| Name | taproot JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | Taproot is a seamlessly scalable AI/ML inference engine designed for deployment across hardware clusters with disparate capabilities. |
| upload_time | 2025-01-30 01:14:13 |
| maintainer | None |
| docs_url | None |
| author | Benjamin Paine |
| requires_python | >=3.8.0 |
| license | None |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<img src="https://github.com/user-attachments/assets/f965fd42-2a95-4552-9b5f-465fc4037a91" width="650" /><br />
<em>An open source real-time AI inference engine for seamless scaling</em>
</div>
<hr/>
<p align="center">
<img src="https://img.shields.io/static/v1?label=painebenjamin&message=taproot&color=234b0e&logo=github" alt="painebenjamin - taproot">
<img src="https://img.shields.io/github/stars/painebenjamin/taproot?style=social" alt="stars - taproot">
<img src="https://img.shields.io/github/forks/painebenjamin/taproot?style=social" alt="forks - taproot"><br />
<a href="https://github.com/painebenjamin/taproot/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache-234b0e" alt="License"></a>
<a href="https://pypi.org/project/taproot"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/taproot?color=234b0e"></a>
<a href="https://pypistats.org/packages/taproot"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/taproot?logo=python&logoColor=white&color=234b0e"></a>
</p>
# About
Taproot is a seamlessly scalable AI/ML inference engine designed for deployment across hardware clusters with disparate capabilities.
## Why Taproot?
Most AI/ML inference engines are built for either large-scale cloud infrastructures or constrained edge devices - Taproot is designed for **medium-scale deployments**, offering flexible and distributed on-premise or PAYG setups. It efficiently uses older or consumer-grade hardware, making it suitable for small networks or ad-hoc clusters, without relying on centralized, hyperscale architectures.
Taproot is also **really, *really* fast** with latency as low as **50 microseconds** per request and transfer rates up to **2 GB/s** on consumer hardware, supporting standard HTTP/S, websockets, and raw TCP or Unix sockets.
<div align="center">
<a href="https://github.com/user-attachments/assets/c39ac7ad-d46b-4ab1-bd79-5bbd6595db52" target="_blank">
<img src="https://github.com/user-attachments/assets/c39ac7ad-d46b-4ab1-bd79-5bbd6595db52" width=640 />
</a><br />
<em>Taproot server/client round-trip echo times for varying packet sizes, grouped by supported protocol.</em>
</div><br/>
Two encryption methods are also supported:
1. `tcps` uses raw `tcp` socket communication with bidirectional AES-NI encryption, configured with a key on server and client.
2. `wss` and `https` use OpenSSL to serve standard TLS connections, configured with a key, certificate and optionally chain.
## Available Models
There are more than 170 models available across 18 task categories. See the [Task Catalog](https://github.com/painebenjamin/taproot/wiki/Task-Catalog) for the complete list, licenses, requirements and citations. Despite the large number of models available, there are many more yet to be added - if you're looking for a particular enhancement, don't hesitate to make an issue on this repository to request it.
### Roadmap
Items with ~strikethrough~ are complete in the main branch.
1. Regular IP Adapter Models for Diffusers Image Generation Pipelines
- ~Stable Diffusion 1.5~
- ~Stable Diffusion XL~
- Stable Diffusion 3.5
- FLUX
2. Face ID IP Adapter Models for Diffusers Image Generation Pipelines
- Stable Diffusion 1.5
- Stable Diffusion XL
3. ControlNet Models for Diffusers Image Generation Pipelines
- ~Stable Diffusion 1.5~
- ~Stable Diffusion XL~
- Stable Diffusion 3.5
- FLUX
4. Additional quantization backends for large models
- Optimum-Quanto Support with FP8
- TorchAO Support with FP8
5. Improved multi-GPU support
- This is currently supported through manual configuration, but usability can be improved.
6. Additional annotators/detectors for image and video
- E.g. Marigold, SAM2
7. Additional audio generation models
- E.g. Stable Audio, AudioLDM, MusicGen
# Installation
```sh
pip install taproot
```
Some additional packages are available to install with the square-bracket syntax (e.g. `pip install taproot[a,b,c]`), these are:
- **tools** - Additional packages for LLM tools like DuckDuckGo Search, BeautifulSoup (for web scraping), etc.
