User will experiment with Vulkan environment variables from launching script.
Keep only CUDA_VISIBLE_DEVICES setting for now.

For stable-diffusion-cpp-python:
- GGML_VK_VISIBLE_DEVICES=
- GGML_VULKAN_DEVICE=

For llama-cpp-python (additional):
- VK_ICD_FILENAMES=/dev/null
- VK_DRIVER_FILES=/dev/null
- VK_LOADER_DRIVERS_DISABLE=*
- VK_LOADER_LAYERS_DISABLE=~all~

All variables are restored on cleanup for subsequent Vulkan models.

- Use VK_ICD_FILENAMES=/dev/null to disable Vulkan ICD and force CUDA
- This is the correct variable for llama.cpp to disable Vulkan
- Restore VK_ICD_FILENAMES on cleanup for subsequent Vulkan models

- Set GGML_DISABLE_VULKAN=1 and GGML_VULKAN_DEVICE='' before loading model
- These must be set before llama_cpp import since it reads them at init
- Restore Vulkan settings on cleanup so subsequent Vulkan models work
- Addresses issue where GGUF models ran on CPU instead of CUDA with --backend nvidia

- Make cleanup() method handle StableDiffusion and other non-ModelManager objects
- Add try-except in on-demand swap to handle cleanup failures gracefully
- Check if cleanup method exists before calling

- Add model_backend_types dict to track backend for each model
- Update set_default_model to accept backend_type parameter
- Modify get_model_for_request to swap models on-demand when in ondemand mode
- Unload current model from VRAM and load new model when request arrives for different model
- Respect --backend flag when loading models on-demand
- Only activates when no --loadall or --loadswap flag is specified

- Add VK_ICD_FILENAMES=/dev/null to disable Vulkan during startup preload
- Previously only set at request time, causing crash during --loadall
- Added check in both startup preload locations (lines ~5254 and ~5811)
- Checks --backend and --image-backend to determine CUDA usage

When --backend nvidia is used, set VK_ICD_FILENAMES=/dev/null
to completely disable Vulkan and force CUDA-only mode for sd.cpp

Print model capabilities (text, image-to-text, image, etc.)
after successful model loading in both NvidiaBackend and VulkanBackend

- Add ModelCapabilities dataclass to represent model capabilities
- Add detect_model_capabilities() function to detect:
  - text_generation (LLM)
  - vision (image understanding)
  - image_generation (Stable Diffusion)
  - speech_to_text (whisper)
  - text_to_speech (TTS)
- Use capability detection for better error messages in image generation endpoint

- Show 'cuda (via llama-cpp-python)' when force_cuda is enabled
- Show original backend in GGUF detection message

GGUF models are for text/LLM and cannot do image generation.

diffusers is required for Stable Diffusion image generation

The package is named 'stable_diffusion_cpp_python', not 'stable_diffusion_cpp'

If --image-model is not specified, try to use the main --model as
the image model fallback when requesting 'default' model.

If loading a cached GGUF model fails with corruption indicators
(invalid, corrupt, magic, header), delete the corrupted cache and
re-download the model automatically.

The cache filename format was inconsistent:
- get_cached_model_path used: {hash}{ext}
- load_model download used: {hash}_{filename}

This caused cache lookups to always fail. Now both use {hash}_{filename}
format to ensure cached models are properly found.

Change from detailed installation instructions to simple message:
'Model does not support image generation'

- If request specifies a model, use that
- If request doesn't specify a model (empty or 'image'), use default
- Legacy 'image:' prefix also falls back to default
- Error handling already exists for when no backend is available

Change message from 'VulkanBackend will use CUDA backend' to
'GGUF model will use CUDA backend (forced by --backend nvidia)'

When user explicitly passes --backend nvidia with a GGUF model,
vulkan is now removed from the available backends list since
llama-cpp-python will use CUDA instead of Vulkan.

- When user specifies --backend nvidia with a GGUF model, show a note
  indicating that the vulkan backend will use CUDA
- This clarifies that Vulkan isn't being used in this scenario

- Add 'all' as a valid backend option
- Change default from 'nvidia' to 'all'
- Add comprehensive 'all' backend section that installs:
  - Base requirements
  - PyTorch with CUDA (nvidia backend)
  - llama-cpp-python with CUDA and Vulkan support
  - stable-diffusion-cpp-python with OpenCL
  - Additional requirements
- Detect available hardware (CUDA, Vulkan, OpenCL) and enable accordingly
- Show summary of available backends after installation

- Store original backend before switching to vulkan for GGUF files
- Pass original_backend to VulkanBackend constructor
- Add force_cuda flag that triggers CUDA environment setup
- Set CUDA_VISIBLE_DEVICES when force_cuda is True
- Update success/error messages to reflect actual backend used
- Add debug output for CUDA detection

The local import of os inside the HTTPS block caused Python to treat os as a local variable throughout the main() function.

- Add python-multipart to requirements.txt, requirements-nvidia.txt, requirements-vulkan.txt
- Add llama-cpp-python to requirements-nvidia.txt for GGUF support
- When using CUDA/nvidia backend with GGUF file, automatically use llama-cpp-python

- Add --vision-model for image/video-to-text models
- When --file-path is set, return URL by default, base64 only if explicitly requested
- Add --https flag with auto-certificate generation
- Add --privkey and --pubkey for custom certificates

- Add --image-cfg-scale CLI option (default 1.0)
- Add get_cfg_scale() helper that auto-detects VRAM
- If Vulkan and VRAM < 16GB, use cfg_scale=1.0 automatically

- Add --image-backend, --audio-backend, --tts-backend CLI args
- Add opencl to backend choices
- Add OpenCL build target in build.sh

- When --debug is enabled, show full command line coderai was called with
- Add --nopreload flag to disable model preloading at startup
- When --nopreload is not specified, skip checking for preloaded sd.cpp models (forces load in worker thread to avoid Vulkan context issues)
- Fix image model preloading to respect --nopreload flag