Sketch-guided Diffusion Models

• Sketch-guided diffusion models are generative models that incorporate explicit sketch cues into the diffusion process for conditional synthesis and structural manipulation.
• They employ diverse architectural paradigms, including direct input fusion, side-branch adapters, and latent-space optimization to integrate spatially structured sketch information.
• Applications span image synthesis, 3D modeling, motion editing, and design, while ongoing research addresses challenges in fidelity, scalability, and multi-modal conditioning.

Sketch-guided diffusion models constitute a class of generative models that introduce one or more explicit sketch-based control signals to a diffusion process, thereby enabling conditional synthesis, editing, and structural manipulation of images, video, or 3D content. These models leverage either learned or “adapter”-based conditioning on spatially structured sketch inputs, ranging from raw freehand input to processed edge or distance field maps, within the noise-to-signal inference process of Denoising Diffusion Probabilistic Models (DDPMs) or Latent Diffusion Models (LDMs). Architectural innovations, optimization strategies, and diverse conditioning mechanisms have emerged, yielding effective spatial/geometric control and improving user interaction for creative, scientific, and commercial purposes.

1. Core Principles and Conditioning Mechanisms

Sketch guidance in diffusion models is typically realized through one of several architectural paradigms:

• Direct input fusion: The sketch is concatenated as an image channel or a latent alongside noisy data passed into a U-Net backbone; classical in DDPM architectures but also used in LDMs (Cheng et al., 2022, Wang et al., 2023).
• Side-branch control/adapters: Structures such as ControlNet (Devmurari et al., 2024, Jin et al., 2024) or learned adapters map the sketch to intermediate features, which are injected into the main denoising path either via residual addition, cross-attention, or channel fusion, allowing user sketches to modulate each U-Net block.
• Embedding projection: Approaches such as CLIP-based adapters or pseudo-text embeddings (Koley et al., 2024) project the sketch into the same space as text conditions, then apply cross-attention or late fusion.
• Latent optimization: Rather than retraining, these approaches manipulate the diffusion trajectory during sampling, optimizing the latent representations so that their internal cross-attention or decoder features match those extracted from a sketch (Ding et al., 2024, Chen et al., 30 Jun 2025).

Such mechanisms can operate either during network fine-tuning or entirely at inference-time with frozen models (adapter- or optimization-based), and often support multi-modal conditioning (e.g., text+sketch, sketch+reference image, sketch+motion) (Kim et al., 2023, Guo et al., 29 May 2025, Jin et al., 2024).

2. Architectural Variants and Conditioning Objectives

Sketch-guided diffusion models can be categorized according to their design and training strategy:

• MLP/U-Net Latent Predictors: Early models used small per-pixel MLPs as "Latent Guidance Predictors" (LGPs) to associate U-Net intermediate features and edge-encoded targets, providing a differentiable sketch matching loss applied during inference (Voynov et al., 2022). U-Sketch generalized this to a U-Net, improving spatial context capture and inference speed (Mitsouras et al., 2024).
• ControlNet and Adapter Approaches: Architectures such as ControlNet use zero-initialized convolutional branches added to each U-Net block to encode sketch features, which are fused with main signal flow (Jin et al., 2024, Devmurari et al., 2024, Koley et al., 2024). T2I-Adapter similarly injects sketch features spatially at multiple scales (Sun, 21 Mar 2025).
• Latent-space Manipulation: Training-free or minimal-overhead designs optimize the latent variables during sampling, enforcing alignment between the current diffusion step and reference sketch features via gradients of cross-attention KL-divergence or CLIP-space matching (Ding et al., 2024, Chen et al., 30 Jun 2025).
• Multi-modal Conditioning and Semantic Harmony: Models targeting sketch-text or sketch-image paired control use multi-branch CLIP embedding, fabric-retrieval, or Q-former-based fusion, with harmonized cross-attention mechanisms that adaptively resolve sketch/text conflicts through gating or interpolation (Guo et al., 29 May 2025, Zhan et al., 11 Apr 2025).