- **cli** - Additional packages for prettifying console output.
- **av** - Additional packages for reading and writing video.
- **uv** - `uvloop` for improved performance on linux systems.
## Installing Tasks
Some tasks are available immediately, but most tasks required additional packages and files. Install these tasks with `taproot install [task:model]+`, e.g:
```sh
taproot install image-generation:stable-diffusion-xl
```
# Usage
## Command-Line
### Introspecting Tasks
From the command line, execute `taproot tasks` to see all tasks and their availability status, or `taproot info` for individual task information. For example:
```sh
taproot info image-generation stable-diffusion-xl
Stable Diffusion XL Image Generation (image-generation:stable-diffusion-xl, available)
Generate an image from text and/or images using a stable diffusion XL model.
Hardware Requirements:
GPU Required for Optimal Performance
Floating Point Precision: half
Minimum Memory (CPU RAM) Required: 231.71 MB
Minimum Memory (GPU VRAM) Required: 7.58 GB
Author:
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna and Robin Rombach
Published in arXiv, vol. 2307.01952, “SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis”, 2023
https://arxiv.org/abs/2307.01952
License:
OpenRAIL++-M License (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)
✅ Attribution Required
✅ Derivatives Allowed
✅ Redistribution Allowed
✅ Copyleft (Share-Alike) Required
✅ Commercial Use Allowed
✅ Hosting Allowed
Files:
image-generation-stable-diffusion-xl-base-vae.fp16.safetensors (334.64 MB) [downloaded]
image-generation-stable-diffusion-xl-base-unet.fp16.safetensors (5.14 GB) [downloaded]
text-encoding-clip-vit-l.bf16.safetensors (246.14 MB) [downloaded]
text-encoding-open-clip-vit-g.fp16.safetensors (1.39 GB) [downloaded]
text-encoding-clip-vit-l-tokenizer-vocab.json (1.06 MB) [downloaded]
text-encoding-clip-vit-l-tokenizer-special-tokens-map.json (588.00 B) [downloaded]
text-encoding-clip-vit-l-tokenizer-merges.txt (524.62 KB) [downloaded]
text-encoding-open-clip-vit-g-tokenizer-vocab.json (1.06 MB) [downloaded]
text-encoding-open-clip-vit-g-tokenizer-special-tokens-map.json (576.00 B) [downloaded]
text-encoding-open-clip-vit-g-tokenizer-merges.txt (524.62 KB) [downloaded]
Total File Size: 7.11 GB
Required packages:
pil~=9.5 [installed]
torch<2.5,>=2.4 [installed]
numpy~=1.22 [installed]
diffusers>=0.29 [installed]
torchvision<0.20,>=0.19 [installed]
transformers>=4.41 [installed]
safetensors~=0.4 [installed]
accelerate~=1.0 [installed]
sentencepiece~=0.2 [installed]
compel~=2.0 [installed]
peft~=0.13 [installed]
Signature:
prompt: Union[str, List[str]], required
prompt_2: Union[str, List[str]], default: None
negative_prompt: Union[str, List[str]], default: None
negative_prompt_2: Union[str, List[str]], default: None
image: ImageType, default: None
mask_image: ImageType, default: None
guidance_scale: float, default: 5.0
guidance_rescale: float, default: 0.0
num_inference_steps: int, default: 20
num_images_per_prompt: int, default: 1
height: int, default: None
width: int, default: None
timesteps: List[int], default: None
sigmas: List[float], default: None
denoising_end: float, default: None
strength: float, default: None
latents: torch.Tensor, default: None
prompt_embeds: torch.Tensor, default: None
negative_prompt_embeds: torch.Tensor, default: None
pooled_prompt_embeds: torch.Tensor, default: None
