Add a new server-side download API that allows frontends and desktop apps
to download models directly into ComfyUI's models directory, eliminating
the need for DOM scraping of the frontend UI.
New files:
- app/download_manager.py: Async download manager with streaming downloads,
pause/resume/cancel, manual redirect following with per-hop host validation,
sidecar metadata for safe resume, and concurrency limiting.
API endpoints (all under /download/, also mirrored at /api/download/):
- POST /download/model - Start a download (url, directory, filename)
- GET /download/status - List all downloads (filterable by client_id)
- GET /download/status/{id} - Get single download status
- POST /download/pause/{id} - Pause (cancels transfer, keeps temp)
- POST /download/resume/{id} - Resume (new request with Range header)
- POST /download/cancel/{id} - Cancel and clean up temp files
Security:
- Gated behind --enable-download-api CLI flag (403 if disabled)
- HTTPS-only with exact host allowlist (huggingface.co, civitai.com + CDNs)
- Manual redirect following with per-hop host validation (no SSRF)
- Path traversal protection via realpath + commonpath
- Extension allowlist (.safetensors, .sft)
- Filename sanitization (no separators, .., control chars)
- Destination re-checked before final rename
- Progress events scoped to initiating client_id
ClosesComfy-Org/ComfyUI-Desktop-2.0-Beta#293
Amp-Thread-ID: https://ampcode.com/threads/T-019d2344-139e-77a5-9f24-1cbb3b26a8ec
Co-authored-by: Amp <amp@ampcode.com>
When training_dtype is set to "none" and the model's native dtype is
float16, GradScaler was unconditionally enabled. However, GradScaler
does not support bfloat16 gradients (only float16/float32), causing a
NotImplementedError when lora_dtype is "bf16" (the default).
Fix by only enabling GradScaler when LoRA parameters are not in
bfloat16, since bfloat16 has the same exponent range as float32 and
does not need gradient scaling to avoid underflow.
Fixes#13124
* Add Number Convert node for unified numeric type conversion
Consolidates fragmented IntToFloat/FloatToInt nodes (previously only
available via third-party packs like ComfyMath, FillNodes, etc.) into
a single core node.
- Single input accepting INT, FLOAT, STRING, and BOOL types
- Two outputs: FLOAT and INT
- Conversion: bool→0/1, string→parsed number, float↔int standard cast
- Follows Math Expression node patterns (comfy_api, io.Schema, etc.)
Refs: COM-16925
* Register nodes_number_convert.py in extras_files list
Without this entry in nodes.py, the Number Convert node file
would not be discovered and loaded at startup.
* Add isfinite guard, exception chaining, and unit tests for Number Convert node
- Add math.isfinite() check to prevent int() crash on inf/nan string inputs
- Use 'from None' for cleaner exception chaining on string parse failure
- Add 21 unit tests covering all input types and error paths
* CURVE node
* remove curve to sigmas node
* feat: add CurveInput ABC with MonotoneCubicCurve implementation (#12986)
CurveInput is an abstract base class so future curve representations
(bezier, LUT-based, analytical functions) can be added without breaking
downstream nodes that type-check against CurveInput.
MonotoneCubicCurve is the concrete implementation that:
- Mirrors frontend createMonotoneInterpolator (curveUtils.ts) exactly
- Pre-computes slopes as numpy arrays at construction time
- Provides vectorised interp_array() using numpy for batch evaluation
- interp() for single-value evaluation
- to_lut() for generating lookup tables
CurveEditor node wraps raw widget points in MonotoneCubicCurve.
* linear curve
* refactor: move CurveEditor to comfy_extras/nodes_curve.py with V3 schema
* feat: add HISTOGRAM type and histogram support to CurveEditor
* code improve
---------
Co-authored-by: Christian Byrne <cbyrne@comfy.org>
There was an issue where the resample split was too early and dropped one
of the rolling convolutions a frame early. This is most noticable as a
lighting/color change between pixel frames 5->6 (latent 2->3), or as a
lighting change between the first and last frame in an FLF wan flow.
The recent PR that added resize_cond_for_context_window methods to
model classes used inline 'import comfy.context_windows' in each
method body. This moves that import to the top-level import section,
replacing 4 duplicate inline imports with a single top-level one.
* Add slice_cond and per-model context window cond resizing
* Fix cond_value.size() call in context window cond resizing
* Expose additional advanced inputs for ContextWindowsManualNode
Necessary for WanAnimate context windows workflow, which needs cond_retain_index_list = 0 to work properly with its reference input.
---------
* chore(api-nodes): mark seedream-3-0-t2i and seedance-1-0-lite models as deprecated
* fix(api-nodes): fixed old regression in the ByteDanceImageReference node
---------
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
* sd: soft_empty_cache on tiler fallback
This doesnt cost a lot and creates the expected VRAM reduction in
resource monitors when you fallback to tiler.
* wan: vae: Don't recursion in local fns (move run_up)
Moved Decoder3d’s recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.
* ltx: vae: Don't recursion in local fns (move run_up)
Mov the recursive run_up out of forward into a class
method to avoid nested closure self-reference cycles. This avoids
cyclic garbage that delays garbage of tensors which in turn delays
VRAM release before tiled fallback.
* ltx: vae: add cache state to downsample block
* ltx: vae: Add time stride awareness to causal_conv_3d
* ltx: vae: Automate truncation for encoder
Other VAEs just truncate without error. Do the same.
* sd/ltx: Make chunked_io a flag in its own right
Taking this bi-direcitonal, so make it a for-purpose named flag.
* ltx: vae: implement chunked encoder + CPU IO chunking
People are doing things with big frame counts in LTX including V2V
flows. Implement the time-chunked encoder to keep the VRAM down, with
the converse of the new CPU pre-allocation technique, where the chunks
are brought from the CPU JIT.
* ltx: vae-encode: round chunk sizes more strictly
Only powers of 2 and multiple of 8 are valid due to cache slicing.
* ltx: vae: add cache state to downsample block
* ltx: vae: Add time stride awareness to causal_conv_3d
* ltx: vae: Automate truncation for encoder
Other VAEs just truncate without error. Do the same.
* sd/ltx: Make chunked_io a flag in its own right
Taking this bi-direcitonal, so make it a for-purpose named flag.
* ltx: vae: implement chunked encoder + CPU IO chunking
People are doing things with big frame counts in LTX including V2V
flows. Implement the time-chunked encoder to keep the VRAM down, with
the converse of the new CPU pre-allocation technique, where the chunks
are brought from the CPU JIT.
* ltx: vae-encode: round chunk sizes more strictly
Only powers of 2 and multiple of 8 are valid due to cache slicing.
On Apple Silicon, `vram_state` is set to `VRAMState.SHARED` because
CPU and GPU share unified memory. However, `text_encoder_device()`
only checked for `HIGH_VRAM` and `NORMAL_VRAM`, causing all text
encoders to fall back to CPU on MPS devices.
Adding `VRAMState.SHARED` to the condition allows non-quantized text
encoders (e.g. bf16 Gemma 3 12B) to run on the MPS GPU, providing
significant speedup for text encoding and prompt generation.
Note: quantized models (fp4/fp8) that use float8_e4m3fn internally
will still fall back to CPU via the `supports_cast()` check in
`CLIP.__init__()`, since MPS does not support fp8 dtypes.
* wan: vae: encoder: Add feature cache layer that corks singles
If a downsample only gives you a single frame, save it to the feature
cache and return nothing to the top level. This increases the
efficiency of cacheability, but also prepares support for going two
by two rather than four by four on the frames.
* wan: remove all concatentation with the feature cache
The loopers are now responsible for ensuring that non-final frames are
processes at least two-by-two, elimiating the need for this cat case.
* wan: vae: recurse and chunk for 2+2 frames on decode
Avoid having to clone off slices of 4 frame chunks and reduce the size
of the big 6 frame convolutions down to 4. Save the VRAMs.
* wan: encode frames 2x2.
Reduce VRAM usage greatly by encoding frames 2 at a time rather than
4.
* wan: vae: remove cloning
The loopers now control the chunking such there is noever more than 2
frames, so just cache these slices directly and avoid the clone
allocations completely.
* wan: vae: free consumer caller tensors on recursion
* wan: vae: restyle a little to match LTX
* ltx: vae: scale the chunk size with the users VRAM
Scale this linearly down for users with low VRAM.
