1. Bibliographic Information

1.1. Title

Canvas-to-Image: Compositional Image Generation with Multimodal Controls

1.2. Authors

The paper lists the following authors: Yusuf Dalva, Guocheng Gordon Qian, Maya Goldenberg, Tsai-Shien Chen, Kfir Aberman, Sergey Tulyakov, Pinar Yanardag, and Kuan-Chieh Jackson Wang. Their affiliations are: Snap Inc., UC Merced, and Virginia Tech. This indicates a collaboration between industrial research (Snap Inc.) and academic institutions (UC Merced, Virginia Tech). The authors likely have backgrounds in computer vision, machine learning, and generative AI.

1.3. Journal/Conference

The paper is published on arXiv, a preprint server for scientific papers. While not a peer-reviewed journal or conference proceedings at the time of its arXiv publication, the research is presented as an arXiv preprint, indicating its readiness for broader academic review. The listed publication date is 2025-11-26T18:59:56.000Z, suggesting it is a very recent or upcoming work. Many high-impact papers in machine learning first appear on arXiv before formal publication at top-tier conferences (e.g., CVPR, ICCV, NeurIPS, ICML) or journals.

1.4. Publication Year

2025

1.5. Abstract

Modern diffusion models, despite their prowess in generating high-quality and diverse images, struggle with maintaining high-fidelity compositional control and multimodal input adherence, especially when users simultaneously provide a variety of control signals such as text prompts, subject references, spatial layouts, pose constraints, and layout annotations. To address this, the paper introduces Canvas-to-Image, a unified framework that consolidates these diverse control signals into a single canvas interface.

The core innovation lies in encoding all heterogeneous control signals into a single composite canvas image. This allows the underlying diffusion model to directly interpret these signals for integrated visual-spatial reasoning. To facilitate this, the authors curate a suite of multi-task datasets and propose a Multi-Task Canvas Training strategy. This strategy optimizes the diffusion model to jointly understand and integrate various control modalities into text-to-image generation within a unified learning paradigm. This joint training enables Canvas-to-Image to perform reasoning across multiple control modalities without relying on task-specific heuristics, and it demonstrates strong generalization to multi-control scenarios during inference.

Extensive experiments show that Canvas-to-Image significantly outperforms state-of-the-art methods in identity preservation and control adherence across challenging benchmarks, including multi-person composition, pose-controlled composition, layout-constrained generation, and multi-control generation.

1.6. Original Source Link

Official Source: https://arxiv.org/abs/2511.21691 PDF Link: https://arxiv.org/pdf/2511.21691v1.pdf Publication Status: Preprint on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem the paper aims to solve is the inherent limitation of modern diffusion models in achieving high-fidelity compositional and multimodal control over image generation. While these models excel at creating realistic and diverse images, they often fall short when users need to simultaneously specify multiple, heterogeneous aspects of an image. For instance, a user might want to generate an image where specific subjects are placed at certain locations, adopting particular poses, described by a text prompt, and constrained by bounding boxes. Existing methods typically handle only isolated aspects (e.g., just spatial layout or just pose), or they rely on complex combinations of separate modules that often lead to artifacts, lack generalizability, or are limited in scope (e.g., only face injection).

This problem is crucial in creative and design applications where precise control over generated content is paramount. The lack of unified, flexible control hinders the usability of powerful generative AI for digital artists, content creators, and designers who need to coordinate various elements to fulfill their creative intent. The specific challenges include reconciling diverse input structures (e.g., an image of a subject, a text description, a skeleton for pose, a bounding box for layout) and semantics, and training a single model that can jointly interpret and balance these signals effectively.

The paper's entry point or innovative idea is to simplify this complex multimodal control problem by unifying all diverse control signals into a single, composite visual canvas image. This Multi-Task Canvas then serves as the primary input to a diffusion model, allowing the model to perform integrated visual-spatial reasoning without needing separate, task-specific modules.

2.2. Main Contributions / Findings

The paper makes several primary contributions:

1. Unified Canvas Framework (Multi-Task Canvas): It introduces a generalized Multi-Task Canvas representation that consolidates heterogeneous control modalities (subject insertion, bounding-box layouts, pose guidance, background composition) into a single RGB image input. This allows for a coherent canvas-to-image formulation, enabling the model to reason across different modalities within a unified input format without architectural changes.

2. Multi-Task Datasets and Training Strategy: The authors curate comprehensive multi-task datasets that align diverse control signals with corresponding target images. They propose a Multi-Task Canvas Training framework which fine-tunes a diffusion model (VLMDiffusion) to collectively reason across these tasks. A key finding is that this joint training fosters emergent generalization, allowing the model to handle complex multi-control scenarios at inference time, even if those specific combinations were not explicitly seen during training. The use of a task indicator prompt is also introduced to prevent task interference.

3. Comprehensive Evaluation and Superior Performance: Extensive experiments on challenging benchmarks (4P Composition, Pose-Guided 4P Composition, Layout-Guided Composition, and Multi-Control Composition) demonstrate that Canvas-to-Image significantly outperforms state-of-the-art methods. It shows clear improvements in identity preservation (maintaining the appearance of specific subjects) and control adherence (faithfully following spatial, pose, and layout instructions). Ablation studies further confirm the effectiveness of the unified multi-task design.

   The key conclusions and findings are that by reformulating diverse control types into a single visual canvas and training a diffusion model with a multi-task strategy, it's possible to achieve robust, precise, and flexible compositional image generation with multimodal controls. This approach simplifies the input interface for users, enhances the model's ability to interpret complex user intent, and generalizes well to novel combinations of controls, solving the problem of fragmented control mechanisms and resulting in more coherent and high-fidelity generations.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand Canvas-to-Image, a reader should be familiar with several core concepts in generative AI and computer vision:

