Title: Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis
Venue: NeurIPS 2025
Accepted: yes
Keywords: video generative models

controllable image generation

image customization

conditional image generation
Abstract: Video generative models can be regarded as world simulators due to their ability to capture dynamic, continuous changes inherent in real-world environments. These models integrate high-dimensional information across visual, temporal, spatial, and causal dimensions, enabling predictions of subjects in various status. A natural and valuable research direction is to explore whether a fully trained video generative model in high-dimensional space can effectively support lower-dimensional tasks such as controllable image generation. In this work, we propose a paradigm for video-to-image knowledge compression and task adaptation, termed \textit{Dimension-Reduction Attack} (\texttt{DRA-Ctrl}), which utilizes the strengths of video models, including long-range context modeling and flatten full-attention, to perform various generation tasks. Specially, to address the challenging gap between continuous video frames and discrete image generation, we introduce a mixup-based transition strategy that ensures smooth adaptation. Moreover, we redesign the attention structure with a tailored masking mechanism to better align text prompts with image-level control. Experiments across diverse image generation tasks, such as subject-driven and spatially conditioned generation, show that repurposed video models outperform those trained directly on images. These results highlight the untapped potential of large-scale video generators for broader visual applications. \texttt{DRA-Ctrl} provides new insights into reusing resource-intensive video models and lays foundation for future unified generative models across visual modalities. The project page is \url{https://dra-ctrl-2025.github.io/DRA-Ctrl/}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this paper, the authors focus on the problem of image generation via a video generative model. They build on top of the HunyuanVideo-I2V model, training it with image pairs set as two frame videos with a mixup transition. They train their model on the Subjects200K and Text-to-Image-2M datasets and evaluate their approach on a number of diverse visual tasks: Canny to Image, Depth to Image, Deblurring, Colorization, and Masked out inpainting, and style transfer. They show quantative results on these tasks, showing improvement over baselines. Finally they perform an ablation analysis.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper focuses on a problem with immediate applications to computer graphics and computational photography: Image Generation\n\n2. The paper shows promising results on a diverse number of tasks, not simply general image synthesis.\n\n3. The approach is simple and makes intuitive sense: Video priors should strengthen image generation performance.\n\nWeaknesses:\n\n1. The paper's result is not particularly surprising. Video models probably outperform strict image models as the data domain of video is much richer.\n\n2. The approach does not have many novel technical contributions, it's largely taking an existing video model and tuning it for better image generation performance. \n\n3. There are some minor grammatical and stylistic issues (such as the choice to put a figure before the abstract), although these do not dramatically detract from readability.\n\nQUESTIONS:\n1. Can you elaborate on the novelty of the approach? It seems that this is just finetuning a video model for image generation.\n\n2. It isn't surprising that video priors would improve image generation. Could you elaborate more on the significance of this result?\n\nLIMITATIONS:\nYes, the authors have addressed the limitations of the work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a shift for the video generation models to use them for controllable image generation. In the paper, the framework, DRA-Ctrl, is proposed for task adaptation and knowledge compression. DRA-Ctrl employs two strategies: 1. Mixup-based transition to bridge the gap between videos and images and 2. Frame-Skip Position Embedding to allow large image transformations without generating many intermediate results. DRA-Ctrl is evaluated on various controllable image generation tasks such as subject-driven generation, style transferring, spatial-aligned controllable generation. The proposed model uses HunyuanVideo-I2V as backbone model.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. Writing is clear and the flow is easy to follow.\n\n2. The proposed model can be used for various condıtional image generation tasks.\n\n3. The paper provides comprehensive evaluation and ablation studies.\n\nWeaknesses:\n\n1. Comparison with SD1.5 in Table 1 can be useless because it is the initial stable diffusion-based model and there are SDXL, SD2, SD3, and SD3.5. I find comparison with the recent models more interesting.\n\n2. Possible long inference time: This is a known limitation of many diffusion-based image generation models, and it is likely a weakness of the proposed model as well. However, the paper provides limited analysis of inference time. While it includes an efficiency comparison with baseline models, this analysis should be extended to include state-of-the-art (SoTA) image editing and generation models. Without this broader comparison, it is difficult to be convinced of the proposed model’s efficiency.\n\nQUESTIONS:\nPlease have a consistent style on the tables. Table 1 highlight the proposed model with yellow, but other tables don’t and they have DRA-Ctrl. In table 1, the best scores are in bold, while, in table 2 the best scores are highlighted red and the second-best scores are blue. For the rest of the tables the best score are in bold for VL results only. Moreover, some tables have arrow near name of metrics, some don’t. Please make tables consistent.\n\nThank you for your acknowledgment and detailed discussion on safeguards  and societal impact with both the potential positive and negative impacts of the proposed model!\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This work targets at controllable image generation by leveraging existing video generation models. Multiple image generation is formulated into keyframe inbetween task in video generation. By carefully designning the keyframe transition between condition image and target one, the video capabilities in smooth transition over time are reused. Experimental results demonstrate the effectiveness of reusing video generative prior in image generation.