* ltx: vae: free non-chunking recursive intermediates
* ltx: vae: cleanup some intermediates
The conv layer can be the VRAM peak and it does a torch.cat. So cleanup
the pieces of the cat. Also clear our the cache ASAP as each layer detect
its end as this VAE surges in VRAM at the end due to the ended padding
increasing the size of the final frame convolutions off-the-books to
the chunker. So if all the earlier layers free up their cache it can
offset that surge.
Its a fragmentation nightmare, and the chance of it having to recache the
pyt allocator is very high, but you wont OOM.
Mark the weight_dtype parameter in UNETLoader (Load Diffusion Model) as
an advanced input to reduce UI complexity for new users. The parameter
is now hidden behind an expandable Advanced section, matching the
pattern used for other advanced inputs like device, tile_size, and
overlap.
Amp-Thread-ID: https://ampcode.com/threads/T-019cbaf1-d3c0-718e-a325-318baba86dec
Write to a temp file in the same directory then os.replace() onto the
target path. If the process crashes mid-write, the original file is
left intact instead of being truncated to zero bytes.
Fixes#11298
Add a GitHub Actions workflow and shell script that scan all commits
in a pull request for Co-authored-by trailers from known AI coding
agents (Claude, Cursor, Copilot, Codex, Aider, Devin, Gemini, Jules,
Windsurf, Cline, Amazon Q, Continue, OpenCode, etc.).
The check fails with clear instructions on how to remove the trailers
via interactive rebase.
* feat(assets): align local API with cloud spec
Unify response models, add missing fields, and align input schemas with
the cloud OpenAPI spec at cloud.comfy.org/openapi.
- Replace AssetSummary/AssetDetail/AssetUpdated with single Asset model
- Add is_immutable, metadata (system_metadata), prompt_id fields
- Support mime_type and preview_id in update endpoint
- Make CreateFromHashBody.name optional, add mime_type, require >=1 tag
- Add id/mime_type/preview_id to upload, relax tags to optional
- Rename total_tags → tags in tag add/remove responses
- Add GET /api/assets/tags/refine histogram endpoint
- Add DB migration for system_metadata and prompt_id columns
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Fix review issues: tags validation, size nullability, type annotation, hash mismatch check, and add tag histogram tests
- Remove contradictory min_length=1 from CreateFromHashBody.tags default
- Restore size field to int|None=None for proper null semantics
- Add Union type annotation to _build_asset_response result param
- Add hash mismatch validation on idempotent upload path (409 HASH_MISMATCH)
- Add unit tests for list_tag_histogram service function
Amp-Thread-ID: https://ampcode.com/threads/T-019cd993-f43c-704e-b3d7-6cfc3d4d4a80
Co-authored-by: Amp <amp@ampcode.com>
* Add preview_url to /assets API response using /api/view endpoint
For input and output assets, generate a preview_url pointing to the
existing /api/view endpoint using the asset's filename and tag-derived
type (input/output). Handles subdirectories via subfolder param and
URL-encodes filenames with spaces, unicode, and special characters.
This aligns the OSS backend response with the frontend AssetCard
expectation for thumbnail rendering.
Amp-Thread-ID: https://ampcode.com/threads/T-019cda3f-5c2c-751a-a906-ac6c9153ac5c
Co-authored-by: Amp <amp@ampcode.com>
* chore: remove unused imports from asset_reference queries
Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c
Co-authored-by: Amp <amp@ampcode.com>
* feat: resolve blake3 hashes in /view endpoint via asset database
Amp-Thread-ID: https://ampcode.com/threads/T-019cda7d-cb21-77b4-a51b-b965af60208c
Co-authored-by: Amp <amp@ampcode.com>
* Register uploaded images in asset database when --enable-assets is set
Add register_file_in_place() service function to ingest module for
registering already-saved files without moving them. Call it from the
/upload/image endpoint to return asset metadata in the response.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>
* Exclude None fields from asset API JSON responses
Add exclude_none=True to model_dump() calls across asset routes to
keep response payloads clean by omitting unset optional fields.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>
* Add comment explaining why /view resolves blake3 hashes
Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>
* Move blake3 hash resolution to asset_management service
Extract resolve_hash_to_path() into asset_management.py and remove
_resolve_blake3_to_path from server.py. Also revert loopback origin
check to original logic.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce023-3384-7560-bacf-de40b0de0dd2
Co-authored-by: Amp <amp@ampcode.com>
* Require at least one tag in UploadAssetSpec
Enforce non-empty tags at the Pydantic validation layer so uploads
with no tags are rejected with a 400 before reaching ingest. Adds
test_upload_empty_tags_rejected to cover this case.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>
* Add owner_id check to resolve_hash_to_path
Filter asset references by owner visibility so the /view endpoint
only resolves hashes for assets the requesting user can access.
Adds table-driven tests for owner visibility cases.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>
* Make ReferenceData.created_at and updated_at required
Remove None defaults and type: ignore comments. Move fields before
optional fields to satisfy dataclass ordering.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>
* Fix double commit in create_from_hash
Move mime_type update into _register_existing_asset so it shares a
single transaction with reference creation. Log a warning when the
hash is not found instead of silently returning None.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>
* Add exclude_none=True to create/upload responses
Align with get/update/list endpoints for consistent JSON output.
Amp-Thread-ID: https://ampcode.com/threads/T-019ce377-8bde-7048-bc28-a9df063409f9
Co-authored-by: Amp <amp@ampcode.com>
* Change preview_id to reference asset by reference ID, not content ID
Clients receive preview_id in API responses but could not dereference it
through public routes (which use reference IDs). Now preview_id is a
self-referential FK to asset_references.id so the value is directly
usable in the public API.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Filter soft-deleted and missing refs from visibility queries
list_references_by_asset_id and list_tags_with_usage were not filtering
out deleted_at/is_missing refs, allowing /view?filename=blake3:... to
serve files through hidden references and inflating tag usage counts.
Add list_all_file_paths_by_asset_id for orphan cleanup which
intentionally needs unfiltered access.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Pass preview_id and mime_type through all asset creation fast paths
The duplicate-content upload path and hash-based creation paths were
silently dropping preview_id and mime_type. This wires both fields
through _register_existing_asset, create_from_hash, and all route
call sites so behavior is consistent regardless of whether the asset
content already exists.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Remove unimplemented client-provided ID from upload API
The `id` field on UploadAssetSpec was advertised for idempotent creation
but never actually honored when creating new references. Remove it
rather than implementing the feature.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Make asset mime_type immutable after first ingest
Prevents cross-tenant metadata mutation when multiple references share
the same content-addressed Asset row. mime_type can now only be set when
NULL (first ingest); subsequent attempts to change it are silently ignored.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Use resolved content_type from asset lookup in /view endpoint
The /view endpoint was discarding the content_type computed by
resolve_hash_to_path() and re-guessing from the filename, which
produced wrong results for extensionless files or mismatched extensions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Merge system+user metadata into filter projection
Extract rebuild_metadata_projection() to build AssetReferenceMeta rows
from {**system_metadata, **user_metadata}, so system-generated metadata
is queryable via metadata_filter and user keys override system keys.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Standardize tag ordering to alphabetical across all endpoints
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Derive subfolder tags from path in register_file_in_place
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Reject client-provided id, fix preview URLs, rename tags→total_tags
- Reject 'id' field in multipart upload with 400 UNSUPPORTED_FIELD
instead of silently ignoring it
- Build preview URL from the preview asset's own metadata rather than
the parent asset's
- Rename 'tags' to 'total_tags' in TagsAdd/TagsRemove response schemas
for clarity
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: SQLite migration 0003 FK drop fails on file-backed DBs (MB-2)
Add naming_convention to Base.metadata so Alembic batch-mode reflection
can match unnamed FK constraints created by migration 0002. Pass
naming_convention and render_as_batch=True through env.py online config.
Add migration roundtrip tests (upgrade/downgrade/cycle from baseline).
Amp-Thread-ID: https://ampcode.com/threads/T-019ce466-1683-7471-b6e1-bb078223cda0
Co-authored-by: Amp <amp@ampcode.com>
* Fix missing tag count for is_missing references and update test for total_tags field
- Allow is_missing=True references to be counted in list_tags_with_usage
when the tag is 'missing', so the missing tag count reflects all
references that have been tagged as missing
- Add update_is_missing_by_asset_id query helper for bulk updates by asset
- Update test_add_and_remove_tags to use 'total_tags' matching the API schema
Amp-Thread-ID: https://ampcode.com/threads/T-019ce482-05e7-7324-a1b0-a56a929cc7ef
Co-authored-by: Amp <amp@ampcode.com>
* Remove unused imports in scanner.py
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Rename prompt_id to job_id on asset_references
Rename the column in the DB model, migration, and service schemas.