• Diffusion Models: These are a class of generative models that learn to reverse a gradual diffusion process. In essence, they are trained to iteratively remove noise from a noisy input, transforming it into a clean data sample (e.g., an image). They consist of two main parts: a forward diffusion process that gradually adds noise to data until it becomes pure noise, and a reverse denoising process, which is learned by a neural network to reconstruct data from noise. Diffusion models have become dominant in high-fidelity image synthesis due to their ability to generate diverse and realistic images.
• Text-to-Image Generation: This refers to the task of generating an image solely from a textual description (a prompt). Modern diffusion models, often conditioned on large text-image datasets, excel at this by learning to map semantic information from text into visual features. The text prompt guides the image generation process.
• Vision-Language Model (VLM): A VLM is a type of AI model that can process and understand both visual information (images) and textual information (language). They are trained on vast datasets of image-text pairs, allowing them to learn alignments between visual concepts and their linguistic descriptions. In Canvas-to-Image, a VLM is used to encode both the canvas image (containing visual control signals) and the text prompt into a unified tokenized representation or semantic embedding.
• Variational Autoencoder (VAE): A VAE is a type of generative neural network that learns a compressed, lower-dimensional representation (called the latent space) of input data. It encodes an input into a distribution in the latent space and then decodes samples from this distribution back into the original data space. In the context of diffusion models like those Canvas-to-Image is built upon, VAEs are often used to encode high-resolution images into a smaller latent representation (VAE latents) to make the diffusion process computationally more efficient, and then decode the generated latent representation back into a high-resolution image.
• LoRA (Low-Rank Adaptation): LoRA is a parameter-efficient fine-tuning technique for large pre-trained models. Instead of fine-tuning all the parameters of a large model, LoRA injects small, trainable low-rank matrices into the model's existing weight matrices. This significantly reduces the number of trainable parameters, making fine-tuning more memory-efficient and faster, while often maintaining or even improving performance. Canvas-to-Image uses LoRA to fine-tune specific layers (attention, image/text modulation) of a pre-trained diffusion model.
• Attention Mechanism: A core component in many modern neural networks, especially Transformers. The attention mechanism allows a model to weigh the importance of different parts of its input when processing a specific element. For example, in text, it helps the model focus on relevant words. In images, it can help focus on relevant regions. In VLMDiffusion architectures, attention layers are crucial for integrating multimodal information (text, visual controls) and generating coherent images.
• Pose Estimation: The task of identifying and localizing key points (joints) on a human body in an image or video. Libraries like OpenPose are common for this. In Canvas-to-Image, pose skeletons (lines connecting key points) are used as a structural control signal to guide the posture of subjects in generated images.
• Bounding Boxes: Rectangular coordinates that define the location and extent of an object within an image. In Canvas-to-Image, bounding boxes combined with textual annotations (textual identifier) are used as a layout annotation to specify where specific subjects or objects should appear and their approximate size.

3.2. Previous Works

The paper frames its contributions against the backdrop of existing generative models and control mechanisms, highlighting their limitations:

• General Diffusion Models: Works like [9, 33, 38] (e.g., Stable Diffusion, SDXL) have achieved impressive realism and diversity in image generation. However, they are inherently stochastic and offer limited flexibility for fine-grained compositional control, meaning it's hard to tell them exactly what to generate and where.
• Personalization Methods: These methods aim to generate specific subjects or identities in novel contexts.
  • Per-concept fine-tuning: Early approaches [10, 21, 39] (e.g., Textual Inversion, DreamBooth) require fine-tuning a model for each new concept/subject, which can be computationally intensive and scale poorly for multiple subjects.
  • Adapter-based solutions: More efficient methods [12, 13, 30, 34, 44, 50] (e.g., IP-Adapter) keep the base model frozen and inject subject-specific representations via lightweight adapters.
  • Multi-concept personalization: This remains challenging. Optimization-based methods [1, 6, 11, 20, 32] often require explicit concept disentanglement, while optimization-free methods [5, 14, 35, 43, 48] might concatenate embeddings, leading to scalability issues or difficulties in combining identities.
  • Limitations: Most personalization methods primarily focus on reference injection (inserting a specific subject) [15, 36] but lack spatial control or the ability to handle multiple subjects with specific poses and layouts simultaneously. Examples like UniPortrait [15] are evaluated in the supplementary, showing it struggles with multi-subject identity preservation.
• Compositional Control Mechanisms (Isolated Tasks):
  • Structural cues: Models like ControlNet [52] and T2I-Adapter [26] use structural inputs such as pose skeletons or depth maps to control aspects like body configurations. They provide excellent control for one modality but aren't designed to combine multiple. Overlay Kontext [18] (a LoRA fine-tune of FLUX.1-Kontext [3]) is mentioned as a baseline.
  • Spatial layout control: Methods such as GLIGEN [22], LayoutDiffusion [54], and CreatiDesign [51] fine-tune generators to interpret bounding boxes or segmentation masks for spatial arrangement. CreatiDesign is a dedicated state-of-the-art model for layout-guided generation and is used as a strong baseline.
  • Limitations: These methods are typically task-specific. For example, layout-guided methods cannot incorporate specific poses or subject identities, and subject injection methods often lack fine-grained spatial control.
• Recent Attempts at Unification: Some recent works, such as StoryMaker [55] and ID-Patch [53], try to combine subject insertion and spatial control. However, they rely on complex combinations of separate modules (e.g., ControlNet with IP-Adapter), which adds complexity, might be limited to specific tasks (like face injection), and generalize poorly to truly multimodal, multi-control scenarios. ID-Patch is specifically highlighted for its pose-guided composition with human identities.

3.3. Technological Evolution

The field of generative AI, particularly image synthesis, has rapidly evolved:

1. Early Generative Models (GANs): Generative Adversarial Networks [19] showed early promise in generating realistic images but often struggled with mode collapse and precise control.

2. Emergence of Diffusion Models: Denoising Diffusion Probabilistic Models (DDPMs) [16, 41] and Latent Diffusion Models (LDMs) [38] revolutionized image quality and diversity. These models learned to iteratively denoise an image, leading to highly realistic outputs.

3. Text-to-Image Powerhouses: Scaling LDMs with massive text-image datasets led to models like DALL-E 2 [37], Imagen [40], and Stable Diffusion [38], enabling open-vocabulary image generation from text prompts.

4. Architectural Advancements: The adoption of Transformers in diffusion models (Diffusion Transformers or DiTs) [3, 9, 31] further improved scalability and quality.

5. Adding Control: Recognizing the need for more granular control beyond text, methods like ControlNet [52] and T2I-Adapter [26] emerged, allowing structural guidance (e.g., pose, depth, edges) to influence generation.

6. Personalization: Techniques like DreamBooth [39] and IP-Adapter [50] enabled users to inject specific subjects or styles into generations.

7. Multimodal Integration: Vision-Language Models (VLMs) [7, 45] began to integrate different modalities more deeply, leading to models that understand both text and image inputs more comprehensively.

   Canvas-to-Image fits into this timeline by addressing the crucial next step: unifying multiple, diverse control signals (identity, pose, layout, text) into a single, coherent interface for compositional image generation, moving beyond isolated control mechanisms or complex module stacking.

3.4. Differentiation Analysis

Compared to the main methods in related work, Canvas-to-Image offers several core differences and innovations:

• Unified Input Representation: The most significant innovation is the Multi-Task Canvas. Unlike prior methods that require separate inputs for different control types (e.g., a text prompt, a ControlNet map, an IP-Adapter embedding), Canvas-to-Image encodes all heterogeneous controls (subject reference, pose, bounding boxes, background) into a single composite RGB image. This simplifies the user interface and the model's input processing.

• Integrated Visual-Spatial Reasoning: By consolidating controls into one visual input, the model can directly interpret and integrate these signals within a common pixel space. This contrasts with approaches that rely on complex module combinations (like ControlNet + IP-Adapter) which often process controls sequentially or in parallel but struggle with deep integration and balancing.

• Multi-Task Canvas Training: Instead of training task-specific models or combining pre-trained modules, Canvas-to-Image employs a Multi-Task Canvas Training strategy. It fine-tunes a single diffusion model to learn different control types (Spatial, Pose, Box) within a unified learning paradigm. This joint training enables the model to reason across modalities collectively and generalize naturally to multi-control scenarios at inference, even for combinations not explicitly seen during training. This emergent generalization is a key advantage over methods that are limited to single-type control or struggle to combine them robustly.

