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SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models

Yuwei Guo, Ceyuan Yang, Anyi Rao, Maneesh Agrawala, Dahua Lin, Bo Dai

arXiv:2311.16933v1cs.CV

TL;DR

Text prompts leave complex video structure ambiguous, while dense structural controls increase the burden of supplying frame-wise conditions. SparseCtrl adds an encoder for temporally sparse sketch, depth, or RGB inputs without modifying the pre-trained T2V generator, and experiments demonstrate control across original and personalized generators.

  • Problem

    Text prompts provide ambiguous control over complex frame composition, while dense structural guidance requires condition maps for each video frame.

  • Method

    SparseCtrl uses an add-on sparse condition encoder that processes temporally sparse maps while leaving the pre-trained T2V generator untouched.

  • Results

    Experiments show that one or a few sketch, depth, or RGB condition maps can control synthetic videos across original and personalized T2V generators.

  • Takeaways & Limitations

    SparseCtrl supports practical applications including storyboarding, depth rendering, image animation, keyframe interpolation, and video prediction.

  • Takeaways & Limitations

    Generated visual quality, semantic composition, and domain coverage remain limited by the pre-trained T2V backbone and training data, especially for out-of-domain inputs.

Abstract

from arXiv · show

The development of text-to-video (T2V), i.e., generating videos with a given text prompt, has been significantly advanced in recent years. However, relying solely on text prompts often results in ambiguous frame composition due to spatial uncertainty. The research community thus leverages the dense structure signals, e.g., per-frame depth/edge sequences, to enhance controllability, whose collection accordingly increases the burden of inference. In this work, we present SparseCtrl to enable flexible structure control with temporally sparse signals, requiring only one or a few inputs, as shown in Figure 1. It incorporates an additional condition encoder to process these sparse signals while leaving the pre-trained T2V model untouched. The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images, providing more practical control for video generation and promoting applications such as storyboarding, depth rendering, keyframe animation, and interpolation. Extensive experiments demonstrate the generalization of SparseCtrl on both original and personalized T2V generators. Codes and models will be publicly available at https://guoyww.github.io/projects/SparseCtrl .

1. Introduction

Text prompts alone provide insufficient control over complex video structure, while existing structural controls require dense frame-wise inputs and often costly retraining. SparseCtrl addresses these burdens with an add-on encoder that uses temporally sparse conditions and supports multiple modalities and T2V backbones.

  • Motivation: Text prompts struggle to specify complex spatial layouts, poses, and shapes, limiting precise control for professional video applications.Users may need repeated trial-and-error to obtain desired outputs.
  • Motivation: Existing controllable T2V methods require structural maps for every generated frame, increasing the practical cost of control.Many approaches also redesign the architecture, requiring costly retraining when a trained T2V model already exists.
  • Approach: SparseCtrl controls T2V generation with temporally sparse condition maps through an efficient add-on encoder.The approach follows a ControlNet-style auxiliary-encoder philosophy while preserving the original generator.
  • Approach: The encoder is trained on top of a pre-trained T2V model, eliminating comprehensive retraining and enabling control of personalized models with AnimateDiff.This preserves the original generator while adding condition inputs.
  • Results: Experiments train sketch, depth, and RGB encoders, showing control from one or a few maps across original and personalized T2V generators.Applications include storyboard animation, depth rendering, image animation, keyframe interpolation, and video prediction.

2. Related Works

Prior work improves T2V control through motion, keyframe, and low-level structural signals, but these approaches commonly require dense sequences or architectural additions. SparseCtrl builds on add-on control networks to target sparse control while preserving pre-trained generators.

