Text2LIVE
autodistill-metaclip
Text2LIVE | autodistill-metaclip | |
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2 | 1 | |
849 | 16 | |
- | - | |
0.0 | 6.4 | |
about 1 year ago | 5 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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Text2LIVE
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The new neural network from NVIDIA can apply special effects to video using simple text commands.
The source code of the neural network can be found on GitHub: https://github.com/omerbt/Text2LIVE
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Text2LIVE: Text-Driven Layered Image and Video Editing. A new zero shot technique to edit the appearances of images and video!
"We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Specifically, given an input image or video and a target text prompt, our goal is to edit the appearance of existing objects (e.g., object's texture) or augment the scene with new visual effects (e.g., smoke, fire) in a semantically meaningful manner. Our framework trains a generator using an internal dataset of training examples, extracted from a single input (image or video and target text prompt), while leveraging an external pre-trained CLIP model to establish our losses. Rather than directly generating the edited output, our key idea is to generate an edit layer (color+opacity) that is composited over the original input. This allows us to constrain the generation process and maintain high fidelity to the original input via novel text-driven losses that are applied directly to the edit layer. Our method neither relies on a pre-trained generator nor requires user-provided edit masks. Thus, it can perform localized, semantic edits on high-resolution natural images and videos across a variety of objects and scenes. Semi-Transparent Effects Text2LIVE successfully augments the input scene with complex semi-transparent effects without changing irrelevant content in the image." demo site: https://text2live.github.io arxiv: https://arxiv.org/abs/2204.02491 github: https://github.com/omerbt/Text2LIVE
autodistill-metaclip
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MetaCLIP – Meta AI Research
I have been playing with MetaCLIP this afternoon and made https://github.com/autodistill/autodistill-metaclip as a pip installable version. The Facebook repository has some guidance but you have to pull the weights yourself, save them, etc.
My inference function (model.predict("image.png")) return an sv.Classifications object that you can load into supervision for processing (i.e. get top k) [1].
The paper [2] notes the following in terms of performance:
> In Table 4, we observe that MetaCLIP outperforms OpenAI CLIP on ImageNet and average accuracy across 26 tasks, for 3 model scales. With 400 million training data points on ViT-B/32, MetaCLIP outperforms CLIP by +2.1% on ImageNet and by +1.6% on average. On ViT-B/16, MetaCLIP outperforms CLIP by +2.5% on ImageNet and by +1.5% on average. On ViT-L/14, MetaCLIP outperforms CLIP by +0.7% on ImageNet and by +1.4% on average across the 26 tasks.
[1] https://github.com/autodistill/autodistill-metaclip
What are some alternatives?
Paint-by-Sketch - Stable Diffusion-based image manipulation method with a sketch and reference image
clip-interrogator - Image to prompt with BLIP and CLIP
SDEdit - PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
open_clip - An open source implementation of CLIP.
ml-gmpi - Official PyTorch implementation of GMPI (ECCV 2022, Oral Presentation)
BLIP - PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
DeepSIM - Official PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral)
NumPyCLIP - Pure NumPy implementation of https://github.com/openai/CLIP
TargetCLIP - [ECCV 2022] Official PyTorch implementation of the paper Image-Based CLIP-Guided Essence Transfer.
sam-clip - Use Grounding DINO, Segment Anything, and CLIP to label objects in images.
sketchedit - SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches, CVPR2022
aphantasia - CLIP + FFT/DWT/RGB = text to image/video