Files
LocalAI/core/config/model_capabilities_test.go
Richard Palethorpe 9058a2bb46 feat: Add 3d generation UI/API and trellis2cpp backend (#10979)
* feat(3d): add Generate3D RPC, FLAG_3D capability, and /v1/3d/generations endpoint

Adds the plumbing for image-conditioned 3D asset generation (binary
glTF / GLB output), modeled on the video generation path:

- backend.proto: Generate3D RPC + Generate3DRequest (staged image src,
  glb dst, seed/step/cfg_scale/texture_steps, quality and background
  enums, params map for backend-specific extras)
- pkg/grpc: thread Generate3D through client, server, embed, base and
  the backend interfaces; connection-evicting and distributed-node
  wrappers (in-flight tracking + file staging) included
- core/config: FLAG_3D usecase (guessed only for the trellis2cpp
  backend), '3d' canonical usecase string mapped to the Generate3D
  method, and a '3d' output modality
- REST: POST /v1/3d/generations (+ unversioned alias) returning
  OpenAIResponse with a /generated-3d URL or b64_json; conditioning
  image accepted as URL, base64, or data URI; quality/background
  validated at the edge; .glb served as model/gltf-binary
- auth: '3d' route feature (default ON); /api/instructions entry

Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(trellis2cpp): add the trellis2.cpp image-to-3D backend

Wraps localai-org/trellis2cpp (C++/GGML port of Microsoft TRELLIS.2,
pbr-textures branch) as a Go+purego backend, following the
stablediffusion-ggml pattern:

- backend/go/trellis2cpp: purego bindings to the flat C ABI (v9,
  asserted at startup), eager pipeline load with model-set validation
  (refuses non-trellis GGUFs; degrades coarse/geometry-only/textured
  exactly like the upstream demo), Generate3D via t2_generate +
  t2_bake_glb writing a binary glTF to dst. Weight-free unit tests
  cover resolution/validation/param mapping — CI never downloads the
  multi-GB GGUF set or runs inference.
- CPU SIMD variants build into per-variant directories (the shared
  libggml sonames collide across variants, unlike sd-ggml's flat
  renamed-.so scheme); run.sh picks one via /proc/cpuinfo.
- CI wiring: backend-matrix entries (cpu, cuda12/13, vulkan
  amd64+arm64, l4t, l4t-cuda13, darwin metal), index.yaml meta +
  latest/master image entries, bump_deps tracking of the pbr-textures
  branch, changed-backends.js mapping, top-level Makefile targets.
- Importer: auto-detects trellis GGUF repos/URIs (registered before
  llama-cpp so the .gguf match isn't stolen) and expands any trellis
  URI to the full 10-file component set spanning the three LocalAI-io
  HF repos.
- Gallery: trellis2-4b (full PBR + 1024 cascade) and
  trellis2-4b-geometry (512 untextured) with verified sha256s.

Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(ui): 3D generation page with native GLB viewer and IndexedDB history

Adds a Studio tab + /app/3d page for the new image-to-3D endpoint:

- GlbViewer ports the trellis2cpp demo's dependency-free WebGL2
  renderer (quaternion trackball, metallic-roughness PBR, ACES,
  hidden-line wireframe with a bounded index budget) and pairs it with
  a minimal GLB parser for the two forms t2_bake_glb emits — dense
  vertex-PBR (linear COLOR_0 + _METALLIC_ROUGHNESS, uploaded as
  normalized integers) and the opt-in UV-atlas textured form. Parsing
  happens before any GL so stats and errors render without WebGL2.
- use3DHistory stores past generations (params, input thumbnail, and
  the GLB blob itself) in IndexedDB with keep-newest-20 eviction —
  GLBs are multi-MB binaries localStorage can't hold — and the page
  offers a download button for the active GLB.
- Wiring: CAP_3D capability constant (FLAG_3D — the exact string
  /api/models/capabilities serves), threeDApi, router entries, Studio
  tab, vite dev proxy, en locale keys.
- e2e: render-smoke entry plus a focused spec that feeds a real
  one-triangle vertex-PBR GLB through the parser/viewer and exercises
  IndexedDB persistence, selection, deletion, and API errors.