negative_pooled_prompt_embeds: torch.Tensor, default: None
clip_skip: int, default: None
seed: SeedType, default: None
pag_scale: float, default: None
pag_adaptive_scale: float, default: None
scheduler: Literal[ddim, ddpm, ddpm_wuerstchen, deis_multistep, dpm_cogvideox, dpmsolver_multistep, dpmsolver_multistep_karras, dpmsolver_sde, dpmsolver_sde_multistep, dpmsolver_sde_multistep_karras, dpmsolver_singlestep, dpmsolver_singlestep_karras, edm_dpmsolver_multistep, edm_euler, euler_ancestral_discrete, euler_discrete, euler_discrete_karras, flow_match_euler_discrete, flow_match_heun_discrete, heun_discrete, ipndm, k_dpm_2_ancestral_discrete, k_dpm_2_ancestral_discrete_karras, k_dpm_2_discrete, k_dpm_2_discrete_karras, lcm, lms_discrete, lms_discrete_karras, pndm, tcd, unipc], default: None
output_format: Literal[png, jpeg, float, int, latent], default: png
output_upload: bool, default: False
highres_fix_factor: float, default: 1.0
highres_fix_strength: float, default: None
spatial_prompts: SpatialPromptInputType, default: None
Returns:
ImageResultType
```
### Invoking Tasks
Run `taproot invoke` to run any task from the command line. All parameters to the task can be passed as flags to the call using kebab-case, e.g.:
```sh
taproot invoke image-generation:stable-diffusion-xl \
--prompt "a photograph of a golden retriever at the park" \
--negative-prompt "fall, autumn, blurry, out-of-focus" \
--seed 12345
Loading task.
100%|███████████████████████████████████████████████████████████████████████████| 7/7 [00:03<00:00, 2.27it/s]
Task loaded in 4.0 s.
Invoking task.
100%|█████████████████████████████████████████████████████████████████████████| 20/20 [00:04<00:00, 4.34it/s]
Task invoked in 6.5 s. Result:
8940aa12-66a7-4233-bfd6-f19da339b71b.png
```
## Python
### Direct Task Usage
```py
from taproot import Task
sdxl = Task.get("image-generation", "stable-diffusion-xl")
pipeline = sdxl()
pipeline.load() # Uses GPU 0 when available
pipeline(prompt="Hello, world!").save("./output.png")
```
### With a Remote Server
```py
import asyncio
from taproot import Tap
async def main() -> None:
tap = Tap()
tap.remote_address = "ws://127.0.0.1:32189"
result = await tap("image-generation", model="stable-diffusion-xl", prompt="Hello, world!")
result.save("./output.png")
asyncio.run(main)
```
### With a Local Server
Also shows usage with `uvloop`.
```py
import uvloop
from taproot import Tap
async def main() -> None:
async with Tap.local() as tap:
# Taproot is now running on ws://127.0.0.1:32189 with a local dispatcher
result = await tap("speech-synthesis", model="kokoro", text="Hello, world!")
result.save("./output.wav")
uvloop.run(main)
```
## Running Servers
Taproot uses a three-roled cluster structure:
1. **Overseers** are entry points into clusters, routing requests to one or more dispatchers.
2. **Dispatchers** are machines capable of running tasks by spawning executors.
3. **Executors** are servers ready to execute a task.
The simplest way to run a server is to run an overseer simultaneously with a local dispatcher like so:
```sh
taproot overseer --local
```
This will run on the default address of `ws://127.0.0.1:32189`, suitable for interaction from python or the browser.
There are many deployment possibilities across networks, with configuration available for encryption, listening addresses, and more. See the wiki for details (coming soon.)
