SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Theorem 4.2. For any  $\epsilon >0$ , if the support of the distribution  $\mathcal{P}_v^{(i)}$  is confined to a bounded space of  $\mathbb{R}^d$ , there exists a FNN  $u(\cdot):\mathbb{R}^d\to \mathbb{R}$  (and thus its gradient  $\nabla u(\cdot):\mathbb{R}^d\to \mathbb{R}^d$ ) with sufficiently large width and depth (depending on  $\epsilon$ ) such that  $\mathcal{W}_2^2 (\mathcal{P}_v^{(i)},\nabla u(\mathcal{G})) < \epsilon$  where  $\nabla u(\mathcal{G})$  is the distribution generated through the mapping  $\nabla u(\xi)$ ,  $\xi \sim a$  d-dim non-degenerate Gaussian distribution.
Another challenge is that the Wasserstein distance between  $\psi_{n}(\pmb{x}_{t-1}^{i}, t)$  and  $\mathcal{P}_{\mathcal{N}_i^s}$  does not have a closed form. Thus, we utilize the empirical Wasserstein distance that can provably approximate the population one as in Peyré et al. [57]. For each forward pass, our CONPREDIFF will get  $q$  sampled target pixel/feature points  $\{\pmb{x}_{(i,j)}^{tar}|1 \leq j \leq q\}$  from  $\mathcal{P}_{\mathcal{N}_i^s}$ ; Next, get  $q$  samples from  $\mathcal{N}(\mu_i, \Sigma_i)$ , denoted by  $\xi_1, \xi_2, \dots, \xi_q$ , and thus  $\{\pmb{x}_{(i,j)}^{pred} = \mathrm{FNN}_n(\xi_j)|1 \leq j \leq q\}$  are  $q$  samples from the prediction  $\psi_{n}(\pmb{x}_{t-1}^{i}, t)$ ; Adopt the following empirical surrogated loss of  $\mathcal{W}_2^2(\psi_n(\pmb{x}_{t-1}^{i}, t), \mathcal{P}_{\mathcal{N}_i^s})$  in Equation (14):
$$
\min  _ {\pi} \sum_ {j = 1} ^ {q} \left\| \boldsymbol {x} _ {(i, j)} ^ {t a r} - \boldsymbol {x} _ {(i, \pi (j))} ^ {p r e d} \right\| ^ {2}, \quad \text {s . t .} \pi \text {i s a b i j e c t i v e m a p p i n g :} [ q ] \rightarrow [ q ]. \tag {17}
$$
The loss function is based on solving a matching problem and needs the Hungarian algorithm with  $O(q^3)$  complexity [33]. A more efficient surrogate loss may be needed, such as Chamfer loss based on greedy approximation [18] or Sinkhorn loss based on continuous relaxation [11], whose complexities are  $O(q^2)$ . In our study, as  $q$  is set to a small constant, we use Equation (17) based on a Hungarian matching and do not introduce much computational overhead. The computational efficiency of design is empirically demonstrated in Sec. 5.3.
4.3 Discrete and Continuous CONPREDIFF
In training process, given previously-estimated  $\pmb{x}_t$ , our CONPREDIFF simultaneously predict both  $\pmb{x}_{t - 1}$  and the neighborhood distribution  $\mathcal{P}_{\mathcal{N}_i^s}$  around each pixel/feature. Because  $\pmb{x}_{t - 1}^i$  can be pixel, feature or discrete token of input image, we can generalize the CONPREDIFF to existing discrete and continuous backbones to form discrete and continuous CONPREDIFF. More concretely, we can substitute the point denoising part in Equation (14) alternatively with the discrete diffusion term  $\mathcal{L}_{t - 1}^{dis}$  (Equation (3)) or the continuous (Equation (6)) diffusion term  $\mathcal{L}_{t - 1}^{con}$  for generalization:
$$
\mathcal {L} _ {\text {C O N P R E D I F F}} ^ {d i s} = \mathcal {L} _ {t - 1} ^ {d i s} + \lambda_ {t} \cdot \sum_ {i = 1} ^ {x \times y} \mathcal {W} _ {2} ^ {2} \left(\psi_ {n} \left(\boldsymbol {x} _ {t - 1} ^ {i}, t\right), \mathcal {P} _ {\mathcal {N} _ {i} ^ {s}}\right), \tag {18}
$$
$$
\mathcal {L} _ {\mathrm {C O N P R E D I F F}} ^ {c o n} = \mathcal {L} _ {t - 1} ^ {c o n} + \lambda_ {t} \cdot \sum_ {i = 1} ^ {x \times y} \mathcal {W} _ {2} ^ {2} (\psi_ {n} (\boldsymbol {x} _ {t - 1} ^ {i}, t), \mathcal {P} _ {\mathcal {N} _ {i} ^ {s}}),
$$
where  $\lambda_t \in [0,1]$  is a time-dependent weight parameter. Note that our CONPREDIFF only performs context prediction in training for optimizing the point denoising network  $\psi_p$ , and thus does not
ConPreDiff (Ours)
"The sunlight shines on the leaves, and every green leaf reflects light."
