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Found 22 papers, 12 papers with code
Piece it Together: Part-Based Concepting with IP-Priors
Advanced generative models excel at synthesizing images but often rely on text-based conditioning.
NeuralSVG: An Implicit Representation for Text-to-Vector Generation
We additionally demonstrate that utilizing a neural representation provides an added benefit of inference-time control, enabling users to dynamically adapt the generated SVG based on user-provided inputs, all with a single learned representation.
InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention
Face image restoration aims to enhance degraded facial images while addressing challenges such as diverse degradation types, real-time processing demands, and, most crucially, the preservation of identity-specific features.
ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation
Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows.
pOps: Photo-Inspired Diffusion Operators
To harness this potential, we introduce pOps, a framework that trains specific semantic operators directly on CLIP image embeddings.
MyVLM: Personalizing VLMs for User-Specific Queries
To effectively recognize a variety of user-specific concepts, we augment the VLM with external concept heads that function as toggles for the model, enabling the VLM to identify the presence of specific target concepts in a given image.
Breathing Life Into Sketches Using Text-to-Video Priors
A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually.
Cross-Image Attention for Zero-Shot Appearance Transfer
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images.
ConceptLab: Creative Concept Generation using VLM-Guided Diffusion Prior Constraints
Recent text-to-image generative models have enabled us to transform our words into vibrant, captivating imagery.
A Neural Space-Time Representation for Text-to-Image Personalization
We observe that one can significantly improve the convergence and visual fidelity of the concept by introducing a textual bypass, where our neural mapper additionally outputs a residual that is added to the output of the text encoder.
TEXTure: Text-Guided Texturing of 3D Shapes
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes.
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
CLIPascene: Scene Sketching with Different Types and Levels of Abstraction
In this paper, we present a method for converting a given scene image into a sketch using different types and multiple levels of abstraction.
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes.
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State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
Third Time's the Charm? Image and Video Editing with StyleGAN3
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
In this work, we introduce this approach into the realm of encoder-based inversion.
StyleFusion: A Generative Model for Disentangling Spatial Segments
Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes.
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner.
Designing an Encoder for StyleGAN Image Manipulation
We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control over the generated image.
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
