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Author SHA1 Message Date
Robert Wojciechowski 593047badf Merge Collector transport with image-list values and native cache reuse 2026-10-14 03:53:29 +00:00
Robert Wojciechowski a9ef36f951 feat: unify Collector media transport and add direct exact returns
Use the existing images/audio Collector for typed serverless pools. Preserve saved legacy_png defaults, map them to serverless png, and retain every transfer format with exact opt-in. Remove the separate Media Collector.

Send direct exact frames through bounded raw tensor multipart transport without 8-bit fallback. Reject incompatible legacy pools, invalid payloads and incomplete exact results. Cover real CPU HTTP round trips, native ComfyUI execution, mixed participant ordering and failure paths.
2026-10-14 03:41:05 +00:00
Robert Wojciechowski 6db5692d78 fix: preserve native image-list processing cache reuse 2026-10-14 03:15:49 +00:00
Robert Wojciechowski 591ffc7396 fix: preserve image-list worker selection across queued runs (#103) 2026-10-13 19:52:29 -07:00
Robert Wojciechowski 5f9a2f0fc6 fix: carry image-list values through End context
Preserve source values without extra wires, collect explicit per-item overrides, and expose ordered bundles through Get Value. Keep legacy sockets and no-override transport compatible.
2026-10-14 01:51:13 +00:00
Robert Wojciechowski c30036ae31 feat: preserve required output utilities in image-list execution 2026-10-13 23:26:25 +00:00
44 changed files with 3669 additions and 1024 deletions
+5 -3
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@@ -36,8 +36,9 @@ async def image_list_capabilities_endpoint(_request):
return web.json_response({
"supported": True,
"execution_targets": True,
"value_results": True,
"protocol_version": PROTOCOL_VERSION,
"node_ids": ["DistributedImageListStart", "DistributedImageListEnd"],
"node_ids": ["DistributedImageListStart", "DistributedImageListEnd", "DistributedImageListGetValue"],
"transport": "typed-float-tensor-json-v1",
})
@@ -52,8 +53,9 @@ async def image_list_item_endpoint(request):
if not isinstance(context_id, str) or not context_id:
raise ValueError("context_id must be a non-empty string")
item = data.get("item")
store_worker_item(data.get("token"), item)
return web.json_response({"status": "stored", "item_index": item["index"]})
identity = store_worker_item(data.get("token"), item)
return web.json_response({"status": "stored", "item_index": item["index"],
"item_identity": identity})
except (ValueError, TypeError) as exc:
return web.json_response({"error": str(exc)}, status=400)
except Exception as exc:
+35
View File
@@ -37,6 +37,8 @@ prompt_server = server.PromptServer.instance
# envelope: JSON { "job_id", "worker_id", "format", "start_index", "count", "width", "height",
# "is_last", "audio"?: {...} }
# media: one part per frame for still formats, or one video file for video formats
# format "exact" adds envelope "descriptors" (one tensor-le-v1 descriptor per
# frame) and sends one raw tensor part per frame; values are not quantized.
def _decode_image_sync(image_path):
@@ -299,6 +301,8 @@ async def _enqueue_worker_items(multi_job_id, items):
def _decode_compressed_chunk_sync(envelope, parts):
"""Decode a compressed chunk into per-frame IMAGE tensors (threadpool)."""
if envelope.get("format") == transfer_codec.EXACT_FORMAT:
return _decode_exact_chunk_sync(envelope, parts)
frames = transfer_codec.decode_chunk(envelope, parts)
return [
ensure_contiguous(torch.from_numpy(transfer_codec.uint8_frame_to_tensor_array(frame)))
@@ -306,6 +310,35 @@ def _decode_compressed_chunk_sync(envelope, parts):
]
def _decode_exact_chunk_sync(envelope, parts):
"""Decode raw exact IMAGE frames, checking each against the envelope geometry."""
from ..serverless_bridge import codec as bridge_codec
count, width, height = envelope.get("count"), envelope.get("width"), envelope.get("height")
for name, value in (("count", count), ("width", width), ("height", height)):
if not isinstance(value, int) or isinstance(value, bool) or value <= 0:
raise transfer_codec.TransferCodecError(f"envelope.{name}: expected positive integer")
descriptors = envelope.get("descriptors")
if not isinstance(descriptors, list) or len(descriptors) != count or len(parts) != count:
raise transfer_codec.TransferCodecError(
f"exact chunk expected {count} descriptors and frames, got "
f"{len(descriptors) if isinstance(descriptors, list) else 'no'} descriptors and {len(parts)} frames"
)
tensors = []
for descriptor, payload in zip(descriptors, parts):
if not isinstance(descriptor, dict) or descriptor.get("encoding") != "tensor-le-v1":
raise transfer_codec.TransferCodecError("exact frames require tensor-le-v1 encoding")
shape = descriptor.get("shape") if isinstance(descriptor, dict) else None
if not isinstance(shape, list) or len(shape) != 4 or shape[:3] != [1, height, width]:
raise transfer_codec.TransferCodecError(
f"exact frame shape {shape} does not match the envelope's 1x{height}x{width} frames"
)
try:
tensors.append(bridge_codec.decode_payload("IMAGE", descriptor, payload))
except ValueError as exc:
raise transfer_codec.TransferCodecError(f"exact frame: {exc}") from None
return tensors
def _validate_compressed_envelope(envelope):
errors = []
if not isinstance(envelope, dict):
@@ -384,6 +417,7 @@ async def _job_complete_compressed(request):
"image_index": start_index + pos,
"is_last": bool(is_last and pos == last_pos),
"audio": decoded_audio if pos == last_pos else None,
"format": envelope["format"],
}
for pos, tensor in enumerate(tensors)
]
@@ -462,6 +496,7 @@ async def job_complete_endpoint(request):
"image_index": int(batch_idx),
"is_last": is_last,
"audio": decoded_audio,
"format": transfer_codec.LEGACY_FORMAT,
}
],
)
+7 -1
View File
@@ -188,7 +188,7 @@ async def dispatch_worker_prompt(
return prompt_id if return_prompt_id else None
async def preflight_image_list_workers(workers, require_idle=True, timeout=3.0):
async def preflight_image_list_workers(workers, require_idle=True, timeout=3.0, *, require_value_results=False):
"""Require protocol/node support and report which direct workers are idle.
This performs read-only GETs only; it never submits a prompt or contacts a
@@ -242,6 +242,12 @@ async def preflight_image_list_workers(workers, require_idle=True, timeout=3.0):
"upgrade that worker before running this feature; no work was submitted"
)
if require_value_results and capability.get("value_results") is not True:
raise RuntimeError(
f"Image-list worker {worker_id} lacks explicit value_results support; "
"upgrade that worker before using End overrides; no work was submitted"
)
check = "queue"
queue_url = build_worker_url(worker, "/queue")
async with session.get(queue_url, timeout=aiohttp.ClientTimeout(total=timeout)) as response:
+2 -22
View File
@@ -401,20 +401,6 @@ def prune_prompt_for_worker(prompt_obj, use_worker_sink=False):
def prepare_delegate_master_prompt(prompt_obj, collector_ids):
"""Prune master prompt so it only executes post-collector nodes in delegate mode."""
# Typed collectors need no fabricated IMAGE placeholder. Sever all media
# links, including STRING scalar branches, before retained-config analysis.
# Their expected_media control was frozen by participant overrides.
if any((prompt_obj.get(cid) or {}).get("class_type") == "DistributedMediaCollector"
for cid in collector_ids):
from ...serverless_bridge.contract import SOCKET_KINDS
prompt_obj = json.loads(json.dumps(prompt_obj))
for cid in collector_ids:
node = prompt_obj.get(cid) or {}
if node.get("class_type") == "DistributedMediaCollector":
inputs = node.setdefault("inputs", {})
inputs.setdefault("expected_media", json.dumps([name for name in SOCKET_KINDS if name in inputs]))
for name in SOCKET_KINDS:
inputs.pop(name, None)
downstream = _find_downstream_nodes(prompt_obj, collector_ids)
nodes_to_keep = set(collector_ids)
nodes_to_keep.update(downstream)
@@ -519,7 +505,6 @@ def generate_job_id_map(prompt_index, prefix):
job_map = {}
distributed_nodes = (
prompt_index.nodes_for_class("DistributedCollector")
+ prompt_index.nodes_for_class("DistributedMediaCollector")
+ prompt_index.nodes_for_class("UltimateSDUpscaleDistributed")
+ prompt_index.nodes_for_class("DistributedImageListStart")
)
@@ -552,10 +537,8 @@ def _override_collector_nodes(
enabled_json,
delegate_master,
):
"""Configure legacy and typed collectors without changing their media."""
collector_ids = (prompt_index.nodes_for_class("DistributedCollector")
+ prompt_index.nodes_for_class("DistributedMediaCollector"))
for node_id in collector_ids:
"""Configure DistributedCollector nodes for master or worker role."""
for node_id in prompt_index.nodes_for_class("DistributedCollector"):
node = prompt_copy.get(node_id)
if not isinstance(node, dict):
continue
@@ -568,9 +551,6 @@ def _override_collector_nodes(
inputs["multi_job_id"] = job_id_map.get(node_id, node_id)
inputs["is_worker"] = not is_master
inputs["enabled_worker_ids"] = enabled_json
if node.get("class_type") == "DistributedMediaCollector":
from ...serverless_bridge.contract import SOCKET_KINDS
inputs["expected_media"] = json.dumps([socket for socket in SOCKET_KINDS if socket in inputs])
if is_master:
inputs["delegate_only"] = bool(delegate_master)
inputs.pop("master_url", None)
+36 -18
View File
@@ -1,8 +1,9 @@
"""Pure compilation of prepared typed-media collectors for serverless workers.
"""Pure compilation of prepared Distributed Collectors for comfy-media-v1 pools.
The legacy image compiler remains separate. This compiler emits one MediaExportV1
execution target per connected socket, retaining only its upstream computation.
It never imports ComfyUI or inspects/executes runtime node classes.
execution target per connected Collector socket (``images`` and/or ``audio``),
retaining only its upstream computation. It never imports ComfyUI or
inspects/executes runtime node classes.
"""
from __future__ import annotations
@@ -13,7 +14,7 @@ import json
from graphlib import CycleError, TopologicalSorter
from ...serverless_bridge.contract import SOCKET_KINDS, identifier, validate_bindings
from ...serverless_bridge.image_transfer import DEFAULT_FORMAT, DEFAULT_QUALITY, validate_options
from ...serverless_bridge.image_transfer import DEFAULT_FORMAT, validate_options
from .serverless_prompt import (
_PRIMITIVE_NODES,
_REMOVED_SCALAR_NODES,
@@ -24,7 +25,24 @@ from .serverless_prompt import (
)
_COLLECTOR = "DistributedMediaCollector"
_COLLECTOR = "DistributedCollector"
# Collector inputs, in output-slot order, that a typed pool returns.
COLLECTOR_SOCKETS = ("images", "audio")
# Collector widget defaults (the saved-workflow contract is legacy_png / 90).
COLLECTOR_DEFAULT_FORMAT = "legacy_png"
COLLECTOR_DEFAULT_QUALITY = 90
def collector_image_policy(inputs):
"""Return the endpoint IMAGE (format, quality) for one Collector.
legacy_png is the Collector's saved default and names a JSON/base64 framing
that only applies to direct workers; serverless returns the same 8-bit
pixels as compressed png. exact stays opt-in. No other format is altered.