• Consistent Computation Cost: The unified canvas approach, especially when combined with adapter-based fine-tuning (LoRA), maintains a constant computation cost regardless of the number or type of controls applied. This is a scalability advantage over methods that might see linear complexity growth with more controls or subjects.

• High-Fidelity Composition and Identity Preservation: The paper demonstrates superior performance in identity preservation and control adherence across complex multi-person and multi-control compositions, outperforming baselines that often struggle with copy-pasting artifacts, identity degradation, or ignoring structural signals.

  In essence, Canvas-to-Image moves from a "modular, piecemeal" approach to compositional control towards a "holistic, integrated" approach, providing a more robust and user-friendly solution for complex image generation tasks.

4. Methodology

4.1. Principles

The core principle behind Canvas-to-Image is to unify diverse, heterogeneous control signals for image generation into a single, coherent visual canvas representation. This Multi-Task Canvas serves as the sole input to a diffusion model, enabling it to perform integrated visual-spatial reasoning across all specified conditions simultaneously. Instead of relying on separate modules or architectural modifications for each control type (e.g., one for pose, another for identity, a third for layout), the model learns to interpret these various controls as distinct visual patterns and cues embedded within a standard RGB image format.

The theoretical basis and intuition are rooted in the idea that if a model is trained on a sufficiently diverse dataset where different types of controls are visually encoded into a common format, it can learn a shared understanding of how these controls relate to image generation. By presenting Spatial Canvas, Pose Canvas, and Box Canvas variants during training, the model learns to associate specific visual patterns (e.g., segmented subjects, pose skeletons, bounding boxes with text) with corresponding compositional instructions. This multi-task training encourages the model to develop a robust and generalizable policy, allowing it to combine these learned control interpretations flexibly during inference, even for combinations it has not explicitly encountered before. This approach leverages the powerful pattern recognition capabilities of deep learning models to overcome the challenges of multimodal input integration.

4.2. Core Methodology In-depth (Layer by Layer)

Canvas-to-Image builds upon a VLMDiffusion architecture and comprises two main components: the Multi-Task Canvas formulation and the Multi-Task Canvas Training strategy.

The overall flow for Multi-Task Canvas Training and Inference is illustrated in the following figure:

VLM Description: The image is a diagram illustrating the process of Multi-Task Canvas Training and Inference. On the left, it shows the combination of spatial canvas, pose canvas, and box canvas used to generate the target image. The right side depicts the inference process where the final image is generated through text prompts and control information.

4.2.1. Multi-Task Canvas Formulation

The Multi-Task Canvas is the central innovation, generalizing different complex compositional tasks into a shared input format: a single RGB image. This "visual canvas" is flexible and multi-modal. The paper details three primary canvas variants, each teaching the model a different type of compositional reasoning:

1. Spatial Canvas:

   • Purpose: Trains the model to render a complete scene based on an explicit visual composition, primarily for multi-subject personalization.
   • Construction: This canvas is a composite RGB image. It's created by visually pasting segmented cutouts of subjects (e.g., $I _ { \mathrm { s u b j e c t.1 } } , I _ { \mathrm { s u b j e c t.2 } }$) at their desired locations on a masked background.
   • Data Source: The construction uses Cross-Frame Sets, which means subjects and backgrounds are drawn from different frames or images, paired together in a way that avoids copy-pasting artifacts. This ensures that the model learns to integrate subjects naturally into new backgrounds, rather than just reproducing the input cutouts.
   • Control Provided: Users can place and resize reference subjects directly on the canvas to guide the generation, effectively controlling identity injection and spatial arrangement.

2. Pose Canvas:

   • Purpose: Enhances the Spatial Canvas by providing a strong visual constraint for subject articulation (pose guidance).
   • Construction: A ground-truth pose skeleton (e.g., derived from OpenPose [4]) is overlaid onto the Spatial Canvas. A specific transparency factor is used for the overlay. This semi-transparent overlay ensures the pose skeleton is clearly recognizable as a structural guide, while also allowing the visual identity from any underlying subject segments (if present on the Spatial Canvas) to still be recovered and interpreted by the model.
   • Data Source: During training, subject segments themselves can be randomly dropped. This means the model sometimes sees only pose skeletons on an empty canvas, training it to support pose control as an independent modality, even without explicit reference image injection.
   • Control Provided: Specifies body configurations and articulation of subjects.

3. Box Canvas:

   • Purpose: Trains the model to interpret explicit layout specifications through bounding boxes with textual annotations.

   • Construction: Each bounding box is drawn directly onto the canvas. Inside each box, a textual identifier (e.g., "Person 1", "Person 2", "Stone") specifies which subject or object should appear in that spatial region and indicates their approximate size. Person identifiers are typically ordered from left to right.

   • Data Source: This canvas variant supports simple spatial control with text annotations without needing reference images, similar to layout-guided methods.

   • Control Provided: Defines spatial regions for specified objects or subjects using textual labels.

     By training the model on these distinct, single-task canvas types, the framework learns a robust and generalizable policy for each control. This design enables the model to generalize beyond single-task learning, allowing for the simultaneous execution of these distinct control signals at inference time, even in combinations not encountered during training.

4.2.2. Model and Multi-Task Training

Canvas-to-Image is built upon a VLMDiffusion architecture. The overall process integrates information from the Multi-Task Canvas and a text prompt to guide the diffusion process.

1. Input Processing:

   • The unified canvas (a single RGB image) is first processed by a Vision-Language Model (VLM) to extract semantic embeddings (a high-dimensional representation of the canvas's content and control signals).
   • Simultaneously, the same canvas image is also encoded by a Variational Autoencoder (VAE) into latent representations (a compressed, lower-dimensional visual representation).
   • The text prompt is encoded by a text encoder (part of the VLM or a separate component) into text embeddings.

2. Conditional Input to Diffusion Model:

   • These VLM embeddings (from the canvas and text prompt), the VAE latents (from the canvas), and the noisy latents (the current noisy image representation in the diffusion process) are concatenated. This concatenated vector forms the comprehensive conditional input to the diffusion model.
   • The diffusion model, typically a U-Net or Diffusion Transformer (DiT) variant, takes this conditional input along with a time step $t$ (indicating the current noise level) and predicts the velocity for denoising.

3. Optimization with Flow-Matching Loss: The model is optimized using a task-aware Flow-Matching Loss, which is a type of objective function used in continuous normalizing flows and diffusion models to learn the reverse process.

