torch.enable_grad() cannot override torch.inference_mode() in older PyTorch.
Add inference_mode(False) as outer wrapper in both train_on and gradient.
Also .clone() tensors from buffer/input before grad tracking to avoid
inference_mode tensor view restrictions.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ComfyUI execution context may have torch.no_grad() active, which kills
gradient tracking needed for training and gradient computation.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
value_function.py: OnlineValueFunction MLP that trains incrementally on
(conditioning, reward) pairs. Uses replay buffer of 100 samples, 20
gradient steps per update. save/load to .pt file via refinement_state_path.
Studio popup: new "Value guidance" toggle. When on, trains the value function
after every rated generation (good and bad). Falls back to no-op if conditioning
deserialization fails.
chain sampler: _load_value_function reads the trained MLP if ready (5+
samples). _build_embed_guidance_wrapper uses value function gradient
(∂reward/∂conditioning) when available, fixed liked direction as fallback.
Log reports which mode is active and sample count.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