"""
fmt, quality = validate_options(inputs.get("transfer_format", COLLECTOR_DEFAULT_FORMAT),
inputs.get("transfer_quality", COLLECTOR_DEFAULT_QUALITY))
return ("png" if fmt == "legacy_png" else fmt), quality
def _validate_identifier(value, context):
@@ -51,7 +69,7 @@ def _validate_prompt(prepared_prompt):
raise ValueError(f"prompt node {node_id!r} must have a nonempty class_type")
if class_type == _COLLECTOR:
collectors.append(node_id)
elif class_type == "DistributedCollector" or _is_unsupported_distributed(class_type):
elif _is_unsupported_distributed(class_type):
raise ValueError(f"unsupported distributed or batch node: {class_type}")
inputs = node.get("inputs", {})
if not isinstance(inputs, dict):
@@ -60,7 +78,7 @@ def _validate_prompt(prepared_prompt):
raise ValueError(f"prompt node {node_id!r} input names must be nonempty strings")
if not collectors:
raise ValueError("prepared prompt contains no DistributedMediaCollector nodes")
raise ValueError("prepared prompt contains no DistributedCollector nodes")
dependencies = {}
for node_id, node in prepared_prompt.items():
@@ -81,18 +99,17 @@ def _validate_prompt(prepared_prompt):
if source_class in _REMOVED_SCALAR_NODES or source_class in _PRIMITIVE_NODES:
if slot != 0:
raise ValueError(f"invalid output slot {slot} for scalar node {source_id!r}")
elif source_class == _COLLECTOR and slot >= len(SOCKET_KINDS):
elif source_class == _COLLECTOR and slot >= len(COLLECTOR_SOCKETS):
raise ValueError(f"invalid output slot {slot} for collector {source_id!r}")
parents.add(source_id)
dependencies[node_id] = parents
for collector_id in collectors:
inputs = prepared_prompt[collector_id].get("inputs", {})
validate_options(inputs.get("transfer_format", DEFAULT_FORMAT),
inputs.get("transfer_quality", DEFAULT_QUALITY))
collector_image_policy(inputs)
for field in ("multi_job_id", "worker_id"):
_validate_identifier(inputs.get(field), f"collector {collector_id!r} {field}")
connected = [socket for socket in SOCKET_KINDS if socket in inputs]
connected = [socket for socket in COLLECTOR_SOCKETS if socket in inputs]
if not connected:
raise ValueError(f"collector {collector_id!r} requires at least one connected media socket")
for socket in connected:
@@ -129,7 +146,8 @@ def _build_exports(source_nodes, collector_ids, request_id):
reserved = set(source_nodes)
for collector_id in collector_ids:
inputs = source_nodes[collector_id].get("inputs", {})
for socket, kind in SOCKET_KINDS.items():
for socket in COLLECTOR_SOCKETS:
kind = SOCKET_KINDS[socket]
if socket not in inputs:
continue
# Hashing avoids separator ambiguity and keeps long valid source
@@ -163,8 +181,7 @@ def _build_exports(source_nodes, collector_ids, request_id):
},
}
if kind == "IMAGE":
fmt, quality = validate_options(inputs.get("transfer_format", DEFAULT_FORMAT),
inputs.get("transfer_quality", DEFAULT_QUALITY))
fmt, quality = collector_image_policy(inputs)
if fmt != DEFAULT_FORMAT:
exports[node_id]["inputs"].update(transfer_format=fmt, transfer_quality=quality)
validate_bindings(bindings)
@@ -174,10 +191,11 @@ def _build_exports(source_nodes, collector_ids, request_id):
def compile_media_prompt(prepared_prompt: dict, request_id: str) -> tuple[dict, list[dict]]:
"""Return a worker graph and canonical typed-media export bindings.
Each DistributedMediaCollector must have prepared literal ``multi_job_id``
and ``worker_id`` identities and at least one linked media input. Present
media inputs (including texts) must be API links; literal text belongs in a
PrimitiveString node. Nonmedia collector controls are not exported.
Each DistributedCollector must have prepared literal ``multi_job_id`` and
``worker_id`` identities and at least one linked ``images``/``audio`` input.
Present media inputs must be API links. Collector transport controls become
the IMAGE export policy (``legacy_png`` is sent as ``png``); other controls
are not exported.
Only MediaExportV1 targets and their ancestors survive. Required ordinary
output/utility nodes are preserved regardless of their runtime OUTPUT_NODE
+46 -66
View File
@@ -153,44 +153,36 @@ def _is_load_balance_enabled(value):
def _prompt_requests_load_balance(prompt_index):
collector_ids = (prompt_index.nodes_for_class("DistributedCollector")
+ prompt_index.nodes_for_class("DistributedMediaCollector"))
for node_id in collector_ids:
for node_id in prompt_index.nodes_for_class("DistributedCollector"):
inputs = prompt_index.inputs_by_node.get(node_id, {})
if _is_load_balance_enabled(inputs.get("load_balance", False)):
return True
return False
def _validate_media_workers(prompt_index, workers):
"""Reject incompatible selections before probing, pruning or dispatching.
def _validate_media_workers(workers, prompt_index=None):
"""Reject unknown serverless media profiles before probing or dispatching.
Typed collectors currently have only a master/serverless transport. Check
all selected workers, not just reachable or load-balanced participants, so
a bad selection cannot silently turn into a different execution mode.
A Distributed Collector accepts direct workers, legacy-image-v1 pools and
comfy-media-v1 pools in the same run; each pool's profile selects its
endpoint contract. Unknown profiles fail rather than guessing a contract.
legacy-image-v1 endpoints return 8-bit files, so a Collector set to exact
cannot use them.
"""
typed = bool(prompt_index.nodes_for_class("DistributedMediaCollector"))
if typed and (prompt_index.nodes_for_class("DistributedCollector")
or prompt_index.nodes_for_class("UltimateSDUpscaleDistributed")):
raise ValueError(
"DistributedMediaCollector cannot be mixed with DistributedCollector or "
"UltimateSDUpscaleDistributed in one prompt"
)
exact = prompt_index is not None and any(
str(prompt_index.inputs_by_node.get(node_id, {}).get("transfer_format", "")).strip().lower() == "exact"
for node_id in prompt_index.nodes_for_class("DistributedCollector")
)
for worker in workers:
if worker.get("type") != "serverless":
if typed:
raise ValueError(
"DistributedMediaCollector does not support selected direct workers; "
"use master execution or comfy-media-v1 serverless pools"
)
continue
profile = worker.get("media_profile", "legacy-image-v1")
if profile not in ("legacy-image-v1", "comfy-media-v1"):
raise ValueError("Unsupported serverless media_profile")
if typed != (profile == "comfy-media-v1"):
if exact and profile == "legacy-image-v1":
raise ValueError(
"DistributedMediaCollector requires comfy-media-v1 serverless pools; "
"legacy collectors require legacy-image-v1"
"transfer_format exact requires comfy-media-v1 serverless pools; "
"legacy-image-v1 endpoints return 8-bit images"
)
@@ -209,7 +201,7 @@ async def _prepare_worker_payload(
):
"""Prepare one worker prompt payload with bounded concurrency and media-sync timeout."""
async with worker_prep_semaphore:
_validate_media_workers(prompt_index, [worker])
_validate_media_workers([worker], prompt_index)
worker_prompt = prompt_index.copy_prompt()
if worker.get("type") == "serverless":
from .orchestration.runpod_client import serialize_request
@@ -217,9 +209,8 @@ async def _prepare_worker_payload(
from ..serverless_bridge.contract import PROFILE, VERSION
from .orchestration.serverless_media_prompt import compile_media_prompt
from .orchestration.serverless_typed_inputs import prepare_media_inputs
# The legacy pruner only recognizes Collector/Upscale roots.
# The typed compiler validates the complete private graph and
# prunes it itself, retaining every connected typed branch.
# prunes it itself, retaining every connected Collector branch.
worker_prompt = apply_participant_overrides(
worker_prompt, worker["id"], enabled_ids, job_id_map,
"", delegate_master, prompt_index,
@@ -332,34 +323,21 @@ async def orchestrate_distributed_execution(
for node in prompt_obj.values())
target_kwargs = {}
if image_list_mode:
if any(isinstance(node, dict) and node.get("class_type") in {
"DistributedCollector", "DistributedMediaCollector", "UltimateSDUpscaleDistributed"
} for node in prompt_obj.values()):
raise ValueError("Distributed Image List cannot be mixed with other distributed collectors")
# Check the source graph before execution-root pruning can remove an
# incompatible mode (or mask it with an unrelated boundary error).
if any(isinstance(node, dict) and node.get("class_type") in (
"DistributedCollector", "UltimateSDUpscaleDistributed") for node in prompt_obj.values()):
raise ValueError(
"Distributed Image List v1 cannot be mixed with Distributed Collector or "
"Ultimate SD Upscale nodes in one prompt"
)
prompt_obj, outer_targets = prepare_image_list_execution(prompt_obj)
target_kwargs["partial_execution_targets"] = outer_targets
prompt_index = PromptIndex(prompt_obj)
typed_media_mode = bool(prompt_index.nodes_for_class("DistributedMediaCollector"))
if typed_media_mode:
from ..serverless_bridge.contract import identifier
# PromptIndex's JSON copy would otherwise turn integer keys into strings.
for node_id in prompt_obj:
identifier(node_id)
image_list_start_ids = prompt_index.nodes_for_class("DistributedImageListStart")
image_list_mode = bool(image_list_start_ids)
if image_list_mode:
# Validate before probing or queueing anything so malformed boundaries and
# old worker releases fail before any upstream image is materialized.
other_distributed = (
prompt_index.nodes_for_class("DistributedCollector")
+ prompt_index.nodes_for_class("DistributedMediaCollector")
+ prompt_index.nodes_for_class("UltimateSDUpscaleDistributed")
)
if other_distributed:
raise ValueError(
"Distributed Image List v1 cannot be mixed with Distributed Collector, "
"DistributedMediaCollector or Ultimate SD Upscale nodes in one prompt"
)
validate_image_list_regions(prompt_obj, explicit_targets=True)
configured_serverless = [
str(worker.get("id")) for worker in config.get("workers", [])
@@ -380,14 +358,14 @@ async def orchestrate_distributed_execution(
"serverless pools are unsupported"
)
else:
if typed_media_mode and requested_ids is not None:
configured_ids = {worker.get("id") for worker in config.get("workers", [])}
if any(worker_id not in configured_ids for worker_id in requested_ids):
raise ValueError("Unknown explicitly selected media worker ID")
workers = _resolve_enabled_workers(config, requested_ids)
_validate_media_workers(prompt_index, workers)
if typed_media_mode and delegate_master and not workers:
raise RuntimeError("Delegate-only DistributedMediaCollector requires a selected comfy-media-v1 pool")
_validate_media_workers(workers, prompt_index)
if any(worker.get("media_profile") == "comfy-media-v1" for worker in workers):
from ..serverless_bridge.contract import identifier
# Validate the source, not PromptIndex's JSON copy (which coerces
# integer keys to strings), before probing or dispatching workers.
for node_id in prompt_obj:
identifier(node_id)
load_balance_requested = _prompt_requests_load_balance(prompt_index)
trace_debug(
execution_trace_id,
@@ -403,8 +381,8 @@ async def orchestrate_distributed_execution(
),
)
