Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Feature Request]: drag gan- drag image manipulations, and vLLM Accelerates HuggingFace Transformers By 24x- link to codes here #11491

Open
1 task done
angrysky56 opened this issue Jun 28, 2023 · 0 comments
Labels
enhancement New feature or request extension-request Items that should be implemented as an extension rather than part of this repo

Comments

@angrysky56
Copy link

Is there an existing issue for this?

  • I have searched the existing issues and checked the recent builds/commits

What would your feature do ?

Synthesizing visual content that meets users' needs often requires flexible and precise controllability of the pose, shape, expression, and layout of the generated objects. Existing approaches gain controllability of generative adversarial networks (GANs) via manually annotated training data or a prior 3D model, which often lack flexibility, precision, and generality. In this work, we study a powerful yet much less explored way of controlling GANs, that is, to "drag" any points of the image to precisely reach target points in a user-interactive manner, as shown in Fig.1. To achieve this, we propose DragGAN, which consists of two main components including: 1) a feature-based motion supervision that drives the handle point to move towards the target position, and 2) a new point tracking approach that leverages the discriminative GAN features to keep localizing the position of the handle points. Through DragGAN, anyone can deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse categories such as animals, cars, humans, landscapes, etc. As these manipulations are performed on the learned generative image manifold of a GAN, they tend to produce realistic outputs even for challenging scenarios such as hallucinating occluded content and deforming shapes that consistently follow the object's rigidity. Both qualitative and quantitative comparisons demonstrate the advantage of DragGAN over prior approaches in the tasks of image manipulation and point tracking. We also showcase the manipulation of real images through GAN inversion.
https://github.com/XingangPan/DragGAN

Meet vLLM: An Open-Source LLM Inference And Serving Library That Accelerates HuggingFace Transformers By 24x
https://www.marktechpost.com/2023/06/24/meet-vllm-an-open-source-llm-inference-and-serving-library-that-accelerates-huggingface-transformers-by-24x/?fbclid=IwAR15kAGDoP7qeE4uyeZz3GQ6vMNKxYDR9eBl681v0ReeIQ1lckOLPjGWVG4

Proposed workflow

for general ui improvements

Additional information

not a coder sorry

@angrysky56 angrysky56 added the enhancement New feature or request label Jun 28, 2023
@catboxanon catboxanon added the extension-request Items that should be implemented as an extension rather than part of this repo label Aug 26, 2023
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
enhancement New feature or request extension-request Items that should be implemented as an extension rather than part of this repo
Projects
None yet
Development

No branches or pull requests

2 participants