* feat(api): add POST /v1/images/upscale endpoint
Add a new image upscaling endpoint that accepts a source image and
returns an upscaled version. Supports selectable upscaler models
(e.g. realesrgan) and a configurable scale factor (2x or 4x).
- backend.proto: add UpscaleImage RPC and UpscaleImageRequest message
- pkg/grpc: implement UpscaleImage in Backend interface, client, server
and embed shim
- core/backend/upscale.go: new backend helper (mirrors ImageGeneration)
- core/http/endpoints/openai/upscale.go: new multipart/form-data handler
- core/http/routes/openai.go: register POST /v1/images/upscale
- core/http/auth/features.go: gate upscale routes under FeatureImages
- backend/python/diffusers/backend.py: implement UpscaleImage — uses
diffusers upscale pipeline when loaded, falls back to Lanczos resize
* fix(grpc): add UpscaleImage stub to Base backend
All Go backends embedding Base now satisfy the AIModel interface
without needing to implement UpscaleImage explicitly.
* fix(images): complete upscale endpoint integration
Store generated upscales under the served images directory, validate scale factors, document and advertise the endpoint, and add a functional Stable Diffusion x4 gallery model.
Assisted-by: Codex:gpt-5
---------
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
GenerateImage hardcoded TilingParamsSetEnabled(vaep, false), so tiled VAE
decoding was unreachable from a model config even though all four upstream
setters were already bound in main.go.
Sampling runs in latent space, but the final VAE decode expands to full
resolution and needs one large compute buffer. At 1024x1024 that buffer
exceeds 8GB, which fails on two kinds of device: cards without the VRAM
for a full-frame decode, and drivers that cap a single allocation
regardless of how much memory is free. Mesa RADV reports a 4GiB
maxMemoryAllocationSize, so a Radeon 8060S with 74GiB of device-local
heap still cannot serve that decode:
[INFO ] sampling completed, taking 251.82s
[INFO ] decoding 1 latents
ggml_vulkan: Requested buffer size exceeds device buffer size limit:
ErrorOutOfDeviceMemory
[ERROR] vae: failed to allocate the compute buffer
[ERROR] decode_first_stage failed for latent 1
Every sampling step completes and then the run is discarded at the last
stage, so the whole generation is wasted.
Add three options, parsed in Load and applied per generation:
vae_tiling:true enable tiled decoding (bare flag also works)
vae_tile_size:512 tile size, or 512x384 for a rectangle
vae_tile_overlap:0.25 overlap between tiles
Tiling stays off unless requested, so existing models are unaffected. Tile
size and overlap only reach the library when the operator set them, which
keeps upstream's defaults rather than pushing a zero, and an unparseable
value is treated as absent for the same reason.
Truthy spellings match what load_model already accepts for its own bool
options, and the bare-flag form matches diffusion_model, so no new
convention is introduced.
Signed-off-by: Dimitris Karakasilis <dimitris@karakasilis.me>
The Hugo relearn theme does not provide an "alert" shortcode, so the
docs deploy failed at the Build site step:
failed to extract shortcode: template for shortcode "alert" not found
docs/content/features/image-generation.md:106
Convert the vae_decode_only note to the theme-supported notice shortcode
used everywhere else in the docs.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
* ⬆️ 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>
* feat(img2vid): Initial support for img2vid
* doc(SD): fix SDXL Example
* Minor fixups for img2vid
* docs(img2img): fix example curl call
* feat(txt2vid): initial support
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
* diffusers: be retro-compatible with CUDA settings
* docs(img2vid, txt2vid): examples
* Add notice on docs
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