Guidance losses may target edge-reconstruction, per-pixel alignment, feature-space similarity, or matching of intermediate activations. Augmented objectives, such as discriminative FG-SBIR losses (Koley et al., 2024), perceptual alignment, or auxiliary classifiers (Wang et al., 2023) may be employed to drive sharpness, sketch fidelity, or class-accuracy.

3. Progressive Control, Realism, and User-driven Refinement

Advanced frameworks expose continuous or discrete control axes for realism, editability, and adherence:

• Fidelity-strength trade-off: Many models offer parameters trading exact sketch adherence for increased realism or diversity. For example, classifier-free guidance weights, early stopping of sketch gradient application, or explicit “realism” filters enable users to explore this continuum (Voynov et al., 2022, Cheng et al., 2022, Devmurari et al., 2024). Latent optimization weights similarly tune correspondence.
• Progressive and staged refinement: Systems such as CoProSketch enable multi-stage workflows—users generate a rough sketch, iteratively edit it, and submit for final, higher-fidelity completion. This is achieved through UDF-based representation and progressive diffusion guided by editable masks, allowing structure refinement before and after completion (Zhan et al., 11 Apr 2025).
• Sequence-aware and modular pipelines: Subjective Camera processes multi-step sketch sequences in user-supplied order, aligning the generative process with the cognitive structure of human scene composition. This sequential update scheme, combined with latent optimization and textual reward priors, enables fine-grained semantic and spatial control without retraining (Chen et al., 30 Jun 2025).

Certain models (e.g., (Kim et al., 2023, Mikaeili et al., 2023)) extend these workflows to localized region editing or 3D object/scene editing, where sketches specify which parts of the object or view to modify.

4. Applications Across Domains and Modalities

Sketch-guided diffusion models have been deployed across a rich spectrum of content domains:

• Image Synthesis and Editing: Spatial control over layout, pose, or local structure in text-to-image generation (Voynov et al., 2022, Mitsouras et al., 2024, Koley et al., 2024).
• 3D Generation and Editing: Sketch-conditioned optimization of neural radiance fields (NeRFs), either for full 3D generation (multi-view sketch input) or localized semantic edits (Mikaeili et al., 2023, Chen et al., 2024).
• Motion and Cinemagraph Synthesis: Freehand sketching of motion fields within images, allowing users to dictate dynamic flows and stylized effects in cinemagraphs (Jin et al., 2024).
• Product, Fashion, and Design: Flat sketch to garment image translation with harmonized cross-modal diffusion and feature-rich semantic enhancement enables practical garment design and editing (Guo et al., 29 May 2025). Sketch-driven product image generation and retrieval in interactive commercial systems leverages adapters and language agents for personalized results (Sun, 21 Mar 2025).
• 3D Shape and Point Clouds: Sketch-text guided denoising pipelines for colored point cloud synthesis, using capsule attention and separate geometry/color diffusion stages, enables controllable 3D modeling (Wu et al., 2023).
• Image Composition and Inpainting: Multi-modal inpainting with region-specific sketch guidance and reference image conditioning demonstrates local, style-respecting compositional editing (Kim et al., 2023, Mao et al., 2023).

5. Quantitative Evaluation and Comparative Performance

Sketch-guided diffusion models are evaluated by multiple axes:

Metric	Description	Used in Papers
FID	Frechét Inception Distance: realism of generated images	(Cheng et al., 2022, Wang et al., 2023, Koley et al., 2024)
LPIPS	Learned Perceptual Image Patch Similarity: perceptual similarity (lower is better)	(Devmurari et al., 2024, Guo et al., 29 May 2025, Peng et al., 2023)
CLIP Score	CLIP-based similarity to prompt or target image	(Guo et al., 29 May 2025, Zhan et al., 11 Apr 2025)
Edge Recall	Edge map overlap/recall between generated image and input sketch	(Mitsouras et al., 2024, Voynov et al., 2022)
MOS	Mean Opinion Score: human evaluation (1–5), realism, or fidelity	(Koley et al., 2024, Mitsouras et al., 2024)
Chamfer/HD	Edge-based distances for sketch-3D or silhouette correspondence	(Chen et al., 2024, Mikaeili et al., 2023)
User Study	Human preference for realism, faithfulness, or overall aesthetics among alternatives	(Mitsouras et al., 2024, Voynov et al., 2022, Cheng et al., 2022)