The API response emits both job_id and prompt_id (deprecated alias)
for backward compatibility with the cloud API.
Amp-Thread-ID: https://ampcode.com/threads/T-019cef41-60b0-752a-aa3c-ed7f20fda2f7
Co-authored-by: Amp <amp@ampcode.com>
* Add index on asset_references.preview_id for FK cascade performance
Amp-Thread-ID: https://ampcode.com/threads/T-019cef45-a4d2-7548-86d2-d46bcd3db419
Co-authored-by: Amp <amp@ampcode.com>
* Add clarifying comments for Asset/AssetReference naming and preview_id
Amp-Thread-ID: https://ampcode.com/threads/T-019cef49-f94e-7348-bf23-9a19ebf65e0d
Co-authored-by: Amp <amp@ampcode.com>
* Disallow all-null meta rows: add CHECK constraint, skip null values on write
- convert_metadata_to_rows returns [] for None values instead of an all-null row
- Remove dead None branch from _scalar_to_row
- Simplify null filter in common.py to just check for row absence
- Add CHECK constraint ck_asset_reference_meta_has_value to model and migration 0003
Amp-Thread-ID: https://ampcode.com/threads/T-019cef4e-5240-7749-bb25-1f17fcf9c09c
Co-authored-by: Amp <amp@ampcode.com>
* Remove dead None guards on result.asset in upload handler
register_file_in_place guarantees a non-None asset, so the
'if result.asset else None' checks were unreachable.
Amp-Thread-ID: https://ampcode.com/threads/T-019cef5b-4cf8-723c-8a98-8fb8f333c133
Co-authored-by: Amp <amp@ampcode.com>
* Remove mime_type from asset update API
Clients can no longer modify mime_type after asset creation via the
PUT /api/assets/{id} endpoint. This reduces the risk of mime_type
spoofing. The internal update_asset_hash_and_mime function remains
available for server-side use (e.g., enrichment).
Amp-Thread-ID: https://ampcode.com/threads/T-019cef5d-8d61-75cc-a1c6-2841ac395648
Co-authored-by: Amp <amp@ampcode.com>
* Fix migration constraint naming double-prefix and NULL in mixed metadata lists
- Use fully-rendered constraint names in migration 0003 to avoid the
naming convention doubling the ck_ prefix on batch operations.
- Add table_args to downgrade so SQLite batch mode can find the CHECK
constraint (not exposed by SQLite reflection).
- Fix model CheckConstraint name to use bare 'has_value' (convention
auto-prefixes).
- Skip None items when converting metadata lists to rows, preventing
all-NULL rows that violate the has_value check constraint.
Amp-Thread-ID: https://ampcode.com/threads/T-019cef87-94f9-7172-a6af-c6282290ce4f
Co-authored-by: Amp <amp@ampcode.com>
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Amp <amp@ampcode.com>
* feat: add essentials_category to nodes and blueprints for Essentials tab
Add ESSENTIALS_CATEGORY or essentials_category to 12 node classes and all
36 blueprint JSONs. Update SubgraphEntry TypedDict and subgraph_manager to
extract and pass through the field.
Fixes COM-15221
Amp-Thread-ID: https://ampcode.com/threads/T-019c83de-f7ab-7779-a451-0ba5940b56a9
* fix: import NotRequired from typing_extensions for Python 3.10 compat
* refactor: keep only node class ESSENTIALS_CATEGORY, remove blueprint/subgraph changes
Frontend will own blueprint categorization separately.
* fix: remove essentials_category from CreateVideo (not in spec)
---------
Co-authored-by: guill <jacob.e.segal@gmail.com>
If a subclass BYO _load_from_state_dict and doesnt call the super() the
needed default init of these weights is missed and can lead to problems
for uninitialized weights.
This is an experimental WIP option that might not work in your workflow but
should lower memory usage if it does.
Currently only the VAE and the load image node will output in fp16 when
this option is turned on.
After a frontend update (e.g. nightly build), browsers could load
outdated cached index.html and JS/CSS chunks, causing dynamically
imported modules to fail with MIME type errors and vite:preloadError.
Hard refresh (Ctrl+Shift+R) was insufficient to fix the issue because
Cache-Control: no-cache still allows the browser to cache and
revalidate via ETags. aiohttp's FileResponse auto-generates ETags
based on file mtime+size, which may not change after pip reinstall,
so the browser gets 304 Not Modified and serves stale content.
Clearing ALL site data in DevTools did fix it, confirming the HTTP
cache was the root cause.
The fix changes:
- index.html: no-cache -> no-store, must-revalidate
- JS/CSS/JSON entry points: no-cache -> no-store
no-store instructs browsers to never cache these responses, ensuring
every page load fetches the current index.html with correct chunk
references. This is a small tradeoff (~5KB re-download per page load)
for guaranteed correctness after updates.
* Implement seek and read for pins
Source pins from an mmap is pad because its its a CPU->CPU copy that
attempts to fully buffer the same data twice. Instead, use seek and
read which avoids the mmap buffering while usually being a faster
read in the first place (avoiding mmap faulting etc).
* pinned_memory: Use Aimdo pinner
The aimdo pinner bypasses pytorches CPU allocator which can leak
windows commit charge.
* ops: bypass init() of weight for embedding layer
This similarly consumes large commit charge especially for TEs. It can
cause a permanement leaked commit charge which can destabilize on
systems close to the commit ceiling and generally confuses the RAM
stats.
* model_patcher: implement pinned memory counter
Implement a pinned memory counter for better accounting of what volume
of memory pins have.
* implement touch accounting
Implement accounting of touching mmapped tensors.
* mm+mp: add residency mmap getter
* utils: use the aimdo mmap to load sft files
* model_management: Implement tigher RAM pressure semantics
Implement a pressure release on entire MMAPs as windows does perform
faster when mmaps are unloaded and model loads free ramp into fully
unallocated RAM.
Make the concept of freeing for pins a completely separate concept.
Now that pins are loadable directly from original file and don' touch
the mmap, tighten the freeing budget to just the current loaded model
- what you have left over. This still over-frees pins, but its a lot
better than before.
So after the pins are freed with that algorithm, bounce entire MMAPs
to free RAM based on what the model needs, deducting off any known
resident-in-mmap tensors to the free quota to keep it as tight as
possible.
* comfy-aimdo 0.2.11
Comfy aimdo 0.2.11
* mm: Implement file_slice path for QT
* ruff
* ops: put meta-tensors in place to allow custom nodes to check geo
* fix(api-nodes): added "texture_image" output to TencentTextToModel and TencentImageToModel nodes. Fixed `OBJ` output when it is zipped
* support additional solid texture outputs
* fixed and enabled Tencent3DTextureEdit node
* Revert "Revert "feat: Add CacheProvider API for external distributed caching …"
This reverts commit d1d53c14be.
* fix: gate provider lookups to outputs cache and fix UI coercion
- Add `enable_providers` flag to BasicCache so only the outputs cache
triggers external provider lookups/stores. The objects cache stores
node class instances, not CacheEntry values, so provider calls were
wasted round-trips that always missed.
- Remove `or {}` coercion on `result.ui` — an empty dict passes the
`is not None` gate in execution.py and causes KeyError when the
history builder indexes `["output"]` and `["meta"]`. Preserving
`None` correctly skips the ui_node_outputs addition.
Bug report in #12651
- to_skip fix: Prevents negative array slicing when the start offset is negative.
- __duration check: Prevents the extraction loop from breaking after a single audio chunk when the requested duration is 0 (which is a sentinel for unlimited).
* feat: Add CacheProvider API for external distributed caching
Introduces a public API for external cache providers, enabling distributed
caching across multiple ComfyUI instances (e.g., Kubernetes pods).