   $ \mathcal { L } _ { \mathrm { f l o w } } = \mathbb { E } _ { \boldsymbol { x } _ { 0 } , \boldsymbol { x } _ { 1 } , t } , \left[ \left. \boldsymbol { v } _ { \theta } \left( \boldsymbol { x } _ { t } , t , \left[ h ; c \right] \right) - \left( \boldsymbol { x } _ { 0 } - \boldsymbol { x } _ { 1 } \right) \right. _ { 2 } ^ { 2 } \right] $

   • $\boldsymbol { x } _ { 0 }$: Represents the target latent, which is the clean, un-noised image latent (the ground truth).
   • $\boldsymbol { x } _ { 1 }$: Represents the noise latent, which is a latent vector sampled from a standard normal distribution.
   • $t$: Represents the time step, a continuous variable typically ranging from 0 to 1, indicating the current point in the diffusion process (from noisy to clean).
   • $\boldsymbol { x } _ { t }$: Represents the interpolated latent, which is a noisy latent image at time $t$, typically obtained by interpolating between $\boldsymbol { x } _ { 0 }$ and $\boldsymbol { x } _ { 1 }$.
   • $\boldsymbol { v } _ { \theta } (\boldsymbol { x } _ { t } , t , [ h ; c ])$: This is the model's prediction of the target velocity. $\boldsymbol { v } _ { \theta }$ is the neural network (parameterized by $\theta$) that predicts how to denoise $\boldsymbol { x } _ { t }$ to move it towards $\boldsymbol { x } _ { 0 }$.
     • $h$: Represents the input condition. This is a concatenation of the VLM embeddings (derived from the canvas and text prompt) and the VAE latents (derived from the same canvas). This combines the high-level semantic understanding with the pixel-level visual guidance from the canvas.
     • $c$: Represents the task indicator. This is a short textual token (e.g., "[Spatial]", "[Pose]", or "[Box]") prepended to the user prompt. Its purpose is to explicitly signal the current control type to the model, disambiguating the task context and preventing mode blending (where the model might confuse the meaning of different canvas types).
   • $(\boldsymbol { x } _ { 0 } - \boldsymbol { x } _ { 1 })$: This term represents the true velocity or the direction and magnitude of change needed to transform $\boldsymbol { x } _ { 1 }$ into $\boldsymbol { x } _ { 0 }$. In flow-matching, the goal is to make the model's predicted velocity $\boldsymbol { v } _ { \theta }$ match this true velocity.
   • The squared L2 norm $$_ { 2 } ^ { 2 }$ indicates that the loss measures the squared difference between the predicted velocity and the true velocity, encouraging the model to accurately predict the denoising direction.

4. Multi-Task Canvas Training Strategy:

   • In each training step, one type of canvas variant (e.g., Spatial, Pose, Box) is randomly sampled and used as the input condition. This ensures that the model is exposed to and learns to handle all control types.
   • This diverse multi-task curriculum enables the model to learn decoupled, generalizable representations for each control type. This decoupling is crucial because it allows the model to combine these controls flexibly at inference time, even if those specific combinations were not explicitly shown during training. For example, a mixed canvas with both pose skeletons and layout boxes might be presented at inference, which the model can handle due to its generalized learning.

5. Fine-tuning Details:

   • The model is built upon Qwen-Image-Edit [45] as the base architecture.
   • During training, LoRA [17] is used to fine-tune specific components: the attention, image modulation, and text modulation layers within each block of the diffusion model. The LoRA rank is 128.
   • Crucially, feed-forward layers are frozen. This is found to be important for preserving the prior image quality of the pre-trained model and preventing negative impact on generalization.
   • Optimization is performed using AdamW [24] with a learning rate of $5 \times 1 0 ^ { - 5 }$ and an effective batch size of 32. The model is trained for 200K steps on 32 NVIDIA A100 GPUs.

5. Experimental Setup

5.1. Datasets

The training of Canvas-to-Image is constructed from two primary data sources:

1. Internal, Human-Centric Dataset:

   • Source: A large-scale internal dataset.
   • Scale: Contains approximately 6 million (~6M) cross-frame images from 1 million (~1M) unique identities. This dataset is human-centric, focusing on human subjects.
   • Characteristics: This dataset enables flexible composition sampling for the Multi-Task Canvas formulation. For example, cross-frame samples allow pairing subjects and backgrounds from different images, which is critical to avoid copy-pasting artifacts and encourage natural integration.
   • Domain: Primarily human subjects.
   • Usage for Canvas Variants:
     • Spatial Canvas: Derived from this dataset by using an internal instance segmentation model to extract human segments for subjects and treating the remaining image areas as backgrounds.
     • Pose Canvas: Utilizes the same dataset; an internal pose estimation model extracts pose skeletons from target frames. These poses are then overlaid onto the Spatial Canvas.

2. CreatiDesign Dataset [51]:

   • Source: An external dataset, CreatiDesign.
   • Characteristics: Provides a large-scale corpus of images already annotated with bounding boxes and named entities. It places a strong emphasis on text rendering capabilities.
   • Usage for Canvas Variants:

     • Box Canvas: This dataset extends the internal data with bounding box annotations, which are directly used for the Box Canvas variant. It helps the model learn to interpret explicit layout specifications with textual annotations.

       During training, task types and their corresponding datasets are sampled with a uniform distribution to ensure balanced multi-task supervision.

For benchmarking, additional datasets are mentioned:

• FFHQ-in-the-Wild [19]: Used to sample input identities for 4P Composition, Pose-Guided 4P Composition, and ID-Object Interaction benchmarks.
• DreamBooth [39]: Used to provide object references for the ID-Object Interaction benchmark.

5.2. Evaluation Metrics

The paper employs a comprehensive suite of metrics to assess identity preservation, visual quality, prompt alignment, and control adherence.

1. ArcFace ID Similarity [8]:

   • Conceptual Definition: Measures the similarity of facial features between two images, commonly used to quantify how well the identity of a person is preserved across different generations. A higher score indicates better identity preservation.
   • Mathematical Formula: The ArcFace loss function typically involves a modified softmax loss with additive angular margin. For evaluating similarity, the cosine similarity between feature embeddings extracted from the ArcFace model is commonly used. Let $f(\boldsymbol{x})$ be the feature embedding extracted by the ArcFace model for an image $\boldsymbol{x}$. The identity similarity between a generated image $\boldsymbol{x}_{\text{gen}}$ and a reference image $\boldsymbol{x}_{\text{ref}}$ is given by: $ \text{ID Similarity} = \cos(\theta) = \frac{f(\boldsymbol{x}{\text{gen}}) \cdot f(\boldsymbol{x}{\text{ref}})}{|f(\boldsymbol{x}{\text{gen}})| |f(\boldsymbol{x}{\text{ref}})| } $
   • Symbol Explanation:
     • $f(\cdot)$: A function representing the feature extraction process by the ArcFace model.
     • $\boldsymbol{x}_{\text{gen}}$: The generated image containing a subject.
     • $\boldsymbol{x}_{\text{ref}}$: The reference image of the subject whose identity is to be preserved.
     • $\cos(\theta)$: The cosine similarity between the two feature vectors.
     • $\cdot$: Dot product of two vectors.
     • $\|\cdot\|$: L2 norm (magnitude) of a vector.

2. HPSv3 (Human Preference Score v3) [25]:

   • Conceptual Definition: An objective metric designed to correlate with human preferences for image quality. It aims to capture aspects like aesthetics, realism, and overall visual appeal, providing a quantitative proxy for how good an image "looks" to humans. A higher score indicates better image quality as perceived by humans.
   • Mathematical Formula: The paper does not provide a specific formula for HPSv3, as it is typically a pre-trained model that outputs a score. Conceptually, it's a learned function $S(\boldsymbol{x}, \text{prompt})$ where $S$ is the HPSv3 model.
   • Symbol Explanation:
     • $S(\cdot)$: The pre-trained HPSv3 model.
     • $\boldsymbol{x}$: The generated image.
     • $\text{prompt}$: The text prompt used for generation.

