--- title: ChatAnything emoji: 🔥 colorFrom: green colorTo: green sdk: gradio sdk_version: 4.2.0 app_file: app.py pinned: false --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference # ChatAnything: Facetime Chat with LLM-Enhanced Personas **Yilin Zhao\*, Shanghua Gao\*, Daquan Zhou\*, Xinbin Yuan\*, Zhijie Lin, Qibin Hou, Jiashi Feng** > What will it be like to Facetime any imaginary concepts? To animate anything, we integrated current open-source models at hand for an animation application for interactive AI-Agent chatting usage. > > To start with, take a look at these incredible faces generated with open-source Civitai models that are to be animated. drawing Here we provide you with ChatAnything. A simple pipeline Enhanced with currently limitless Large Language Models, yielding imaginary Facetime chats with intented visual appearance! Remember, the repo and application are totally based on pre-trained deep learning methods and haven't included any training yet. We give all the credit to the open-source community (shout out to you). For detail of the pipeline, see our technical report (TODO: link here) ## Release & Features & Future Plans - [ ] Fine-tune face rendering module. - [ ] Better TTS module & voice render module. - [ ] Adding Open-source Language Models. - [x] Initial release - Facetime Animation. - Multiple model choices for initial frame generation. - Multiple choices for voices. # Install & Run Just follow the instructions. Every thing would be simple (hopefully). Reach out if you met with any problems! ### Install first, install the virtual environment. ``` conda env create -f environment.yaml # then install conda env update --name chatanything --file environment.yaml ``` The Pipeline integrated Open-Source Models. All Models are to be found online(see [Acknowledgement](#acknowledgement)). We put some important models together on huggingface remotes just to make life easier. Prepare them for the first run with this Python script [prepare_models.py](./python_scripts/prepare_models.py): ``` # prepare the local models python python_scripts/prepare_models.py ``` ### Building Docker Try build a docker if you find it easier. This part is not fully tested. If you find a anything wrong, feel free to contribute~ ``` docker build --network=host -t chatanything . # docker run -dp 127.0.0.1:8901:8901 chatanything docker run -p 127.0.0.1:8901:8901 -it --gpus all chatanything docker run -it --gpus all chatanything bash ``` ### Run specify a port for the gradio application to run on and set off! ``` PORT=8809 python app.py $PORT ``` # Configuring: From User Input Concept to Appearance & Voice The first step of the pipeline is to generate a image for SadTalker and at the same time set up the Text to Sound Module for voice chat. The pipeline would query a powerful LLM (ChatGPT) for the selection in a zero-shot multi-choice selection format. Three Questions are asked upon the initial of every conversation(init frame generation): 1. Provide a imagen personality for the user input concept. 2. Select a Generative model for the init frame generation. 3. Select a Text To Sound Voice(Model) for the character base on the personality. We have constructed the model selection to be extendable. Add your ideal model with just a few lines of Configuring! The rest of this section would breifly introduce the steps to add a init-frame generator/language voice. ### Image Generator Configure the models in the [Model Config](./resources/models.yaml). This Config acts as the memory (or an image-generating tool pool) for the LLM. The prompt sets up this selection process. Each sub field of the "models" would turn into an option in the multiple-choice question. the "**desc**" field of each element is what the Language Model would see. The key is not provided to the LM as it would sometimes mislead it. the others are used for the image generation as listed: 1. model_dir: the repo-path for diffusers package. As the pretrained Face-landmark ControlNet is based on stable-diffusion-v1-5, we currently only supports the derivatives of it. 2. lora_path: LoRA derivatives are powerful, try a LoRA model also for better stylization. Should directly point to the parameters binary file. 3. prompt_template & negative_prompt: this is used for prompting the text-to-image diffusion model. Find a ideal prompt for your model and stick with it. A "{}" should be in the prompt template for inserting the user input concept. Here are some **Tips** for configuring you own model. 1. Provide the LLM with a simple description of the generative model. It is worth noting that the description needs to be concise and accurate for a correct selection. 