"Some ancient stone pillars stand upon the earth, arranged in an orderly manner and spaced apart from each other."
"There are black suits and white pants hanging in the wardrobe."
"On a rainy day street, the air near the ground is filled with raindrops."
"A bunch of flowers bloomed in the grass, with dewdrops on each petal."
Imagen
oennnnnne nnnnnnne
"The sky was blanketed with thick snow, while the ground lay adorned with stones."
Figure 2: Synthesis examples demonstrating text-to-image capabilities of for various text prompts with LDM, Imagen, and ConPreDiff (Ours). Our model can better express local contexts and semantics of the texts marked in blue.
"A box contains apples, each displaying a touch of green hue."
"A rainbow rise after the rain, a group of cows and sheep graze in the fields. Cows are black and sheep are white."
"An elderly fisherman sits in a boat, his fishing rod set aside, gazing towards the distant horizon. The last rays of the sun reflect off the ripples on the water's surface."
"On a town's nighttime streets, light strips were pulled up on the busy streets. People dressed in various attire and holding umbrellas strolled along the streets."
introduce extra parameters to the inference stage, which is computationally efficient. Equipped with our proposed context prediction term, existing diffusion models consistently gain performance promotion. Next, we use extensive experimental results to prove the effectiveness.
Datasets and Metrics Regarding unconditional image generation, we choose four popular datasets for evaluation: CelebA-HQ [34], FFHQ [35], LSUN-Church-outdoor [102], and LSUN-bedrooms [102]. We evaluate the sample quality and their coverage of the data manifold using FID [26] and Precision-and-Recall [43]. For text-to-image generation, we train the model with LAION [73, 74]
Table 1: Quantitative evaluation of FID on MS-COCO for  ${256} \times  {256}$  image resolution.
Approach	Model Type	FID-30K	Zero-shot FID-30K
AttnGAN [96]	GAN	35.49	-
DM-GAN [113]	GAN	32.64	-
DF-GAN [86]	GAN	21.42	-
DM-GAN + CL [100]	GAN	20.79	-
XMC-GAN [107]	GAN	9.33	-
LAFITE [112]	GAN	8.12	-
Make-A-Scene [22]	Autoregressive	7.55	-
DALL-E [61]	Autoregressive	-	17.89
LAFITE [112]	GAN	-	26.94
LDM [65]	Continuous Diffusion	-	12.63
GLIDE [54]	Continuous Diffusion	-	12.24
DALL-E 2 [62]	Continuous Diffusion	-	10.39
Improved VQ-Diffusion [85]	Discrete Diffusion	-	8.44
Simple Diffusion [31]	Continuous Diffusion	-	8.32
Imagen [69]	Continuous Diffusion	-	7.27
Parti [104]	Autoregressive	-	7.23
Muse [7]	Non-Autoregressive	-	7.88
eDiff-I [3]	Continuous Diffusion	-	6.95
CONPREDIFFdis	Discrete Diffusion	-	6.67
CONPREDIFFcon	Continuous Diffusion	-	6.21
and some internal datasets, and conduct evaluations on MS-COCO dataset with zero-shot FID and CLIP score [25, 59], which aim to assess the generation quality and resulting image-text alignment. For image inpainting, we choose CelebA-HQ [34] and ImageNet [12] for evaluations, and evaluate all 100 test images of the test datasets for the following masks: Wide, Narrow, Every Second Line, Half Image, Expand, and Super-Resolve. We report the commonly reported perceptual metric LPIPS [109], which is a learned distance metric based on the deep feature space.
