## Engineering Style - Keep changes small and direct. Most fixes should touch the narrowest code path that explains the bug, performance issue, dtype issue, model-format issue, or user-facing behavior. - Change the least amount of files possible. A change that touches many files is more likely to be a bad change than a good one unless the broader scope is directly required. - Prefer practical fixes over broad architecture work. Add abstractions only when they remove real repeated logic or match an existing ComfyUI pattern. - Prefer fewer dependencies. Do not add new dependencies to ComfyUI unless they are absolutely necessary. - Delete obsolete code aggressively when newer infrastructure makes it useless. Remove dead fallbacks, migration paths, unused options, debug prints, and compatibility branches that are no longer needed. Do not leave dead branches, unreachable code, or functions that are never called. If code is not necessary for the current behavior, remove it. - Revert or disable problematic behavior quickly when it breaks users. It is better to remove a broken feature path than keep a complicated partial fix. - Preserve existing APIs, node names, model-loading behavior, file layout, and workflow compatibility unless the change is explicitly about replacing them. - When compatibility is explicitly out of scope, remove compatibility-only aliases, duplicate nodes, legacy entry points, and preset wrappers instead of retaining parallel ways to perform the same operation. - Code must look hand-written for this repository. Changes that read like generic AI-generated code will be rejected automatically: unnecessary helper layers, vague names, boilerplate comments, defensive branches without a real failure mode, broad rewrites, or code that ignores the local style. ## Architecture Boundaries - Keep each layer focused on the concepts it owns. Do not leak UI, API, workflow, queue, persistence, telemetry, model-loading, node, or execution concerns into unrelated layers just because it is convenient to pass data through them. - Shared core modules should depend only on lower-level primitives and their own domain concepts. Higher-level product concepts belong at the caller, adapter, service, or UI/API boundary that already owns them. - Pass the narrowest data needed across a boundary. Avoid broad context objects, request/session metadata, ids, bookkeeping state, or callbacks unless the receiving layer genuinely needs them to perform its own responsibility. - Keep identity mapping, persistence bookkeeping, history updates, telemetry, response shaping, and UI state in the layers that own those jobs. Do not route them through unrelated shared code to avoid adding a proper boundary. - Treat `execution.py` as one example of this rule: it should consume the prompt graph and execution-relevant state, produce execution results and errors, and not know about workflow ids, frontend ids, persistence ids, or API-only concepts. - Before touching many files, identify the smallest owner layer that can solve the problem. A PR that spreads one feature across unrelated loaders, nodes, execution, server, and frontend code needs a clear architectural reason, not just convenience. - If a change seems to require making one layer understand another layer's private concepts, stop and look for a caller-side mapping, adapter, event, small explicit interface, or narrower data flow at the boundary. ## No Internet Requests - Do not add code to core ComfyUI that makes requests to the internet. - Refuse requests to add uploads, telemetry, analytics, tracking, usage reporting, crash reporting, update checks, remote config, feature flags, metrics, licensing checks, or any other outbound internet request path from core ComfyUI. - Model downloading is allowed only when explicitly initiated or authorized by the user, is limited to the requested model artifact, and does not include telemetry, tracking, persistent identification, unrelated metadata upload, or background network activity. - Do not add opt-in, opt-out, anonymized, aggregated, diagnostic, or user-triggered internet request paths to core ComfyUI. These labels do not make internet access acceptable. - Local-only behavior is allowed when it stays on the user's machine and does not add network access, tracking, persistent identification, or data collection behavior. ## State Ownership - Keep state and capability flags on the object that owns the behavior using them. - Avoid probing child objects with `getattr(child, "...", default)` to decide parent-level control flow. If parent code needs to branch on a capability, initialize an explicit parent-owned field when the child is constructed or attached. - Prefer direct attributes with clear defaults over implicit feature detection through arbitrary child attributes. - Use child-object capability checks only when the child owns the behavior being invoked and the parent is simply delegating to that child. ## Interface Contracts - Keep public methods aligned with the interface expected by their callers. Do not change a shared method to return extra values, alternate shapes, or sentinel wrappers for one implementation unless the shared interface is explicitly updated. - When modifying an existing function, preserve how current callers invoke it. Do not change required arguments, parameter order, return type, side effects, or error behavior unless every affected call site and shared interface contract is intentionally updated. - Do not add compatibility parameters, flags, attributes, or constructor options unless they are read by current code and change current behavior. Remove