Assisted-by: Claude:claude-fable-5 [Claude Code]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* fix(3d): address API correctness and UX issues

Keep 3D generation on the LocalAI-specific /3d/generations route and ensure authentication and permissions cover it.

Propagate distributed transfer failures, publish a portable ARM64 backend image, honor importer overrides, and align discovery, upload validation, and touch controls.

Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* feat(3d): add previewable print remeshing

Add a single-detail CGAL Alpha Wrap workflow for existing Trellis GLBs, including PBR reprojection, API documentation, tracing, and an in-browser preview before download.

Allow the remesh route to enforce its 512 MiB upload cap independently of the smaller global default so generated high-resolution meshes can be processed.

Assisted-by: Codex:gpt-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* build(trellis2cpp): centralize remesh dependency pins

Assisted-by: Codex:GPT-5 [apply_patch] [exec_command]
Signed-off-by: Richard Palethorpe <io@richiejp.com>

* fix(kokoros): implement Generate3D stub for new proto RPC

The Generate3D RPC added to backend.proto for the trellis2cpp backend
made tonic's generated Backend trait require generate3_d, breaking the
kokoros-grpc build. Return unimplemented like the other unsupported
modalities.

Assisted-by: Claude Code:claude-fable-5
Signed-off-by: Richard Palethorpe <io@richiejp.com>

---------

Signed-off-by: Richard Palethorpe <io@richiejp.com>
Co-authored-by: localai-org-maint-bot <bot-opensource@localaisrl.com>
2026-07-29 16:15:04 +02:00