New files:
- comfy_execution/cache_provider.py: CacheProvider ABC, CacheContext/CacheValue
dataclasses, thread-safe provider registry, serialization utilities
Modified files:
- comfy_execution/caching.py: Add provider hooks to BasicCache (_notify_providers_store,
_check_providers_lookup), subcache exclusion, prompt ID propagation
- execution.py: Add prompt lifecycle hooks (on_prompt_start/on_prompt_end) to
PromptExecutor, set _current_prompt_id on caches
Key features:
- Local-first caching (check local before external for performance)
- NaN detection to prevent incorrect external cache hits
- Subcache exclusion (ephemeral subgraph results not cached externally)
- Thread-safe provider snapshot caching
- Graceful error handling (provider errors logged, never break execution)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: use deterministic hash for cache keys instead of pickle
Pickle serialization is NOT deterministic across Python sessions due
to hash randomization affecting frozenset iteration order. This causes
distributed caching to fail because different pods compute different
hashes for identical cache keys.
Fix: Use _canonicalize() + JSON serialization which ensures deterministic
ordering regardless of Python's hash randomization.
This is critical for cross-pod cache key consistency in Kubernetes
deployments.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* test: add unit tests for CacheProvider API
- Add comprehensive tests for _canonicalize deterministic ordering
- Add tests for serialize_cache_key hash consistency
- Add tests for contains_nan utility
- Add tests for estimate_value_size
- Add tests for provider registry (register, unregister, clear)
- Move json import to top-level (fix inline import)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* style: remove unused imports in test_cache_provider.py
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: move _torch_available before usage and use importlib.util.find_spec
Fixes ruff F821 (undefined name) and F401 (unused import) errors.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* fix: use hashable types in frozenset test and add dict test
Frozensets can only contain hashable types, so use nested frozensets
instead of dicts. Added separate test for dict handling via serialize_cache_key.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* refactor: expose CacheProvider API via comfy_api.latest.Caching
- Add Caching class to comfy_api/latest/__init__.py that re-exports
from comfy_execution.cache_provider (source of truth)
- Fix docstring: "Skip large values" instead of "Skip small values"
(small compute-heavy values are good cache targets)
- Maintain backward compatibility: comfy_execution.cache_provider
imports still work
Usage:
from comfy_api.latest import Caching
class MyProvider(Caching.CacheProvider):
def on_lookup(self, context): ...
def on_store(self, context, value): ...
Caching.register_provider(MyProvider())
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: clarify should_cache filtering criteria
Change docstring from "Skip large values" to "Skip if download time > compute time"
which better captures the cost/benefit tradeoff for external caching.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* docs: make should_cache docstring implementation-agnostic
Remove prescriptive filtering suggestions - let implementations
decide their own caching logic based on their use case.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* feat: add optional ui field to CacheValue
- Add ui field to CacheValue dataclass (default None)
- Pass ui when creating CacheValue for external providers
- Use result.ui (or default {}) when returning from external cache lookup
This allows external cache implementations to store/retrieve UI data
if desired, while remaining optional for implementations that skip it.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* refactor: rename _is_cacheable_value to _is_external_cacheable_value
Clearer name since objects are also cached locally - this specifically
checks for external caching eligibility.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* refactor: async CacheProvider API + reduce public surface
- Make on_lookup/on_store async on CacheProvider ABC
- Simplify CacheContext: replace cache_key + cache_key_bytes with
cache_key_hash (str hex digest)
- Make registry/utility functions internal (_prefix)
- Trim comfy_api.latest.Caching exports to core API only
- Make cache get/set async throughout caching.py hierarchy
- Use asyncio.create_task for fire-and-forget on_store
- Add NaN gating before provider calls in Core
- Add await to 5 cache call sites in execution.py
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove unused imports (ruff) and update tests for internal API
- Remove unused CacheContext and _serialize_cache_key imports from
caching.py (now handled by _build_context helper)
- Update test_cache_provider.py to use _-prefixed internal names
- Update tests for new CacheContext.cache_key_hash field (str)
- Make MockCacheProvider methods async to match ABC
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address coderabbit review feedback
- Add try/except to _build_context, return None when hash fails
- Return None from _serialize_cache_key on total failure (no id()-based fallback)
- Replace hex-like test literal with non-secret placeholder
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use _-prefixed imports in _notify_prompt_lifecycle
The lifecycle notification method was importing the old non-prefixed
names (has_cache_providers, get_cache_providers, logger) which no
longer exist after the API cleanup.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: add sync get_local/set_local for graph traversal
ExecutionList in graph.py calls output_cache.get() and .set() from
sync methods (is_cached, cache_link, get_cache). These cannot await
the now-async get/set. Add get_local/set_local that bypass external
providers and only access the local dict — which is all graph
traversal needs.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* chore: remove cloud-specific language from cache provider API
Make all docstrings and comments generic for the OSS codebase.
Remove references to Kubernetes, Redis, GCS, pods, and other
infrastructure-specific terminology.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* style: align documentation with codebase conventions
Strip verbose docstrings and section banners to match existing minimal
documentation style used throughout the codebase.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: add usage example to Caching class, remove pickle fallback
- Add docstring with usage example to Caching class matching the
convention used by sibling APIs (Execution.set_progress, ComfyExtension)
- Remove non-deterministic pickle fallback from _serialize_cache_key;
return None on JSON failure instead of producing unretrievable hashes
- Move cache_provider imports to top of execution.py (no circular dep)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: move public types to comfy_api, eager provider snapshot
Address review feedback:
- Move CacheProvider/CacheContext/CacheValue definitions to
comfy_api/latest/_caching.py (source of truth for public API)
- comfy_execution/cache_provider.py re-exports types from there
- Build _providers_snapshot eagerly on register/unregister instead
of lazy memoization in _get_cache_providers
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: generalize self-inequality check, fail-closed canonicalization
Address review feedback from guill:
- Rename _contains_nan to _contains_self_unequal, use not (x == x)
instead of math.isnan to catch any self-unequal value
- Remove Unhashable and repr() fallbacks from _canonicalize; raise
ValueError for unknown types so _serialize_cache_key returns None
and external caching is skipped (fail-closed)
- Update tests for renamed function and new fail-closed behavior
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: suppress ruff F401 for re-exported CacheContext
CacheContext is imported from _caching and re-exported for use by
caching.py. Add noqa comment to satisfy the linter.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: enable external caching for subcache (expanded) nodes
Subcache nodes (from node expansion) now participate in external
provider store/lookup. Previously skipped to avoid duplicates, but
the cost of missing partial-expansion cache hits outweighs redundant
stores — especially with looping behavior on the horizon.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: wrap register/unregister as explicit static methods
Define register_provider and unregister_provider as wrapper functions
in the Caching class instead of re-importing. This locks the public
API signature in comfy_api/ so internal changes can't accidentally
break it.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use debug-level logging for provider registration
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: follow ProxiedSingleton pattern for Caching class
Add Caching as a nested class inside ComfyAPI_latest inheriting from
ProxiedSingleton with async instance methods, matching the Execution
and NodeReplacement patterns. Retains standalone Caching class for
direct import convenience.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: inline registration logic in Caching class
Follow the Execution/NodeReplacement pattern — the public API methods
contain the actual logic operating on cache_provider module state,
not wrapper functions delegating to free functions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: single Caching definition inside ComfyAPI_latest
Remove duplicate standalone Caching class. Define it once as a nested
class in ComfyAPI_latest (matching Execution/NodeReplacement pattern),
with a module-level alias for import convenience.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: remove prompt_id from CacheContext, type-safe canonicalization
Remove prompt_id from CacheContext — it's not relevant for cache
matching and added unnecessary plumbing (_current_prompt_id on every
cache). Lifecycle hooks still receive prompt_id directly.
Include type name in canonicalized primitives so that int 7 and
str "7" produce distinct hashes. Also canonicalize dict keys properly
instead of str() coercion.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address review feedback on cache provider API
- Hold references to pending store tasks to prevent "Task was destroyed
but it is still pending" warnings (bigcat88)
- Parallel cache lookups with asyncio.gather instead of sequential
awaits for better performance (bigcat88)
- Delegate Caching.register/unregister_provider to existing functions
in cache_provider.py instead of reimplementing (bigcat88)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
* fix: guard torch.AcceleratorError for compatibility with torch < 2.8.0
torch.AcceleratorError was introduced in PyTorch 2.8.0. Accessing it
directly raises AttributeError on older versions. Use a try/except
fallback at module load time, consistent with the existing pattern used
for OOM_EXCEPTION.
* fix: address review feedback for AcceleratorError compat
- Fall back to RuntimeError instead of type(None) for ACCELERATOR_ERROR,
consistent with OOM_EXCEPTION fallback pattern and valid for except clauses
- Add "out of memory" message introspection for RuntimeError fallback case
- Use RuntimeError directly in discard_cuda_async_error except clause
---------
Comfy Aimdo 0.2.10 fixes the aimdo allocator hook for legacy cudaMalloc
consumers. Some consumers of cudaMalloc assume implicit synchronization
built in closed source logic inside cuda. This is preserved by passing
through to cuda as-is and accouting after the fact as opposed to
integrating these hooks with Aimdos VMA based allocator.