3. VQAScore [23]:

   • Conceptual Definition: Measures the alignment between an image and a given text prompt. It evaluates how well the generated image semantically matches the description provided in the text. A higher score indicates better text-image alignment.
   • Mathematical Formula: The paper does not provide a specific formula for VQAScore, as it is typically derived from a Vision-Question Answering (VQA) model or similar image-to-text generation capabilities. Conceptually, it represents the likelihood or similarity score $A(\boldsymbol{x}, \text{prompt})$ where $A$ is the VQAScore model.
   • Symbol Explanation:
     • $A(\cdot)$: The pre-trained VQAScore model.
     • $\boldsymbol{x}$: The generated image.
     • $\text{prompt}$: The text prompt used for generation.

4. Control-QA:

   • Conceptual Definition: A novel, LLM-based scoring system introduced in this paper to assess the fidelity with respect to applied controls (identity, pose, box layout). It uses GPT-4o [28] (a multimodal expert) to rate generated compositions against provided control images on a 1-to-5 scale. This score is designed to holistically evaluate how well a generation adheres to the combined set of control inputs. Higher scores indicate better adherence.
   • Mathematical Formula: Not a mathematical formula in the traditional sense, but rather a score derived from an LLM's evaluation based on a specific rubric.
   • Symbol Explanation:
     • LLM: Large Language Model (specifically GPT-4o).
     • Input Canvas: The image containing control signals.
     • Generated Scene: The output image.
     • Score: A numerical rating (1-5) based on predefined criteria (Identity Preservation, Spatial Order, Pose Fidelity, Realism & Integration, depending on the benchmark). The detailed System Prompt content and scoring rubrics are provided in the Appendix (Tables VI, VII, VIII, IX).

5. PoseAP_{0.5} (Pose Average Precision @ IOU=0.5):

   • Conceptual Definition: A metric used to strictly measure spatial adherence for pose. It reports the Average Precision (AP) for extracted pose keypoints, with an Intersection Over Union (IOU) threshold of 0.5. A higher score indicates that the generated poses more accurately match the target pose skeletons.
   • Mathematical Formula: Average Precision is typically calculated as the area under the precision-recall curve. For pose estimation, it involves comparing detected keypoints against ground-truth keypoints within a certain tolerance (e.g., PCK - Percentage of Correct Keypoints). $\text{PoseAP}_{0.5}$ would specifically refer to AP calculated using an Object Keypoint Similarity (OKS) or IOU threshold of 0.5 for defining a correct match. The precise formula for PoseAP in general is: $ \text{AP} = \sum_{r \in {0.0, 0.01, \dots, 1.0}} (\text{max}_{r' \ge r} P(r')) \Delta r $ Where $\text{AP}_{0.5}$ implies the filtering or scoring mechanism for matching keypoints uses a threshold of 0.5.
   • Symbol Explanation:
     • P(r'): Precision at recall $r'$.
     • $\Delta r$: Change in recall.
     • The subscript 0.5 specifies the Intersection Over Union (IOU) threshold for matching detected keypoints to ground-truth keypoints or pose instances.

6. DINOv2 [29]:

   • Conceptual Definition: A self-supervised vision transformer model that learns robust visual features without supervision. In this context, DINOv2 is used to measure object preservation by calculating similarity scores between features of generated objects and reference objects, similar to how ArcFace is used for identity. A higher score indicates better object preservation.
   • Mathematical Formula: Similar to ArcFace ID Similarity, for evaluation, DINOv2 similarity would typically be the cosine similarity between the feature embeddings of generated and reference objects. Let $g(\boldsymbol{o})$ be the DINOv2 feature embedding for an object $\boldsymbol{o}$. $ \text{Object Similarity} = \cos(\phi) = \frac{g(\boldsymbol{o}{\text{gen}}) \cdot g(\boldsymbol{o}{\text{ref}})}{|g(\boldsymbol{o}{\text{gen}})| |g(\boldsymbol{o}{\text{ref}})| } $
   • Symbol Explanation:
     • $g(\cdot)$: A function representing the feature extraction process by the DINOv2 model.
     • $\boldsymbol{o}_{\text{gen}}$: The generated object.
     • $\boldsymbol{o}_{\text{ref}}$: The reference object.
     • $\cos(\phi)$: The cosine similarity between the two feature vectors.

5.3. Baselines

The paper compares Canvas-to-Image against several state-of-the-art models and commercial products, categorized by their relevance to the evaluated tasks:

• General Purpose/Commercial Models:
  • Qwen-Image-Edit [45]: The base architecture upon which Canvas-to-Image is built. It's a powerful VLMDiffusion model. Comparing against it shows the improvement gained by the Multi-Task Canvas and training strategy.
  • Gemini 2.5 Flash Image (Nano-Banana) [42]: A state-of-the-art commercial editing model. This represents a strong, closed-source competitor, often indicating industrial capabilities.
• Compositional/Layout-Guided Models:
  • CreatiDesign [51]: A dedicated state-of-the-art model for layout-to-image generation using a conditional diffusion transformer. It specializes in interpreting bounding boxes and named entities, making it a crucial baseline for the Layout-Guided Composition benchmark.
• Structural/Overlay Models:
  • Overlay Kontext [18]: A LoRA fine-tune of FLUX.1-Kontext [3], designed for image overlay tasks. It's relevant for scenarios involving structural cues or elements overlaid on images.
• Personalization/Multi-Concept Models (primarily in Supplementary Material):

  • UniPortrait [15]: A unified framework for identity-preserving single- and multi-human image personalization.

  • FLUX Kontext [3]: A flow-matching model for in-context image generation.

  • UNO [47]: A method for unlocking more controllability by in-context generation.

  • OmniGen2 [46]: An exploration into advanced multimodal generation.

  • DreamO [27]: A unified framework for image customization.

  • ID-Patch [53]: A method specifically designed for robust ID association for group photo personalization, often integrated with ControlNet for pose.

    These baselines are chosen because they represent different facets of controllable image generation (general quality, commercial systems, layout control, structural control, personalization) and allow for a comprehensive evaluation of Canvas-to-Image's unified approach. For the main paper, the focus is on baselines that also operate on a single image input or are closely related to the core tasks.

5.4. Benchmarks

The paper evaluates Canvas-to-Image across four distinct benchmarks, with an additional one in the supplementary:

1. 4P Composition Benchmark:

   • Purpose: Evaluates the model's ability to compose multiple personalized subjects while preserving their identities and spatial arrangements. "4P" likely refers to "four persons" or "four subjects."
   • Canvas Type: Utilizes the Spatial Canvas.
   • Construction: Randomly samples four human identities from FFHQ-in-the-Wild [19]. A synthetic "prior image" is generated using FLUX.1-Dev [2] from a target prompt, and human instances are detected to obtain realistic bounding boxes. The segmented FFHQ identities are then placed into these positions on the canvas.