# Keep the legacy Collector fallback. Image-list and typed-media delegate
# modes must not silently enable master participation.
# Keep the legacy Collector fallback, but never override image-list
# orchestrator-only mode merely because its direct workers are unavailable.
if not image_list_mode and not workers and delegate_master:
trace_debug(
execution_trace_id,
@@ -419,19 +397,22 @@ async def orchestrate_distributed_execution(
trace_execution_id=execution_trace_id,
probe_concurrency=worker_probe_concurrency,
)
if typed_media_mode and delegate_master and not active_workers:
raise RuntimeError("No selected comfy-media-v1 worker is reachable for delegate-only DistributedMediaCollector")
if image_list_mode:
from .orchestration.dispatch import preflight_image_list_workers
if not active_workers and delegate_master:
raise RuntimeError(
"No selected direct worker is reachable for Distributed Image List in orchestrator-only mode"
)
idle_ids = (await preflight_image_list_workers(active_workers, require_idle=delegate_master)
if active_workers else {})
active_workers = [worker for worker in active_workers if worker["id"] in idle_ids]
if not active_workers and delegate_master:
raise RuntimeError("No feature-compatible direct worker is idle in orchestrator-only mode")
from ..image_list.runtime import END_CLASS, end_override_names
requires_values = any(end_override_names(node.get("inputs", {}))
for node in prompt_obj.values() if node.get("class_type") == END_CLASS)
value_preflight = {"require_value_results": True} if requires_values else {}
# Persist candidates, not this submission-time idle snapshot: a preceding
# master prompt may still be using them. The image-list runtime rechecks
# capability and availability before assigning the first item.
if active_workers:
await preflight_image_list_workers(active_workers, require_idle=False,
**value_preflight)
else:
delegate_master = selected_delegate_master
@@ -520,8 +501,7 @@ async def orchestrate_distributed_execution(
)
if delegate_master and not image_list_mode:
collector_ids = (find_nodes_by_class(master_prompt, "DistributedCollector")
+ find_nodes_by_class(master_prompt, "DistributedMediaCollector"))
collector_ids = find_nodes_by_class(master_prompt, "DistributedCollector")
upscale_nodes = find_nodes_by_class(master_prompt, "UltimateSDUpscaleDistributed")
if upscale_nodes:
debug_log(
+29 -4
View File
@@ -141,13 +141,16 @@ Also, for collector-based flows:
## Endpoint: `POST /distributed/job_complete`
Submit one completed worker image back to the master collector queue.
Submit direct-worker image/audio results to the master Collector queue. The route
accepts canonical JSON completions or multipart image chunks, including opt-in
exact floating-point IMAGE transport. Serverless typed results use their separate
bridge/provider contract.
### URL
- `http://<master-host>:<master-port>/distributed/job_complete`
### Request Body
### JSON request body
```json
{
@@ -164,13 +167,35 @@ Submit one completed worker image back to the master collector queue.
- `job_id` (string, required)
- `worker_id` (string, required)
- `batch_idx` (integer >= 0, required)
- `image` (string, required)
- `image` (string, optional if `audio` is present)
- PNG payload as either:
- data URL: `data:image/png;base64,...`
- raw base64 PNG bytes
- `is_last` (boolean, required)
Legacy multipart/tensor payload formats are no longer accepted on this endpoint.
At least one of `image` or `audio` must be present. Optional `audio` is an object
with `sample_rate` (positive integer), `shape` (`[batch, channels, samples]`),
`dtype: "float32"`, and `data` (base64 waveform bytes). Audio-only completions
still include `batch_idx`; the Collector uses `0` and `is_last: true`.
### Multipart image chunks
Send `multipart/form-data` with a JSON `envelope` field and repeated binary
`media` file fields. The envelope contains `job_id`, `worker_id`, `format`,
`start_index` (nonnegative integer), `count`, `width`, `height`, `is_last`, and
optional canonical `audio`.
- `png`, `jpeg`, `webp`: one `media` part per frame.
- `ffv1`, `h264`, `h265` (experimental), `av1`: one video part per chunk.
- `exact`: one raw tensor part per frame and an envelope `descriptors` list with
one `tensor-le-v1` IMAGE descriptor per part. Each descriptor shape must be
`[1, height, width, channels]`; decoding preserves supported float dtype and
values instead of quantizing to 8-bit.
`legacy_png` uses JSON, not multipart. Exact transport requires support on both
sides and never retries as an 8-bit format when an older master rejects it.
See [result transfer](compressed-result-transfer.md) for format controls,
non-exact compatibility fallbacks and request limits.
### CORS note
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@@ -1,38 +1,49 @@
# Compressed Result Transfer
# Compressed and Exact Result Transfer
**Distributed Collector** has two optional widgets that control how workers send their images back to the master. This matters most when workers run in the cloud and you pay for data leaving it (egress).
**Distributed Collector** (`DistributedCollector`) collects images and/or audio. Its `transfer_format` and `transfer_quality` widgets control **remote image returns only**; they do not re-encode master-local images or change audio transport. The saved defaults remain `legacy_png` and quality `90`.
| `transfer_format` | Type | Notes |
| `transfer_format` | Fidelity | Direct-worker transport |
|---|---|---|
| `legacy_png` (default) | lossless | Original behaviour: one JSON request per frame, uncompressed PNG in base64. Works with any master version. |
| `png` | lossless | Compressed PNG, binary, several frames per request. Same tensors as `legacy_png`. |
| `ffv1` | lossless | Whole batch as one FFV1 video. Same tensors as `legacy_png`; best lossless choice for frame sequences. |
| `jpeg`, `webp` | lossy | Per-frame stills. Good for image batches. |
| `h264`, `h265`, `av1` | lossy | Whole batch as one video. By far the smallest for video frame batches. |
| `legacy_png` (default) | 8-bit pixels | Original behaviour: one JSON request per frame, uncompressed PNG in base64. Compatible with legacy masters. |
| `png` | Lossless 8-bit pixels | Compressed PNG, binary, several frames per request. |
| `ffv1` | Lossless 8-bit pixels | FFV1 video chunks; useful for frame sequences. |
| `jpeg`, `webp` | Lossy 8-bit images | Per-frame stills. |
| `h264`, `h265` (**experimental**), `av1` | Lossy 8-bit images | Video chunks; useful for similar consecutive frames. |
| `exact` (opt-in) | Original floating-point tensors | Binary multipart chunks with one raw tensor payload and `tensor-le-v1` descriptor per frame. No 8-bit quantization. |
`transfer_quality` (1–100, default 90) applies to the lossy formats. JPEG/WebP use it directly; video codecs map it to CRF (90 → CRF 13 for h264/h265, CRF 16 for av1; 100 → CRF 0).
“Lossless” PNG/FFV1 means lossless **after 8-bit quantization**, not preservation of the source float tensor. Direct non-exact returns decode to RGB float32. `exact` preserves supported floating-point dtype, shape, channels and values: float16, bfloat16, float32 or float64, finite dense BHWC tensors with 1, 3 or 4 channels. It generally costs substantially more bandwidth.
Measured on a 121-frame 1280×720 batch (one worker's result):
`transfer_quality` (1–100, default 90) applies to lossy formats, not `exact`, PNG or FFV1. JPEG/WebP use it directly; video codecs map it to CRF (90 → CRF 13 for h264/h265, CRF 16 for av1; 100 → CRF 0). A quality of 100 is not a substitute for exact float transport.
## Serverless profiles
- **`comfy-media-v1`:** the same Collector supplies the endpoint's image-return policy. The saved `legacy_png` default is mapped to compressed **`png`** with the same 8-bit pixel precision; normal serverless returns are not exact by default. All other formats retain their selection, including opt-in `exact` and experimental `h265`. Missing codecs or rejected formats fail instead of selecting another codec. Typed image returns preserve channel count and carry alpha separately where needed. See [serverless images and audio](serverless-media.md).
- **`legacy-image-v1`:** retains its image-file/`SaveImage` contract, not the typed return-format controls. Selecting a legacy pool when any Collector requests `exact` is rejected before dispatch; there is no 8-bit fallback. Audio requires direct workers or `comfy-media-v1`.
## Direct-worker compatibility and limits
- Choose the format on the master; the value is sent to workers with the workflow.
- **`exact` requires support on both master and worker.** If an older master rejects an exact chunk, the worker raises an error and never resends it as `legacy_png` or another 8-bit format. An exact Collector also rejects direct-worker image returns labelled with a non-exact format. Update both sides or explicitly select a non-exact format.
- For **non-exact compressed** formats only, the specific first-request older-master rejection (`400` with `Invalid JSON payload`) causes a retry using `legacy_png`. Other rejections, including HTTP 413, remain errors.
- For direct video returns only, an unavailable encoder falls back to `h264` for lossy video if available, otherwise `png`; unavailable `ffv1` falls back to `png`. The worker logs this. This compatibility fallback does **not** apply to `exact` or typed serverless returns.
- Multipart requests use a 32 MiB chunk target (`COMFYUI_DISTRIBUTED_TRANSFER_CHUNK_BYTES`) and a 96 MiB request budget (`COMFYUI_DISTRIBUTED_TRANSFER_MAX_REQUEST_BYTES`), reserving space for the envelope and accounting separately for audio. A single large frame may exceed the chunk target but must fit the request budget. Keep the request budget below the master's `--max-upload-size` (default 100 MB).
- Non-exact compressed chunks are all encoded before sending. Exact transfer instead plans frame sizes from shape/dtype before sending and encodes one chunk at a time. An oversized individual frame is rejected before any chunks are sent; choose another format or raise the worker and master limits together.
- Audio is attached to the final image chunk when it fits, otherwise sent separately. Audio-only completions use the canonical JSON envelope. Direct audio uses float32 waveform bytes regardless of the image format; `exact` is an IMAGE option, not a new audio codec.
- Each worker logs the format and bytes sent. These request limits are for direct multipart transfers; serverless request/artifact limits are documented separately.
## Previously documented size example
The following measurements are retained from a 121-frame 1280×720 slow-pan batch (one worker's result); they are not new validation of the current implementation or exact transport.
| Format | Sent | vs legacy | PSNR |
|---|---|---|---|
| `legacy_png` | 446 MB | 1× | lossless |
| `png` | 163 MB | 2.7× | lossless |
| `ffv1` | 128 MB | 3.5× | lossless |
| `legacy_png` | 446 MB | 1× | lossless at 8-bit precision |
| `png` | 163 MB | 2.7× | lossless at 8-bit precision |
| `ffv1` | 128 MB | 3.5× | lossless at 8-bit precision |
| `jpeg` q90 | 27 MB | 17× | 42.7 dB |
| `webp` q90 | 13 MB | 33× | 40.6 dB |
| `h264` q90 | 2.4 MB | 186× | 40.2 dB |
| `h265` q90 | 1.0 MB | 434× | 40.1 dB |
| `h265` q90 (experimental) | 1.0 MB | 434× | 40.1 dB |
| `av1` q90 | 0.4 MB | 1031× | 40.5 dB |
The test clip is a slow pan, which video codecs compress especially well; expect smaller (still large) gains on content with more motion.
Notes:
- Choose the format on the master; the value is sent to workers with the workflow.
- The master must run this version to accept compressed results. A worker sending to an older master gets a 400 on its first request and automatically resends with `legacy_png`.
- If a worker's PyAV build lacks the requested video encoder, it falls back to `h264` (lossy formats) or `png` (`ffv1`), and logs it.
- Lossy formats only affect frames produced by workers; the master's own frames are never re-encoded.
- Request size is bounded on the final encoded bytes. Frames are batched into requests of up to 32 MiB (`COMFYUI_DISTRIBUTED_TRANSFER_CHUNK_BYTES`); a single large frame may go alone in a request of up to 96 MiB (`COMFYUI_DISTRIBUTED_TRANSFER_MAX_REQUEST_BYTES`). Keep the latter below the master's `--max-upload-size` (default 100 MB) if you change either.
