GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation

Summary

The paper introduces a zero-shot layout technique for spatially-grounded text-to-image generation with the power of transformer-based diffusion architecture. Prior methods tend to manipulate the latent image during reverse process for grounding, solely relying on cross-attention maps that show the alignment between specific text tokens and spatial regions of the target object to place in. This shows a drawback in generating a target object that fits within the region of bounding boxes. By utilizing transformer architecture, the method can divide the generation process into two branches: (1) first branch is to generate a target image patch of the bounding box with an arbitrary size by a transformer, (2) the second branch is the original generation process of the whole image. At every denoising step, the generated object is then copied into the generated image of the second branch.

Strengths

- Addressing the limitation of attention-based method in generating arbitrary object sizes within bounding boxes, the method proposes to utilize transformer architecture (namely DiT) which is free from fixed-resolution constraints and enables patch-level generation. - The paper is well-written and easy to follow. - Model efficacy is superior to other methods in Figure 4 and Table 1.

Weaknesses

- 158-161: It is obvious that different noise inputs certainly yield different outputs. So I do not understand the main point of this part. - Semantic sharing part is an interesting observation. It is more helpful if the authors compare it with image interpolation. Does it work when two images have different semantic classes like dog and human? Wang, Clinton, and Polina Golland. "Interpolating between images with diffusion models." (2023). - Eq 5: What is formulation of $ \mathcal{L}(x_t, G_i, A_i)$? - In reverse process: Have the authors applied semantic sharing on bounding boxes like concat tokens of multiple objects for simultaneous denoising? I assume that they share the same semantic class. - In Fig 3, I saw the size of object image is much bigger than the size of bounding box. Is it intentional? If so, please explain. How to define noisy object image $z_{i, t}$? As far as I understand, the method need to reserve the aspect ratio of the bounding box by scaling it correspondingly to match the size of main image. Is it possible to disable this scaling step and denoise an object image with the same size as the bounding box? - Does Noisy Patch transplantation guarantee the consistent content of the output image when pasting the generated image with a binary mask (Eq. 9)? Again in Eq 9, the size of $x_{t-1}$ and $B_{i, t-1}$ are not the same as illustrated in Figure 3 so it is not clear how Hadamard product is possible.

Questions

NA

Rating

6

Confidence

4

Soundness

3

Presentation

2

Contribution

3

Limitations

Limitations and societal impacts are included in the paper.

Summary

The paper targets exploiting a pre-trained PixArt-alpha (a text-to-image multi-aspect diffusion transformer) to generate images conditioned on a set of text-labeled bounding boxes. The authors start from the idea that, at inference time, a transformer can be given an arbitrary number of tokens, and that these tokens can have positional embedding corresponding to multiple images of multiple aspect ratios at once. Empirically, they observe that in this situation, the diffusion transformer is able to handle the generation process consistently and the outputs are multiple images with shared content but with different aspect ratios corresponding to the input PEs. When generating the content of the bounding boxes, they take advange of this idea by simultaneously generating two version of the same bounding-box, one with positional embeddings corresponding to its location in the full image, and the other one with positional embeddings making the crop appear as a full image. This ensures that the label is represented in the full-image version, and thanks to the shared content, also in the original box version, that can then be transplanted back into the image.

Strengths

- The paper confirms an interesting property of multi-aspect diffusion transformers, in that tokens are able to interact at inference time, across combinations of positions that are not seen during training. - They propose a clever way to exploit this capability in the context of a multi-aspect diffusion transformer. - Handling spatial relations correctly is one of the major challenges that state-of-the-art text-to-image models have yet to overcome. The targetted task of grounding is very relevant as it is one way to circumvent the issue. - The proposed solution obtains very good performance on the considered benchmark.

Weaknesses

Mainly, I find that the exposition of the idea not very clear and could be improved: - How the different branches, the two-stage process, and the diffusion scheduling interact is not clear: - Appendix A.1 reveals that Stage 1 and 2 are merged in a single full 50 steps denoising schedule, with Stage 1 only being used in the 25 first steps, which is not exactly in line with what is presented in the main paper. This information needs to be in the main paper, and ideally the presentation should be made more consistent with the implementation. - Figure 3 and Line 200 suggest that in the main branch x_t is denoised in parallel to the object branch, but it does not appear in Algorithm 1. This is inconsistent. And if the main brain does anything, it needs to be explained and detailed. - Section 3 should present how multi-aspect/size is handled in diffusion transformers, as it is a key requirement for the proposed method. Also, Eq.3 introduces $w$ without definition. - a smaller point that could be improved: in 4.2, while it can be derived from context and references that $\mathcal{L}$ needs to be a grounding loss, this is nowhere explicitly stated. If I strictly adhered to what is written in this paragraph, the authors could very well be arguing that any loss function that takes as input $(x_t, \mathcal(G)_i, \mathcal(A)_i)$ would be effective, which is obviously not what they are trying to say.