Pytorch only filters for OOMs in its own allocators however there are
paths that can OOM on allocators made outside the pytorch allocators.
These manifest as an AllocatorError as pytorch does not have universal
error translation to its OOM type on exception. Handle it. A log I have
for this also shows a double report of the error async, so call the
async discarder to cleanup and make these OOMs look like OOMs.
Comfy-aimdo 0.2.9 fixes a context issue where if a non-main thread does
a spurious garbage collection, cudaFrees are attempted with bad
context.
Some new APIs for displaying aimdo stats in UI widgets are also added.
These are purely additive getters that dont touch cuda APIs.
Sync the compute stream before freeing the cast buffers. This can cause
use after free issues when the cast stream frees the buffer while the
compute stream is behind enough to still needs a casted weight.
* mp: respect model_defined_dtypes in default caster
This is needed for parametrizations when the dtype changes between sd
and model.
* audio_encoders: archive model dtypes
Archive model dtypes to stop the state dict load override the dtypes
defined by the core for compute etc.
* feat: add EagerEval dataclass for frontend-side node evaluation
Add EagerEval to the V3 API schema, enabling nodes to declare
frontend-evaluated JSONata expressions. The frontend uses this to
display computation results as badges without a backend round-trip.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: add Math Expression node with JSONata evaluation
Add ComfyMathExpression node that evaluates JSONata expressions against
dynamically-grown numeric inputs using Autogrow + MatchType. Sends
input context via ui output so the frontend can re-evaluate when
the expression changes without a backend round-trip.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: register nodes_math.py in extras_files loader list
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address CodeRabbit review feedback
- Harden EagerEval.validate with type checks and strip() for empty strings
- Add _positional_alias for spreadsheet-style names beyond z (aa, ab...)
- Validate JSONata result is numeric before returning
- Add jsonata to requirements.txt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: remove EagerEval, scope PR to math node only
Remove EagerEval dataclass from _io.py and eager_eval usage from
nodes_math.py. Eager execution will be designed as a general-purpose
system in a separate effort.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: use TemplateNames, cap inputs at 26, improve error message
Address Kosinkadink review feedback:
- Switch from Autogrow.TemplatePrefix to Autogrow.TemplateNames so input
slots are named a-z, matching expression variables directly
- Cap max inputs at 26 (a-z) instead of 100
- Simplify execute() by removing dual-mapping hack
- Include expression and result value in error message
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add unit tests for Math Expression node
Add tests for _positional_alias (a-z mapping) and execute() covering
arithmetic operations, float inputs, $sum(values), and error cases.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: replace jsonata with simpleeval for math evaluation
jsonata PyPI package has critical issues: no Python 3.12/3.13 wheels,
no ARM/Apple Silicon wheels, abandoned (last commit 2023), C extension.
Replace with simpleeval (pure Python, 3.4M downloads/month, MIT,
AST-based security). Add math module functions (sqrt, ceil, floor,
log, sin, cos, tan) and variadic sum() supporting both sum(values)
and sum(a, b, c). Pin version to >=1.0,<2.0.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: update tests for simpleeval migration
Update JSONata syntax to Python syntax ($sum -> sum, $string -> str),
add tests for math functions (sqrt, ceil, floor, sin, log10) and
variadic sum(a, b, c).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* refactor: replace MatchType with MultiType inputs and dual FLOAT/INT outputs
Allow mixing INT and FLOAT connections on the same node by switching
from MatchType (which forces all inputs to the same type) to MultiType.
Output both FLOAT and INT so users can pick the type they need.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: update tests for mixed INT/FLOAT inputs and dual outputs
Add assertions for both FLOAT (result[0]) and INT (result[1]) outputs.
Add test_mixed_int_float_inputs and test_mixed_resolution_scale to
verify the primary use case of multiplying resolutions by a float factor.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* feat: make expression input multiline and validate empty expression
- Add multiline=True to expression input for better UX with longer expressions
- Add empty expression validation with clear "Expression cannot be empty." message
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* test: add tests for empty expression validation
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* fix: address review feedback — safe pow, isfinite guard, test coverage
- Wrap pow() with _safe_pow to prevent DoS via huge exponents
(pow() bypasses simpleeval's safe_power guard on **)
- Add math.isfinite() check to catch inf/nan before int() conversion
- Add int/float converters to MATH_FUNCTIONS for explicit casting
- Add "calculator" search alias
- Replace _positional_alias helper with string.ascii_lowercase
- Narrow test assertions and add error path + function coverage tests
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
* Update requirements.txt
---------
Co-authored-by: Claude Opus 4.6 <noreply@anthropic.com>
Co-authored-by: Jedrzej Kosinski <kosinkadink1@gmail.com>
Co-authored-by: Christian Byrne <abolkonsky.rem@gmail.com>
Allows explicit control over the causal_fix flag passed to
latent_to_pixel_coords. Defaults to frame_idx == 0 when not
specified, fixing the previous heuristic.
Comfy-aimdo 0.2.7 fixes a crash when a spurious cudaAsyncFree comes in
and would cause an infinite stack overflow (via detours hooks).
A lock is also introduced on the link list holding the free sections
to avoid any possibility of threaded miscellaneous cuda allocations
being the root cause.
* ops: dont unpin nothing
This was calling into aimdo in the none case (offloaded weight). Whats worse,
is aimdo syncs for unpinning an offloaded weight, as that is the corner case of
a weight getting evicted by its own use which does require a sync. But this
was heppening every offloaded weight causing slowdown.
* mp: fix get_free_memory policy
The ModelPatcherDynamic get_free_memory was deducting the model from
to try and estimate the conceptual free memory with doing any
offloading. This is kind of what the old memory_memory_required
was estimating in ModelPatcher load logic, however in practical
reality, between over-estimates and padding, the loader usually
underloaded models enough such that sampling could send CFG +/-
through together even when partially loaded.
So don't regress from the status quo and instead go all in on the
idea that offloading is less of an issue than debatching. Tell the
sampler it can use everything.
Define a threshold below which a weight loading takes priority. This
actually makes the offload consistent with non-dynamic, because what
happens, is when non-dynamic fills ints to_load list, it will fill-up
any left-over pieces that could fix large weights with small weights
and load them, even though they were lower priority. This actually
improves performance because the timy weights dont cost any VRAM and
arent worth the control overhead of the DMA etc.
* Fix VideoFromComponents.save_to crash when writing to BytesIO
When `get_container_format()` or `get_stream_source()` is called on a
tensor-based video (VideoFromComponents), it calls `save_to(BytesIO())`.
Since BytesIO has no file extension, `av.open` can't infer the output
format and throws `ValueError: Could not determine output format`.
The sibling class `VideoFromFile` already handles this correctly via
`get_open_write_kwargs()`, which detects BytesIO and sets the format
explicitly. `VideoFromComponents` just never got the same treatment.
This surfaces when any downstream node validates the container format
of a tensor-based video, like TopazVideoEnhance or any node that calls
`validate_container_format_is_mp4()`.
Three-line fix in `comfy_api/latest/_input_impl/video_types.py`.
* Add docstring to save_to to satisfy CI coverage check
* respect model dtype in non-comfy caster
* utils: factor out parent and name functionality of set_attr
* utils: implement set_attr_buffer for torch buffers
* ModelPatcherDynamic: Implement torch Buffer loading
If there is a buffer in dynamic - force load it.
* model_management: Remove non-comfy dynamic _v caster
* Force pre-load non-comfy weights to GPU in ModelPatcherDynamic
Non-comfy weights may expect to be pre-cast to the target
device without in-model casting. Previously they were allocated in
the vbar with _v which required the _v fault path in cast_to.
Instead, back up the original CPU weight and move it directly to GPU
at load time.
* draft zeta (z-image pixel space)
* revert gitignore
* model loaded and able to run however vector direction still wrong tho
* flip the vector direction to original again this time
* Move wrongly positioned Z image pixel space class
* inherit Radiance LatentFormat class
* Fix parameters in classes for Zeta x0 dino
* remove arbitrary nn.init instances
* Remove unused import of lru_cache
---------
Co-authored-by: silveroxides <ishimarukaito@gmail.com>
Comfy Aimdo 0.2.4 fixes a VRAM buffer alignment issue that happens in
someworkflows where action is able to bypass the pytorch allocator
and go straight to the cuda hook.
