inpainting. To properly feed in the known image conditions, they propose to use a pixel-wise noisy injection with different levels, and a spatial-variant timesteps map. Therefore, during training and inference, it allows to add minimum levels of noises to known image regions, while only diffuse the unknown regions following the normal diffusion process. The idea is simple, effective and easy to implement. Extensive experiments also demonstrate its effectiveness in maintaining good inpainting quality while reducing inpainting complexity compared to RePaint.", "questions": "- During training, the authors use noise levels close to zero to generate noises in known region, while uses zero during inference. Any specific reasons of adding noises during training? Is it possible to simply set them as zero?\n- How did the authors design the time step conditions input to the network? It is not clearly stated in the paper.\n- How is the inpainting performance of the model compared with state-of-the-art commercial inpainter like Photoshop Generative Fill, MidJourney Editor, and DALL-E inpainter? No need to do detailed comparison, but a few figures / images to show the advantages or gaps will be sufficient. \n- The pixel-wise time embedding is actually a region-wise time embedding? Is it possible to formulate it as only two time embeddings + one binary mask, so as to reduce the learning complexity of the model. The reviewer is not sure about the learning stability of the model. Also pixel-wise embedding can be well generalized to SDEdit, but it can be very hard to train.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The design of spatial-variant time embedding for inpainting tasks bring two major advantages: (1) no architecture change form T2I, and (2) relative faster inpainting process. \n- The authors provide extensive and scientific evaluation on different masks, and the proposed method achieved good inpainting quality.", "weaknesses": "- Repaint seems not the state-of-the-art inpainting model, and recently models based on latest latent diffusion model or DiT framework achieve much better results, for instance, powerpaint(https://powerpaint.github.io/). Some comparison needs to be updated. \n- Since the model is trained on diffusion-based framework, is it still necessary to list ConvNet-based or transformer-based model in the comparison? They will be not comparable given the inference speed and model capacity. The comparisons should focus on latest diffusion-based model in quality and speed. \n- To the best of the reviewer's knowledge, recent diffusion-based inpainting is no longer using the way of RePaint to do inference. The problems mentioned by the authors like 'consistency' are no longer an issue. It can be simply achieved by adding an additional conditions concatenated with the noisy input in UNet, or adding additional tokens for DiT-based framework. Some controlnet-based method do have the composition and harmonization problems.\n- It is not clear how the framework can be generalized to image editing, when the mask is soft or we need some levels of content preservation under the mask region. Without having the original image as a condition, it will be very hard to generalize the model to other editing tasks, like color changing. Therefore, the proposed framework will be limited as a foundation model, or used to finetuned for other tasks. \n- The masks used in inpainting literature are mostly not suitable for real practical application because they are not used by the real inpainter users. Better to try some object-shaped masks and use those for user study. \n- The proposed method may lack mask precision in a latent diffusion framework, especially for tasks like background replacement. For latent diffusion, the masks are usually applied to the pixel space, and the masked image will be encoded using the latent encoder for compression, and server as the condition. In this case, the latent diffusion model can still learn to reconstruct the high-precision mask boundary to preserve the details around the boundary. If we apply the low-res mask in the latent and apply different t in the precision of latent mask, it won't be able to leverage the advantages of the high-quality encoding.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kzRYf5sidC", "reviewer": "Reviewer_VMkS", "summary": "This paper proposes a method that can do faster inpainting in diffusion models by introducing time-aware denoising process.", "questions": "The paper's contribution to the community is not that great, as time improvement is not that significant.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. It can generate high-quality inpainting on various mask sizes and shapes.\n2. It is faster compared to other stare-of-art methods.\n3. It has extensive evaluations", "weaknesses": "1. The visual result is not much better than other baselines\n2. Even though they have many evaluation results, the improvement in image quality with scores in not much.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fDZgkxfrzS", "reviewer": "Reviewer_DEmo", "summary": "The authors proposed a new training scheme of diffusion-based inpainting model that can perfectly maintain the regions inside the mask.", "questions": "1. The training masks are always square. How can you applied the trained model on random masks during the inference?\n2. Is it possible that this method can be extended to the latent diffsusion?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The training strategy is novel and the performance is good.", "weaknesses": "1. Any evaluations about how good the unchanged regions are maintained?\n2. I admit that the training scheme is novel, but I am not sure if the novelty is enough for publishing. Since the entire method is a slight modification of existing diffusion model.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)