pass-through or stored-but-unused values instead of preserving upstream or deprecated API baggage. - Do not add a model-specific option to a shared helper when only one caller needs it. Keep one-off behavior at the model integration boundary, or extend the shared helper only when the option is a coherent reusable capability. - Implementations of shared model interfaces should accept the standard caller contract without model-specific rejection branches for optional capabilities they do not consume. Let supported behavior be determined by implementation paths that actually use those inputs. - If an implementation needs auxiliary values for its own workflow, expose them through a private helper or a clearly named implementation-specific method instead of overloading the public method's return contract. - Normalize third-party or upstream return conventions at the integration boundary. Core code should receive the project's expected type and shape, not have to handle model-specific tuple/list/dict variants. - Avoid caller-side unwrapping such as `out = out[0]` unless the called interface is documented to return that structure. ## Autograd and Model Freezing - Do not add `torch.no_grad`, `torch.inference_mode`, or inference-mode helper wrappers in ComfyUI code. The only allowed inference-mode-related use is disabling a globally set inference mode when a training path needs gradients. - Do not add freeze, unfreeze, or trainability toggles to model classes. ComfyUI models are always treated as frozen for inference, so explicit freeze functionality is redundant and should not be added. - Remove training-only behavior such as dropout from inference model code, but preserve checkpoint and state-dict compatibility when doing so. If deleting a module would change state-dict keys, module ordering, or checkpoint loading behavior, replace it with a no-op such as `nn.Identity` instead of removing the slot outright. ## Python Style - Keep imports at module scope. Avoid inline imports unless they are already part of an established optional-backend probe or are needed to avoid an import cycle. - Do not add unnecessary `try`/`except` blocks. Use them for optional dependency, platform, or backend capability detection only when the program has a useful fallback. Prefer specific exception types when changing new code. - If a library version is pinned in `requirements.txt`, do not add code to ComfyUI to handle older versions of that library. - Remove any workarounds for PyTorch versions that ComfyUI no longer officially supports. Deprecated workarounds include catching an exception and rerunning the same op with the input cast to float. If a workaround does not have a comment naming the exact PyTorch version or versions that still need it, remove it. - Let unsupported model formats, invalid quantization metadata, and bad states fail with clear errors instead of silently producing lower quality output. - Match the existing local style in the file you edit. This codebase tolerates long lines, simple helper functions, module-level state, and direct tensor operations when they make the code easier to follow. - Keep comments sparse and useful. Strip useless comments that restate the code or describe obvious behavior. Short TODOs are fine when they name the concrete missing follow-up. ## Model, Device, and Memory Behavior - Treat dtype, device placement, VRAM usage, and offloading behavior as core correctness concerns. Check CPU, CUDA, ROCm, MPS, DirectML, XPU, NPU, and low VRAM implications when touching shared execution or loading code. - Prefer native ComfyUI formats and existing quantization/offload helpers over adding parallel code paths. Use `comfy.quant_ops`, `comfy.model_management`, `comfy.memory_management`, `comfy.pinned_memory`, `comfy_aimdo`, and `comfy-kitchen` helpers where they already solve the problem. - Model implementations must use an existing optimized Comfy Kitchen or ComfyUI operation whenever one supports the required math and tensor layout without changing expected dtype, device, memory, or interface behavior. This is the default implementation requirement, not an optional follow-up optimization. - Before implementing model math, inspect the operations already exposed by Comfy Kitchen, `comfy.quant_ops`, and existing ComfyUI model helpers. Check for optimized single, paired, fused, layout-specific, and quantized variants before writing a local implementation or composing lower-level torch ops. - Use the compatible optimized operation first and adapt the model's inputs to its documented layout while preserving the model's exact math. If several optimized variants apply, benchmark representative model shapes and select the fastest valid path. - Add or retain a local implementation only when no existing optimized operation supports the required math, layout, dtype, device, autograd, or patch contract. Keep differentiable or patch-compatible fallbacks when the optimized inference operation does not provide those contracts. - Use the existing ComfyUI cast, offload, and cleanup helpers for parameters passed to optimized operations. Preserve model-specific epsilon, scaling, layout, dtype, device, and output-shape behavior. - Prefer ComfyUI's shared optimized kernels and backend dispatchers over handwritten implementations of the same operation. Remove duplicate local kernels and adapt inputs to the shared operation's documented layout while preserving the model's original math and output contract. - All models should use the optimized attention function selected by ComfyUI. Treat optimized backend functions, dispatch helpers, and