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4 Commits
Author SHA1 Message Date
Claude 38e80f8fb8 Add BEN2 and BiRefNet-HR as selectable Remove Background models 
BEN2 becomes the new default local backend (clean cutouts, strong on
hair/fur edges), with BiRefNet-HR available as a high-res/print
alternate and U2Net kept as the lightweight fallback. Both are
MIT-licensed and download weights from HuggingFace on first use
(cached via the existing hf_cache bind mount), unlike U2Net/SAM which
need an explicit download script.

- config: new BG_REMOVAL_MODEL setting (default "ben2")
- tools.py: remove-background-base64 now tries local backends in
  order (request.model override > BG_REMOVAL_MODEL > ben2/u2net),
  falling back to rembg's birefnet-general session as a last resort
- requirements.gpu.txt / Dockerfile.gpu: add ben2 + transformers deps
  needed for the new backends, with a build-time smoke test for ben2
- frontend: model dropdown in the Remove Background dialog, threaded
  through api.js to the new request field

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ro4PwQKvSc3CH19LSN21Ht
 2026-06-18 14:20:24 +00:00
Claude 59b26b6d5e Fix diffusers/torch compat, SAM docs, and SelectionActions UX 
- Pin diffusers to >=0.28.0,<0.29.0 to avoid AttributeError on torch.xpu
  (diffusers 0.29+ requires PyTorch 2.4 but base image ships 2.1.2)
- Pin transformers to <4.40.0 to match
- README: add SAM offline download troubleshooting for DNS-blocked containers
- SelectionActions panel: 'Selection ready' title + subtitle makes clear
  nothing has fired yet; AI actions show inline description (not just tooltip);
  section labels 'AI Actions' / 'Classic Tools'; scale hint updates live;
  Enter key submits AI edit prompt; _actionCard hover border for clickability

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
 2026-06-13 23:53:21 +00:00
Claude fe4d911a00 Dynamic GPU capability detection: probe CC, VRAM, feature flags, pick best model 
Replaces fixed tier table with real hardware probing and dynamic model selection.

gpu_detect.py — complete rewrite:
- Reads torch.cuda.get_device_properties + mem_get_info for actual free VRAM
- Detects: fp16 (CC≥6.0), bf16 (CC≥8.0), fp8 (CC≥8.9 Ada/Hopper),
           int8 (CC≥7.0), tensor_cores (CC≥7.0), xformers presence
- Pre-Pascal (CC<6.0): effective_vram halved (fp32 weights are 2× larger)
- Subtracts 400MB driver overhead from free VRAM before model selection
- _select_txt2img / _select_inpaint / _select_img2img / _select_upscale:
    eff≥20GB  → FLUX.1-schnell (no offload)
    eff≥10GB  → FLUX.1-schnell (model_cpu_offload)
    eff≥7.5GB → SDXL
    eff≥5.5GB → SDXL + attention_slicing
    eff≥3.5GB → SD 2.1
    eff≥2.5GB → SD 2.1-base + attention_slicing
    eff≥1.7GB → SD 1.5
    else      → SD 1.5 + sequential_cpu_offload
- ModelSpec carries: model_id, family, memory_opt, native_res, vram_fp16_gb
- Warnings: old CC, pre-Pascal fp32, fp8 upgrade hint, xformers install tip
- Compatibility shim get_model_ids() retained for existing callers
- infer_spec_from_model_id() auto-detects family from HF_MODEL_* overrides

local_diffusion.py — refactored to use ModelSpec:
- Reads spec from GpuCapabilities.recommended[op] instead of tier table
- FLUX.1-schnell: FluxPipeline / FluxImg2ImgPipeline, 4 steps, guidance=0.0
- SD families: family-aware pipeline class selection (sd15/sd2x/sdxl)
- Memory opts applied per ModelSpec.memory_opt field
- xformers attention enabled automatically when xformers detected

gpu_status.py — richer response:
- Exposes all feature flags (fp16/bf16/fp8/int8/tensor_cores/xformers)
- Returns full ModelSpec per operation (model_id, family, memory_opt, native_res)

ai_tools.py — /api/config exposes:
- gpu_vram_total, gpu_vram_free, gpu_cc, gpu_fp16, gpu_bf16, gpu_fp8,
  gpu_tensor_cores, gpu_eff_vram, local_gpu_warnings

requirements.gpu.txt:
- diffusers bumped to >=0.29.0 (FLUX pipeline added in 0.29)
- transformers bumped to >=4.40.0
- sentencepiece added (FLUX T5 tokenizer)

scripts/gpu_setup.py:
- Prints full model table at startup (op → model_id, family, memory_opt, res)
- Shows all feature flags in one line

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
 2026-06-13 15:35:37 +00:00
Claude 8fe8498df2 Add local GPU inference: auto-detect GPU, auto-download best diffusion models 
Adds AI_PROVIDER=local_gpu — a fully self-contained GPU inference engine
using HuggingFace Diffusers that requires zero InvokeAI/ComfyUI setup.
All existing providers (InvokeAI, ComfyUI, OpenAI, Replicate) remain intact
and can be mixed with local GPU via per-operation overrides.

New features:
- GPU auto-detection (CUDA/NVIDIA, MPS/Apple Silicon, CPU fallback)
- VRAM-tiered model selection:
    ultra ≥16 GB → SDXL inpaint + SDXL base
    high  8-16 GB → SDXL inpaint + SDXL base
    medium 4-8 GB → SD 2.x inpaint + SD 2.1
    low  <4 GB   → SD 2.x (small)
- Auto-download model weights to HuggingFace disk cache at startup
  (background task; first request loads from local disk, not internet)
- LRU pipeline cache evicts oldest GPU pipeline when VRAM limit reached
- Per-operation model overrides via HF_MODEL_INPAINT / HF_MODEL_TXT2IMG etc.
- Optional HF_TOKEN for gated/private HuggingFace models

New files:
- backend/app/services/gpu_detect.py   — GPU detection + tier/model mapping
- backend/app/services/local_diffusion.py — Diffusers provider + LRU cache
- backend/app/routers/gpu_status.py    — GET /api/gpu/status, POST /api/gpu/prefetch
- backend/requirements.gpu.txt         — Diffusers ecosystem deps (GPU only)
- docker-compose.gpu.yml               — NVIDIA GPU compose (one-command startup)
- Dockerfile.gpu                       — pytorch/pytorch:2.1.0-cuda12.1 base image
- scripts/gpu_setup.py                 — Startup GPU info logger

Modified:
- backend/app/config.py                — local_gpu settings added
- backend/app/services/remote_provider.py — local_gpu registered as provider
- backend/app/routers/ai_tools.py      — /api/config exposes GPU tier + caps
- backend/app/main.py                  — GPU router + background prefetch task
- backend/entrypoint.sh                — runs gpu_setup.py at container start
- .env.example                         — local_gpu documented as first option

Quick start with GPU:
  docker compose -f docker-compose.gpu.yml up --build

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM
 2026-06-13 15:08:41 +00:00