Baselines To demonstrate the effectiveness of CONPREDIFF, we compare with the latest diffusion and non-diffusion models. Specifically, for unconditional image generation, we choose ImageBART[16], U-Net GAN (+aug) [72], UDM [39], StyleGAN [36], ProjectedGAN [71], DDPM [28] and ADM [14] for comparisons. As for text-to-image generation, we choose DM-GAN [113], DF-GAN [86], DM-GAN + CL [100], XMC-GAN [107] LAFITE [112], Make-A-Scene [22], DALL-E [61], LDM [65], GLIDE [54], DALL-E 2 [62], Improved VQ-Diffusion [85], Imagen-3.4B [69], Parti [104], Muse [7], and eDiff-I [3] for comparisons. For image inpainting, we choose autoregressive methods (DSI [56] and ICT [91]), the GAN methods (DeepFillv2 [103], AOT [105], and LaMa [84]) and diffusion based model (RePaint [49]). All the reported results are collected from their published papers or reproduced by open source codes.
Implementation Details For text-to-image generation, similar to Imagen [68], our continuous diffusion model  $\mathrm{CONPREDIFF}_{con}$  consists of a base text-to-image diffusion model  $(64\times 64)$  [53], two super-resolution diffusion models [29] to upsample the image, first  $64\times 64\rightarrow 256\times 256$  and then  $256\times 256\to 1024\times 1024$ . The model is conditioned on both T5 [60] and CLIP [59] text embeddings. The T5 encoder is pre-trained on a C4 text-only corpus and the CLIP text encoder is trained on an image-text corpus with an image-text contrastive objective. We use the standard Adam optimizer with a learning rate of 0.0001, weight decay of 0.01, and a batch size of 1024 to optimize the base model and two super-resolution models on NVIDIA A100 GPUs, respectively, equipped with multi-scale training technique (6 image scales). We generalize our context prediction to discrete diffusion models [24, 85] to form our  $\mathrm{CONPREDIFF}_{dis}$ . For image inpainting, we adopt a same pipeline as RePaint [49], and retrain its diffusion backbone with our context prediction loss. We use  $\mathrm{T} = 250$  time steps, and applied  $\mathrm{r} = 10$  times resampling with jumpy size  $\mathrm{j} = 10$ . For unconditional generation tasks, we use the same denoising architecture like LDM [65] for fair comparison. The max channels are 224, and we use  $\mathrm{T} = 2000$  time steps, linear noise schedule and an initial learning rate of 0.000096.
Table 2: Quantitative evaluation of image inpainting on CelebA-HQ and ImageNet.
CelebA-HQ
Method	Wide
LPIPS ↓	Narrow
LPIPS ↓	Super-Resolve 2×
LPIPS ↓	Altern. Lines
LPIPS ↓	Half
LPIPS ↓	Expand
LPIPS ↓
AOT [105]	0.104	0.047	0.714	0.667	0.287	0.604
DSI [56]	0.067	0.038	0.128	0.049	0.211	0.487
ICT [91]	0.063	0.036	0.483	0.353	0.166	0.432
DeepFillv2 [103]	0.066	0.049	0.119	0.049	0.209	0.467
LaMa [84]	0.045	0.028	0.177	0.083	0.138	0.342
RePaint [49]	0.059	0.028	0.029	0.009	0.165	0.435
CONPREDIFF	0.042	0.022	0.023	0.022	0.139	0.297
ImageNet
Method	Wide
LPIPS ↓	Narrow
LPIPS ↓	Super-Resolve 2×
LPIPS ↓	Altern. Lines
LPIPS ↓	Half
LPIPS ↓	Expand
LPIPS ↓
DSI [56]	0.117	0.072	0.153	0.069	0.283	0.583
ICT [91]	0.107	0.073	0.708	0.620	0.255	0.544
LaMa [84]	0.105	0.061	0.272	0.121	0.254	0.534
RePaint [49]	0.134	0.064	0.183	0.089	0.304	0.629
CONPREDIFF	0.098	0.057	0.129	0.107	0.285	0.506
Our context prediction head contains two non-linear blocks (e.g., Conv-BN-ReLU, resnet block or transformer block), and its choice can be flexible according to specific task. The prediction head does not incur significant training costs, and can be removed in inference stage without introducing extra testing costs. We set the neighborhood stride to 3 for all experiments, and carefully choose the specific layer for adding context prediction head near the end of denoising networks.