2. Pose-Guided 4P Composition Benchmark:

   • Purpose: Tests the model's capacity to simultaneously handle identity preservation and pose guidance for multiple subjects.
   • Canvas Type: Utilizes the Pose Canvas.
   • Construction: Builds upon the 4P Composition setup. The same FLUX.1-Dev [2] prior images are used, but instead of just bounding boxes, internal pose estimation extracts target poses. These target poses are then placed alongside the reference identities on the input canvas.

3. Layout-Guided Composition Benchmark:

   • Purpose: Assesses the model's capability to interpret explicit layout specifications through bounding boxes with textual annotations.
   • Canvas Type: Utilizes the Box Canvas.
   • Construction: Uses the test set of the CreatiDesign [51] dataset. The dataset is filtered to select samples compatible with Canvas-to-Image's canvas format, which involves text overlaid directly on the image.

4. Multi-Control Composition Benchmark:

   • Purpose: The most challenging benchmark, designed to evaluate the model's ability to jointly satisfy identity preservation, pose guidance, and box annotations within a single generation.
   • Canvas Type: A mixed canvas that combines elements of Spatial Canvas, Pose Canvas, and Box Canvas.
   • Construction: Leverages text prompts and named entity annotations from the CreatiDesign [51] test set, specifically filtering for samples involving human subjects. A synthetic prior image is generated using Qwen-Image-Edit [45] (the base model) only to extract the target skeletal pose. This pose, a sampled reference identity, and the named entity annotations (for text rendering) from CreatiDesign are combined to form the final input canvas. The pixel data of the prior image is not used as direct input.

5. ID-Object Interaction Benchmark (Supplementary):

   • Purpose: Demonstrates the generalizability of the approach beyond human subjects and evaluates scenarios involving natural interactions between subjects and objects.
   • Construction: Pairs human identities from FFHQ-in-the-Wild [19] with object references from the DreamBooth [39] dataset to create challenging ID-Object pairs.

5.5. Implementation Details

• Base Architecture: Qwen-Image-Edit [45] serves as the foundation.
• Input Processing: The canvas image and text prompt are processed by a VLM for semantic embeddings. The canvas image is also encoded by a VAE into latents.
• Diffusion Model Input: VLM embeddings, VAE latents, and noisy latents are concatenated and fed into the diffusion model.
• Fine-tuning Strategy: LoRA [17] is employed for fine-tuning.
  • Target Layers: Attention, image modulation, and text modulation layers within each block are fine-tuned.
  • Frozen Layers: Crucially, feed-forward layers are frozen to preserve the prior image quality of the pre-trained model.
  • LoRA Rank: 128.
• Optimizer: AdamW [24].
• Learning Rate: $5 \times 1 0 ^ { - 5 }$.
• Batch Size: Effective batch size of 32.
• Training Duration: 200,000 steps.
• Hardware: 32 NVIDIA A100 GPUs.

6. Results & Analysis

6.1. Core Results Analysis

The experimental results demonstrate that Canvas-to-Image consistently outperforms state-of-the-art methods across various challenging benchmarks, showcasing superior identity preservation, control adherence, and visual quality in compositional image generation.

6.1.1. Quantitative Comparisons

The following are the results from Table 1 of the original paper:

Analysis of Table 1:

• 4P Composition: Canvas-to-Image (Ours) achieves the highest scores across all metrics (ArcFace, HPSv3, VQAScore, Control-QA). This indicates superior identity preservation, image quality, text-image alignment, and overall control adherence when composing multiple subjects. Nano-Banana shows decent ArcFace but suffers in HPSv3, often producing copy-pasted artifacts, as highlighted qualitatively.

• Pose Guided 4P Composition: Again, Canvas-to-Image leads in ArcFace, VQAScore, and Control-QA, and maintains strong HPSv3. This demonstrates its ability to accurately follow target poses while preserving identities and generating high-quality images. Baselines struggle significantly with identity preservation under pose constraints.

• Layout-Guided Composition: Canvas-to-Image achieves the highest HPSv3 and VQAScore, and matches CreatiDesign for Control-QA. This is particularly impressive as CreatiDesign is a dedicated layout-guided model trained on this specific task. This shows Canvas-to-Image's ability to interpret complex layout specifications effectively.

• Multi-Control Composition: In this most complex scenario, Canvas-to-Image excels across all metrics, achieving the highest ArcFace, HPSv3, VQAScore, and Control-QA. This validates its capacity to seamlessly integrate identity preservation, pose guidance, and box annotations simultaneously.

  The consistent outperformance across diverse and complex benchmarks validates the effectiveness of the unified Multi-Task Canvas framework and Multi-Task Canvas Training strategy. The balanced performance across control adherence and identity preservation confirms that encoding heterogeneous signals into a single canvas successfully enables the simultaneous execution of spatial, pose, and identity constraints. All these results are generated by the same unified Canvas-to-Image model, demonstrating its strong generalization from single-control training samples to complex multi-control scenarios at inference.

6.1.2. Qualitative Comparisons

• 4P Composition (Figure 3): VLM Description: The image is a comparative display showcasing five different algorithm-generated images, including Overlay Kontext, Nano-Banana, Qwen-Image-Edit, the generation results of this research, and their corresponding input descriptions. The images illustrate various scenes generated under different control signals, highlighting the performance of the proposed method across multiple tasks.

  该图像是一个对比展示，包含五个不同算法生成的图像比较，包括 Overlay Kontext、Nano-Banana、Qwen-Image-Edit、本研究的生成结果和对应的输入说明。图像展示了不同控制信号下生成的多个场景，体现了研究方法在多个任务上的性能。

  Canvas-to-Image demonstrates superior identity preservation and spatial alignment. Nano-Banana often produces copy-pasted human segments, a flaw addressed by Canvas-to-Image's Cross-Frame Sets training. Overlay Kontext and Qwen-Image-Edit fail to preserve subject identities, showing blurriness or changes in appearance.

• Pose-Guided 4P Composition (Figure 4): VLM Description: The image is a chart showcasing the results of different methods in the paper, featuring various generated crowd scenes for comparison. The first row displays the synthesis results of three methods, the second row shows game interaction scenes, and the third row illustrates shopping scenes on the street, reflecting the study's multimodal control and high-fidelity generation capabilities.

  该图像是论文中展示不同方法效果的图表，包含了多种人群场景的生成对比。第一行展示了三种方法的合成结果，第二行展示了游戏互动场景，第三行展示了街道上的购物场景，所示结果体现了该研究的多模态控制和高保真生成能力。

  Canvas-to-Image is the only method that accurately follows the target poses ("Pose Prior" column) while maintaining high identity fidelity and visual realism. Baselines like Nano-Banana and Qwen-Image-Edit struggle to follow complex poses or preserve identity.

• Layout-Guided Composition (Figure 5): VLM Description: The image is a diagram showcasing the performance of different image generation methods on the layout-guided composition benchmark, including CreatiDesign, Nano-Banana, Qwen-Image-Edit, and our model. The top of the image displays the generated images, while the bottom shows the corresponding input conditions, highlighting the model's capability in handling multiple input signals.