This was previously considering the pool of dynamic models as one giant
entity for the sake of smart memory, but that isnt really the useful
or what a user would reasonably expect. Make Dynamic VRAM properly purge
its models just like the old --disable-smart-memory but conditioning
the dynamic-for-dynamic bypass on smart memory.
Re-enable dynamic smart memory.
Multi-step samplers (eg. dpmpp_2s_ancestral) call the model at intermediate sigma values not present in the schedule. This caused set_step to crash with "No sample_sigmas matched current timestep" when context windows were enabled.
The fix is to keep self._step from the last exact match when a substep sigma is encountered, since substeps are still logically part of their parent step and should use the same context windows.
Co-authored-by: ozbayb <17261091+ozbayb@users.noreply.github.com>
* sd: add support for clip model reconstruction
* nodes: SetClipHooks: Demote the dynamic model patcher
* mp: Make dynamic_disable more robust
The backup need to not be cloned. In addition add a delegate object
to ModelPatcherDynamic so that non-cloning code can do
ModelPatcherDynamic demotion
* sampler_helpers: Demote to non-dynamic model patcher when hooking
* code rabbit review comments
Allow non QuantizedTensor layer to set want_requant to get the post lora
calculation stochastic cast down to the original input dtype.
This is then used by the legacy fp8 Linear implementation to set the
compute_dtype to the preferred lora dtype but then want_requant it back
down to fp8.
This fixes the issue with --fast fp8_matrix_mult is combined with
--fast dynamic_vram which doing a lora on an fp8_ non QT model.
Users should either use the cu126 one or the regular one (cu130 at the moment)
The cu128 portable is still included in the latest github release but I will stop including it as soon as it becomes slightly annoying to deal with. This might happen as soon as next week.
Some custom node packs are naughty, and violate the
dont-load-torch-on-load rule. This causes aimdo to lose preference on
its allocator hook on linux.
Go super early on the aimdo first-stage init before custom nodes
are mentioned at all.
Move essentials_category from deprecated/incorrect nodes to their replacements:
- ImageBatch → BatchImagesNode (ImageBatch is deprecated)
- Blur → removed (should use subgraph blueprint)
- GetVideoComponents → Video Slice
Amp-Thread-ID: https://ampcode.com/threads/T-019c8340-4da2-723b-a09f-83895c5bbda5
Implements per-guide attention attenuation via log-space additive bias
in self-attention. Each guide reference tracks its own strength and
optional spatial mask in conditioning metadata (guide_attention_entries).
Comfy Aimdo 0.2.2 moves the cuda allocator hook from the cudart API to
the cuda driver API on windows. This is needed to handle Windows+cu13
where cudart is statically linked.
* utils: dont use comfy sft loader in aimdo fallback
This was going to the raw command line switch and should respect main.py
probe of whether aimdo actually loaded successfully.
* ops: dont use deferred linear load in Aimdo fallback
Avoid changes of behaviour on --fast dynamic_vram when aimdo doesnt work.
* mp: attach re-construction arguments to model patcher
When making a model-patcher from a unet or ckpt, attach a callable
function that can be called to replay the model construction. This
can be used to deep clone model patcher WRT the actual model.
Originally written by Kosinkadink
https://github.com/Comfy-Org/ComfyUI/commit/f4b99bc62389af315013dda85f24f2bbd262b686
* mp: Add disable_dynamic clone argument
Add a clone argument that lets a caller clone a ModelPatcher but disable
dynamic to demote the clone to regular MP. This is useful for legacy
features where dynamic_vram support is missing or TBD.
* torch_compile: disable dynamic_vram
This is a bigger feature. Disable for the interim to preserve
functionality.
Skip entries in the prompt dict that don't contain a class_type key
in apply_replacements(), preventing crashes on metadata or non-node
entries.
FixesComfy-Org/ComfyUI#12517
* chore: tune CodeRabbit config to limit review scope and disable for drafts
- Add tone_instructions to focus only on newly introduced issues
- Add global path_instructions entry to ignore pre-existing issues in moved/reformatted code
- Disable draft PR reviews (drafts: false) and add WIP title keywords
- Disable ruff tool to prevent linter-based outside-diff-range comments
Addresses feedback from maintainers about CodeRabbit flagging pre-existing
issues in code that was merely moved or de-indented (e.g., PR #12557),
which can discourage community contributions and cause scope creep.
Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba
* chore: add 'DO NOT MERGE' to ignore_title_keywords
Amp-Thread-ID: https://ampcode.com/threads/T-019c82de-0481-7253-ad42-20cb595bb1ba
On Windows, Python defaults to cp1252 encoding when no encoding is
specified. JSON files containing UTF-8 characters (e.g., non-ASCII
characters) cause UnicodeDecodeError when read with cp1252.
This fixes the error that occurs when loading blueprint subgraphs
on Windows systems.
https://claude.ai/code/session_014WHi3SL9Gzsi3U6kbSjbSb
Co-authored-by: Claude <noreply@anthropic.com>
Integrate comfy-aimdo 0.2 which takes a different approach to
installing the memory allocator hook. Instead of using the complicated
and buggy pytorch MemPool+CudaPluggableAlloctor, cuda is directly hooked
making the process much more transparent to both comfy and pytorch. As
far as pytorch knows, aimdo doesnt exist anymore, and just operates
behind the scenes.
Remove all the mempool setup stuff for dynamic_vram and bump the
comfy-aimdo version. Remove the allocator object from memory_management
and demote its use as an enablment check to a boolean flag.
Comfy-aimdo 0.2 also support the pytorch cuda async allocator, so
remove the dynamic_vram based force disablement of cuda_malloc and
just go back to the old settings of allocators based on command line
input.
Add 24 non-cloud essential blueprints from comfyui-wiki/Subgraph-Blueprints.
These cover common workflows: text/image/video generation, editing,
inpainting, outpainting, upscaling, depth maps, pose, captioning, and more.
Cloud-only blueprints (5) are excluded and will be added once
client-side distribution filtering lands.
Amp-Thread-ID: https://ampcode.com/threads/T-019c6f43-6212-7308-bea6-bfc35a486cbf
gemini-3-pro-image-preview nondeterministically returns image/jpeg
instead of image/png. get_image_from_response() hardcoded
get_parts_by_type(response, "image/png"), silently dropping JPEG
responses and falling back to torch.zeros (all-black output).
Add _mime_matches() helper using fnmatch for glob-style MIME matching.
Change get_image_from_response() to request "image/*" so any image
format returned by the API is correctly captured.
This check was far too broad and the dtype is not a reliable indicator
of wanting the requant (as QT returns the compute dtype as the dtype).
So explictly plumb whether fp8mm wants the requant or not.
* lora: add weight shape calculations.
This lets the loader know if a lora will change the shape of a weight
so it can take appropriate action.
* MPDynamic: force load flux img_in weight
This weight is a bit special, in that the lora changes its geometry.
This is rather unique, not handled by existing estimate and doesn't
work for either offloading or dynamic_vram.
Fix for dynamic_vram as a special case. Ideally we can fully precalculate
these lora geometry changes at load time, but just get these models
working first.
* lora_extract: Add a trange
If you bite off more than your GPU can chew, this kinda just hangs.
Give a rough indication of progress counting the weights in a trange.
* lora_extract: Support on-the-fly patching
Use the on-the-fly approach from the regular model saving logic for
lora extraction too. Switch off force_cast_weights accordingly.
This gets extraction working in dynamic vram while also supporting
extraction on GPU offloaded.
Get rid of the cat and unary negation and inplace add-cmul the two
halves of the rope. Precompute -sin once at the start of the model
rather than every transformer block.
This is slightly faster on both GPU and CPU bound setups.
The current behaviour of the default ModelPatcher is to .to a model
only if its fully loaded, which is how random non-leaf weights get
loaded in non-LowVRAM conditions.
The however means they never get loaded in dynamic_vram. In the
dynamic_vram case, force load them to the GPU.
* model_management: lazy-cache aimdo_tensor
These tensors cosntructed from aimdo-allocations are CPU expensive to
make on the pytorch side. Add a cache version that will be valid with
signature match to fast path past whatever torch is doing.