- All chunks are encoded before anything is sent. If a single frame cannot fit within the request limit, the worker fails with an error and sends nothing; use a lossy format or raise both limits.
- A rejection other than the older-master case above (for example the master's size limit, HTTP 413) is reported as an error rather than retried as `legacy_png`.
- Each worker logs what it sent, e.g. `Sent 121 image(s) as h264 in 1 request(s): 2.40 MB (raw 334.49 MB, 139.4x smaller)`.
Video codecs compress this slow pan especially well; gains depend on content and motion.
+38 -2
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@@ -52,7 +52,31 @@ Start outputs, in order: `image`, `mask`, `item_index` (zero-based), `context`,
Values that cannot be transported exactly (models, latents, conditioning and other objects) are rejected. Load models inside the region from a loader connected directly to the processing nodes, as described below.
End accepts `images`, optional `masks` / `texts`, and `context`. It returns `image_list`, `masks`, and `texts` in original item order. Each job must produce exactly one image, at most one mask, and at most one text value. Unconnected optional outputs contain `None` entries.
End accepts `images`, optional `masks` / `texts`, and `context`. Its first three outputs remain `image_list`, `masks`, and `texts` in original item order. Each job must produce exactly one image, at most one mask, and at most one text value. Unconnected optional mask/text outputs contain `None` entries.
### Keep values through End without extra wires
Start's existing **context** connection now also lets End recover each item's original `value_1` … `value_10`. You do not need to wire those values individually into End. Context remains a lightweight execution reference, not a general-purpose data pipe.
End appends a **values** output (`DISTRIBUTED_IMAGE_LIST_VALUES`): one value bundle per image, in the same source order. Connect it to **Distributed Image List Get Value**, then choose `value_name` to extract the field you need. The getter preserves its type and ComfyUI maps it across the bundles, producing a list aligned with End's images. Missing fields fail clearly instead of producing empty placeholders.
For example, to preserve filenames:
```text
filenames → Start.value_1
Start.context ─────────────→ End.context
Start.image → processing → End.images
├─ image_list → Save.images
└─ values → Get Value [value_1] → Save.filename_prefix
```
**New or changed per-item values:** connect the calculated result to End's optional `value_N` input. It replaces the original field with that number, or adds it if Start did not supply it. Every connected override must produce exactly one supported value for each item. A tensor batch remains one value, just as it does at Start. Image, mask, string, integer, float and boolean values are supported; arbitrary objects/models/latents and non-finite numbers are not.
End shows override inputs progressively. Use **Add value override** to expose a later slot without connecting the earlier ones. Disconnecting an override never renumbers the other End fields: `value_3` always means `value_3`. Unlike Start's native Autogrow inputs, End overrides must keep these identities stable. Existing image/mask/text/context sockets and output indices are unchanged. If the frontend extension is unavailable, Python still declares all optional override inputs and the values output.
Original values are retained on the master; workers only return explicitly connected overrides, avoiding a redundant return trip for unchanged reference images or other values. Overrides use the existing exact value transport and size limits. Selected workers must advertise `value_results: true` when overrides are requested, checked before execution and again at dispatch. Original-value passthrough alone does not require that additional capability.
Values leaving the region must come from **End**, not directly from Start. To chain regions, extract an End value and connect it to the next Start's corresponding input. Values remain available downstream after End cleans up the execution context; they do not depend on a later lookup of expired job state.
Models are loaded and cached on each processing participant, not serialized between machines. Connect loader outputs directly to the processing nodes. The master needs the region's model files when participating, as does each selected worker. The master still validates the prompt: it needs the relevant node definitions and any model-name/file availability required by their validation (including combo lists). This is not a model-free-master guarantee.
@@ -62,10 +86,22 @@ End's optional `texts` is a connection-only socket. For workflows saved before t
File-based dependencies keep ComfyUI's `[output]` / `[temp]` root annotations and are read on the master. Their worker copies use separate content-addressed paths under `input/distributed-media/`; each worker's rewritten prompt is reused across items without reusing item tokens.
## Native cache reuse
Repeated items can reuse **ComfyUI's native processing cache** on the same participant. Start uses a deterministic identity of the exact image, optional mask, all supplied value slots and source index; tensor dtype, shape and bytes are preserved. Fresh job IDs, assignment tokens and callback addresses live at End, so they no longer invalidate otherwise unchanged upstream processing. End still validates the current assignment and delivers results in source order on every run.
- Processing settings, seeds, links and node fingerprints still participate in ComfyUI's normal signatures. Changed content invalidates reuse; nodes that declare themselves noncacheable still run.
- The master's private executor retains one native LRU output cache between successful slots, capped at **128 entries when retained**. An active graph may temporarily exceed that target; oversized generations are not retained. Failed or cancelled local slots do not seed reuse, and `--cache-none` disables the local output cache. Direct workers use their own configured native caches.
- Cache hits are best-effort, not guaranteed: eviction, restarts, changed ordering/index, changed settings and assignment to a different worker can cause misses. There is no worker affinity, disk cache or separate completed-result store.
- Reuse retains tensor/model references and can increase RAM/VRAM use; the entry cap is **not a byte limit**. Exact identity calculation also adds CPU hashing and, for GPU-resident inputs, a CPU copy.
- Remote items are still uploaded. Each upload returns a worker-computed identity; if a compatible older worker does not return a valid identity, that assignment uses the legacy graph without stable-input reuse. A worker downgrade between items also falls back safely. Assignment expiry and current-item validation apply even when processing hits the cache.
- Keep side-effect nodes outside the region when they must run on every submission. A cached processing node follows ordinary ComfyUI semantics and does not repeat its save/preview side effects; End's delivery and downstream saving still run.
## Scope and failure behavior
- This version supports the master and direct workers, not Runpod serverless pools. Unsupported pools are excluded without contacting the provider for this job.
- Worker eligibility is determined before execution and checked again before dispatch. Busy or transiently unreachable workers are excluded; healthy candidates can continue. Malformed capability/queue responses and incompatible versions fail explicitly. Workers joining later are not added to an already-running region.
- Queue submission checks selected direct workers for reachability and feature compatibility, but does not freeze the worker list to that moment's idle snapshot. A selected worker busy with an earlier prompt remains a candidate for the queued run. Immediately before a region starts assigning images, its saved candidates are checked again: workers still busy or transiently unreachable are excluded; healthy idle candidates can continue. Malformed capability/queue responses and incompatible versions fail explicitly. Workers excluded by the initial reachability probe are not retained, and newly selected workers are not added to an already-queued run. Workers becoming free after the region starts are not added mid-region.
- **Orchestrator Only** requires a selected, reachable, feature-compatible direct worker at submission, but can queue while all selected workers are busy. At region start it requires at least one idle compatible direct worker and fails if none are available; it never falls back to master processing. The queue response's `worker_count` counts saved remote candidates, not guaranteed execution-time participants.
- Omitted worker IDs select enabled direct workers. Explicit IDs select the named configured direct workers, including disabled ones; an empty list stays empty, unknown IDs fail, and serverless workers are excluded. Local workers use the master's loopback callback; remote workers use its configured public address.
- Only the enclosed processing is distributed. List creation and downstream saving run on the master in both modes. With participation enabled, the master is a normal scheduler participant and can run alone when no direct workers are available. **Orchestrator-only mode never silently falls back to master processing.** Failed items are not reassigned automatically.
- Local items execute while the enclosing master prompt waits; they are not queued behind that prompt on the master's `/prompt` queue. The enclosed branch is not also evaluated as a full list by the outer prompt.
+70 -41
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@@ -1,12 +1,19 @@
# Typed serverless media (PR2)
# Serverless images and audio
`comfy-media-v1` adds typed list results, exact by default, for `IMAGE`, `AUDIO`, `MASK`,
`STRING`, and native ComfyUI `VIDEO`. It is an **explicit endpoint profile**, not
an automatically detected capability. Omitting `media_profile` still selects
`legacy-image-v1`; existing image collectors and handlers keep their old contract.
**Distributed Collector** (`DistributedCollector`) collects `IMAGE` and/or `AUDIO`
from direct workers and serverless pools. There is no separate
`DistributedMediaCollector`; replace that removed node with `DistributedCollector`
for image/audio workflows. The Collector has no MASK, STRING, or native VIDEO
output sockets.
`comfy-media-v1` is an **explicit endpoint profile**, not an automatically detected
capability. It returns typed image/audio items to the Collector. The standalone
bridge's generic contract also supports `MASK`, `STRING`, and native ComfyUI
`VIDEO`, but that does not add those outputs to the Collector. Omitting
`media_profile` still selects the image-only `legacy-image-v1` contract.
See [legacy serverless setup](serverless-workers.md) for credentials and recovery.
## Use a typed collector
## Use the Collector with a typed endpoint
1. Prepare an endpoint with the [standalone media bridge](../serverless_bridge/README.md)
and the workflow's models/custom nodes. The endpoint needs only the bridge,
@@ -15,9 +22,9 @@ See [legacy serverless setup](serverless-workers.md) for credentials and recover
**Typed media v1 (requires endpoint bridge)** (`media_profile: "comfy-media-v1"`).
Start with **Images per run = 1**. This existing setting counts remote jobs,
not the number of typed items each job returns.
3. Use **Distributed Media Collector** (`DistributedMediaCollector`), connect at
least one typed input, and connect its outputs to the appropriate downstream
nodes. For literal text, connect a `PrimitiveString` node to `texts`.
3. Use **Distributed Collector** (`DistributedCollector`), connect `images`,
`audio`, or both, and connect the corresponding outputs downstream. Image-only
and audio-only workflows use the same node.
4. Queue through Distributed. **Delegate Only** excludes local upstream
generation; normal collection includes the master's local values.
@@ -25,41 +32,55 @@ See [legacy serverless setup](serverless-workers.md) for credentials and recover
installation, model availability, or typed execution. Selecting a profile does
not install or upgrade anything on the endpoint.
The collector has five optional inputs and five **list outputs**, in this order:
The Collector accepts optional `images` and `audio` inputs, including ComfyUI list
inputs, and returns two ordinary outputs—not list outputs:
| Socket / output index | Native type | Each list item |
| Socket / output index | Native type | Aggregation |
| --- | --- | --- |
| `images` / 0 | `IMAGE` | One BHWC tensor, including its batch dimension |
| `audio` / 1 | `AUDIO` | `{ "waveform": BCT_tensor, "sample_rate": positive_int }` |
| `masks` / 2 | `MASK` | One BHW tensor |
| `texts` / 3 | `STRING` | Exact text, including whitespace or an empty string |
| `video` / 4 | `VIDEO` | One supported native video object |
| `images` / 0 | `IMAGE` | Concatenate BHWC tensors along the batch dimension |
| `audio` / 1 | `AUDIO` | Concatenate waveforms along the sample dimension |
For each connected socket, order is **master first, then the prepared participant
For each connected socket, order is **master first, then prepared participant
order, then that participant's item order**—never completion order. Delegate-only
mode omits the master. An unconnected socket returns `[]`. A connected remote
socket must return 1–64 items; an empty string is an item, an empty list is not.
Lists are not concatenated into one tensor, resampled, resized, or aligned across
sockets. Different items may have different valid shapes or audio sample rates.
The entire remote response is validated and decoded before any of its results
are delivered; missing, duplicate, mismatched, or corrupt results fail collection.
mode omits the master. Image dimensions/channels and audio batch/channel dimensions
must be compatible for concatenation. Use a common audio sample rate; the Collector
does not resample or align streams. Audio-only collection returns `None` for images;
when no audio is supplied it returns a silent placeholder, not an empty list.
