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MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel
TL;DR
Text-to-image diffusion models provide strong image synthesis but remain difficult to control and costly to adapt across tasks. MultiDiffusion binds multiple regional diffusion processes over a pretrained model through shared constraints and optimization. It supports aspect-ratio and spatial controls while achieving high-quality controlled generation without further training or finetuning.
Problem
Text-to-image diffusion models lack intuitive user control, while adapting them to new tasks commonly requires costly retraining, finetuning, or task-specific methods.
Method
MultiDiffusion binds multiple reference diffusion generation processes with shared parameters or constraints and reconciles their denoising steps through optimization.
Results
MultiDiffusion achieves state-of-the-art controlled-generation quality against relevant baselines, including methods specifically trained for the tasks, without computational overhead.
Takeaways & Limitations
A pretrained text-to-image model can support desired aspect ratios, resolutions, and rough region-based text prompts through a common generation process.
Takeaways & Limitations
Results depend on the generative prior and diffusion paths supplied by the reference model, so bad seeds or biased prompts can affect outputs.
Abstract
from arXiv · showhide
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fast adaptation to new tasks still remains an open challenge, currently mostly addressed by costly and long re-training and fine-tuning or ad-hoc adaptations to specific image generation tasks. In this work, we present MultiDiffusion, a unified framework that enables versatile and controllable image generation, using a pre-trained text-to-image diffusion model, without any further training or finetuning. At the center of our approach is a new generation process, based on an optimization task that binds together multiple diffusion generation processes with a shared set of parameters or constraints. We show that MultiDiffusion can be readily applied to generate high quality and diverse images that adhere to user-provided controls, such as desired aspect ratio (e.g., panorama), and spatial guiding signals, ranging from tight segmentation masks to bounding boxes. Project webpage: https://multidiffusion.github.io
1. Introduction
MultiDiffusion addresses limited controllability and costly adaptation in text-to-image diffusion by binding multiple reference-model generation processes into one controllable process. It supports aspect-ratio and spatial controls while achieving high controlled-generation quality without computational overhead.
- Motivation: Text-to-image diffusion models offer high-quality synthesis, but intuitive user control remains challenging and existing solutions require extensive training or task-specific adaptations.The introduction describes training or finetuning as compute-intensive and prior pretrained-model methods as concentrated on specific tasks.
- Approach: MultiDiffusion binds several reference diffusion processes with shared parameters or constraints and reconciles their regional denoising steps through least-squares optimization.The reference model predicts denoising steps for different image regions, while MultiDiffusion computes a global step that follows them jointly.
- Capabilities: The framework adapts a pretrained text-to-image model to desired resolutions or aspect ratios and rough region-based text prompts within a common generation process.The controls can be solved simultaneously rather than through separate task-specific adaptations.
- Capabilities: Figure 1 illustrates unified control over generated content through desired aspect ratios and rough region-based text prompts.The figure presents these controls as examples of flexible text-to-image generation.
- Results: MultiDiffusion produces state-of-the-art controlled-generation quality against relevant baselines, including methods specifically trained for the evaluated tasks.The authors also report that the method works efficiently without introducing computational overhead.
2. Related Work
Diffusion models generate images by progressively denoising Gaussian noise, while prior controllable-generation methods use conditioning, generation-process manipulation, or lightweight finetuning. MultiDiffusion instead fuses regional diffusion paths into a unified process.
- Diffusion Models: Diffusion models transform Gaussian noise into samples through a series of gradual denoising steps.Their objective is to approximate a data distribution while remaining easy to sample from.
- Controllable Generation: Conditional diffusion models use guiding inputs such as semantic layouts or category labels for conditional image generation, including text-to-image synthesis.The passage identifies recent text-to-image diffusion models as prominent examples of this approach.
- MultiDiffusion: MultiDiffusion defines a new generation process over a pretrained reference model and fuses denoising directions from multiple crops into a global step.The figure describes this fusion as reconciling potentially inconsistent regional directions into a seamless image.
- Controllable Generation: Other controllable-generation methods manipulate a pretrained model’s generation process or apply lightweight finetuning, often tailoring techniques to particular editing tasks.Examples include inpainting, object replacement, style manipulation, and layout control.
3. Method
MultiDiffusion adapts a pre-trained diffusion model to a target image space without training or finetuning by fusing multiple constrained diffusion paths. Its optimization reconciles regional denoising updates while preserving consistency with the reference model.