2. Set the model_dir to a local directory of diffusers stable-diffusion-v1-5 derivatives. Also, you can provide a repo-id on the huggingface hub model space. The model would be downloaded when first chosen, wait for it. 3. To better utilize the resources from the community, we also add in support of the LoRA features. To add the LoRA module, you would need to give the path to the parameter files. 4. Carefully write the prompt template and negative prompt. These which affect the initial face generation a lot. Be aware that the prompt template should contain only one pair of "{}" to insert the concept that users wrote on the application webpage. We support the Stable-Diffusion-Webui prompt style as implemented by diffusers, feel free to copy the prompt from Civitai for better prompting the generation and put in the "{}" to the original prompt for ChatAnything! Again, this model's config acts as an extended tool pool for the LM, the application would drive the LM to choose from this config and use the chosen model to generate. Sometimes the LM fails to choose the correct model or choosing any available model, this would cause the Chatanything app to fail on a generation. Notice we currently support ONLY stable-diffusion-v1.5 derivatives (Sdxl Pipelines are under consideration, however not yet implemented as we lack a face-landmark ControlNet for it. Reach out if you're interested in training one!) ### Voice TTS We are using the edge_tts package for text-to-speech support. The voice selection and [voice configuration file](./resources/voices_edge.yaml) is constructed similarly to the Image generation model selection, except now the LM is supposed to choose the voice base on the personality description given by itself earlier. "**gender**" and "**language**" field corresponds to edge_tts. # On-going tasks. ### Customized Voice. There is a Voice Changer TextToSpeach-SpeachVoiceConversion Pipeline app, which ensures a better customized voice. We are trying to leverage its TTS functionality. Reach out if you want to add a voice of your own or your hero! Here are the possible steps for You would need to change a little bit in the code first: 1. Alter this [code](./utils.py#14) to import a TTSTalker from chat_anything/tts_talker/tts_voicechanger.py. 2. switch the config to another one, change [code](./utils.py#14) "resources/voices_edge.yaml" -> "resources/voices_voicechanger.yaml" The try running a [Voice Changer](https://huggingface.co/spaces/kevinwang676/Voice-Changer) on your local machine. Simply set up git-lfs and install the repo and run it for the TTS voice service. The TTS caller was set to port 7860. make sure the client class is set up with the same port in [here](chat_anything/tts_talker/tts_voicechanger.py#5) ```python client = Client("http://127.0.0.1:7860/") ``` # Acknowledgement Again, the project hasn't yet included any training. The pipeline is totally based on these incredible awesome packages and pretrained models. Don't hesitate to take a look and explore the amazing open-source generative communities. We love you, guys. - [ChatGPT](https://openai.com/chatgpt): GOD - [SadTalker](https://github.com/OpenTalker/SadTalker): The Core Animation Module - [Face-Landmark-ControlNet](https://huggingface.co/georgefen/Face-Landmark-ControlNet): An Awesome ControlNet with Face landmark using Stable Diffusion 1.5 as base Model. - [diffusers](https://github.com/huggingface/diffusers): GOAT of Image Generative Framework🥳. - [langchain](https://github.com/langchain-ai/langchain): An Awesome Package for Dealing with LLM. - [edge-tts](https://github.com/rany2/edge-tts): An Awesome Package for Text To Sound Solutions. - [gradio](https://www.gradio.app/): GOAT😄 Machine Learning based App framework. - [Civitai](https://civitai.com/models) and [Huggingface_hub](https://huggingface.co/models): Find your ideal Image Generative Model on Civitai. These Communities are Crazy🥂. Here are Some Fantastic Derivatives of [stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5): - [Game Icon Institute_mode](https://civitai.com/models/47800?modelVersionId=76533) - [dreamshaper](https://civitai.com/models/4384/dreamshaper) - [3D_Animation_Diffusion](https://civitai.com/models/118086?modelVersionId=128046) - [anything-v5](https://huggingface.co/stablediffusionapi/anything-v5) # Citation If you like our pipeline and application, don't hesitate to reach out! Let's work on it and see how far it would go! ```bibtex @misc{zhao2023ChatAnything, title={ChatAnything: Facetime Chat with LLM-Enhanced Personas}, author={Yilin, Zhao and Shanghua, Gao and Daquan, Zhou and Xinbin, Yuan and Qibin, Hou and Jiashi, Feng}, publisher={}, year={2023}, } ```