The typed wire contract carries 1–64 items per connected socket. The entire remote
response is validated and decoded before any of its results are delivered;
missing, duplicate, mismatched, or corrupt typed results fail collection. Validated
wire lists are then combined into the Collector's ordinary outputs.
Direct workers, `legacy-image-v1` pools and `comfy-media-v1` pools can share an
image-only Collector run. Audio requires direct workers or `comfy-media-v1` pools.
If any Collector requests `exact`, a selected `legacy-image-v1` pool is rejected
before dispatch instead of silently returning 8-bit images.
## Image return format and quality
`transfer_format` defaults to **`exact`** and `transfer_quality` to **90**.
The Collector's saved `transfer_format` default remains **`legacy_png`**, with
`transfer_quality` **90**. For `comfy-media-v1`, the compiler maps `legacy_png` to
**`png`**, so normal serverless returns use compressed 8-bit PNG. `exact` is opt-in.
These controls affect **remote IMAGE returns only**, not master-local images,
uploaded inputs, MASK, AUDIO, STRING or native VIDEO values.
uploaded inputs or AUDIO. The bridge's generic MASK, STRING and native VIDEO
codecs are also unaffected.
- `exact`: preserve floating-point dtype and values, with no image quantization.
- `exact`: preserve supported floating-point dtype and values, with no image
quantization. Available for direct workers and `comfy-media-v1` pools; it is
never downgraded to an 8-bit format. An older master that rejects exact transfer
causes an error, not a legacy retry. Update both sides before selecting it.
- `legacy_png`, `png`, `ffv1`: lossless **8-bit image pixels**, not exact float
tensors. Image values are clipped to [0,1] and quantized as in the existing
Collector. `legacy_png` uses PNG without compression; `png` compresses it.
tensors. For typed serverless returns, image values are clipped to [0,1] and
quantized; `legacy_png` is sent as `png`. Direct workers retain the original
JSON/base64 framing for `legacy_png`.
- `jpeg`, `webp`: lossy still-image compression.
- `h264`, `h265`, `av1`: lossy video encoding of each IMAGE tensor's frame batch.
This does not transcode the native `video` socket or combine separate list items.
- `h264`, `h265` (**experimental**), `av1`: lossy video encoding of each IMAGE
tensor's frame batch. This is an image-return encoding, not a native VIDEO output.
- Quality 1–100 affects lossy formats only, using the existing Collector's
quality-to-CRF mapping for video. Dimensions, frame order and channel count
are preserved; alpha is carried separately where needed, not silently dropped.
are preserved for typed serverless returns; alpha is carried separately where
needed, not silently dropped.
Both master and endpoint must have this bridge revision for compressed image
returns. Pillow handles still formats and CPU/software PyAV encoders handle video;
@@ -67,8 +88,11 @@ unavailable codecs fail clearly rather than silently changing the selection.
Compression can reduce result egress and inline response size, but does not relax
encoded/decoded byte budgets or the input request limit. Compressed IMAGE batches
are capped at 240 frames and 8192 pixels per dimension, with a conservative
256 MiB aggregate decode budget (including codec scratch and alpha). The original Collector's
settings, default and fallback behavior are unchanged.
256 MiB aggregate decode budget (including codec scratch and alpha). Direct
workers have different limits and non-exact compatibility fallbacks; see
[result transfer](compressed-result-transfer.md). The bridge's internal
`MediaExportV1` default is still `exact` for generic callers that omit controls;
the Collector compiler explicitly supplies `png` for the normal saved default.
## Inputs and fidelity
@@ -92,7 +116,11 @@ becoming endpoint-local paths. Models and custom nodes are never transferred.
the pure codec itself rejects non-CPU storage. Remote decoding returns
independently owned CPU values. STRING transport is exact UTF-8 (`utf8-v1`).
### Native VIDEO is not a generic video converter
### Bridge-level native VIDEO support
The generic bridge can import/export native VIDEO, and the typed input adapter
can stage `LoadVideo` for upstream processing. This is not a Collector VIDEO
output or a generic video converter.
The adapter supports the pinned native classes **exactly**, not arbitrary
subclasses or objects exposing `save_to()` / `get_components()`:
@@ -127,11 +155,12 @@ invalidate a returned VIDEO.
**10,000,000 bytes**. Larger results require an operator-supplied publisher
returning expiring public HTTPS artifact URLs. See the bridge guide for
bounded download/upload responsibilities and retention.
- Remote typed transport is **serverless only**. Direct-worker typed transport
is not implemented. Do not mix a typed collector with legacy image collectors,
unsupported distributed/batch nodes, or collector-to-collector dependencies.
Downstream processing stays on the master. Image-list scheduling is separate
PR3 work, not part of this contract.
- The generic five-kind typed envelope is a serverless bridge contract. Direct
workers support Collector images/audio, including opt-in exact IMAGE transport,
not generic MASK/STRING/VIDEO Collector outputs. The typed compiler rejects
unsupported distributed/batch nodes and collector-to-collector dependencies.
Downstream processing stays on the master. [Image-list scheduling](image-list-queue.md)
is a separate execution mode.
## Timeout is not proof that Comfy stopped
@@ -154,5 +183,5 @@ the request or turn every handler exception into a safely reusable terminal
provider job. Master-side provider status/reconciliation alone is not proof
that a surviving local Comfy process or callback has stopped.
These docs describe the implementation and its offline tests; they do not claim
live Runpod/provider or GPU end-to-end validation.
These docs describe the implementation, not a test result or live Runpod/provider
or GPU end-to-end validation.
+19 -8
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@@ -1,9 +1,12 @@
# Runpod serverless workers (preview)
A **Serverless (Runpod)** worker is a queue-based endpoint, not a permanent
ComfyUI machine. Runpod chooses the actual GPU workers. Distributed submits one
normal ComfyUI API workflow per requested image and collects the returned images.
The endpoint workers do not need ComfyUI-Distributed installed.
ComfyUI machine. Runpod chooses the actual GPU workers. This page describes the
default **`legacy-image-v1`** profile: Distributed submits normal ComfyUI API
workflows and collects returned image files. For image/audio collection and
opt-in exact IMAGE returns, use [the `comfy-media-v1` profile](serverless-media.md)
with the same **Distributed Collector** (`DistributedCollector`). The endpoint
workers do not need the full ComfyUI-Distributed extension installed.
## Setup
@@ -14,7 +17,8 @@ The endpoint workers do not need ComfyUI-Distributed installed.
4. Save, then choose **Test Saved Endpoint**. This checks authentication and
endpoint health only; it does not start GPU inference or prove model/node compatibility.
5. Enable the worker when you want queued Distributed workflows to use it.
6. Use an image **Distributed Collector** and queue through Distributed as usual.
6. Connect the **Distributed Collector**'s `images` input and queue through
Distributed as usual. Leave `audio` unconnected for this legacy profile.
Enable **Delegate Only** if the master should collect without generating locally.
Keys are write-only. Leave the key field blank to retain a saved key, or select
@@ -152,9 +156,16 @@ Recovery uses the same trusted-master API as the panel; it never resubmits a job
Worker setting changes become available once all its local tasks and unresolved records
are finished. No credentials or ledger files need to be edited by hand.
## First-version limits
## Legacy profile limits
- Image collectors only. Audio/video transfer and audio collectors are rejected.
- Only the Collector's image branch is supported by `legacy-image-v1`; connected
audio is rejected. `comfy-media-v1` adds audio through the same Collector and
supports the configured image-return formats.
- `exact` is opt-in and requires direct workers or `comfy-media-v1` pools. If any
Collector requests it, a selected legacy pool is rejected before dispatch,
not used as an 8-bit fallback. Other Collector transfer-format controls do not
change this legacy profile's `SaveImage`/image-file contract. With
`comfy-media-v1`, the saved `legacy_png` default is mapped to compressed `png`.
- Coordinated `UltimateSDUpscaleDistributed` workloads and unsupported Distributed
node types are rejected before submission.
- Downstream post-collector processing remains local; the remote graph contains
@@ -168,6 +179,6 @@ are finished. No credentials or ledger files need to be edited by hand.
- No remote GPU discovery, endpoint provisioning, model synchronization, automatic
scaling changes, or inbound master callback/tunnel is needed.
This feature is verified with offline provider/transport tests. A successful
health test is not a paid end-to-end generation test; test your deployed handler
These docs describe the implementation, not a test result. A successful health
check is not a paid end-to-end generation test; validate your deployed handler
and model inventory with one image per run before increasing it.
+2 -1
View File
@@ -6,6 +6,7 @@ import sys
from .runtime import (
END_CLASS,
END_RESULT_INPUTS,
_ancestors_for_inputs,
_downstream_until,
_image_list_pairs,
@@ -49,7 +50,7 @@ def prepare_image_list_execution(prompt: dict) -> tuple[dict, list[str]]:
required = _reachable(boundaries, upstream)
discarded = set()
for start_id, end_id in pairs:
region_nodes = _ancestors_for_inputs(prepared, end_id, start_id, ("images", "masks", "texts"))
region_nodes = _ancestors_for_inputs(prepared, end_id, start_id, END_RESULT_INPUTS)
outside = _downstream_until(prepared, start_id, end_id) - region_nodes
# Preserve bypass joins and their whole chain into post-End computation.
# They must fail boundary validation, not disappear with debug previews.
+40 -1
View File
@@ -24,6 +24,34 @@ from .runtime import ImageListCancelledError
_EXECUTION_LOCK = threading.Lock()
_CACHE_LOCK = threading.Lock()
# One process-local native output cache, not a second completed-result cache.
# Core's LRU can temporarily exceed its target for the active graph; oversized
# generations are discarded rather than retained after an item/run finishes.
NATIVE_CACHE_MAX_ENTRIES = 128
_RETAINED_OUTPUTS = None
def _take_outputs(execution):
global _RETAINED_OUTPUTS
with _CACHE_LOCK:
outputs, _RETAINED_OUTPUTS = _RETAINED_OUTPUTS, None
if getattr(execution.args, "cache_none", False):
return execution.NullCache()
return outputs if outputs is not None else execution.LRUCache(
execution.CacheKeySetInputSignature, max_size=NATIVE_CACHE_MAX_ENTRIES)
def _retain_outputs(outputs):
global _RETAINED_OUTPUTS
# Only the bounded native LRU is eligible. NullCache and test doubles are not.
if getattr(outputs, "max_size", None) != NATIVE_CACHE_MAX_ENTRIES:
return
outputs.clean_unused()
if len(outputs.cache) > NATIVE_CACHE_MAX_ENTRIES:
return
with _CACHE_LOCK:
_RETAINED_OUTPUTS = outputs
class LocalExecutionSlot:
@@ -32,6 +60,7 @@ class LocalExecutionSlot:
self._stop = threading.Event()
self._future = None
self._caches = None
self._failed = False
self._model_callback = model_callback
self._closed = False
self._loop = None
@@ -55,6 +84,10 @@ class LocalExecutionSlot:
self._loop.call_soon_threadsafe(self._task.cancel)
async def close(self):
if self._closed:
return
# A stop requested before close indicates cancellation, not normal drain.
cancelled = self._stop.is_set()
self.request_stop()
try:
if self._future is not None:
@@ -72,6 +105,8 @@ class LocalExecutionSlot:
waiter.exception()
finally:
self._pool.shutdown(wait=True, cancel_futures=True)
if not cancelled and not self._failed and self._caches is not None:
_retain_outputs(self._caches.outputs)
self._caches = None
self._closed = True
@@ -84,7 +119,11 @@ class LocalExecutionSlot:
try:
asyncio.run(self._execute(prompt, prompt_id, end_id, workflow_meta))
except asyncio.CancelledError as exc:
self._failed = True
raise ImageListCancelledError("Local image-list execution cancelled") from exc
except BaseException:
self._failed = True
raise
finally:
with self._state_lock:
self._loop = self._task = None
@@ -125,7 +164,7 @@ class LocalExecutionSlot:
try:
with torch.inference_mode():
if self._caches is None:
outputs = execution.HierarchicalCache(execution.CacheKeySetInputSignature)
outputs = _take_outputs(execution)
objects = execution.HierarchicalCache(execution.CacheKeySetID)
self._caches = SimpleNamespace(outputs=outputs, objects=objects, all=[outputs, objects])
caches = self._caches
+242 -30
View File
@@ -7,9 +7,11 @@ from __future__ import annotations
import asyncio
import base64
import hashlib
import json
import math
import secrets
import struct
import threading
import time
from dataclasses import dataclass, field
@@ -29,6 +31,7 @@ VALUE_OUT_BASE = 4
MAX_VALUE_SLOTS = 10
VALUE_NAMES = tuple(f"value_{i}" for i in range(1, MAX_VALUE_SLOTS + 1))
VALUES_INPUT = "values"
END_RESULT_INPUTS = ("images", "masks", "texts") + VALUE_NAMES
MAX_PAYLOAD_BYTES = 50 * 1024 * 1024
MAX_REQUEST_BYTES = MAX_PAYLOAD_BYTES * 2 + 1024 * 1024
MAX_ITEMS = 4096
@@ -179,6 +182,50 @@ def _normalize_values(values: Any, count: int) -> dict[str, list[Any]]:
return aligned
def end_override_names(inputs: dict[str, Any]) -> tuple[str, ...]:
"""Fixed keys stay stable even when an intermediate socket disconnects."""
for name in inputs:
if (name.startswith("value_") or name.startswith("values.")) and name not in VALUE_NAMES:
raise ValueError(f"Unknown End value slot {name!r}")
return tuple(name for name in VALUE_NAMES if name in inputs)
def _validate_result_values(values: Any) -> dict[str, Any]:
"""Validate flat override values; tensors are values, never frame lists."""
if not isinstance(values, dict):
raise ValueError("End result values must be a mapping of value_N slots")
for name, value in values.items():
if name not in VALUE_NAMES:
raise ValueError(f"Unknown End value slot {name!r}")
kind = value_kind(value, name)
if kind == "TENSOR":
_tensor_dtype_name(value)
if not _torch().isfinite(value).all().item():
raise ValueError(f"End {name} tensor values must be finite")
return values
def normalize_value_overrides(values: Any) -> dict[str, Any]:
if not isinstance(values, dict):
raise ValueError("End overrides must be a mapping of value_N slots")
normalized = {}
for name, raw in values.items():
items = list(raw) if isinstance(raw, (list, tuple)) else [raw]
if len(items) != 1:
raise ValueError(f"Worker End override {name} expects exactly one value, received {len(items)}")
normalized[name] = items[0]
return _validate_result_values(normalized)
def _checked_overrides(result: dict[str, Any], expected: tuple[str, ...]) -> dict[str, Any]:
values = _validate_result_values(result.get("values", {}))
missing, extra = set(expected) - values.keys(), values.keys() - set(expected)
if missing or extra:
raise ValueError(f"End result overrides do not match connected slots: "
f"missing {sorted(missing)}, unexpected {sorted(extra)}")
return values
def normalize_image_list_inputs(images: Any, mask: Any = None, values: Any = None) -> list[dict[str, Any]]:
"""Split images into items and pair masks/generic values by the 1-or-N rule."""
frames = _image_frames(images, "images")
@@ -197,6 +244,64 @@ def normalize_image_list_inputs(images: Any, mask: Any = None, values: Any = Non
]
def is_valid_item_identity(identity: Any) -> bool:
return (isinstance(identity, str) and len(identity) == 71
and identity.startswith("sha256:")
and all(char in "0123456789abcdef" for char in identity[7:]))
def item_content_identity(item: dict[str, Any]) -> str:
"""Hash normalized content, not transport bytes or dispatch metadata.
Length-framed, typed fields distinguish scalars, absent masks, slot names,
shapes and exact tensor bits (including bfloat16). CPU copying changes neither
dtype nor values; local items deliberately have no HTTP transport-size limit.
"""
digest = hashlib.sha256(b"distributed-image-list-item-v1\0")
def field(tag, raw):
digest.update(tag.encode("ascii") + b"\0")
digest.update(len(raw).to_bytes(8, "big"))
digest.update(raw)
def value(raw, name):
if raw is None:
field("NONE", b"")
return
kind = value_kind(raw, name)
if kind == "TENSOR":
cpu = raw.detach().to("cpu").contiguous()
field("DTYPE", str(cpu.dtype).encode("ascii"))
field("SHAPE", json.dumps(list(cpu.shape), separators=(",", ":")).encode("ascii"))
field("TENSOR", memoryview(cpu.view(_torch().uint8).numpy()).cast("B"))
elif kind == "FLOAT":
field(kind, struct.pack("!d", raw))
elif kind == "BOOLEAN":
field(kind, b"1" if raw else b"0")
elif kind == "INT":
field(kind, str(raw).encode("ascii"))
else:
field(kind, raw.encode("utf-8", errors="surrogatepass"))
index = item["index"]
if isinstance(index, bool) or not isinstance(index, int) or index < 0:
raise ValueError("item.index must be a non-negative integer")
field("INDEX", str(index).encode("ascii"))
field("IMAGE", b"")
value(item["image"], "image")
field("MASK", b"")
value(item.get("mask"), "mask")
values = item.get("values") or {}
if not isinstance(values, dict) or any(name not in VALUE_NAMES for name in values):
raise ValueError("item.values must be a mapping of value_N slots")
# Canonical socket order, not the producer's mapping insertion order.
for name in VALUE_NAMES:
if name in values:
field("SLOT", name.encode("ascii"))
value(values[name], name)
return "sha256:" + digest.hexdigest()
def _tensor_dtype_name(tensor: Any) -> str:
name = str(tensor.dtype).removeprefix("torch.")
if name not in _ALLOWED_FLOAT_DTYPES:
@@ -346,26 +451,64 @@ def _sweep_worker_items() -> None:
del _WORKER_ITEMS[token]
def store_worker_item(token: str, item: dict[str, Any]) -> None:
def store_worker_item(token: str, item: dict[str, Any]) -> str:
if not isinstance(token, str) or len(token) < 32 or len(token) > 128:
raise ValueError("token must be a 32-128 character capability")
clean = decode_item(item)
# Never trust a master-supplied digest; hash the decoded worker-owned item.
identity = item_content_identity(clean)
_sweep_worker_items()
with _LOCK:
if token in _WORKER_ITEMS:
raise ValueError("duplicate item token")
_WORKER_ITEMS[token] = (time.monotonic() + _WORKER_ITEM_TTL_SECONDS, clean)
return identity
def consume_worker_item(token: str) -> dict[str, Any]:
def _pop_worker_item(token: str) -> tuple[float, dict[str, Any]]:
if not isinstance(token, str) or not token:
raise ValueError("worker item token is required")
_sweep_worker_items()
with _LOCK:
entry = _WORKER_ITEMS.pop(token, None)
if entry is None:
if entry is None or entry[0] <= time.monotonic():
raise RuntimeError("Image-list item is missing, expired, or already consumed")
return entry[1]
return entry
def _check_worker_item_identity(item: dict[str, Any], identity: str, index: int) -> None:
if not is_valid_item_identity(identity):
raise ValueError("Invalid worker item identity")
actual = item_content_identity(item) if "image" in item else item.get("item_identity")
if actual != identity:
raise ValueError("Worker item identity does not match the staged item")
if isinstance(index, bool) or not isinstance(index, int) or item["index"] != index:
raise ValueError("Worker item index does not match the staged item")
def consume_worker_item(token: str, *, item_identity: str | None = None,
item_index: int | None = None) -> dict[str, Any]:
with _LOCK:
expires, item = _pop_worker_item(token)
if "image" not in item:
raise RuntimeError("Image-list item is already consumed")
if item_identity is not None:
_check_worker_item_identity(item, item_identity, item_index)
if expires <= time.monotonic():
raise RuntimeError("Image-list item is expired")
# Start misses release staged tensors, but retain a tiny per-token
# receipt until End. This is NOT a completed-result/content cache.
# Keeping the original deadline prevents cache hits bypassing TTL.
_WORKER_ITEMS[token] = (expires, {"item_identity": item_identity, "index": item["index"]})
return item
def finish_worker_item(token: str, item_identity: str, item_index: int) -> None:
"""Validate and release this dispatch, whether Start ran or hit native cache."""
expires, item = _pop_worker_item(token)
_check_worker_item_identity(item, item_identity, item_index)
if expires <= time.monotonic():
raise RuntimeError("Image-list item is expired")
def delete_worker_item(token: str) -> bool:
@@ -544,10 +687,11 @@ def validate_image_list_regions(prompt: dict[str, Any], *, explicit_targets: boo
if "images" not in prompt[end_id].get("inputs", {}):
raise ValueError(f"End node {end_id}.images must be connected to the per-item result branch")
end_inputs = prompt[end_id].get("inputs", {})
for name in ("images", "masks", "texts"):
end_override_names(end_inputs)
for name in END_RESULT_INPUTS:
if name in end_inputs:
_direct_link(end_inputs[name], prompt, end_id, name)
region_nodes = _ancestors_for_inputs(prompt, end_id, start_id, ("images", "masks", "texts"))
region_nodes = _ancestors_for_inputs(prompt, end_id, start_id, END_RESULT_INPUTS)
if start_id not in region_nodes:
raise ValueError(f"Start node {start_id} is not an ancestor of End node {end_id}")
forbidden = {n for n in region_nodes if n != start_id and n != end_id and
@@ -592,18 +736,28 @@ def validate_image_list_regions(prompt: dict[str, Any], *, explicit_targets: boo
def build_worker_prompt(prompt: dict[str, Any], region: dict[str, Any], item_token: str,
context_id: str, item_index: int, callback_url: str) -> dict[str, Any]:
"""Copy only the per-image branch and its worker-side dependencies."""
context_id: str, item_index: int, callback_url: str, *,
item_identity: str | None = None) -> dict[str, Any]:
"""Copy the branch; opt into stable ancestry only after a worker handshake."""