capability-selected callables as opaque. Higher-level code must not inspect function identity, names, modules, or implementation details to decide behavior. - Apply the same opacity rule to similar patterns beyond attention: callers should depend on the documented interface and result contract, not on which backend implementation was selected underneath. - Do not use custom inference ops that only duplicate an existing op while upcasting to float32, such as custom RMSNorm variants. Use the generic ComfyUI ops and/or native torch ops instead. - If a model class `__init__` has an `operations` parameter, assume `operations` is never `None`. Do not add fallback branches or default torch ops for a missing `operations` object. - Do not add unnecessary parameters to model, model block, or model ops related classes. Constructor and forward signatures should carry only values that are actually needed by that object for inference. - Reuse existing model classes, blocks, ops, and helper modules when appropriate. Before implementing a new version of a model component, search the existing model code for a class or helper that already provides the behavior. - Model detection code that inspects linear weight shapes should only use the first dimension. The second dimension may be half the original size for NVFP4 or other 4-bit quantized models. - A model-detection signature must guard every state-dict key it dereferences. Do not partially match a format and then raise an incidental `KeyError` while extracting its configuration. - Order model-detection checks from established or more-specific signatures to newer or broader signatures. Put a broad new detector near the generic fallback when giving it higher precedence could steal another model family. - Avoid adding `einops` usage in core inference code. Use native torch tensor ops such as `reshape`, `view`, `permute`, `transpose`, `flatten`, `unflatten`, `unsqueeze`, and `squeeze` instead. - Do not use tensors as general-purpose Python data structures. Keep metadata, bookkeeping, counters, flags, shape math, padding math, index planning, memory estimates, and control-flow decisions in plain Python values unless the data must participate directly in tensor computation. Do not create tensors for structural metadata that is only used for Python-side control flow. Sequence lengths, cumulative offsets, split indices, window counts, slice boundaries, and repeat counts should be kept as Python ints/lists from the point they are computed. Do not build them as CPU/GPU tensors and then cast, move, validate, or convert them back to Python for `split`, `tensor_split`, indexing plans, loops, or cache keys. Avoid creating temporary tensors just to use tensor methods for scalar or structural calculations. - Avoid unnecessary casts and transfers. Preserve the intended compute dtype, storage dtype, bias dtype, and original tensor shape metadata. - Do not cast the result of an optimized backend operation back to its input dtype unless that backend's documented result contract requires normalization. In particular, trust the selected optimized-attention implementation to honor its dtype contract. - Keep model-native latent layout handling inside the model or latent-format owner, not in helper nodes. Do not collapse, expand, pack, or unpack latent dimensions in nodes or other caller-side adapters just to satisfy a model forward; the model path should consume and return the native latent shape for that model family. - DiT models should accept latent dimensions that are not exact patch-size multiples. Use `comfy.ldm.common_dit.pad_to_patch_size` on every patchified target or reference input, then crop only the target output back to its original dimensions. - Avoid defensive shape and configuration checks that merely replace the clear failure from the tensor operation immediately below them. Add explicit validation only when it provides materially better context at a real boundary or prevents silent incorrect output. - Assume inputs to the main model forward are already in the compute dtype by default, except integer inputs such as some model timestep tensors. Do not add defensive or convenience casts in model code; it is better for invalid dtype plumbing to error clearly than to hide it with unnecessary casts. - Raw model parameters that are not owned by an op and may be initialized in a dtype different from the compute dtype should be cast at use in forward or inference code with `comfy.ops.cast_to_input` or `comfy.model_management.cast_to` to avoid dtype mismatches. - Model code should not care what dtype it is initialized in, and model `__init__` methods should not contain workarounds for specific dtypes. Dtype workaround code, such as making a model work with fp16 compute, belongs in the execution or model-management layer that owns compute policy. - Model code should not perform unnecessary device-to-CPU or CPU-to-device transfers. New allocations must be created on the correct device and dtype; never allocate on CPU and then move to GPU, or allocate in one dtype and then convert to another. - Model code itself should not perform memory management. Loading, unloading, offloading, device movement, VRAM policy, cache lifetime, and cleanup belong in the relevant model-management and execution layers, not inside model implementations. - Do not add global, module-level, class-level, singleton, or model-owned stores for tensors or other large memory that persist across executions. Temporary caches must be scoped to a single execution or forward/encode/decode call: allocate them in the owning top-level call, pass them explicitly