Handle old/low-VRAM GPUs and document nvidia-container-toolkit requirement

GPU tier table extended:
  ultra   ≥16 GB → SDXL (unchanged)
  high    8-16 GB → SDXL (unchanged)
  medium  4-8 GB → SD 2.x (unchanged)
  legacy  2-4 GB → SD 1.5 (~1.7 GB fp16)  ← new: GTX 970/1060/RX 580 etc.
  minimal <2 GB  → SD 1.5 + sequential CPU offload  ← new: very old/integrated GPUs

gpu_detect.py:
- Detects CUDA compute capability (CC); fp16 disabled for CC < 6.0 (pre-Pascal)
- GpuInfo gains compute_capability and warnings fields
- _make_warnings() emits human-readable warnings for low VRAM and old CC
- model tier fallback updated from 'low' to 'legacy'

local_diffusion.py:
- minimal/legacy tiers use enable_sequential_cpu_offload() + enable_attention_slicing(1)
- target resolution per tier: ultra/high=1024, medium=768, legacy/minimal=512
- .to(device) skipped when sequential CPU offload is active

gpu_status.py:
- Response now includes compute_capability and warnings

docker-compose.gpu.yml:
- Full nvidia-container-toolkit install instructions in header comment
- nvidia-docker2 (legacy) fallback documented as comment block inline
- AMD ROCm swap-in instructions added
- GPU tier table documented in header

scripts/gpu_setup.py:
- Prints compute capability, fp16 status, tier, and model selection at startup
- Prints per-tier warnings (old CC, low VRAM)

https://claude.ai/code/session_01WVDg7amsy1TTtxvpku7bcM

backend/app/routers/gpu_status.py

@@ -26,8 +26,10 @@ async def gpu_status():
         "backend": info.backend,
         "device_name": info.device_name,
         "vram_gb": info.vram_gb,
+        "compute_capability": info.compute_capability,
         "tier": info.tier,
         "fp16": info.fp16,
+        "warnings": info.warnings,
         "capabilities": info.capabilities,
         "models": {
             op: {"model_id": mid, "available": mid is not None}