- MultiDiffusion process: Mappings F_i from target to reference image spaces and λ_i from target to reference conditions make each step follow multiple reference diffusion paths.The mappings are application-dependent and allow different target regions or conditions to be handled through one framework.
- Optimization: The MultiDiffuser minimizes an FTD loss that reconciles denoising steps suggested for different regions, using per-pixel weights in a least-squares objective.When mappings are direct pixel samples, the quadratic least-squares solution is computed analytically as a weighted average of diffusion updates.
- Consistency property: Under the stated zero-loss conditions, the mapped outputs reproduce reference diffusion paths, and the final mapped images are distributed identically to samples from the reference model.This proposition supplies the consistency property motivating adaptation without retraining.
- Illustration: Figure 3 contrasts incoherent independent crop generation with MultiDiffusion, which steers paths initialized from the same noise into a consistent high-quality panorama.The comparison uses four crops and shows the benefit of fusing initially unrelated diffusion paths.
4. Applications
The framework instantiates controlled generation for panoramas and region-based text prompts, including tight-mask guidance. These applications use analytically solved weighted updates and a time-dependent bootstrapping strategy for mask fidelity.
- 4.1. Panorama: Panoramic generation applies a square-image reference model to overlapping crops covering a larger target image space.The target dimensions satisfy H′ ≥ H and W′ ≥ H, while each F_i extracts an H × W crop and uses the same text condition.
- 4.1. Panorama: Independent application to four non-overlapping crops yields incoherent regions, whereas fused paths produce a high-quality coherent panorama.Both approaches start from the same initial noise in the comparison.
- 4.2. Region-based text-to-image generation: For region-based text-to-image generation, masks M_i assign prompts y_i to image regions so each masked segment depicts its desired content.The target and reference image spaces are identical, and the condition space contains the set of regional prompts.
- 4.2. Region-based text-to-image generation: The regional update averages the pretrained model’s prompt-specific diffusion directions for every pixel covered by the corresponding masks.Pixel weights are set to the masks, and the least-squares solution is calculated analytically.
- 4.2. Region-based text-to-image generation: Tight-mask fidelity uses early diffusion steps focused on each region before later steps incorporate the full image context for harmonization.The method introduces time-dependent mappings and a bootstrapping phase; one reported setting uses T_init = 800, or 20% of generation.
5. Results
MultiDiffusion is evaluated for panorama and region-based generation using Stable Diffusion, showing controlled, high-quality outputs across aspect-ratio and spatial-guidance tasks. It outperforms relevant baselines quantitatively and qualitatively, while bootstrapping improves fidelity to tight masks.
- Panorama Generation: The evaluation uses Stable Diffusion v2 and tests panorama generation at 512 × 4608 resolution, nine times wider than the original training resolution.Panoramas are compared with Blended Latent Diffusion and Stable Inpainting.
- Panorama Generation: MultiDiffusion produces seamless, high-quality panoramas, whereas baselines often show seams, discontinuities, repetitive content, or visual differences across the image.The method combines diffusion paths from all crops during generation rather than generating crops sequentially.
- Panorama Generation: Across FID, CLIP text-image score, and CLIP aesthetic score, MultiDiffusion outperforms the evaluated panorama baselines.The evaluation uses 2,000 samples across eight text-conditioning settings.
- Region-based Text-to-Image Generation: For region-based generation, MultiDiffusion follows spatial constraints and produces consistent images qualitatively comparable to a task-specific method, while an autoregressive BLD adaptation often produces incoherent scenes.Comparisons include Make-A-Scene, SpaText, and BLD; the first two were trained specifically for this task.
- Region-based Text-to-Image Generation: On a COCO subset, MultiDiffusion outperforms Stable Inpainting and BLD using segmentation-model Intersection over Union against ground-truth masks.The evaluation uses 1,000 samples with two to four foreground objects and excludes people and masks occupying less than 5% of the image.
- Region-based Text-to-Image Generation: Bootstrapping makes generated objects fit tight masks more closely, while removing it still places the desired object within the mask region.The ablation is evaluated qualitatively and quantitatively.
6. Discussion and Conclusions
MultiDiffusion addresses controllable generation by defining a new process over a fixed pre-trained diffusion model. Its flexibility and efficiency come with dependence on the reference model’s generative prior, while broader optimization formulations remain a future direction.
