首先介绍一下open-set Grounded Text2Img Generation,它是一个框架,它可以根据文本描述和定位指令生成图像。定位指令提供有关图像的附加信息,例如边界框、深度图、语义地图等。所提出的框架可以在不同类型的定位指令上进行训练,例如检测数据、检测+字幕数据和定位数据。该模型在COCO2014数据集上进行评估,同时在图像质量...
Abstract Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propose GLIGEN, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the...
It is important to note that our model GLIGEN is designed for open-world grounded text-to-image generation with caption and various condition inputs (e.g. bounding box). However, we also recognize the importance of responsible AI considerations and the need to clearly communicate the capabilitie...
Grounded SAM 2: Ground and Track Anything in Videos with Grounding DINO, Florence-2 and SAM 2 - Grounded-SAM-2/grounded_sam2_florence2_image_demo.py at main · IDEA-Research/Grounded-SAM-2
Image-grounded emotional response generation (IgERG) tasks requires chatbots to generate a response with the understanding of both textual contexts and speakers' emotions in visual signals. Pre-training models enhance many NLP and CV tasks and image-text pre-training also helps multimodal tasks. ...
The unification brings two benefits: 1) it allows GLIP to learn from both detection and grounding data to im- prove both tasks and bootstrap a good grounding model; 2) GLIP can leverage massive image-text pairs by generat- ing grounding boxes in a self-training fashion, making the ...
”}, ] #合并消息以进行批量 messages=[消息1,消息1] #批量推断准备文本=[ processor.apply_chat_template(msg,词元化=假,add_generation_prompt=真)用于消息中的消息 ] image_inputs,video_inputs=process_vision_info(消息)输入=处理器( text=文本, images=图像输入,视频=视频输入, paddingneneneea=真, ...
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Im...
However, the status quo is to use text input alone, which can impede controllability. In this work, we propose Gligen, Grounded-Language-to-Image Generation, a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them ...
Official implementation of "IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation". - WUyinwei-hah/IFAdapter