Text-to-Image Synthesis We conduct text-to-image generation on MS-COCO dataset, and quantitative comparison results are listed in Tab. 1. We observe that both discrete and continuous CONPREDIFF substantially surpasses previous diffusion and non-diffusion models in terms of FID score, demonstrating the new state-of-the-art performance. Notably, our discrete and continuous CONPREDIFF achieves an FID score of 6.67 and 6.21 which are better than the score of 8.44 and 7.27 achieved by previous SOTA discrete and continuous diffusion models. We visualize text-to-image generation results in Figure 2, and find that our CONPREDIFF can synthesize images that are semantically better consistent with text prompts. It demonstrates our CONPREDIFF can make promising cross-modal semantic understanding through preserving visual context information generating process, and effectively associating with contextual text information. Moreover, we observe that CONPREDIFF can synthesize complex objects and scenes consistent with text prompts as demonstrated by Figure 7 in Appendix A.4, proving the effectiveness of our designed neighborhood context prediction. Human evaluations are provided in Appendix A.5.
Image Inpainting Our CONPREDIFF naturally fits image inpainting task because we directly predict the neighborhood context of each pixel/feature in diffusion generation. We compare our CONPREDIFF against state-of-the-art on standard mask distributions, commonly employed for benchmarking. As in Tab. 2, our CONPREDIFF outperforms previous SOTA method for most kinds of masks. We also put some qualitative results in Figure 3, and observe that CONPREDIFF produces a semantically meaningful filling, demonstrating the effectiveness of our context prediction.
Unconditional Image Synthesis We list the quantitative results about unconditional image generation in Tab. 3 of Appendix A.3. We observe that our CONPREDIFF significantly improves upon the state-of-the-art in FID and Precision-and-Recall scores on FFHQ and LSUN-Bedrooms datasets. The CONPREDIFF obtains high perceptual quality superior to prior GANs and diffusion models, while maintaining a higher coverage of the data distribution as measured by recall.
5.3 The Impact and Efficiency of Context Prediction
In Sec. 4.2, we tackle the complexity problem by transforming the decoding target from entire neighborhood features to neighborhood distribution. Here we investigate both impact and efficiency of the proposed neighborhood context prediction. For fast experiment, we conduct ablation study with the diffusion backbone of LDM [65]. As illustrated in Figure 4, the FID score of CONPREDIFF
Figure 3: Inpainting examples generated by our CONPREDIFF.
is better with the neighbors of more strides and 1-stride neighbors contribute the most performance gain, revealing that preserving local context benefits the generation quality. Besides, we observe that increasing neighbor strides significantly increases the training cost when using feature decoding, while it has little impact on distribution decoding with comparable FID score. To demonstrate the generalization ability, we equip previous diffusion models with our context prediction head. From the results in Figure 5, we find that our context prediction can consistently and significantly improve the FID scores of these diffusion models, sufficiently demonstrating the effectiveness and extensibility of our method. We further conduct ablation study on the trade-off between CLIP and FID scores across a range of guidance weights in Appendix A.2, the results exhibit our superior generation ability.
Figure 4: Bar denotes FID and line denotes time cost.
Figure 5: Equip diffusion models with our context prediction.
In this paper, we for the first time propose CONPREDIFF to improve diffusion-based image synthesis with context prediction. We explicitly force each point to predict its neighborhood context with an efficient context decoder near the end of diffusion denoising blocks, and remove the decoder for inference. CONPREDIFF can generalize to arbitrary discrete and continuous diffusion backbones and consistently improve them without extra parameters. We achieve new SOTA results on unconditional image generation, text-to-image generation and image inpainting tasks.
This work was supported by the National Natural Science Foundation of China (No.61832001 and U22B2037).
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A.1 Limitations and Broader Impact
Limitations While our ConPreDiff boosts performance of both discrete and continuous diffusion models without introducing additional parameters in model inference, our models still have more trainable parameters than other types of generative models, e.g. GANs. Furthermore, we note the long sampling times of both and compared to single step generative approaches like GANs or VAEs. However, this drawback is inherited from the underlying model class and is not a property of our context prediction approach. Neighborhood context decoding is fast and incurs negligible computational overhead in training stage. For future work, we will try to find more intrinsic information to preserve for improving existing point-wise denoising diffusion models, and extend to more challenging tasks like text-to-3D and text-to-video generation.
Broader Impact Recent generative image models enable creative applications and autonomous media creation, but can also be viewed as a dual-use technology with negative implications. In this paper, we use human face datasets only for evaluating the image inpainting performance of our method, and our method is not intended to create content that is used to mislead or deceive. However, like other related image generation methods, it could still potentially be misused in the realm of human impersonation. A notorious example are so-called "deep fakes" that have been used, for example, to create pornographic "undressing" applications. We strongly disapprove of any actions aimed at producing deceptive or harmful content featuring real individuals. Besides, generative methods have the capacity to be harnessed for other malicious intentions, including harassment and misinformation spread [20], and give rise to significant concerns pertaining to societal and cultural exclusion as well as biases [83, 82]. These considerations guide our decision not to release the source code or a public demo at this point in time.
