f44034021e
* ⬆️ Update leejet/stable-diffusion.cpp Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> * fix(stablediffusion-ggml): adapt gosd.cpp to upstream sd_ctx_params_t API The bump to 5a34bc7 restructured sd_ctx_params_t: the boolean CPU-offload knobs (offload_params_to_cpu, keep_clip_on_cpu, keep_vae_on_cpu, keep_control_net_on_cpu) were replaced by backend assignment specs (backend/params_backend), and vae_decode_only / free_params_immediately were dropped entirely. The build broke with "no member named ..." on every arch. Translate the legacy options we still accept from gallery configs into the new backend assignment specs, mirroring prepare_backend_assignments() in the upstream CLI, so offload_params_to_cpu / keep_*_on_cpu keep working. vae_decode_only is parsed and ignored for config compatibility. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(stablediffusion-ggml): expose backend/params placement options The upstream bump introduced new sd_ctx_params_t fields for device and memory placement (backend, params_backend, rpc_servers, max_vram, stream_layers) plus PuLID-Flux weights (pulid_weights_path). Wire them up as backend options so models can be split across CPU/GPU/disk/RPC: - backend: per-component compute placement (e.g. clip=cpu,vae=cuda0) - params_backend: per-component weight storage incl. disk mmap - max_vram / stream_layers: graph-cut segmented parameter offload budget - rpc_servers: offload compute to remote RPC servers - pulid_weights_path: PuLID-Flux identity injection The legacy keep_*_on_cpu / offload_params_to_cpu booleans now seed and compose with the explicit backend/params_backend specs, matching upstream prepare_backend_assignments(). Option values are taken as everything after the first ':' so colon-bearing values (rpc_servers host:port) survive parsing. Documented the new options in the image-generation guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(stablediffusion-ggml): distributed RPC across ggml workers Enable the ggml RPC backend (-DSD_RPC=ON) so image generation can be sharded across remote rpc-server workers. The ggml rpc-server is backend-agnostic, so this reuses the exact same worker pool as the llama.cpp backend - one set of `local-ai worker llama-cpp-rpc` / `p2p-llama-cpp-rpc` workers accelerates both text and image generation. RPC servers are selected by precedence: - the explicit `rpc_servers` option, else - the LLAMACPP_GRPC_SERVERS env var, which LocalAI's p2p worker mode populates automatically with discovered workers (the backend inherits it from the parent process env), so distributed image generation needs no per-model configuration. Documented manual and p2p setup in the image-generation guide. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
LocalAI website
LocalAI documentation website
Requirement
In this project, the Docsy theme component is pulled in as a Hugo module, together with other module dependencies:
$ hugo mod graph
hugo: collected modules in 566 ms
hugo: collected modules in 578 ms
github.com/google/docsy-example github.com/google/docsy@v0.5.1-0.20221017155306-99eacb09ffb0
github.com/google/docsy-example github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/twbs/bootstrap@v4.6.2+incompatible
github.com/google/docsy/dependencies@v0.5.1-0.20221014161617-be5da07ecff1 github.com/FortAwesome/Font-Awesome@v0.0.0-20220831210243-d3a7818c253f
If you want to do SCSS edits and want to publish these, you need to install PostCSS
npm install
Running the website locally
Building and running the site locally requires a recent extended version of Hugo.
You can find out more about how to install Hugo for your environment in our
Getting started guide.
Once you've made your working copy of the site repo, from the repo root folder, run:
hugo server
Running a container locally
You can run docsy-example inside a Docker
container, the container runs with a volume bound to the docsy-example
folder. This approach doesn't require you to install any dependencies other
than Docker Desktop on
Windows and Mac, and Docker Compose
on Linux.
-
Build the docker image
docker-compose build -
Run the built image
docker-compose upNOTE: You can run both commands at once with
docker-compose up --build. -
Verify that the service is working.
Open your web browser and type
http://localhost:1313in your navigation bar, This opens a local instance of the docsy-example homepage. You can now make changes to the docsy example and those changes will immediately show up in your browser after you save.
Cleanup
To stop Docker Compose, on your terminal window, press Ctrl + C.
To remove the produced images run:
docker-compose rm
For more information see the Docker Compose documentation.
Troubleshooting
As you run the website locally, you may run into the following error:
➜ hugo server
INFO 2021/01/21 21:07:55 Using config file:
Building sites … INFO 2021/01/21 21:07:55 syncing static files to /
Built in 288 ms
Error: Error building site: TOCSS: failed to transform "scss/main.scss" (text/x-scss): resource "scss/scss/main.scss_9fadf33d895a46083cdd64396b57ef68" not found in file cache
This error occurs if you have not installed the extended version of Hugo. See this section of the user guide for instructions on how to install Hugo.
Or you may encounter the following error:
➜ hugo server
Error: failed to download modules: binary with name "go" not found
This error occurs if you have not installed the go programming language on your system.
See this section of the user guide for instructions on how to install go.