## Outside Python
- [taproot.js](https://github.com/painebenjamin/taproot.js) - for the browser and node.js, available in ESM, UMD and IIFE
- taproot.php - coming soon
Raw data
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"author": "Benjamin Paine",
"author_email": "painebenjamin@gmail.com",
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"description": "<div align=\"center\">\n<img src=\"https://github.com/user-attachments/assets/f965fd42-2a95-4552-9b5f-465fc4037a91\" width=\"650\" /><br />\n<em>An open source real-time AI inference engine for seamless scaling</em>\n</div>\n<hr/>\n<p align=\"center\">\n <img src=\"https://img.shields.io/static/v1?label=painebenjamin&message=taproot&color=234b0e&logo=github\" alt=\"painebenjamin - taproot\">\n <img src=\"https://img.shields.io/github/stars/painebenjamin/taproot?style=social\" alt=\"stars - taproot\">\n <img src=\"https://img.shields.io/github/forks/painebenjamin/taproot?style=social\" alt=\"forks - taproot\"><br />\n <a href=\"https://github.com/painebenjamin/taproot/blob/main/LICENSE\"><img src=\"https://img.shields.io/badge/License-Apache-234b0e\" alt=\"License\"></a>\n <a href=\"https://pypi.org/project/taproot\"><img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/taproot?color=234b0e\"></a>\n <a href=\"https://pypistats.org/packages/taproot\"><img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/taproot?logo=python&logoColor=white&color=234b0e\"></a>\n</p>\n\n# About\n\nTaproot is a seamlessly scalable AI/ML inference engine designed for deployment across hardware clusters with disparate capabilities.\n\n## Why Taproot?\n\nMost AI/ML inference engines are built for either large-scale cloud infrastructures or constrained edge devices - Taproot is designed for **medium-scale deployments**, offering flexible and distributed on-premise or PAYG setups. It efficiently uses older or consumer-grade hardware, making it suitable for small networks or ad-hoc clusters, without relying on centralized, hyperscale architectures.\n\nTaproot is also **really, *really* fast** with latency as low as **50 microseconds** per request and transfer rates up to **2 GB/s** on consumer hardware, supporting standard HTTP/S, websockets, and raw TCP or Unix sockets.\n\n<div align=\"center\">\n<a href=\"https://github.com/user-attachments/assets/c39ac7ad-d46b-4ab1-bd79-5bbd6595db52\" target=\"_blank\">\n<img src=\"https://github.com/user-attachments/assets/c39ac7ad-d46b-4ab1-bd79-5bbd6595db52\" width=640 />\n</a><br />\n<em>Taproot server/client round-trip echo times for varying packet sizes, grouped by supported protocol.</em>\n</div><br/> \n\nTwo encryption methods are also supported:\n1. `tcps` uses raw `tcp` socket communication with bidirectional AES-NI encryption, configured with a key on server and client.\n2. `wss` and `https` use OpenSSL to serve standard TLS connections, configured with a key, certificate and optionally chain.\n\n## Available Models\n\nThere are more than 170 models available across 18 task categories. See the [Task Catalog](https://github.com/painebenjamin/taproot/wiki/Task-Catalog) for the complete list, licenses, requirements and citations. Despite the large number of models available, there are many more yet to be added - if you're looking for a particular enhancement, don't hesitate to make an issue on this repository to request it.\n\n### Roadmap\n\nItems with ~strikethrough~ are complete in the main branch.\n\n1. Regular IP Adapter Models for Diffusers Image Generation Pipelines\n - ~Stable Diffusion 1.5~\n - ~Stable Diffusion XL~\n - Stable Diffusion 3.5\n - FLUX\n2. Face ID IP Adapter Models for Diffusers Image Generation Pipelines\n - Stable Diffusion 1.5\n - Stable Diffusion XL\n3. ControlNet Models for Diffusers Image Generation Pipelines\n - ~Stable Diffusion 1.5~\n - ~Stable Diffusion XL~\n - Stable Diffusion 3.5\n - FLUX\n4. Additional quantization backends for large models\n - Optimum-Quanto Support with FP8\n - TorchAO Support with FP8\n5. Improved multi-GPU support\n - This is currently supported through manual configuration, but usability can be improved.