horde-worker-reGen
The default client software to create images for the AI-Horde
Stars: 102
This repository provides the latest implementation for the AI Horde Worker, allowing users to utilize their graphics card(s) to generate, post-process, or analyze images for others. It offers a platform where users can create images and earn 'kudos' in return, granting priority for their own image generations. The repository includes important details for setup, recommendations for system configurations, instructions for installation on Windows and Linux, basic usage guidelines, and information on updating the AI Horde Worker. Users can also run the worker with multiple GPUs and receive notifications for updates through Discord. Additionally, the repository contains models that are licensed under the CreativeML OpenRAIL License.
README:
Welcome to the AI Horde, a free and open decentralized platform for collaborative AI! The AI Horde enables people from around the world to contribute their GPU power to generate images, text, and more. By running a worker on your local machine, you can earn kudos which give you priority when making your own requests to the horde.
A worker is a piece of software that handles jobs from the AI Horde, such as generating an image from a text prompt. When your worker successfully completes a job, you are rewarded with kudos. The more kudos you have, the faster your own requests will be processed.
You can read about kudos, the reward granted to you for running a worker, including some reasons for running a worker on our detailed kudos explanation.
- AI Horde Worker reGen
Before installing the worker:
- Register an account on the AI Horde website.
- Securely store the API key you receive. Treat this key like a password.
-
Install git for Windows if you haven't already.
-
Open PowerShell or Command Prompt.
-
Navigate to the folder where you want to install the worker:
cd C:\path\to\install\folder
-
Clone the repository:
git clone https://github.com/Haidra-Org/horde-worker-reGen.git cd horde-worker-reGen
- Download the zipped worker files.
- Extract to a folder of your choice.
Open a terminal and run:
git clone https://github.com/Haidra-Org/horde-worker-reGen.git
cd horde-worker-reGen
AMD support is experimental, and Linux-only for now:
- Use
horde-bridge-rocm.sh
andupdate-runtime-rocm.sh
in place of the standard versions. - WSL support is highly experimental.
- Join the AMD discussion on Discord if you're interested in trying.
Experimental Support for DirectML has been added. See Running on DirectML for more information and further instructions. You can now follow this guide using update-runtime-directml.cmd
and horde-bridge-directml.cmd
where appropriate. Please note that DirectML is several times slower than ANY other methods of running the worker.
- Copy
bridgeData_template.yaml
tobridgeData.yaml
. - Edit
bridgeData.yaml
following the instructions inside. - Set a unique
dreamer_name
- If the name is already taken, you'll get a "Wrong Credentials" error. The name must be unique across the entire horde network.
Tailor settings to your GPU, following these pointers:
-
24GB+ VRAM (e.g. 3090, 4090):
- queue_size: 1 # <32GB RAM: 0, 32GB: 1, >32GB: 2 - safety_on_gpu: true - high_memory_mode: true - high_performance_mode: true - unload_models_from_vram_often: false - max_threads: 1 # 2 is often viable for xx90 cards - post_process_job_overlap: true - queue_size: 2 # Set to 1 if max_threads: 2 - max_power: 64 # Reduce if max_threads: 2 - max_batch: 8 # Increase if max_threads: 1, decrease if max_threads: 2 - allow_sdxl_controlnet: true
-
12-16GB VRAM (e.g. 3080 Ti, 4070 Ti, 4080):
- queue_size: 1 # <32GB RAM: 0, 32GB: 1, >32GB: 2 - safety_on_gpu: true # Consider false if using Cascade/Flux - moderate_performance_mode: true - unload_models_from_vram_often: false - max_threads: 1 - max_power: 50 - max_batch: 4 # Or higher
-
8-10GB VRAM (e.g. 2080, 3060, 4060, 4060 Ti):
- queue_size: 1 # <32GB RAM: 0, 32GB: 1, >32GB: 2 - safety_on_gpu: false - max_threads: 1 - max_power: 32 # No higher - max_batch: 4 # No higher - allow_post_processing: false # If using SDXL/Flux, else can be true - allow_sdxl_controlnet: false
- Also minimize other VRAM-consuming apps while the worker runs.
-
Lower-end GPUs / Under-performing Workers:
-
extra_slow_worker: true
gives more time per job, but users must opt-in. Only use if <0.3 MPS/S or <3000 kudos/hr consistently with correct config. -
limit_max_steps: true
caps total steps per job based on model. -
preload_timeout: 120
allows longer model load times. Avoid misusing to prevent kudos loss or maintenance mode.
-
-
Systems with less than 32GB of System RAM:
- Be sure to only run SD15 models and queue_size: 0.
- Set
load_large_models: false
- To your
models_to_skip
addALL SDXL
,ALL SD21
, and the 'unpruned' models (see config) to prevent running out of memory
- Set
- Be sure to only run SD15 models and queue_size: 0.