  • Text-to-video diffusion models: T2V models have evolved from video models trained from scratch toward extensions of powerful pre-trained T2I generators.These extensions add temporal layers to model cross-frame motion and consistency.
  • Controllable text-to-video generation: Controllable T2V research addresses high-level motion, keyframes, and fine-grained spatial structure using trajectories, motion vectors, poses, images, sketches, and depth.Different methods inject these controls through LoRA layers, concatenation, separate encoding, or feature injection.
  • Controllable text-to-video generation: Fine-grained control methods generally rely on temporally dense structural sequences, including depth, sketch, and other per-frame signals.The related-work passage identifies dense sequences as a recurring requirement for precise controllability.
  • Add-on network for additional control: Add-on networks incorporate extra conditions by training auxiliary encoders while maintaining the original generative backbone.ControlNet reintegrates encoder outputs through zero-initialized layers, while T2I-Adapter and IP-Adapter use lightweight or embedding-based injection.
  • Add-on network for additional control: SparseCtrl follows add-on network principles but specifically targets sparse control for video generation.Its design motivation is to add sparse conditions without replacing the pre-trained generator.

3. SparseCtrl

SparseCtrl adds temporally sparse conditioning to pre-trained T2V generators through dedicated encoders, temporal propagation, and modality-specific inputs. The design addresses frame-wise inconsistency and avoids letting copied noisy latents overwhelm sparse conditions.

  • 3.1. Text-to-Video Diffusion Models: SparseCtrl extends pre-trained T2I-derived video generators, whose temporal layers exchange information across frames to model motion and consistency.The T2V training objective uses an MSE loss on noisy video latents conditioned on text embeddings.
  • 3.2. Sparse Condition Encoder: Sparse condition encoders accept sparse condition maps and reintegrate their outputs into the original T2V model through an add-on design.The comparison with vanilla ControlNet highlights zero-initialized layers and channel-wise condition processing.
  • 3.2. Sparse Condition Encoder: A frame-wise encoder replicates ControlNet across time, inserts conditions at selected keyframes, and uses zero images plus a mask for unconditioned frames.This straightforward design can fail to maintain temporal consistency when only sparse frames are conditioned.
  • 3.2. Sparse Condition Encoder: Temporal layers such as temporal attention with positional encoding propagate conditional signals between frames to infer intermediate states.The design targets the backbone’s difficulty inferring conditions for unconditioned frames.
  • 3.2. Sparse Condition Encoder: Vanilla ControlNet can degrade visual quality because copied noised samples may cause the sparse encoder to rely on noise rather than condition maps.SparseCtrl therefore removes the noised-sample input and accepts concatenated condition maps and masks instead.
  • 3.3. Supported Modalities and Applications: SparseCtrl implements encoders for sketches, depth maps, and RGB images, while identifying skeletons and edge maps as future modalities.Sketch inputs support storyboard-based video shaping and sparse sketches for coarse motion.

4. Experiments

The experiments evaluate SparseCtrl across qualitative applications, sparse-control tasks, and ablation studies. The stated evaluation includes comparisons with baseline methods and tests of textual prompts and unrelated conditions.

  • Experimental Settings: The experiments cover implementation details, one- or few-condition applications, baseline comparisons, ablations, and responses to textual or unrelated conditions.The passage specifically cites sparse depth-to-video generation and image animation among the comparison tasks.

4.1. Implementation Details

SparseCtrl is implemented on AnimateDiff as an add-on sparse condition encoder, preserving the pretrained T2V backbone while supporting original and personalized generators.

  • Text-to-video generator: SparseCtrl integrates a sparse condition encoder into a pretrained AnimateDiff-based T2V backbone.The encoder is trained on top of the original generator rather than retraining the full model.
  • Training: The training strategy randomly selects between 1 and N frames to receive conditions during each iteration.Conditions are retained only at sampled frame indices, encouraging controllability under varying numbers of conditioned frames.
  • Text-to-video generator: The implementation supports both Stable Diffusion V1.5-based AnimateDiff and personalized backbones including RealisticVision and ToonYou.
  • Training: SparseCtrl is trained on WebVid-10M with conditions extracted on the fly.

4.2. Main Results

SparseCtrl generates videos that adhere closely to sparse sketch, depth, and RGB controls while maintaining temporal consistency across original and personalized T2V settings. A single sketch or as few as two conditions can support complex compositions and intermediate-state inference.