* dynamic_vram: Minimize fast path CPU work
Move as much as possible inside the not resident if block and cache
the formed weight and bias rather than the flat intermediates. In
extreme layer weight rates this adds up.
* Fix bypass dtype/device moving
* Force offloading mode for training
* training context var
* offloading implementation in training node
* fix wrong input type
* Support bypass load lora model, correct adapter/offloading handling
This was missing the stochastic rounding required for fp8 downcast
to be consistent with model_patcher.patch_weight_to_device.
Missed in testing as I spend too much time with quantized tensors
and overlooked the simpler ones.
If there are non-trivial python objects nested in the model_options, this
causes all sorts of issues. Traverse lists and dicts so clones can safely
overide settings and BYO objects but stop there on the deepclone.
* feat(api-nodes-Kling): add new models (V3, O3)
* remove storyboard from VideoToVideo node
* added check for total duration of storyboards
* fixed other small things
* updated display name for nodes
* added "fake" seed
* revert threaded model loader change
This change was only needed to get around the pytorch 2.7 mempool bugs,
and should have been reverted along with #12260. This fixes a different
memory leak where pytorch gets confused about cache emptying.
* load non comfy weights
* MPDynamic: Pre-generate the tensors for vbars
Apparently this is an expensive operation that slows down things.
* bump to aimdo 1.8
New features:
watermark limit feature
logging enhancements
-O2 build on linux
Torch has alignment enforcement when viewing with data type changes
but only relative to itself. Do all tensor constructions straight
off the memory-view individually so pytorch doesnt see an alignment
problem.
The is needed for handling misaligned safetensors weights, which are
reasonably common in third party models.
This limits usage of this safetensors loader to GPU compute only
as CPUs kernnel are very likely to bus error. But it works for
dynamic_vram, where we really dont want to take a deep copy and we
always use GPU copy_ which disentangles the misalignment.
* feat(comfy_api): add basic 3D Model file types
* update Tripo nodes to use File3DGLB
* update Rodin3D nodes to use File3DGLB
* address PR review feedback:
- Rename File3D parameter 'path' to 'source'
- Convert File3D.data property to get_data()
- Make .glb extension check case-insensitive in nodes_rodin.py
- Restrict SaveGLB node to only accept File3DGLB
* Fixed a bug in the Meshy Rig and Animation nodes
* Fix backward compatability
This is using a different layers weight with .to(). Change it to use
the ops caster if the original layer is a comfy weight so that it picks
up dynamic_vram and async_offload functionality in full.
Co-authored-by: Rattus <rattus128@gmail.com>
* mp: fix full dynamic unloading
This was not unloading dynamic models when requesting a full unload via
the unpatch() code path.
This was ok, i your workflow was all dynamic models but fails with big
VRAM leaks if you need to fully unload something for a regular ModelPatcher
It also fices the "unload models" button.
* mm: load models outside of Aimdo Mempool
In dynamic_vram mode, escape the Aimdo mempool and load into the regular
mempool. Use a dummy thread to do it.
This function has a dtype argument that allows the caller to set the
dtype in the cast. TIL Some models override this on weight casts, which
means its the highest priority.
Priority scheme is: argument > model dtype > state dict dtype
pinned memory was converted back to pinning the CPU side weight without
any changes. Fix the pinner to use the CPU weight and not the model defined
geometry. This will either save RAM or stop buffer overruns when the types
mismatch.
Fix the model defined weight caster to use the [ s.weight, s.bias ]
interpretation, as xfer_dest might be the flattened pin now. Fix the detection
of needing to cast to not be conditional on !pin.
- Change error type from 'invalid_prompt' to 'missing_node_type' for frontend detection
- Add extra_info with node_id, class_type, and node_title (from _meta.title)
- Improve user-facing message: 'Node X not found. The custom node may not be installed.'
Move count increment before isinstance(item, dict) check so that
non-dict output items (like text strings from PreviewAny node)
are included in outputs_count.
This aligns OSS Python with Cloud's Go implementation which uses
len(itemsArray) to count ALL items regardless of type.
Amp-Thread-ID: https://ampcode.com/threads/T-019c0bb5-14e0-744f-8808-1e57653f3ae3
Co-authored-by: Amp <amp@ampcode.com>
When a node is declared as dev-only, it doesn't show in the default UI
unless the dev mode is enabled in the settings. The intention is to
allow nodes related to unit testing to be included in ComfyUI
distributions without confusing the average user.
The code throughout is None safe to just skip the feature cache saving
step if none. Set it none in single frame use so qwen doesn't burn VRAM
on the unused cache.
* ops: introduce autopad for conv3d
This works around pytorch missing ability to causal pad as part of the
kernel and avoids massive weight duplications for padding.
* wan-vae: rework causal padding
This currently uses F.pad which takes a full deep copy and is liable to
be the VRAM peak. Instead, kick spatial padding back to the op and
consolidate the temporal padding with the cat for the cache.
* wan-vae: implement zero pad fast path
The WAN VAE is also QWEN where it is used single-image. These
convolutions are however zero padded 3d convolutions, which means the
VAE is actually just 2D down the last element of the conv weight in
the temporal dimension. Fast path this, to avoid adding zeros that
then just evaporate in convoluton math but cost computation.
* Disable timestep embed compression when inpainting
Spatial inpainting not compatible with the compression
* Reduce crossattn peak VRAM
* LTX2: Refactor forward function for better VRAM efficiency
- Add search_aliases for discoverability: resize, scale, dimensions, etc.
- Add node description for hover tooltip
- Add tooltips to all inputs explaining their behavior
- Reorder options: most common (scale dimensions) first, most technical (scale to multiple) last
Addresses user feedback that 'resize' search returned nothing useful and
options like 'match size' and 'scale to multiple' were not self-explanatory.
- Add search_aliases for discoverability: resize, scale, dimensions, etc.
- Add node description for hover tooltip
- Add tooltips to all inputs explaining their behavior
- Reorder options: most common (scale dimensions) first, most technical (scale to multiple) last
Addresses user feedback that 'resize' search returned nothing useful and
options like 'match size' and 'scale to multiple' were not self-explanatory.
tone_instructions:"Only comment on issues introduced by this PR's changes. Do not flag pre-existing problems in moved, re-indented, or reformatted code."
reviews:
profile:"chill"
request_changes_workflow:false
high_level_summary:false
poem:false
review_status:false
review_details:false
commit_status:true
collapse_walkthrough:true
changed_files_summary:false
sequence_diagrams:false
estimate_code_review_effort:false
assess_linked_issues:false
related_issues:false
related_prs:false
suggested_labels:false
auto_apply_labels:false
suggested_reviewers:false
auto_assign_reviewers:false
in_progress_fortune:false
enable_prompt_for_ai_agents:true
path_filters:
- "!comfy_api_nodes/apis/**"
- "!**/generated/*.pyi"
- "!.ci/**"
- "!script_examples/**"
- "!**/__pycache__/**"
- "!**/*.ipynb"
- "!**/*.png"
- "!**/*.bat"
path_instructions:
- path:"**"
instructions:|
IMPORTANT: Only comment on issues directly introduced by this PR's code changes.
Do NOT flag pre-existing issues in code that was merely moved, re-indented,
de-indented, or reformatted without logic changes. If code appears in the diff
only due to whitespace or structural reformatting (e.g., removing a `with:` block),
treat it as unchanged. Contributors should not feel obligated to address
pre-existing issues outside the scope of their contribution.
- path:"comfy/**"
instructions:|
Core ML/diffusion engine. Focus on:
- Backward compatibility (breaking changes affect all custom nodes)
- Memory management and GPU resource handling
- Performance implications in hot paths
- Thread safety for concurrent execution
- path:"comfy_api_nodes/**"
instructions:|
Third-party API integration nodes. Focus on:
- No hardcoded API keys or secrets
- Proper error handling for API failures (timeouts, rate limits, auth errors)
- Correct Pydantic model usage
- Security of user data passed to external APIs
- path:"comfy_extras/**"
instructions:|
Community-contributed extra nodes. Focus on:
- Consistency with node patterns (INPUT_TYPES, RETURN_TYPES, FUNCTION, CATEGORY)
- No breaking changes to existing node interfaces
- path:"comfy_execution/**"
instructions:|
Execution engine (graph execution, caching, jobs). Focus on:
Please make sure that you post ALL your ComfyUI logs in the bug report. A bug report without logs will likely be ignored.
