LAMP
YiVal
LAMP | YiVal | |
---|---|---|
2 | 2 | |
223 | 2,436 | |
- | 0.8% | |
7.5 | 9.6 | |
12 days ago | 13 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LAMP
YiVal
-
YiVal——Unlocking Your Data's Power to Create Customized GenAI Apps
- 🤖Github:https://github.com/YiVal/YiVal/pull/189
- Show HN: YiVal, Auto-Tuning Assistant for GenAI Applications
What are some alternatives?
collage-diffusion-ui - An open source, layer-based web interface for Collage Diffusion - use a familiar Photoshop-like interface and let the AI harmonize the details.
AI-Image-PromptGenerator - A flexible UI script to help create and expand on prompts for generative AI art models, such as Stable Diffusion and MidJourney. Get inspired, and create.
ReVersion - ReVersion: Diffusion-Based Relation Inversion from Images
awesome-open-gpt - Collection of Open Source Projects Related to GPT,GPT相关开源项目合集🚀、精选🔥🔥
zero123plus - Code repository for Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model.
llm-client-sdk - SDK for using LLM
Gen-L-Video - The official implementation for "Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising".
Awesome-AIGC-Tutorials - Curated tutorials and resources for Large Language Models, AI Painting, and more.
sliders - Concept Sliders for Precise Control of Diffusion Models
aihandler - A simple engine to help run diffusers and transformers models
ziplora-pytorch - Implementation of "ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs"
llm-prompt-testing - Prompt Testing framework for LLMs (specifically OpenAI models). Compute NLP and Responsible AI metrics for each model-generated answer.