  • Qualitative results: SparseCtrl produces videos that closely follow control signals while maintaining temporal consistency across different numbers of conditioning frames.The qualitative demonstrations cover sketch, depth, and RGB modalities in both original and personalized T2V settings.
  • Sparse semantic control: A single sketch can trigger rare semantic compositions that pretrained T2V models struggle to generate from textual descriptions alone.The paper illustrates this with a panda standing on a surfboard.
  • Sparse temporal control: As few as two conditions can support inference of intermediate states using the pretrained T2V model’s learned real-world motion knowledge.This result suggests temporally dense control may not be necessary for some generation settings.

4.3. Comparisons on Popular Tasks

SparseCtrl is evaluated on sparse depth-to-video generation and image animation against dense-control and specialized animation baselines. It maintains comparable sparse-control error as sparsity increases and is favored for first-frame fidelity while delivering comparable image-animation performance.

  • Sparse depth-to-video generation: SparseCtrl is compared on sparse depth-to-video generation against VideoComposer, Text2Video-Zero, and AnimateDiff with frame-wise ControlNet.VideoComposer and Text2Video-Zero provide dense depth controls, while the AnimateDiff baseline applies frame-wise controls to conditioned keyframes.
  • Sparse depth-to-video generation: The error of AnimateDiff with frame-wise ControlNet increases as control becomes sparser, indicating that it tends to ignore sparse condition signals.
  • Image animation: For image animation, SparseCtrl is evaluated against two open-source animation baselines using eight in-the-wild images and 24 generated samples.Metrics cover first-frame fidelity to the input image using LPIPS and temporal consistency using CLIP similarity.
  • Image animation: SparseCtrl achieves comparable performance with specifically designed animation pipelines and is favored for fidelity to the first frame.

4.4. Ablative Study

The ablations examine sparse-encoder designs, unrelated conditions, and textual-prompt influence. Results favor propagation without noised-sample input, while showing that prompts continue directing generated content.

  • Design of Sparse Encoder: All three encoder variations preserve fidelity to the input image in the first generated frame.This comparison is reported for qualitative image animation results.
  • Design of Sparse Encoder: The full encoder with propagation layers and noised-sample removal maintains condition fidelity and temporal consistency in both evaluated settings.The frame-wise design fails to propagate controls to unconditioned frames, while propagation with noised-sample input degrades quality.
  • Unrelated Conditions: Unrelated RGB inputs can still produce smooth interpolations, indicating robustness for visual-effects synthesis.The experiment interpolates between two unrelated images supplied to the RGB image encoder.
  • Response to Textual Prompt: Text prompts influence outcomes by steering generated contents toward corresponding directions under the same input condition.The figure compares different textual prompts while holding the input condition fixed.
  • Response to Textual Prompt: With sketch conditions, proper content generation requires a completed prompt rather than an insufficient or incomplete description.The comparison uses prompts that omit useful information, partially describe the scene, or describe every content.

5. Discussion and Conclusion

SparseCtrl adds temporally sparse, multimodal controls to pre-trained text-to-video generators through an add-on encoder, supporting practical video-generation applications. Its effectiveness generalizes across original and personalized generators, while output quality and domain coverage remain constrained by the backbone and training data.

  • SparseCtrl adds temporally sparse controls to pre-trained text-to-video generators through a unified add-on encoder network.The approach accommodates depth, sketches, and RGB images without replacing the pre-trained generator.
  • The method supports practical applications including sketch-to-video, image animation, and keyframe interpolation.Its multimodal flexibility is presented as valuable for diverse video-generation use cases.
  • Experiments validate SparseCtrl’s effectiveness and generalizability across original and personalized text-to-video generators.The authors characterize it as promising for real-world usage.
  • Generated visual quality, semantic composition, and domain coverage remain limited by the pre-trained text-to-video backbone and training data.The reported failures mostly involve out-of-domain inputs such as anime image animation, which are scarce in the WebVid-10M pre-training data.
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