Please make sure that you post ALL your ComfyUI logs in the bug report **even if there is no crash**. Just paste everything. The startup log (everything before "To see the GUI go to: ...") contains critical information to developers trying to help. For a performance issue or crash, paste everything from "got prompt" to the end, including the crash. More is better - always. A bug report without logs will likely be ignored.
See what ComfyUI can do with the [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
### Cloud
#### [Comfy Cloud](https://www.comfy.org/cloud)
- Our official paid cloud version for those who can't afford local hardware.
## Examples
See what ComfyUI can do with the [newer template workflows](https://comfy.org/workflows) or old [example workflows](https://comfyanonymous.github.io/ComfyUI_examples/).
## Features
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
@@ -189,8 +196,6 @@ The portable above currently comes with python 3.13 and pytorch cuda 13.0. Updat
[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z).
[Portable with pytorch cuda 12.6 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu126.7z) (Supports Nvidia 10 series and older GPUs).
#### How do I share models between another UI and ComfyUI?
@@ -208,7 +213,7 @@ comfy install
## Manual Install (Windows, Linux)
Python 3.14 works but you may encounter issues with the torch compile node. The free threaded variant is still missing some dependencies.
Python 3.14 works but some custom nodes may have issues. The free threaded variant works but some dependencies will enable the GIL so it's not fully supported.
Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
@@ -227,11 +232,11 @@ Put your VAE in: models/vae
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
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{"revision":0,"last_node_id":13,"last_link_id":0,"nodes":[{"id":13,"type":"cf95b747-3e17-46cb-8097-cac60ff9b2e1","pos":[1120,330],"size":[240,58],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":null},{"name":"model_name","type":"COMBO","widget":{"name":"model_name"},"link":null}],"outputs":[{"localized_name":"VIDEO","name":"VIDEO","type":"VIDEO","links":[]}],"title":"Video Upscale(GAN x4)","properties":{"proxyWidgets":[["-1","model_name"]],"cnr_id":"comfy-core","ver":"0.14.1"},"widgets_values":["RealESRGAN_x4plus.safetensors"]}],"links":[],"version":0.4,"definitions":{"subgraphs":[{"id":"cf95b747-3e17-46cb-8097-cac60ff9b2e1","version":1,"state":{"lastGroupId":0,"lastNodeId":13,"lastLinkId":19,"lastRerouteId":0},"revision":0,"config":{},"name":"Video Upscale(GAN x4)","inputNode":{"id":-10,"bounding":[550,460,120,80]},"outputNode":{"id":-20,"bounding":[1490,460,120,60]},"inputs":[{"id":"666d633e-93e7-42dc-8d11-2b7b99b0f2a6","name":"video","type":"VIDEO","linkIds":[10],"localized_name":"video","pos":[650,480]},{"id":"2e23a087-caa8-4d65-99e6-662761aa905a","name":"model_name","type":"COMBO","linkIds":[19],"pos":[650,500]}],"outputs":[{"id":"0c1768ea-3ec2-412f-9af6-8e0fa36dae70","name":"VIDEO","type":"VIDEO","linkIds":[15],"localized_name":"VIDEO","pos":[1510,480]}],"widgets":[],"nodes":[{"id":2,"type":"ImageUpscaleWithModel","pos":[1110,450],"size":[320,46],"flags":{},"order":1,"mode":0,"inputs":[{"localized_name":"upscale_model","name":"upscale_model","type":"UPSCALE_MODEL","link":1},{"localized_name":"image","name":"image","type":"IMAGE","link":14}],"outputs":[{"localized_name":"IMAGE","name":"IMAGE","type":"IMAGE","links":[13]}],"properties":{"cnr_id":"comfy-core","ver":"0.10.0","Node name for S&R":"ImageUpscaleWithModel"}},{"id":11,"type":"CreateVideo","pos":[1110,550],"size":[320,78],"flags":{},"order":3,"mode":0,"inputs":[{"localized_name":"images","name":"images","type":"IMAGE","link":13},{"localized_name":"audio","name":"audio","shape":7,"type":"AUDIO","link":16},{"localized_name":"fps","name":"fps","type":"FLOAT","widget":{"name":"fps"},"link":12}],"outputs":[{"localized_name":"VIDEO","name":"VIDEO","type":"VIDEO","links":[15]}],"properties":{"cnr_id":"comfy-core","ver":"0.10.0","Node name for S&R":"CreateVideo"},"widgets_values":[30]},{"id":10,"type":"GetVideoComponents","pos":[1110,330],"size":[320,70],"flags":{},"order":2,"mode":0,"inputs":[{"localized_name":"video","name":"video","type":"VIDEO","link":10}],"outputs":[{"localized_name":"images","name":"images","type":"IMAGE","links":[14]},{"localized_name":"audio","name":"audio","type":"AUDIO","links":[16]},{"localized_name":"fps","name":"fps","type":"FLOAT","links":[12]}],"properties":{"cnr_id":"comfy-core","ver":"0.10.0","Node name for S&R":"GetVideoComponents"}},{"id":1,"type":"UpscaleModelLoader","pos":[750,450],"size":[280,60],"flags":{},"order":0,"mode":0,"inputs":[{"localized_name":"model_name","name":"model_name","type":"COMBO","widget":{"name":"model_name"},"link":19}],"outputs":[{"localized_name":"UPSCALE_MODEL","name":"UPSCALE_MODEL","type":"UPSCALE_MODEL","links":[1]}],"properties":{"cnr_id":"comfy-core","ver":"0.10.0","Node name for S&R":"UpscaleModelLoader","models":[{"name":"RealESRGAN_x4plus.safetensors","url":"https://huggingface.co/Comfy-Org/Real-ESRGAN_repackaged/resolve/main/RealESRGAN_x4plus.safetensors","directory":"upscale_models"}]},"widgets_values":["RealESRGAN_x4plus.safetensors"]}],"groups":[],"links":[{"id":1,"origin_id":1,"origin_slot":0,"target_id":2,"target_slot":0,"type":"UPSCALE_MODEL"},{"id":14,"origin_id":10,"origin_slot":0,"target_id":2,"target_slot":1,"type":"IMAGE"},{"id":13,"origin_id":2,"origin_slot":0,"target_id":11,"target_slot":0,"type":"IMAGE"},{"id":16,"origin_id":10,"origin_slot":1,"target_id":11,"target_slot":1,"type":"AUDIO"},{"id":12,"origin_id":10,"origin_slot":2,"target_id":11,"target_slot":2,"type":"FLOAT"},{"id":10,"origin_id":-10,"origin_slot":0,"target_id":10,"target_slot":0,"type":"VIDEO"},{"id":15,"origin_id":11,"origin_slot":0,"target_id":-20,"target_slot":0,"type":"VIDEO"},{"id":19,"origin_id":-10,"origin_slot":1,"target_id":1,"target_slot":0,"type":"COMBO"}],"extra":{"workflowRendererVersion":"LG"},"category":"Video generation and editing/Enhance video"}]},"extra":{}}
@@ -146,6 +148,8 @@ parser.add_argument("--reserve-vram", type=float, default=None, help="Set the am
parser.add_argument("--async-offload",nargs='?',const=2,type=int,default=None,metavar="NUM_STREAMS",help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
parser.add_argument("--disable-dynamic-vram",action="store_true",help="Disable dynamic VRAM and use estimate based model loading.")
parser.add_argument("--enable-dynamic-vram",action="store_true",help="Enable dynamic VRAM on systems where it's not enabled by default.")
parser.add_argument("--force-non-blocking",action="store_true",help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")
@@ -220,6 +224,8 @@ parser.add_argument("--user-directory", type=is_valid_directory, default=None, h
parser.add_argument("--enable-download-api",action="store_true",help="Enable the model download API. When set, ComfyUI exposes endpoints that allow downloading model files directly into the models directory. Only HTTPS downloads from allowed hosts (huggingface.co, civitai.com) are permitted.")
parser.add_argument("--database-url",type=str,default=f"sqlite:///{database_default_path}",help="Specify the database URL, e.g. for an in-memory database you can use 'sqlite:///:memory:'.")
parser.add_argument("--disable-assets-autoscan",action="store_true",help="Disable asset scanning on startup for database synchronization.")
parser.add_argument("--enable-assets",action="store_true",help="Enable the assets system (API routes, database synchronization, and background scanning).")
ifcomfy.options.args_parsing:
args=parser.parse_args()
@@ -257,3 +263,8 @@ elif args.fast == []:
# '--fast' is provided with a list of performance features, use that list
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