\n6. Additional annotators/detectors for image and video\n - E.g. Marigold, SAM2\n7. Additional audio generation models\n - E.g. Stable Audio, AudioLDM, MusicGen\n\n# Installation\n\n```sh\npip install taproot\n```\n\nSome additional packages are available to install with the square-bracket syntax (e.g. `pip install taproot[a,b,c]`), these are:\n- **tools** - Additional packages for LLM tools like DuckDuckGo Search, BeautifulSoup (for web scraping), etc.\n- **cli** - Additional packages for prettifying console output.\n- **av** - Additional packages for reading and writing video.\n- **uv** - `uvloop` for improved performance on linux systems.\n\n## Installing Tasks\n\nSome tasks are available immediately, but most tasks required additional packages and files. Install these tasks with `taproot install [task:model]+`, e.g: \n\n```sh\ntaproot install image-generation:stable-diffusion-xl\n```\n\n# Usage\n\n## Command-Line\n\n### Introspecting Tasks\n\nFrom the command line, execute `taproot tasks` to see all tasks and their availability status, or `taproot info` for individual task information. For example:\n\n```sh\ntaproot info image-generation stable-diffusion-xl\n\nStable Diffusion XL Image Generation (image-generation:stable-diffusion-xl, available)\n Generate an image from text and/or images using a stable diffusion XL model.\nHardware Requirements: \n GPU Required for Optimal Performance \n Floating Point Precision: half \n Minimum Memory (CPU RAM) Required: 231.71 MB \n Minimum Memory (GPU VRAM) Required: 7.58 GB \nAuthor: \n Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M\u00fcller, Joe Penna and Robin Rombach\n Published in arXiv, vol. 2307.01952, \u201cSDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis\u201d, 2023\n https://arxiv.org/abs/2307.01952 \nLicense:\n OpenRAIL++-M License (https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md)\n \u2705 Attribution Required\n \u2705 Derivatives Allowed\n \u2705 Redistribution Allowed\n \u2705 Copyleft (Share-Alike) Required\n \u2705 Commercial Use Allowed\n \u2705 Hosting Allowed\nFiles: \n image-generation-stable-diffusion-xl-base-vae.fp16.safetensors (334.64 MB) [downloaded]\n image-generation-stable-diffusion-xl-base-unet.fp16.safetensors (5.14 GB) [downloaded]\n text-encoding-clip-vit-l.bf16.safetensors (246.14 MB) [downloaded]\n text-encoding-open-clip-vit-g.fp16.safetensors (1.39 GB) [downloaded]\n text-encoding-clip-vit-l-tokenizer-vocab.json (1.06 MB) [downloaded]\n text-encoding-clip-vit-l-tokenizer-special-tokens-map.json (588.00 B) [downloaded]\n text-encoding-clip-vit-l-tokenizer-merges.txt (524.62 KB) [downloaded]\n text-encoding-open-clip-vit-g-tokenizer-vocab.json (1.06 MB) [downloaded]\n text-encoding-open-clip-vit-g-tokenizer-special-tokens-map.json (576.00 B) [downloaded]\n text-encoding-open-clip-vit-g-tokenizer-merges.txt (524.62 KB) [downloaded]\n Total File Size: 7.11 GB\nRequired packages:\n pil~=9.5 [installed]\n torch<2.5,>=2.4 [installed]\n numpy~=1.22 [installed]\n diffusers>=0.29 [installed]\n torchvision<0.20,>=0.19 [installed]\n transformers>=4.41 [installed]\n safetensors~=0.4 [installed]\n accelerate~=1.0 [installed]\n sentencepiece~=0.2 [installed]\n compel~=2.0 [installed]\n peft~=0.13 [installed]\nSignature:\n prompt: Union[str, List[str]], required\n prompt_2: Union[str, List[str]], default: None\n negative_prompt: Union[str, List[str]], default: None\n negative_prompt_2: Union[str, List[str]], default: None\n image: ImageType, default: None\n mask_image: ImageType, default: None\n guidance_scale: float, default: 5.0\n guidance_rescale: float, default: 0.0\n num_inference_steps: int, default: 20\n num_images_per_prompt: int, default: 1\n height: int, default: None\n width: int, default: None\n timesteps: List[int], default: None\n sigmas: List[float], default: None\n