Performance over GAN, encoder-decoder, and previous diffusion-based methods is consistently improved across these metrics, with sharper textures, better structural alignment, and improved user study preference, particularly in cases involving abstract or freehand, out-of-domain, or multi-modal sketches (Wang et al., 2023, Voynov et al., 2022, Koley et al., 2024, Guo et al., 29 May 2025).

6. Limitations, Challenges, and Research Directions

Challenges for sketch-guided diffusion frameworks include:

• Fidelity under abstraction: Extremely sparse or ambiguous sketches, domain gaps between training edge-maps and human sketching, and over-constrained or under-defined user input can yield artifacts or semantic ambiguity (Koley et al., 2024, Voynov et al., 2022).
• Guidance tuning: Parameters such as guidance strength, noising schedule, and where in the denoising chain to apply sketch losses must be adapted to the desired structure-realism trade-off and can be sensitive to seed and initial conditions (Mitsouras et al., 2024, Voynov et al., 2022).
• Scalability and performance: Sampling speed and memory requirements remain a bottleneck for high-resolution, multi-modal, or 3D/4D outputs; distillation or accelerated samplers are pressing directions (Sun, 21 Mar 2025, Wang et al., 2023).
• Generalization and robustness: Out-of-distribution sketching, stylized lines, or highly abstract input still challenge generalization, motivating approaches that utilize discriminative guidance or learned adapters in the language/image embedding space (Koley et al., 2024).
• Limitations in fine structure: Models trained exclusively on edge maps or simple sketches may under-represent fine-grained spatial details, complex compositions, or rare semantic categories; hybrid pipelines with perceptual or cycle consistency losses may partially address this (Zhan et al., 11 Apr 2025, Peng et al., 2023).

Future research is oriented toward better multi-modal harmonization (e.g., sketch+text+reference), extension to higher spatial/temporal/3D resolution, interactive/incremental editing, and training-free or zero-shot adaptation strategies.

---

References:

• “Sketch-Guided Text-to-Image Diffusion Models” (Voynov et al., 2022)
• “U-Sketch: An Efficient Approach for Sketch to Image Diffusion Models” (Mitsouras et al., 2024)
• “Training-Free Sketch-Guided Diffusion with Latent Optimization” (Ding et al., 2024)
• “It’s All About Your Sketch: Democratising Sketch Control in Diffusion Models” (Koley et al., 2024)
• “DiffSketching: Sketch Control Image Synthesis with Diffusion Models” (Wang et al., 2023)
• “CoProSketch: Controllable and Progressive Sketch Generation with Diffusion Model” (Zhan et al., 11 Apr 2025)
• “HiGarment: Cross-modal Harmony Based Diffusion Model for Flat Sketch to Realistic Garment Image” (Guo et al., 29 May 2025)
• “Sketch-Guided Motion Diffusion for Stylized Cinemagraph Synthesis” (Jin et al., 2024)
• “VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation” (Devmurari et al., 2024)
• “Subjective Camera: Bridging Human Cognition and Visual Reconstruction through Sequence-Aware Sketch-Guided Diffusion” (Chen et al., 30 Jun 2025)
• “d-Sketch: Improving Visual Fidelity of Sketch-to-Image Translation with Pretrained Latent Diffusion Models without Retraining” (Roy et al., 19 Feb 2025)
• “Reference-based Image Composition with Sketch via Structure-aware Diffusion Model” (Kim et al., 2023)
• “SKED: Sketch-guided Text-based 3D Editing” (Mikaeili et al., 2023)
• “Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation” (Chen et al., 2024)
• “Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation” (Wu et al., 2023)
• “Adaptively-Realistic Image Generation from Stroke and Sketch with Diffusion Model” (Cheng et al., 2022)