Furthermore, the immediate availability of mass-produced high-quality images can be used to spread misinformation and spam, which in turn can be used for targeted manipulation in social media. Datasets are crucial for deep learning as they are the main input of information [101, 92, 93, 97]. Large-scale data requirements of text-to-image models have led researchers to rely heavily on large, mostly uncurated, web-scraped datasets. While this approach has enabled rapid algorithmic advances recently, datasets of this nature have been critiqued and contested along various ethical dimensions. One should consider the ability to curate the database to exclude (or explicitly contain) potential harmful source images. Creating a public API could offer a cheaper way to offer a safe model than retraining a model on a filtered subset of the training data or doing difficult prompt engineering. Conversely, including only harmful content is an easy way to build a toxic model.
A.2 Guidance Scale vs. FID
To further demonstrate the effectiveness of our proposed context prediction, we quantitatively conduct evaluations about the trade-off between MS-COCO zero-shot FID [26] and CLIP scores. The results in Figure 6 indicate that the guidance hurts the diversity of GLIDE much more than DALL-E 2 and CONPREDIFF. The phenomenon reveals that the proposed CONPREDIFF can overall improve the generation quality of diffusion models.
Figure 6: Trade-off between guidance scale and FID.
A.3 More Quantitative Results
We list the unconditional generation results on FFHQ, CelebA-HQ, LSUN-Churches, and LSUN-Bedrooms in Tab. 3. We find CONPREDIFF consistently outperforms previous methods, demonstrating the effectiveness of the CONPREDIFF.
Table 3: Evaluation results for unconditional image synthesis.
FFHQ 256 × 256
Method	FID ↓	Prec. ↑	Recall ↑
ImageBART[16]	9.57	-	-
U-Net GAN (+aug) [72]	7.6	-	-
UDM [39]	5.54	-	-
StyleGAN [36]	4.16	0.71	0.46
ProjectedGAN [71]	3.08	0.65	0.46
LDM [65]	4.98	0.73	0.50
CONPREDIFF	2.24	0.81	0.61
LSUN-Bedrooms 256 × 256
Method	FID ↓	Prec. ↑	Recall ↑
ImageBART [16]	5.51	-	-
DDPM [28]	4.9	-	-
UDM [39]	4.57	-	-
StyleGAN [36]	2.35	0.59	0.48
ADM [14]	1.90	0.66	0.51
ProjectedGAN [71]	1.52	0.61	0.34
LDM-4 [65]	2.95	0.66	0.48
CONPREDIFF	1.12	0.73	0.59
CelebA-HQ 256 × 256
Method	FID ↓	Prec. ↑	Recall ↑
DC-VAE [55]	15.8	-	-
VQGAN+T. [17] (k=400)	10.2	-	-
PGGAN [43]	8.0	-	-
LSGM [87]	7.22	-	-
UDM [39]	7.16	-	-
LDM [65]	5.11	0.72	0.49
CONPREDIFF	3.22	0.83	0.57
LSUN-Churches 256 × 256
Method	FID ↓	Prec. ↑	Recall ↑
DDPM [28]	7.89	-	-
ImageBART [16]	7.32	-	-
PGGAN [43]	6.42	-	-
StyleGAN [36]	4.21	-	-
StyleGAN2 [37]	3.86	-	-
ProjectedGAN [71]	1.59	0.61	0.44
LDM [65]	4.02	0.64	0.52
CONPREDIFF	1.78	0.74	0.61
A.4 More Synthesis Results
We visualize more text-to-image synthesis results on MS-COCO dataset in Figure 7. We observe that compared with previous powerful LDM and DALL-E 2, our CONPREDIFF generates more natural and smooth images that preserve local continuity.
"A photo of a dark Goth house"
"A teddy bear sitting on a chair."
"A person holding a bunch of bananas on a table."
"Green frog on green grass"
"The plane wing above the clouds."
"A group of elephants walking in muddy water."
"A big round hole in brick wall"
"Reflection of tree in lake"
"An orange ball is put on the ground."
"Trees on African grassland"
"Pancakes with ketchup"
Figure 7: Synthesis examples demonstrating text-to-image capabilities of for various text prompts.
"Cat fell asleep on the owner's bed."
"A photo of an adult lion."
"A red hydrant sitting in the snow."
"A photo of an white garlic ice cream"
A.5 Human Evaluations