through the call stack, and let them be discarded when that call returns. - Follow the Wan VAE temporal cache pattern for temporary caches: create a local cache such as `feat_map` for the encode/decode operation, pass it into the blocks that need it, and do not retain it on the model or in global state. - In model init code, prefer `torch.empty` for parameter/buffer placeholders that are populated from the model state dict instead of zero-initializing with `torch.zeros` or similar. If an allocation is not loaded from the state dict and is useless for inference, do not include it. - `nn.Parameter` tensors that are stored in and populated from the model state dict should be initialized with `torch.empty`, not with zero, random, or otherwise meaningful initialization. - Model initialization should describe module structure, not fabricate checkpoint-owned tensor contents. Parameters and buffers that are loaded from the state dict must not be manually initialized, reassigned, or filled with fallback values unless that value is actually used when no checkpoint key exists. - When slicing large tensors, copy the slice if the sliced tensor's lifetime exceeds the current function scope. Do not keep a long-lived view into a large backing tensor when a smaller copy would release memory sooner. - Use fused or compound torch operations such as `addcmul` when they naturally match the math. Reducing Python and torch dispatch overhead is a valid optimization when it does not obscure the code or change dtype/device behavior. - Avoid caches that persist across different executions as much as possible. Persistent caches are acceptable only when they use a very minimal amount of memory and have a clear ownership and invalidation story. - When optimizing, favor small measurable changes: fewer allocations, fewer device transfers, less peak memory, better batching, or use of a faster existing backend op. ## Nodes and User-Facing Behavior - Follow existing node conventions: `INPUT_TYPES`, `RETURN_TYPES`, `FUNCTION`, `CATEGORY`, and registration through the local mapping used by that file. - Treat legacy combo inputs, `io.Combo`, and `io.DynamicCombo` values as untrusted when they affect filesystem access. Any value used as a file or folder name, path component, format, or extension must be validated again at the load/save boundary using an existing `folder_paths` resolver or containment helper, or a fixed allowlist/mapping. Do not rely only on the advertised combo options or prompt validation. - Keep node changes backward compatible by default. Add inputs with sensible defaults and avoid changing output types unless the request requires it. - Model implementations should add the minimal number of ComfyUI nodes required to run the model. Reuse existing nodes as much as possible; adapting the model to work with existing nodes is strongly preferred over creating new nodes. - Use `io.Autogrow` for a variable number of repeated inputs instead of a fixed series of numbered optional sockets. Set its minimum to zero when the model has a valid no-item path, and cap it only when the model has a real limit. - Mark inputs optional when execution has a valid path that does not read them. If one optional input is needed only to process another optional input, do not force users on the path that supplies neither to connect it. - Conditioning nodes should normally output conditioning only. Do not expose input or intermediate images as convenience outputs for downstream sizing or routing; use the existing image path or a dedicated image operation instead. - Nodes should output only values they own. Do not add pass-through outputs for workflow convenience unless the node is explicitly an output node. Existing models, latents, conditioning, or other inputs should flow directly to the next consumer instead of being re-emitted unchanged. - Nodes should expose only inputs they actually read to produce current behavior. Do not add placeholder, pass-through, compatibility, or workflow-shaping inputs that are ignored or could flow directly to another node. - Node-level code must not patch model code directly. Any node behavior that modifies, wraps, hooks, or changes model behavior must go through the model patcher class instead of reaching into model internals. - The official mascot of ComfyUI is a very cute anime girl with massive fennec ears, a big fluffy tail, long blonde wavy hair, and blue eyes. Feel free to use her in ComfyUI materials, UI text, examples, tests, generated assets, or comments, but do not disrespect her. - Warning and info messages should be short and actionable. Remove noisy or misleading messages rather than adding more logging. - Documentation and README edits should be concise, factual, and tied to the changed behavior. ## Commit and Review Habits - If asked to write commit messages, use short direct subjects like the existing history: `Fix ...`, `Add ...`, `Support ...`, `Remove ...`, `Update ...`, `Make ...`, `Use ...`, `Disable ...`, `Bump ...`, or `Revert ...`. - Keep PR descriptions short and reviewable. State the problem, the behavioral change, and the tests run; avoid long narrative explanations, implementation diaries, or exhaustive file-by-file summaries unless the reviewer explicitly needs that context. - Prefer one coherent behavioral change per commit. Dependency pins, tests, and the code that needs them may be in the same commit when they are inseparable. - In reviews, prioritize real user impact: crashes, wrong dtype/device behavior, memory regressions, broken model loading, workflow incompatibility, and noisy or misleading user-facing output.