denoising_end: float, default: None\n strength: float, default: None\n latents: torch.Tensor, default: None\n prompt_embeds: torch.Tensor, default: None\n negative_prompt_embeds: torch.Tensor, default: None\n pooled_prompt_embeds: torch.Tensor, default: None\n negative_pooled_prompt_embeds: torch.Tensor, default: None\n clip_skip: int, default: None\n seed: SeedType, default: None\n pag_scale: float, default: None\n pag_adaptive_scale: float, default: None\n scheduler: Literal[ddim, ddpm, ddpm_wuerstchen, deis_multistep, dpm_cogvideox, dpmsolver_multistep, dpmsolver_multistep_karras, dpmsolver_sde, dpmsolver_sde_multistep, dpmsolver_sde_multistep_karras, dpmsolver_singlestep, dpmsolver_singlestep_karras, edm_dpmsolver_multistep, edm_euler, euler_ancestral_discrete, euler_discrete, euler_discrete_karras, flow_match_euler_discrete, flow_match_heun_discrete, heun_discrete, ipndm, k_dpm_2_ancestral_discrete, k_dpm_2_ancestral_discrete_karras, k_dpm_2_discrete, k_dpm_2_discrete_karras, lcm, lms_discrete, lms_discrete_karras, pndm, tcd, unipc], default: None\n output_format: Literal[png, jpeg, float, int, latent], default: png\n output_upload: bool, default: False\n highres_fix_factor: float, default: 1.0\n highres_fix_strength: float, default: None\n spatial_prompts: SpatialPromptInputType, default: None\nReturns:\n ImageResultType\n```\n\n### Invoking Tasks\n\nRun `taproot invoke` to run any task from the command line. All parameters to the task can be passed as flags to the call using kebab-case, e.g.:\n\n```sh\ntaproot invoke image-generation:stable-diffusion-xl \\\n --prompt \"a photograph of a golden retriever at the park\" \\\n --negative-prompt \"fall, autumn, blurry, out-of-focus\" \\\n --seed 12345\nLoading task.\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:03<00:00, 2.27it/s]\nTask loaded in 4.0 s.\nInvoking task.\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 20/20 [00:04<00:00, 4.34it/s]\nTask invoked in 6.5 s. Result:\n8940aa12-66a7-4233-bfd6-f19da339b71b.png\n```\n\n## Python\n\n### Direct Task Usage\n\n```py\nfrom taproot import Task\nsdxl = Task.get(\"image-generation\", \"stable-diffusion-xl\")\npipeline = sdxl()\npipeline.load() # Uses GPU 0 when available\npipeline(prompt=\"Hello, world!\").save(\"./output.png\")\n```\n\n### With a Remote Server\n\n```py\nimport asyncio\nfrom taproot import Tap\n\nasync def main() -> None:\n tap = Tap()\n tap.remote_address = \"ws://127.0.0.1:32189\"\n result = await tap(\"image-generation\", model=\"stable-diffusion-xl\", prompt=\"Hello, world!\")\n result.save(\"./output.png\")\n\nasyncio.run(main)\n```\n\n### With a Local Server\n\nAlso shows usage with `uvloop`.\n\n```py\nimport uvloop\nfrom taproot import Tap\n\nasync def main() -> None:\n async with Tap.local() as tap:\n # Taproot is now running on ws://127.0.0.1:32189 with a local dispatcher\n result = await tap(\"speech-synthesis\", model=\"kokoro\", text=\"Hello, world!\")\n result.save(\"./output.wav\")\n\nuvloop.run(main)\n```\n\n## Running Servers\n\nTaproot uses a three-roled cluster structure:\n1. **Overseers** are entry points into clusters, routing requests to one or more dispatchers.\n2. **Dispatchers** are machines capable of running tasks by spawning executors.\n3. **Executors** are servers ready to execute a task.\n\nThe simplest way to run a server is to run an overseer simultaneously with a local dispatcher like so:\n\n```sh\ntaproot overseer --local\n```\n\nThis will run on the default address of `ws://127.0.0.1:32189`, suitable for interaction from python or the browser.\n\nThere are many deployment possibilities across networks, with configuration available for encryption, listening addresses, and more. See the wiki for details (coming soon.)\n\n## Outside Python\n\n- [taproot.js](https://github.com/painebenjamin/taproot.js) - for the browser and node.js, available in ESM, UMD and IIFE\n- taproot.php - coming soon\n\n\n",
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