- MultiDiffusion defines a new generation process on top of a pre-trained and fixed diffusion model for controllable image generation.
- The framework requires no further training or finetuning, supports multiple generation tasks, and can be solved efficiently for many tasks.The optimization can have a closed-form solution and ensures convergence to the global optimum of the objective.
- The method’s results depend on the diffusion paths supplied by the reference model, so bad seeds or biased prompts can affect the output.Additional constraints or prompt engineering can mitigate some such effects.
- A more general optimization problem is proposed as one possible direction for extending MultiDiffusion.
- The optimization framework combines a cost function with hard constraints that incorporate other priors or design constraints.
A. Additional Results
The appendix provides additional results and comparisons for the applications presented in the main paper.
- The appendix presents additional results for the applications shown in the main paper.
- It includes additional comparisons alongside the supplementary application results.
- The material extends the results reported for the main-paper applications.
A.1. Panorama Generation
Additional qualitative panorama results compare MultiDiffusion with Stable Inpainting and Blended Latent Diffusion, including a vertical panorama example.
- Panorama Generation: Figure 10 compares MultiDiffusion with Stable Inpainting and Blended Latent Diffusion for text-to-panorama generation.
- Panorama Generation: The appendix provides additional qualitative results for the text-to-panorama task.
- Panorama Generation: A vertical panorama result is shown in Figure 12 left.
A.2. Region-based Text-to-Image Generation
The appendix adds qualitative region-based generation results and comparisons, including examples from a COCO validation subset.
- Region-based Text-to-Image Generation: Figures 12 and 14 present additional qualitative results and comparisons for region-based generation.
- Region-based Text-to-Image Generation: The appendix includes sample results and comparisons on a subset of the COCO validation set.
- Region-based Text-to-Image Generation: The COCO experiment is described in more detail in Section 5.2.
B. Additional Implementation Details.
Panorama generation represents the desired panorama with overlapping fixed-size crops and applies the reference diffusion model to these crop mappings during iterative sampling. The crop updates are fused into a decoded RGB panorama, with parallel computation reducing execution overhead.
- Panorama representation: Panorama maps Fi define overlapping fixed-size crops from the full panorama.The crops use size H = W = 64 in latent space.
- Sampling inputs: Algorithm 2 takes a pre-trained diffusion model, target resolution, crop mappings, and a conditioned text prompt as inputs.Sampling begins from a noise initialization JT and iterates over denoising steps.
- Sampling process: At each denoising step, per-crop diffusion updates are computed and combined through MultiDiffuser.The resulting global latent state is updated before the next denoising step.
- Output: The final latent panorama is decoded to RGB space to produce the output panorama.Algorithm 2 denotes this decoding operation as Panorama ← D(J0).
- Efficiency: Per-crop diffusion updates can be computed in parallel in a batch, requiring T · n / b calls to the reference diffusion model.Here, T is the number of denoising steps, n the number of crops, and b the batch size.
B.2. Bootstrapping (Sec. 4.2)
Bootstrapping is introduced to preserve high fidelity to tight masks by initializing crop maps from a noised encoded constant-color image. The appendix also presents additional qualitative results for panoramas, rough layouts, combined regions, and comparisons with other methods.
- Bootstrapping: Bootstrapping improves fidelity to tight masks by precomputing the crop maps Fi from a noised latent initialization.The initialization is obtained by encoding a random constant RGB image and adding diffusion noise at each timestep.
- Bootstrapping: The bootstrapping construction encodes a random constant-color image into Stable Diffusion’s latent space before timestep-specific noising.The resulting St values use the diffusion noise scheduler parameters µt and σt.
- Additional results: Additional figures show text-to-panorama results and comparisons associated with the panorama-generation section.Figure 10 is explicitly described as providing additional text-to-panorama results and comparisons to Section 5.1.
- Additional results: Additional qualitative results cover generation from rough scene layouts, with multiple samples shown for each spatial layout and text prompt.Figure 11 emphasizes sample diversity under the same layout and prompt conditions.
- Comparisons: Further figures provide COCO validation samples and qualitative comparisons with BLD, SI, Make-A-Scene, and Spa-Text.These figures direct readers to Section 5.2 for additional evaluation details.