  该图像是一个示意图，展示了不同图像生成方法在布局引导组合基准测试中的表现，包括CreatiDesign、Nano-Banana、Qwen-Image-Edit和我们的模型。该图的顶部显示的是各自生成的图像，而底部则展示了对应的输入条件，突显了模型在处理多种输入信号时的能力。

  Canvas-to-Image produces semantically coherent compositions adhering to box constraints. Nano-Banana and Qwen-Image-Edit frequently ignore structural signals or show annotation rendering artifacts. Even against the specialized CreatiDesign, Canvas-to-Image performs comparably or better in terms of alignment and quality.

• Multi-Control Composition (Figure 6): VLM Description: The image is a schematic diagram that compares the performance of different methods (Nano-Banana, Qwen-Image-Edit, and our framework) in generating images under multimodal controls. It shows input images, generated results, and their respective control elements. The image displays items like rain gear, whiteboards, and beaches, reflecting the ability to analyze location and pose.

  该图像是一个示意图，比较了不同方法（Nano-Banana、Qwen-Image-Edit和我们的框架）在处理多模态控制下的图像生成效果。展示了输入图像、生成的结果以及各自的控制元素。图像中展示了雨具、白板和沙滩等元素，体现了对位置和姿势的分析能力。

  When identity preservation, pose guidance, and box annotations must be satisfied jointly, Canvas-to-Image achieves the highest compositional fidelity. It integrates reference subjects and multiple control cues seamlessly, whereas baselines often produce artifacts or fail to satisfy all input constraints.

6.1.3. Supplementary Quantitative Comparisons (Appendix A)

The following are the results from Table I of the original paper:

The following are the results from Table II of the original paper:

The following are the results from Table III of the original paper:

Analysis of Supplementary Tables:

• 4P Composition with Personalization Baselines (Table I): Canvas-to-Image maintains its leading position across all metrics when compared against a broader range of personalization methods (DreamO, OmniGen2, ID-Patch, UniPortrait, Flux Kontext, UNO). This further solidifies its superior performance in multi-subject identity preservation and composition. Qualitative examples in Figure 9 confirm these findings, showing clearer identities and better integration.
• Pose-Guided 4P Composition with ID-Patch (Table II): ID-Patch achieves a higher PoseAP_{0.5}``, indicating its strength in replicating target poses. However, Canvas-to-Image achieves the highest Control-QA score (which jointly considers pose accuracy and identity preservation) and a higher ArcFace score. This suggests ID-Patch, despite precise pose reproduction, might struggle with maintaining the correct number of subjects or consistent identities, as shown qualitatively in Figure 10. Canvas-to-Image strikes a better balance between pose fidelity and identity preservation.
• ID-Object Interaction Benchmark (Table III): Canvas-to-Image shows the highest ArcFace (human identity), DINOv2 (object preservation), and Control-QA scores. This demonstrates its generalizability beyond human-only compositions and its ability to handle natural interactions between subjects and objects with high fidelity. Qualitative results in Figure 11 reinforce this, showing Canvas-to-Image produces coherent compositions with faithful preservation of both human identity and object fidelity, maintaining correct proportions and natural interactions.

6.1.4. User Study Results (Appendix E)

The following are the results from Table V of the original paper:

The user studies confirm Canvas-to-Image's superiority. Users preferred Canvas-to-Image over Qwen-Image-Edit 67.3% for Control Following and 77.3% for Identity Preservation. Against Nano Banana, Canvas-to-Image was preferred 78.9% for Control Following and 73.8% for Identity Preservation. This user preference aligns with the quantitative metrics and serves as a strong validation for the Control-QA score. It also highlights a trade-off among competitors: Nano Banana performs stronger on identity but weaker on control, while Qwen-Image-Edit shows the opposite trend, whereas Canvas-to-Image excels in both.

6.2. Ablation Studies / Parameter Analysis

6.2.1. Multi-Task Canvas Training Ablation

The following are the results from Table 2 of the original paper:

This ablation study evaluates the impact of progressively adding different canvas tasks to the training curriculum, using the Multi-Control Benchmark.

• Baseline (Spatial Canvas only): A model trained only on Spatial Canvas shows moderate ArcFace and Control-QA.

• + Pose Canvas: Adding Pose Canvas to the training slightly decreases ArcFace but improves VQAScore, HPSv3, and Control-QA. The decrease in ArcFace might be due to the increased complexity of simultaneously handling identity and pose, but the overall control adherence (Control-QA) improves.

• + Box Canvas (Full Model): Incrementally adding Box Canvas (leading to the full Canvas-to-Image model) further improves VQAScore, HPSv3, and Control-QA. The ArcFace score slightly recovers. This demonstrates that as more canvas tasks are incorporated, there are consistent gains in image quality (HPSv3) and control adherence (Control-QA). The qualitative results (Figure 7) confirm this: the Spatial Canvas-only baseline fails to follow pose and layout instructions, while the full model successfully handles all multi-control inputs. VLM Description: The image is a diagram illustrating the process of generating images under different control modalities, including a spatial-only canvas, a pose canvas, and a box canvas, as well as inputs and pose priors. The examples in the image demonstrate how to adjust the output of image generation using locational and pose information.

  该图像是示意图，展示了在不同控制模式下生成图像的过程，包括仅空间画布、添加姿势画布和框架画布，以及输入和姿势先验。图中的示例说明了如何通过位置和姿势信息来调整图像生成的输出效果。

6.2.2. Ablations on Model Architecture (Appendix B)

The following are the results from Table IV of the original paper:

This study investigates the impact of different LoRA fine-tuning configurations on the 4P Composition benchmark, starting from the Qwen-Image-Edit base and comparing against the full Canvas-to-Image model.

• Ours w/o Text Branch: Removing the fine-tuning on the text branch significantly decreases ArcFace (from 0.5915 to 0.4917), HPSv3, and VQAScore. This highlights that effective identity preservation requires the joint training of both the text and image branches.

• Ours w/o Image Branch: Similarly, removing fine-tuning on the image branch also leads to a drop in ArcFace (to 0.4687), HPSv3, and VQAScore. This reinforces the need for both branches to be trained for optimal performance.

• Ours w/ Feed-Forward: Fine-tuning the feed-forward layers (instead of freezing them) results in lower HPSv3 and VQAScore compared to the full model, despite a decent ArcFace. This indicates that training the feed-forward layers negatively impacts the model's generalization, leading to a deterioration in visual quality and prompt alignment. The decision to freeze these layers is justified.

• Ours w/o Task Indicator: Removing the explicit task indicator (c) leads to a degradation across all metrics (ArcFace to 0.5217, HPSv3 to 12.6046, VQAScore to 0.8555). This confirms that the task indicator prompt is crucial for the model to resolve ambiguity and effectively switch between different compositional reasoning modes, preventing task interference (mode mix-up). Qualitative results in Figure 14 further illustrate this, where omitting the task indicator causes unwanted text artifacts to appear in background, incorrectly transferring text-rendering behavior from the Box Canvas task to the Spatial Canvas. VLM Description: The image is a diagram illustrating the impact of removing the task indicator on image generation results. The left shows the input, the center displays the output without the task indicator, and the right shows the result generated using our method. It can be observed that without the task indicator, the generated images suffer from task mix-up, leading to unwanted text artifacts in the background where text rendering is not required.