start_id, end_id = region["start_id"], region["end_id"]
_direct_link(prompt[end_id].get("inputs", {}).get("context"), prompt, end_id, "context")
kept = {node_id: json.loads(json.dumps(prompt[node_id])) for node_id in region["node_ids"]}
start_inputs = kept[start_id].setdefault("inputs", {})
# Per-item data reaches workers only through the staged item token.
for name in list(start_inputs):
if name in ("images", "mask") or name.startswith(VALUES_INPUT + "."):
start_inputs.pop(name)
start_inputs.update({"worker_item_token": item_token, "worker_context_id": context_id})
end_inputs = kept[end_id].setdefault("inputs", {})
if item_identity is not None:
if not is_valid_item_identity(item_identity):
raise ValueError("Invalid worker item identity")
# Native ancestry signatures include EVERY literal, not just a custom
# fingerprint. Remove inherited execution IDs, callbacks and all source
# inputs wholesale; transient delivery state belongs downstream at End.
kept[start_id]["inputs"] = {"worker_item_identity": item_identity}
end_inputs.update(worker_context_id=context_id, worker_item_identity=item_identity)
else:
# Preserve old raw /prompt and older-worker behavior byte-for-byte.
start_inputs = kept[start_id].setdefault("inputs", {})
for name in list(start_inputs):
if name in ("images", "mask") or name.startswith(VALUES_INPUT + "."):
start_inputs.pop(name)
start_inputs.update({"worker_item_token": item_token, "worker_context_id": context_id})
end_inputs.update({"worker_result_token": item_token,
"worker_item_index": int(item_index),
"master_callback_url": callback_url})
@@ -713,6 +867,7 @@ def set_master_result(context_id: str, item_index: int, token: str, result: dict
raise ValueError("Image-list result token does not match an assigned item")
if item_index in state.received_indexes:
raise ValueError(f"Duplicate image-list result for item {item_index}")
_checked_overrides(result, end_override_names(state.original_prompt[state.end_id].get("inputs", {})))
state.received_indexes.add(item_index)
state.results[item_index] = result
event = state.event
@@ -744,10 +899,24 @@ def decode_result(result: Any) -> dict[str, Any]:
if mask is not None and (mask.ndim != 3 or mask.shape[0] != 1):
raise ValueError("result.mask must contain exactly one MASK frame")
text = decode_value(result.get("text"), "STRING")
return {"image": image, "mask": mask, "text": text}
decoded = {"image": image, "mask": mask, "text": text}
if "values" in result:
raw_values = result["values"]
if not isinstance(raw_values, dict):
raise ValueError("result.values must be an object")
values = {}
for name, encoded in raw_values.items():
if name not in VALUE_NAMES:
raise ValueError(f"result.values has unknown slot {name!r}")
if not isinstance(encoded, dict) or encoded.get("type") not in _VALUE_KINDS:
raise ValueError(f"result.values.{name} has an unsupported type")
values[name] = decode_value(encoded, encoded["type"])
decoded["values"] = _validate_result_values(values)
return decoded
def normalize_worker_result(images: Any, masks: Any = None, texts: Any = None) -> dict[str, Any]:
def normalize_worker_result(images: Any, masks: Any = None, texts: Any = None,
values: Any = None) -> dict[str, Any]:
def one(value, name, kind):
if value is None:
return None
@@ -770,17 +939,29 @@ def normalize_worker_result(images: Any, masks: Any = None, texts: Any = None) -
text = one(texts, "texts", "STRING")
if text is not None and not isinstance(text, str):
raise TypeError("Worker End text result must be a string")
return {"image": image, "mask": mask, "text": text}
result = {"image": image, "mask": mask, "text": text}
if values is not None:
overrides = normalize_value_overrides(values)
if overrides:
result["values"] = overrides
return result
def encode_result(result: dict[str, Any]) -> dict[str, Any]:
normalized = normalize_worker_result(result.get("image"), result.get("mask"), result.get("text"))
return {"image": encode_value(normalized["image"], "IMAGE"),
"mask": encode_value(normalized["mask"], "MASK"),
"text": encode_value(normalized["text"], "STRING")}
encoded = {"image": encode_value(normalized["image"], "IMAGE"),
"mask": encode_value(normalized["mask"], "MASK"),
"text": encode_value(normalized["text"], "STRING")}
if "values" in result:
values = _validate_result_values(result["values"])
if values:
encoded["values"] = {name: encode_value(value, value_kind(value, name))
for name, value in values.items()}
return encoded
def _collect_output_lists(results: list[dict[str, Any]]) -> tuple[list[Any], list[Any], list[Any]]:
def _collect_output_lists(results: list[dict[str, Any]], *, state: ImageListJob | None = None,
include_values: bool = False) -> tuple[list[Any], ...]:
if not results:
raise RuntimeError("Image-list execution returned no results")
images, masks, texts = [], [], []
@@ -790,10 +971,20 @@ def _collect_output_lists(results: list[dict[str, Any]]) -> tuple[list[Any], lis
images.append(result["image"])
masks.append(result.get("mask"))
texts.append(result.get("text"))
return images, masks, texts
if not include_values:
return images, masks, texts
if state is None or len(results) != len(state.items):
raise RuntimeError("Image-list values require exactly one result per source item")
expected = end_override_names(state.original_prompt[state.end_id].get("inputs", {}))
# Reconstruct unchanged values from the master source, not worker transport.
# Do this before the context is removed, and only after all items succeeded.
values = [{**item.get("values", {}), **_checked_overrides(result, expected)}
for item, result in zip(state.items, results)]
return images, masks, texts, values
async def run_image_list_job(context_id: str, *, model_callback=None) -> tuple[list[Any], list[Any], list[Any]]:
async def run_image_list_job(context_id: str, *, model_callback=None,
include_values: bool = False) -> tuple[list[Any], ...]:
"""Schedule a shared queue across the optional master and direct workers."""
state = get_master_job(context_id)
if state is None:
@@ -807,7 +998,7 @@ async def run_image_list_job(context_id: str, *, model_callback=None) -> tuple[l
timer = asyncio.timeout(deadline if deadline > 0 else None)
try:
async with timer:
return await _run_image_list_job(state)
return await _run_image_list_job(state, include_values=include_values)
except TimeoutError:
if timer.expired():
raise TimeoutError(f"Image-list total deadline exceeded ({deadline:g}s); cleanup attempted") from None
@@ -820,7 +1011,7 @@ async def run_image_list_job(context_id: str, *, model_callback=None) -> tuple[l
remove_master_job(context_id)
async def _run_image_list_job(state: ImageListJob):
async def _run_image_list_job(state: ImageListJob, *, include_values: bool = False):
context_id = state.context_id
state.event = asyncio.Event()
state.event_loop = asyncio.get_running_loop()
@@ -838,7 +1029,10 @@ async def _run_image_list_job(state: ImageListJob):
if not workers and not state.master_participates:
raise RuntimeError("Distributed Image List requires enabled direct workers; serverless pools are unsupported")
# Recheck worker capability and idle state immediately before the first item.
worker_statuses = await preflight_image_list_workers(workers, require_idle=not state.master_participates) if workers else {}
override_names = end_override_names(state.original_prompt[state.end_id].get("inputs", {}))
value_preflight = {"require_value_results": True} if override_names else {}
worker_statuses = (await preflight_image_list_workers(
workers, require_idle=not state.master_participates, **value_preflight) if workers else {})
workers = [worker for worker in workers if worker["id"] in worker_statuses]
if not workers and not state.master_participates:
raise RuntimeError("No free, reachable, feature-compatible direct workers are available; master execution is disabled")
@@ -871,7 +1065,8 @@ async def _run_image_list_job(state: ImageListJob):
# /prompt queue, or repeated execution of the original source.
with _LOCK:
_WORKER_ITEMS[token] = (time.monotonic() + _WORKER_ITEM_TTL_SECONDS, state.items[index])
prompt = build_worker_prompt(state.original_prompt, region, token, context_id, index, "")
prompt = build_worker_prompt(state.original_prompt, region, token, context_id, index, "",
item_identity=item_content_identity(state.items[index]))
prompt[state.start_id]["inputs"]["image_list_local_execution"] = True
prompt[state.end_id]["inputs"]["image_list_local_execution"] = True
future = local_executor.submit(prompt, f"{context_id}:{index}", state.end_id, state.workflow_meta)
@@ -881,6 +1076,13 @@ async def _run_image_list_job(state: ImageListJob):
if worker is None:
raise RuntimeError(f"Worker {worker_id} was removed before dispatch")
assert session is not None
if override_names:
# A worker can restart/downgrade between items. Check before upload,
# not merely once at queue preparation or at the start of the run.
ready = await preflight_image_list_workers([worker], require_idle=False,
require_value_results=True)
if worker_id not in ready:
raise RuntimeError(f"Image-list worker {worker_id} is unavailable for value overrides")
token = create_worker_task_token()
register_assignment(context_id, index, worker_id, token)
item_payload = {"context_id": context_id, "item": encode_item(state.items[index])}
@@ -894,6 +1096,14 @@ async def _run_image_list_job(state: ImageListJob):
timeout=aiohttp.ClientTimeout(total=60)) as response:
if response.status >= 400:
raise RuntimeError(f"Worker {worker_id} rejected image-list item {index}: HTTP {response.status}")
try:
stored = await response.json()
except (ValueError, aiohttp.ClientError):
stored = None
# A per-upload acknowledgement gates the new graph. Older workers may
# return no identity; do not change global protocol/preflight contracts.
identity = stored.get("item_identity") if isinstance(stored, dict) else None
identity_options = {"item_identity": identity} if is_valid_item_identity(identity) else {}
callback_url = build_master_callback_url(worker, config=config)
if worker_id not in prepared_worker_prompts:
worker_prompt = build_worker_prompt(state.original_prompt, region, token, context_id,
@@ -906,9 +1116,11 @@ async def _run_image_list_job(state: ImageListJob):
from ..api.orchestration.media_sync import sync_worker_media
await asyncio.wait_for(sync_worker_media(worker, worker_prompt), timeout=60)
prepared_worker_prompts[worker_id] = worker_prompt
# Reuse media rewrites, never assignment identity or a mutable submitted graph.
# Reuse legacy media rewrites, never assignment identity or a mutable
# submitted graph. Keeping this template legacy also permits a worker to
# downgrade between items without inheriting a previous stable identity.
worker_prompt = build_worker_prompt(prepared_worker_prompts[worker_id], region, token,
context_id, index, callback_url)
context_id, index, callback_url, **identity_options)
handle["submission_pending"] = True
prompt_id = await dispatch_worker_prompt(
worker, worker_prompt, state.workflow_meta,
@@ -1041,7 +1253,7 @@ async def _run_image_list_job(state: ImageListJob):
slots = ([master_slot] if local_executor is not None else []) + [worker["id"] for worker in workers]
results = await dynamic_schedule(len(state.items), slots,
dispatch, wait_next, cleanup, interrupted)
return _collect_output_lists(results)
return _collect_output_lists(results, state=state, include_values=include_values)
finally:
if local_executor is not None:
# No timeout here: Python cannot safely kill a synchronous node.
+188 -3
View File
@@ -27,6 +27,39 @@ def _encode_all_chunks(frames, fmt, quality, max_chunk_bytes, max_frame_bytes):
return list(transfer_codec.encode_chunks(frames, fmt, quality, max_chunk_bytes, max_frame_bytes))
def _exact_frame_bytes(image_batch):
"""Wire size of one exact frame, computed from shape/dtype before encoding."""
from ..serverless_bridge import codec as bridge_codec
if not isinstance(image_batch, torch.Tensor) or image_batch.layout != torch.strided or image_batch.is_nested:
raise transfer_codec.TransferCodecError("exact transfer requires a dense IMAGE tensor")
if image_batch.dtype not in bridge_codec._DTYPE_NAMES:
raise transfer_codec.TransferCodecError(f"exact transfer does not support dtype {image_batch.dtype}")
if image_batch.ndim != 4:
raise transfer_codec.TransferCodecError(
f"Expected IMAGE tensor [B,H,W,C], got shape {tuple(image_batch.shape)}")
batch, height, width, channels = (int(value) for value in image_batch.shape)
if min(batch, height, width) <= 0 or channels not in (1, 3, 4):
raise transfer_codec.TransferCodecError("exact IMAGE dimensions must be positive with 1, 3 or 4 channels")
size = height * width * channels * image_batch.element_size()
if size > bridge_codec.MAX_PAYLOAD_BYTES:
raise transfer_codec.FrameTooLargeError("exact frame exceeds the tensor codec byte limit")
return size
def _encode_exact_frames(frames):
"""Encode [1,H,W,C] CPU slices losslessly (runs in an executor)."""
from ..serverless_bridge import codec as bridge_codec
descriptors, parts = [], []
for frame in frames:
try:
descriptor, payload = bridge_codec.encode_payload("IMAGE", frame)
except ValueError as exc:
raise transfer_codec.TransferCodecError(f"exact transfer cannot encode this IMAGE: {exc}") from None
descriptors.append(descriptor)
parts.append(payload)
return descriptors, parts
class DistributedCollectorNode:
INPUT_IS_LIST = True
EMPTY_AUDIO = {"waveform": torch.zeros(1, 2, 1), "sample_rate": 44100}
@@ -200,6 +233,10 @@ class DistributedCollectorNode:
"""Send an image batch, optionally with audio, or an audio-only completion."""
batch_size = 0 if image_batch is None else image_batch.shape[0]
fmt = self._resolve_transfer_format(transfer_format) if batch_size else transfer_codec.LEGACY_FORMAT
if fmt == transfer_codec.EXACT_FORMAT:
# Never downgrade exact to 8-bit: an older master fails the job clearly.
await self._send_exact_batch(image_batch, audio, multi_job_id, master_url, worker_id)
return
if fmt != transfer_codec.LEGACY_FORMAT:
sent = await self._send_compressed_batch(
image_batch, audio, multi_job_id, master_url, worker_id, fmt, transfer_quality
@@ -329,6 +366,100 @@ class DistributedCollectorNode:
)
return True
async def _send_exact_batch(self, image_batch, audio, multi_job_id, master_url, worker_id):
"""Send original floating-point frames as binary multipart chunks.
