Backend
- Fix FLUX.2 klein family default base_repo: black-forest-labs/FLUX.2-klein
does not exist on the Hub. Point at the Apache 2.0 4B Base instead so
the from_pretrained call works out of the box for ungated users.
- Serialise concurrent load_model calls with a dedicated _load_lock so
two /images/load requests cannot both reach pipeline_cls.from_pretrained
at the same time (would double-spend VRAM and corrupt _pipe).
- When the caller passes a full diffusers repo (no gguf_filename),
use repo_id directly instead of silently substituting the family
default. Closes the load-the-wrong-model regression flagged by review.
- Drop negative_prompt from the pipeline call when the loaded pipeline
does not accept it (FLUX.2 / FLUX.2 klein). Inspect __call__ via
inspect.signature so we do not maintain a manual class list.
- Best-effort unload the chat backend (llama-server) before a diffusion
load so a 24 GB consumer GPU can swap between chat and diffusion
without manual unload steps.
Frontend
- Replace the four curated entries with the actual filenames published
on the Hub (lowercase flux-2-klein-Nb-Q4_K_S.gguf and flux2-dev*).
- Add an explicit base_repo per curated entry so the backend never
falls back to the family default for the curated picker.
- Add the Apache 2.0 FLUX.2 klein base 4B entry so first-time users
have an ungated, no-token-required default.
- Hide the negative prompt field for FLUX.2 / FLUX.2 klein and show a
small explanatory note instead.
Tests
- Add 6 new backend tests: base_repo override, full-repo (no GGUF)
no-substitution, concurrent serialise race, signature-based kwarg
filter, negative_prompt strip on FLUX.2, negative_prompt preserved
on supporting pipelines. 33 tests passing.
Backend
- core/inference/diffusion.py: DiffusionBackend singleton that loads
diffusion GGUFs from Hugging Face via diffusers.GGUFQuantizationConfig
and runs them on the active CUDA / MPS / CPU device. Supports FLUX.2,
FLUX.2 klein, FLUX.1, Qwen-Image, Stable Diffusion 3, and SDXL.
- routes/inference.py: POST /api/inference/images/load,
POST /api/inference/images/generate, POST /api/inference/images/unload,
GET /api/inference/images/status mirroring the llama-server lifecycle.
- models/inference.py: DiffusionLoadRequest, DiffusionGenerateRequest,
DiffusionGenerateResponse pydantic schemas with prompt / step / size
validation up front so callers get clear 422s rather than VAE crashes.
- requirements/no-torch-runtime.txt: pin gguf alongside the existing
diffusers entry so GGUFQuantizationConfig works out of the box.
- tests/test_diffusion_backend.py + tests/test_diffusion_routes.py:
27 unit tests covering family detection, validation, lifecycle, and
the full FastAPI round trip with the backend stubbed. No torch /
diffusers / GPU required to run.
Frontend
- features/images/: standalone images-page.tsx with curated model picker
(FLUX.2 klein 4B / 9B, FLUX.2 dev, FLUX.1 dev), HF token field,
prompt + negative prompt, resolution presets, steps + guidance
sliders, seed input, and a result gallery that renders base64 PNGs
inline.
- app/routes/images.tsx: lazy /images route wired into router.tsx.
- components/app-sidebar.tsx: PaintBrush02Icon nav item between
Recipes and Export, hidden in chat-only mode.