  该图像是图表，展示了去除任务指示器对图像生成效果的影响。左侧为输入，中央为未使用任务指示器的生成结果，右侧为使用我们的方法生成的结果。可以看到，去除任务指示器后，生成的图像存在任务混淆现象，导致不需文本渲染的背景出现了不必要的文本伪影。

6.2.3. Convergence Behavior (Appendix B)

VLM Description: The image is a chart showing the training dynamics of Canvas-to-Image. The ControlQA score steadily improves during early training and converges around 50K iterations, indicating that the model effectively learns consistent control and composition. Training continues up to 200K iterations to refine local details and enhance robustness in generation quality.

该图像是一个图表，展示了 Canvas-to-Image 的训练动态。ControlQA 分数在训练初期持续提升，并在约 50K 次迭代后趋于稳定，表明模型有效学习了控制和组合的一致性。同时，训练至 200K 次迭代以细化局部细节和提升生成质量的鲁棒性。

Figure 8 (Figure I in the supplementary) shows the training dynamics. The Control-QA score (indicating control adherence) steadily improves during early training, with rapid gains up to 50K iterations, where convergence is largely achieved. While key metrics plateau beyond 50K iterations, training is continued up to 200K iterations to refine local details and enhance robustness in generation quality. This suggests that the core control understanding is learned relatively early, but further training improves the aesthetic and fine-grained aspects of the generated images.

6.3. Additional Applications (Appendix D)

Canvas-to-Image also demonstrates background-aware composition. It can inject humans or objects into a scene via reference image pasting or bounding box annotation, with the inserted elements naturally interacting with the background. An example is provided in Figure 15 (Figure VIII in the supplementary), showcasing subjects naturally integrated into various environments. VLM Description: The image is a diagram illustrating the relationship between the input canvas and the generated output images. The left side shows the input canvas, while the right shows the output processed by the Canvas-to-Image model, highlighting the model's generation capability under multimodal control.

该图像是示意图，展示了输入画布与生成的输出图像之间的关系。左侧为输入画布，右侧为经过 Canvas-to-Image 模型处理后的输出，体现了模型在多模态控制下的生成能力。

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper successfully introduced Canvas-to-Image, a novel and unified framework that addresses the significant challenge of achieving high-fidelity compositional and multimodal control in diffusion-based image generation. Its core innovation lies in the Multi-Task Canvas, a single RGB image input that consolidates diverse control signals including subject references, pose constraints, spatial layouts, and textual annotations. By reformulating these heterogeneous controls into a single canvas-conditioned paradigm, the model is enabled to perform integrated visual-spatial reasoning.

Through a dedicated Multi-Task Canvas Training strategy on a suite of comprehensive datasets, Canvas-to-Image learns to jointly understand and integrate these varied control modalities. A key finding is the emergent generalization where the model, trained on individual control tasks, can effectively handle complex multi-control scenarios during inference, even for combinations not explicitly seen during training. Extensive experiments and user studies validate that Canvas-to-Image significantly surpasses state-of-the-art methods in identity preservation and control adherence across various benchmarks, demonstrating robust performance in multi-person composition, pose-controlled composition, layout-constrained generation, and combined multi-control tasks.

7.2. Limitations & Future Work

The authors acknowledge a primary limitation of the Canvas-to-Image framework:

• Information Density of Single RGB Interface: While the "visual canvas" format offers significant usability and flexibility, it is strictly bounded by the available pixel space. As the number of concepts or controls to be specified increases, the canvas can become crowded and harder to interpret for the model. For instance, successfully handling up to 4P composition (four persons) has been demonstrated, but beyond that, the single RGB canvas might reach its limits in conveying complex, non-overlapping information.

  For future work, the authors suggest exploring:

• Layered Controls: Designing the input canvas with an additional alpha channel (RGBA) could provide a richer information density, allowing for more complex and granular control without overcrowding the primary RGB channels. This would enable the model to distinguish different layers of control more effectively. This robust foundation can be built upon to enable even richer forms of visual and semantic control.

7.3. Personal Insights & Critique

The Canvas-to-Image paper presents a highly intuitive and powerful approach to multimodal control in image generation. The core idea of unifying heterogeneous controls into a single visual canvas is brilliant in its simplicity and effectiveness. It transforms a complex problem of integrating disparate data types into a more manageable image-to-image translation problem, which diffusion models are inherently good at.

One of the most inspiring aspects is the emergent generalization observed from multi-task training. This suggests that by carefully designing the training data and objectives, models can learn abstract representations of control that are flexible enough to combine in novel ways during inference. This principle could be highly valuable for other multimodal AI challenges, where explicit programming for every combination of inputs is infeasible.

The choice of LoRA for fine-tuning on a strong base model (Qwen-Image-Edit) is also a practical and efficient strategy, demonstrating how lightweight adaptation can unlock significant new capabilities. The meticulous ablation studies, especially concerning the task indicator and frozen feed-forward layers, provide strong empirical justification for their architectural and training design choices.

Potential Issues/Areas for Improvement:

1. Scalability to Extreme Complexity: While the alpha channel suggestion is a good step, the fundamental pixel space limitation might still become an issue for truly dense scenes with dozens of interacting entities or highly detailed, fine-grained controls that require pixel-perfect precision for many distinct elements. More advanced data structures beyond simple channels, perhaps an explicit graph-based representation of objects and their relations, could be explored if the canvas becomes too abstract or ambiguous.
2. Ambiguity in Visual Encoding: Relying purely on visual encoding of controls might introduce ambiguity. For instance, a semi-transparent pose skeleton might be confused with actual image content or shadows in very complex backgrounds. While the task indicator helps, subtle visual cues can be interpreted differently.
3. User Interface for Canvas Creation: While the concept of a canvas interface is intuitive, the practical process for users to create such a sophisticated Multi-Task Canvas with accurate subject cutouts, pose overlays, and bounding box annotations might still require specialized tools or significant manual effort. The paper focuses on the model's side; the user experience of generating these complex canvas inputs could be a bottleneck.
4. Implicit Bias from Training Data: The heavy reliance on an "internal, human-centric dataset" implies potential biases (e.g., demographic, pose diversity, scene types) inherent in that dataset. While augmenting with CreatiDesign helps, the generalization to entirely novel domains or highly diverse populations might still be constrained by the dominant training data.

Transferability: The methodology of unifying diverse control signals into a single, standardized input format is highly transferable. This "universal input" paradigm could be applied to:

• Video Generation: Control motion, character identities, and scene layouts in video synthesis.

• 3D Content Creation: Guide 3D model generation or scene composition using 2D canvases that encode depth, material, and object placement.

• Interactive Design Tools: Serve as the backend for intuitive graphic design or digital art tools, where users can "paint" their intent directly on a canvas, and the AI generates the high-fidelity output.

• Scientific Visualization: Control complex data visualizations where multiple parameters need to be simultaneously represented visually.

  Overall, Canvas-to-Image represents a significant step towards more controllable and user-friendly generative AI, demonstrating that clever input design and multi-task learning can unlock powerful compositional capabilities.