Questions

I find this submission to be very interesting and show a lot of promise, but the description of the method is very confusing. For the discussion period, I hope the authors can provide the necessary clarifications and leave enough time for us to have a discussion together around their updated description. Indeed, until the main points are convincingly clarified, succintly reviewed again, and we are confident that changes will be made, I cannot recommend acceptance. My main question is then: - Can the authors clarify if anything happens in the main branch? Perhaps a full description of how the method works, including the reverse diffusion iterations would help. Also, as a comparatively minor point, diffusion models are known for the flexibility of their sampling process. Considering how each stage and branch have compatible objectives, have the authors considered doing every update simultaneously in a single reverse diffusion process instead of multi-stage?

Rating

7

Confidence

3

Soundness

2

Presentation

1

Contribution

3

Limitations

Limitations and impacts are discussed adequately.

Summary

This paper explores the zero-shot layout-to-image generation problem using bounding boxes and texts as conditional inputs. The authors propose a method called GrounDiT, which builds upon the recent Diffusion Transformers (DiT) model. Leveraging DiT's emergent property of semantic sharing, where two noisy images of different sizes can progressively converge during sampling, the authors introduce a key innovation: a separate object denoising branch for each bounding box condition, running in parallel to the main denoising branch. In each object branch, a noisy object patch is generated and then implanted into the original image. The authors compare GrounDiT with SOTA zero-shot methods on the HRS and DrawBench datasets, demonstrating that GrounDiT can generate complete objects in the correct regions while maintaining high overall image fidelity.

Strengths

- The paper is well-written, with a clear and concise background and motivation. - The idea of having a separate object branch for each bounding box is simple and intuitive, making the method easy to understand and implement. - The authors conduct an extensive comparison with SOTA baselines, including R&B, BoxDiff, Attention-Refocusing, and Layout-Guidance, demonstrating the effectiveness of their approach.

Weaknesses

- The paper lacks a comparison of prompt fidelity on the HRS dataset. Except for PixArt-R&B( a variant of GrounDiT), it would also be useful to include results from other baselines like R&B and BoxDiff to ensure the completeness of the work, even if they may use different backbones. - In Table 1, GrounDiT outperforms its variant PixArt-R&B in grounding accuracy, which is expected given the special design of noisy object patch cultivation and transplantation. However, in Table 2, GrounDiT continue to surpass PixArt-R&B in prompt fidelity scores like CLIP score and ImageReward. I am curious to know where these improvements come from, as object patch cultivation and transplantation should only benefit grounding ability. Did the authors perform multiple runs for the experiments? - The paper does not evaluate the MS-COCO subset presented in the R&B paper, which would be a valuable addition to the experiments. - The proposed method would increase the compuation cost for image generation. However, there is no quantitative evidence showing the exact cost.

Questions

Please refer to the weaknesses part.

Rating

5

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

In the conclusion part, the authors admit that the proposed method would increase the compuation time for image generation.

Summary

This paper proposes a method to use a pre-trained text-to-image diffusion model to guide the generation into placing objects at given locations determined by bounding boxes. The challenge is to develop this capability without requiring fine-tuning of the model.

Strengths

- The problem of guided generation without requiring fine-tuning of a diffusion model is a relevant one, and the method seems effective. - Experimental results show the proposed method outperforms compared baselines.

Weaknesses

- The quality of the presentation can be improved. For instance, one fundamental component of the method, $\math{L}_{AGG}$, is not defined. The evaluation metrics (spatial, size, color) are not properly defined, unless I missed it. - Further analysis or discussion on why the proposed approach is superior to simply directly transferring the patch would make the paper more convincing. - Experimental comparison is limited to two datasets. Qualitatively, it's not entirely clear there is a significant difference between the R&B baseline and the proposed approach.

Questions

See weaknesses.

Rating

4

Confidence

4

Soundness

2

Presentation

1

Contribution

2

Limitations

Yes