- Use an SSD, especially for multiple models. HDDs should offer one model only, loading within 60s.
- Configure 8GB (preferably 16GB+) of swap space, even on Linux.
- Keep
threads
≤2 unless using a 48GB+ VRAM data center GPU. - Worker RAM usage scales with
queue_size
. Use 1 for <32GB RAM, and optimize further for <16GB. - SDXL needs ~9GB free RAM consistently (32GB+ total recommended).
- Flux and Stable Cascade need ~20GB free RAM consistently (48GB+ total recommended).
- Disable sleep/power-saving modes while the worker runs.
Note: The worker is resource-intensive. Avoid gaming or other heavy tasks while it runs. Turn it off or limit to small models at reduced settings if needed.
- Install the worker as described in the Installation section.
- Run
horde-bridge.cmd
(Windows) orhorde-bridge.sh
(Linux).-
AMD: Use
horde-bridge-rocm
versions.
-
AMD: Use
- Press
Ctrl+C
in the worker's terminal. - It will finish current jobs before exiting.
Watch the terminal for progress, completed jobs, kudos earned, stats, and errors.
Detailed logs are in the logs
directory:
-
bridge*.log
: All info-
bridge.log
is the main window -
bridge_n.log
is process-specific (n
is the process number)
-
-
trace*.log
: Errors and warnings only-
trace.log
is the main window -
trace_n.log
is process-specific
-
Future versions won't need multiple worker instances
For now, start a separate worker per GPU.
On Linux, specify the GPU for each instance:
CUDA_VISIBLE_DEVICES=0 ./horde-bridge.sh -n "Instance 1"
CUDA_VISIBLE_DEVICES=1 ./horde-bridge.sh -n "Instance 2"
Warning: High RAM (32-64GB+) is needed for multiple workers. queue_size
and max_threads
greatly impact RAM per worker.
The worker is constantly improving. Follow development and get update notifications in our Discord.
Script names below assume Windows (.cmd
) and NVIDIA. For Linux use .sh
, for AMD use -rocm
versions.
- Stop the worker with
Ctrl+C
. - Update the files:
- If you used
git clone
:- Open a terminal in the worker folder
- Run
git pull
- If you used the zip download:
- Delete the old
horde_worker_regen
folder - Download the latest zip
- Extract to the original location, overwriting existing files
- Delete the old
- If you used
- Continue with Updating the Runtime below.
Warning: Some antivirus software (e.g. Avast) may interfere with the update. If you get
CRYPT_E_NO_REVOCATION_CHECK
errors, disable antivirus, retry, then re-enable.
- Run
update-runtime
for your OS to update dependencies.- Not all updates require this, but run it if unsure
- Advanced users: see README_advanced.md for manual options
- Start the worker again
Serving custom models not in our reference requires the customizer
role. Request it on Discord.
With the role:
-
Download your model files locally.
-
Reference them in
bridgeData.yaml
:custom_models: - name: My Custom Model baseline: stable_diffusion_xl filepath: /path/to/model/file.safetensors
-
Add the model
name
to yourmodels_to_load
list.
If set up correctly, custom_models.json
will appear in the worker directory on startup.
Notes:
- Custom model names can't match our existing model names
- The horde will treat them as SD 1.5 for kudos rewards and safety checks
Docker images are at https://hub.docker.com/r/tazlin/horde-worker-regen/tags.
Detailed guide: Dockerfiles/README.md
Manual worker setup: README_advanced.md
Check the #local-workers Discord channel for the latest info and community support.
Common issues and fixes:
- Download failures: Check disk space and internet connection.
-
Job timeouts:
- Remove large models (Flux, Cascade, SDXL)
- Lower
max_power
- Disable
allow_post_processing
,allow_controlnet
,allow_sdxl_controlnet
, and/orallow_lora
-
Out of memory: Decrease
max_threads
,max_batch
, orqueue_size
to reduce VRAM/RAM use. Close other intensive programs. - I have less kudos than I expect: As a new user, 50% of your job reward kudos and 100% of uptime kudos are held in escrow until you become trusted after ~1 week of worker uptime. You'll then receive the escrowed kudos and earn full rewards immediately going forward.
-
My worker is in maintenance mode: You can log into artbot here and use the manage workers page with the worker on and click "unpause" to take your worker out of maintenance mode.
- Note: Workers are put into maintenance mode automatically by the server when the worker is failing to perform fast enough or if it is reporting that it failed too many jobs. You should investigate the logs (search for "ERROR") to see what led to the issue. You can also open an issue or ask in the #local-workers channel in our Discord.
Open an issue to report bugs or request features. We appreciate your help!
Many bundled models use the CreativeML OpenRAIL License. Please review it before use.
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