150 lines
6.4 KiB
Go

package config
import (
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func usecaseBits(flags ModelConfigUsecase) *ModelConfigUsecase {
return &flags
}
var _ = Describe("Model capabilities derivation", func() {
Describe("VisionSupported", func() {
It("is false for a plain text chat model", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
Expect(cfg.VisionSupported()).To(BeFalse())
})
It("is true when the FLAG_VISION bit is declared", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT | FLAG_VISION), Backend: "llama.cpp"}
Expect(cfg.VisionSupported()).To(BeTrue())
})
It("is true when image input is declared explicitly", func() {
cfg := &ModelConfig{
KnownUsecases: usecaseBits(FLAG_CHAT),
KnownInputModalities: []string{ModalityText, ModalityImage},
}
Expect(cfg.VisionSupported()).To(BeTrue())
})
It("is true when an mmproj projector is set", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
cfg.MMProj = "mmproj.gguf" // promoted field from the embedded options struct
Expect(cfg.VisionSupported()).To(BeTrue())
})
It("does not fall for the GuessUsecases FLAG_VISION false positive", func() {
// A chat model with a chat template would make HasUsecases(FLAG_VISION)
// return true via the guess heuristic; VisionSupported must not.
cfg := &ModelConfig{Backend: "llama.cpp"}
cfg.TemplateConfig.Chat = "{{.Input}}"
Expect(cfg.VisionSupported()).To(BeFalse())
})
})
Describe("AudioInputSupported / VideoInputSupported", func() {
It("honors explicit model modality declarations", func() {
cfg := &ModelConfig{
KnownInputModalities: []string{ModalityAudio, ModalityVideo},
}
Expect(cfg.AudioInputSupported()).To(BeTrue())
Expect(cfg.VideoInputSupported()).To(BeTrue())
})
It("detects vLLM omni audio input via limit_mm_per_prompt", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
cfg.LimitMMPerPrompt.LimitAudioPerPrompt = 1
Expect(cfg.AudioInputSupported()).To(BeTrue())
Expect(cfg.VideoInputSupported()).To(BeFalse())
})
It("detects vLLM omni video input via limit_mm_per_prompt", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
cfg.LimitMMPerPrompt.LimitVideoPerPrompt = 2
Expect(cfg.VideoInputSupported()).To(BeTrue())
})
})
Describe("Capabilities + modalities", func() {
It("a text-only chat model exposes chat and text-only modalities", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "llama.cpp"}
Expect(cfg.Capabilities()).To(ContainElement(UsecaseChat))
Expect(cfg.Capabilities()).NotTo(ContainElement(UsecaseVision))
Expect(cfg.Capabilities()).NotTo(ContainElement(UsecaseTranscript))
Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
})
It("a vision chat model accepts text+image input", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT | FLAG_VISION), Backend: "llama.cpp"}
Expect(cfg.Capabilities()).To(ContainElements(UsecaseChat, UsecaseVision))
Expect(cfg.InputModalities()).To(Equal([]string{"text", "image"}))
Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
})
It("an omni chat model accepts text+audio input without an audio capability flag", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_CHAT), Backend: "vllm"}
cfg.LimitMMPerPrompt.LimitAudioPerPrompt = 1
// audio-in is a modality, not a usecase string — this is exactly the
// case a plain capability list cannot express.
Expect(cfg.Capabilities()).To(ContainElement(UsecaseChat))
Expect(cfg.InputModalities()).To(Equal([]string{"text", "audio"}))
})
It("a transcription model reads audio and writes text", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_TRANSCRIPT), Backend: "parakeet-cpp"}
Expect(cfg.Capabilities()).To(Equal([]string{UsecaseTranscript}))
Expect(cfg.InputModalities()).To(Equal([]string{"audio"}))
Expect(cfg.OutputModalities()).To(Equal([]string{"text"}))
})
It("an image-generation model reads text and writes an image", func() {
// stablediffusion-ggml is image-only; plain "stablediffusion" is also
// in GuessUsecases' video-backend list, so it would report video too.
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_IMAGE), Backend: "stablediffusion-ggml"}
Expect(cfg.Capabilities()).To(Equal([]string{UsecaseImage}))
Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
Expect(cfg.OutputModalities()).To(Equal([]string{"image"}))
})
It("guesses the 3d usecase from the trellis2cpp backend and only that backend", func() {
cfg := &ModelConfig{Backend: "trellis2cpp"}
Expect(cfg.HasUsecases(FLAG_3D)).To(BeTrue())
Expect(cfg.Capabilities()).To(ContainElement(Usecase3D))
other := &ModelConfig{Backend: "llama-cpp"}
Expect(other.HasUsecases(FLAG_3D)).To(BeFalse())
})
It("a 3D-generation model reads an image and writes a 3D asset", func() {
// Pins the wire strings the UI depends on: capability "3d",
// input modality "image" (no text prompt — TRELLIS.2 is
// image-conditioned only), output modality "3d".
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_3D), Backend: "trellis2cpp"}
Expect(cfg.Capabilities()).To(Equal([]string{Usecase3D}))
Expect(cfg.InputModalities()).To(Equal([]string{ModalityImage}))
Expect(cfg.OutputModalities()).To(Equal([]string{Modality3D}))
})
It("conditioned video uses declared modalities without backend-specific inference", func() {
cfg := &ModelConfig{
KnownUsecases: usecaseBits(FLAG_VIDEO),
KnownInputModalities: []string{ModalityAudio, ModalityImage, ModalityText, ModalityAudio, "unknown"},
KnownOutputModalities: []string{ModalityVideo},
}
Expect(cfg.Capabilities()).To(Equal([]string{UsecaseVideo}))
Expect(cfg.InputModalities()).To(Equal([]string{ModalityText, ModalityImage, ModalityAudio}))
Expect(cfg.OutputModalities()).To(Equal([]string{ModalityVideo}))
})
It("a TTS model reads text and writes audio", func() {
cfg := &ModelConfig{KnownUsecases: usecaseBits(FLAG_TTS), Backend: "piper"}
Expect(cfg.Capabilities()).To(ContainElement(UsecaseTTS))
Expect(cfg.InputModalities()).To(Equal([]string{"text"}))
Expect(cfg.OutputModalities()).To(Equal([]string{"audio"}))
})
})
})