STRENGTHS AND WEAKNESSES:
This paper is well-organized and clearly presented. 
The motivation for reusing the video generative prior is insightful for image applications.
The experimental details are thoroughly provides including training and evaluation, which helps reproduction.
The comparison against prior efforts is comprehensive. Ablations further help the audience better understand every components.

QUESTIONS:
- In terms of number of transition frames, what if we have no transition frames? That is, we directly feed the condition image and target one.
- As mentioned at L159, I2V model over-preserves similarity to the condition image and exhibits poor prompt adherence. After keyframe fine-tuning, is prompt following better?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

comments: SUMMARY:
This work proposes using video generation models (e.g., Hunyuan Video) to achieve spatial structure-controlled image generation.
A mix-based transition strategy is introduced to bridge the gap between videos and images.
The authors also propose a Frame-Skip Position Embedding, which expands temporal intervals in the latent space.
Experiments are conducted under various types of conditions to verify the effectiveness of the proposed approach.

STRENGTHS AND WEAKNESSES:
Weaknesses   
The generation quality is sometimes worse than that of Flux, but I think it is acceptable.  
I wonder whether the inference speed is significantly slower when using video models for image generation.   
The authors are encouraged to provide an analysis of why using video models can greatly improve controllability, and to include any failure cases where the method struggles to provide effective control.    

Strengths    
The improvement in controllability is impressive.    
The topic explores an interesting and promising direction.  
The experiments are comprehensive.

QUESTIONS:
How does the image generation speed compare to that of Flux and SD?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- This work proposes Dimension-Reduction Attack (DRA-Ctrl), a method that utilizes the long-range context modeling and full attention in video generative models to perform various controllable image generation tasks.\n- Specifically, authors leverage priors from Image-to-Video (I2V) models for the tasks of 1) subject-driven generation, image-to-image translation and style transfer, and demonstrate that such repurposed video models outperform those trained only on images.\n- The main idea of the paper is to construct a synthetic video with the condition image as the first frame and the target image as its last frame. The intermediate frames are generated using a temporal position-aware mixup strategy between the first and the last frames. An I2V model is then trained on these synthetic videos to solve several image-to-image translation tasks. To avoid computation costs due to processing a lot of intermediate frames, they propose Frame-skip position embedding (FSPE) to augment the 3D RoPE embeddings. FSPE \"simulates\" a longer video in the latent space by expanding the PEs.\n- Experiments demonstrate that video generative models can be repurposed to solve controllable image generation tasks and consistently outperform methods built upon image generation tasks\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The paper is well written and easy to follow. The paper has adequate details for fair reproduction.\n- The results on image-to-image translation (Table-1) and subject-driven generation are impressive.\n- The use of mixup, a data augmentation strategy widely used in self-supervised learning, for media generation is novel and this line of work can potentially improve the \"mixup\" of ideas from both the domains.\n- The idea that leveraging video generation models for improved controlled image generation tasks is very interesting and can potentially open up several valuable research problems.\n\n**Weaknesses**\n- My main issue with this work is the evaluation setup. Evaluation setup compares DRA-Ctrl with contemporary work. It fails to normalize the results with compute. A more fair way to evaluate DRA-Ctrl would be to report FLOPs and GPU hours spent by the pre-training models and the finetuning step and then comparing results. \n- Here is the setup that I believe is fair. 1) Image vs Video pre-trained model initialization: Compare a method that uses an image generation model's (trained in N gpu hours, M Flops) weights as initialization to DRA-Ctrl, that initializes from a video generation model trained with the same GPU hours and FLOPs. This experiment would be convincing that the priors learned by video generation model can indeed improve image generation. With the current setup it is not clear if the compute budget of pre-trained model or the video generation task itself is helping. 2) Number of parameters/compute budget in the fine-tuning stage: Compare with methods that use similar compute budgets/parameters in the 2nd stage to fully understand where the improvements are coming from.\n\nQUESTIONS:\n- Why is the method called dimension-reduction \"attack\"? What is the attack that is being done in the proposed method? The rest of the title conveys the workings of the proposed method but the name is very misleading.\n- For the VL score, how do you ensure that the model returns the same score for a particular image pair in a randomized validation set? In my experience a VLM outputs different scores for the same input when asked at a later point in time. This makes the metric very unreliable. \n- What is the Oracle score in Table 2? If it is computed using the ground truth images, why can't the VL-Score metric be computed for this row?\n- Please comment on my issue with the experimental setup described in the weaknesses section.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 2, "DATE": "2025-07-05"}