Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
- URL: http://arxiv.org/abs/2406.07540v1
- Date: Tue, 11 Jun 2024 17:59:01 GMT
- Title: Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance
- Authors: Kuan Heng Lin, Sicheng Mo, Ben Klingher, Fangzhou Mu, Bolei Zhou,
- Abstract summary: Recent controllable generation approaches bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models without training auxiliary modules.
This work presents Ctrl-X, a simple framework for T2I diffusion controlling structure and appearance without additional training or guidance.
- Score: 36.50036055679903
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent controllable generation approaches such as FreeControl and Diffusion Self-guidance bring fine-grained spatial and appearance control to text-to-image (T2I) diffusion models without training auxiliary modules. However, these methods optimize the latent embedding for each type of score function with longer diffusion steps, making the generation process time-consuming and limiting their flexibility and use. This work presents Ctrl-X, a simple framework for T2I diffusion controlling structure and appearance without additional training or guidance. Ctrl-X designs feed-forward structure control to enable the structure alignment with a structure image and semantic-aware appearance transfer to facilitate the appearance transfer from a user-input image. Extensive qualitative and quantitative experiments illustrate the superior performance of Ctrl-X on various condition inputs and model checkpoints. In particular, Ctrl-X supports novel structure and appearance control with arbitrary condition images of any modality, exhibits superior image quality and appearance transfer compared to existing works, and provides instant plug-and-play functionality to any T2I and text-to-video (T2V) diffusion model. See our project page for an overview of the results: https://genforce.github.io/ctrl-x
Related papers
- FBSDiff: Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation [19.65838242227773]
This paper contributes a novel, concise, and efficient approach that adapts pre-trained large-scale text-to-image (T2I) diffusion model to the image-to-image (I2I) paradigm in a plug-and-play manner.
Our method allows flexible control over both guiding factor and guiding intensity of the reference image simply by tuning the type and bandwidth of the substituted frequency band.
arXiv Detail & Related papers (2024-08-02T04:13:38Z) - AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation [24.07613591217345]
Linguistic control enables effective content creation, but struggles with fine-grained control over image generation.
AnyControl develops a novel Multi-Control framework that extracts a unified multi-modal embedding to guide the generation process.
This approach enables a holistic understanding of user inputs, and produces high-quality, faithful results under versatile control signals.
arXiv Detail & Related papers (2024-06-27T07:40:59Z) - FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation [99.4649330193233]
Controllable text-to-image (T2I) diffusion models generate images conditioned on both text prompts and semantic inputs of other modalities like edge maps.
We propose a novel Flexible and Efficient method, FlexEControl, for controllable T2I generation.
arXiv Detail & Related papers (2024-05-08T06:09:11Z) - Object-Attribute Binding in Text-to-Image Generation: Evaluation and Control [58.37323932401379]
Current diffusion models create images given a text prompt as input but struggle to correctly bind attributes mentioned in the text to the right objects in the image.
We propose focused cross-attention (FCA) that controls the visual attention maps by syntactic constraints found in the input sentence.
We show substantial improvements in T2I generation and especially its attribute-object binding on several datasets.
arXiv Detail & Related papers (2024-04-21T20:26:46Z) - FreeControl: Training-Free Spatial Control of Any Text-to-Image
Diffusion Model with Any Condition [41.92032568474062]
FreeControl is a training-free approach for controllable T2I generation.
It supports multiple conditions, architectures, and checkpoints simultaneously.
It achieves competitive synthesis quality with training-based approaches.
arXiv Detail & Related papers (2023-12-12T18:59:14Z) - SparseCtrl: Adding Sparse Controls to Text-to-Video Diffusion Models [84.71887272654865]
We present SparseCtrl to enable flexible structure control with temporally sparse signals.
It incorporates an additional condition to process these sparse signals while leaving the pre-trained T2V model untouched.
The proposed approach is compatible with various modalities, including sketches, depth maps, and RGB images.
arXiv Detail & Related papers (2023-11-28T16:33:08Z) - Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models [82.19740045010435]
We introduce Uni-ControlNet, a unified framework that allows for the simultaneous utilization of different local controls and global controls.
Unlike existing methods, Uni-ControlNet only requires the fine-tuning of two additional adapters upon frozen pre-trained text-to-image diffusion models.
Uni-ControlNet demonstrates its superiority over existing methods in terms of controllability, generation quality and composability.
arXiv Detail & Related papers (2023-05-25T17:59:58Z) - UniControl: A Unified Diffusion Model for Controllable Visual Generation
In the Wild [166.25327094261038]
We introduce UniControl, a new generative foundation model for controllable condition-to-image (C2I) tasks.
UniControl consolidates a wide array of C2I tasks within a singular framework, while still allowing for arbitrary language prompts.
trained on nine unique C2I tasks, UniControl demonstrates impressive zero-shot generation abilities.
arXiv Detail & Related papers (2023-05-18T17:41:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.