Frame sizes are known from shape and dtype, so every request is planned
(and an oversized frame rejected) before anything is sent. Frames are
encoded one chunk at a time to avoid holding a second copy of the batch.
Any master rejection raises: there is no lossy or 8-bit fallback.
"""
encoded_audio = encode_audio_payload(audio)
session = await get_client_session()
url = f"{master_url}/distributed/job_complete"
loop = asyncio.get_running_loop()
media_limit = max(1, TRANSFER_MAX_REQUEST_BYTES - self.TRANSFER_ENVELOPE_RESERVE_BYTES)
chunk_limit = min(TRANSFER_MAX_CHUNK_BYTES, media_limit)
frame_bytes = _exact_frame_bytes(image_batch)
total = int(image_batch.shape[0])
if frame_bytes > media_limit:
raise transfer_codec.FrameTooLargeError(
f"Frame 0 is {frame_bytes} bytes as exact, above the {media_limit}-byte request limit. "
"Use png or a lossy format, or raise the master's --max-upload-size together with "
"COMFYUI_DISTRIBUTED_TRANSFER_MAX_REQUEST_BYTES."
)
# Bound descriptor/part-header overhead even for tiny frames.
per_chunk = min(128, max(1, chunk_limit // frame_bytes))
ranges = [(start, min(per_chunk, total - start)) for start in range(0, total, per_chunk)]
audio_bytes = len(json.dumps(encoded_audio)) if encoded_audio is not None else 0
last_media = ranges[-1][1] * frame_bytes
audio_inline = encoded_audio is not None and last_media + audio_bytes <= media_limit
audio_separate = encoded_audio is not None and not audio_inline
height, width = int(image_batch.shape[1]), int(image_batch.shape[2])
if audio_separate and audio_bytes > media_limit:
raise transfer_codec.FrameTooLargeError("exact completion audio exceeds the request limit")
cpu_batch = ensure_contiguous(image_batch.detach().cpu())
started = time.time()
sent_bytes = 0
for index, (start, count) in enumerate(ranges):
frames = [cpu_batch[i:i + 1] for i in range(start, start + count)]
descriptors, parts = await loop.run_in_executor(None, _encode_exact_frames, frames)
is_final_chunk = index == len(ranges) - 1
envelope = {
"job_id": str(multi_job_id),
"worker_id": str(worker_id),
"format": transfer_codec.EXACT_FORMAT,
"start_index": start,
"count": count,
"width": width,
"height": height,
"descriptors": descriptors,
"is_last": bool(is_final_chunk and not audio_separate),
}
if is_final_chunk and audio_inline:
envelope["audio"] = encoded_audio
chunk_bytes = sum(len(p) for p in parts)
body = self._build_multipart(envelope, parts)()
if body.size is None or body.size > TRANSFER_MAX_REQUEST_BYTES:
raise transfer_codec.FrameTooLargeError("exact multipart body exceeds the request limit")
timeout_seconds = min(600, 60 + chunk_bytes // (256 * 1024))
try:
async with session.post(
url,
data=body,
timeout=aiohttp.ClientTimeout(total=timeout_seconds),
) as response:
if response.status >= 400:
body = await response.text()
raise RuntimeError(
f"Master rejected exact chunk {index + 1}/{len(ranges)} ({chunk_bytes} bytes) "
f"with HTTP {response.status}: {body[:300]}. exact needs a master with exact "
"transfer support and requests within its --max-upload-size; it is never "
"resent as an 8-bit format. Update the master or choose png."
)
except Exception as e:
log(f"Worker - Failed to send exact chunk to master: {e}")
debug_log(f"Worker - Full error details: URL={url}")
raise
sent_bytes += chunk_bytes
requests = len(ranges)
if audio_separate:
await self._post_json_completion(session, url, {
"job_id": str(multi_job_id),
"worker_id": str(worker_id),
"batch_idx": 0,
"audio": encoded_audio,
"is_last": True,
})
requests += 1
log(
f"Worker - Sent {total} image(s) as exact in {requests} request(s): "
f"{sent_bytes / 1e6:.2f} MB in {time.time() - started:.1f}s"
)
@staticmethod
async def _post_json_completion(session, url, payload):
timeout_seconds = 60 if "image" in payload else 600
@@ -457,6 +588,31 @@ class DistributedCollectorNode:
worker_images[worker_id][image_index] = tensor
return 1
# Collector sockets a comfy-media-v1 serverless pool may return.
TYPED_MEDIA_SOCKETS = ("images", "audio")
def _store_typed_media(self, worker_images: dict, worker_audio: dict, worker_id: str, media) -> int:
"""Store one atomic typed serverless delivery: ordered IMAGE/AUDIO item lists.
Each IMAGE item is a BHWC batch kept at its list position; AUDIO items are
joined in order, exactly as list inputs are on the master.
"""
if (not isinstance(media, dict) or not media
or any(socket not in self.TYPED_MEDIA_SOCKETS for socket in media)):
raise RuntimeError(f"Invalid serverless media delivery from {worker_id}")
for socket, items in media.items():
if not isinstance(items, list) or not items or any(item is None for item in items):
raise RuntimeError(f"Serverless participant {worker_id} returned no {socket} items")
stored = 0
for index, tensor in enumerate(media.get("images", [])):
if not isinstance(tensor, torch.Tensor) or tensor.ndim != 4:
raise RuntimeError(f"Serverless participant {worker_id} returned an invalid IMAGE item")
worker_images.setdefault(worker_id, {})[index] = tensor
stored += 1
if "audio" in media:
worker_audio[worker_id] = self._normalize_audio_input(media["audio"])
return stored
def _reorder_and_combine_tensors(
self,
worker_images: dict,
@@ -534,6 +690,10 @@ class DistributedCollectorNode:
serverless_manager = self._get_serverless_manager()
return serverless_manager
try:
exact_requested = transfer_codec.normalize_format(transfer_format) == transfer_codec.EXACT_FORMAT
except transfer_codec.TransferCodecError:
exact_requested = False
num_workers = len(expected_workers)
if num_workers == 0:
return (images, audio if audio is not None else self.EMPTY_AUDIO)
@@ -609,6 +769,27 @@ class DistributedCollectorNode:
raise RuntimeError(f"Serverless participant {participant} failed: {message}")
result_worker_id = str(result["worker_id"])
if "media" in result:
if result_worker_id not in serverless_workers:
raise RuntimeError(f"Unexpected serverless media participant {result_worker_id}")
if result_worker_id in workers_done:
raise RuntimeError(f"Duplicate serverless media delivery from {result_worker_id}")
if result.get("is_last") is not True:
raise RuntimeError("Serverless media delivery must be atomic and final")
collected_count += self._store_typed_media(
worker_images, worker_audio, result_worker_id, result["media"]
)
debug_log(f"Master - Got typed media from serverless participant {result_worker_id}")
mark_worker_done(result_worker_id)
return
if (exact_requested and result.get("tensor") is not None
and result.get("format") != transfer_codec.EXACT_FORMAT):
raise RuntimeError(
f"Worker {result_worker_id} sent {result.get('format')} images, but this Collector "
"requested exact. Update ComfyUI-Distributed on that worker; exact is never "
"replaced by an 8-bit format."
)
is_last = result.get("is_last", False)
collected_count += self._store_worker_result(worker_images, result)
debug_log(
@@ -733,6 +914,10 @@ class DistributedCollectorNode:
consume_result(item)
break
if exact_requested and workers_done != expected_workers:
raise RuntimeError(
f"Exact transfer incomplete: missing completion from {sorted(expected_workers - workers_done)}"
)
total_collected = sum(len(imgs) for imgs in worker_images.values())
# Remove the queue before combining so a late provider callback cannot
@@ -753,10 +938,10 @@ class DistributedCollectorNode:
f"(master: {master_batch_size}, workers: {combined.shape[0] - master_batch_size})")
return (combined, combined_audio)
except Exception as e:
if serverless_results:
if serverless_results or exact_requested:
raise RuntimeError(
"Serverless result batch shape mismatch while combining participants "
f"{sorted(serverless_results)}: {e}"
"Serverless or exact result batch shape mismatch while combining participants "
f"{sorted(worker_images)}: {e}"
) from e
log(f"Master - Error combining images: {e}")
# Preserve collected audio for regular workers when image assembly fails.
+131 -13
View File
@@ -10,6 +10,7 @@ from ..image_list.runtime import (
MAX_VALUE_SLOTS,
VALUE_NAMES,
VALUES_INPUT,
end_override_names,
create_master_job,
normalize_image_list_inputs,
normalize_worker_result,
@@ -17,9 +18,40 @@ from ..image_list.runtime import (
encode_result,
encode_request_payload,
unwrap_single,
is_valid_item_identity,
)
def _stable_worker_binding(prompt, start_id, identity):
"""Resolve transient state only from the exact Start -> End context wire."""
if not is_valid_item_identity(identity):
raise ValueError("Invalid worker item identity")
start = prompt.get(start_id) if isinstance(prompt, dict) else None
if (not isinstance(start, dict) or start.get("class_type") != "DistributedImageListStart"
or start.get("inputs", {}).get("worker_item_identity") != identity):
raise RuntimeError("Worker item identity is missing its paired Start")
candidates = []
for end_id, node in prompt.items():
if not isinstance(node, dict) or node.get("class_type") != "DistributedImageListEnd":
continue
inputs = node.get("inputs", {})
link = inputs.get("context")
if isinstance(link, (list, tuple)) and list(link) == [start_id, 3]:
candidates.append((end_id, inputs))
if len(candidates) != 1:
raise RuntimeError("Worker Start must have exactly one paired End context wire")
end_id, inputs = candidates[0]
if inputs.get("worker_item_identity") != identity:
raise ValueError("Paired End worker item identity does not match Start")
for name in ("worker_result_token", "worker_context_id"):
if not isinstance(inputs.get(name), str) or not inputs[name]:
raise RuntimeError(f"Worker paired End is missing {name}")
index = inputs.get("worker_item_index")
if isinstance(index, bool) or not isinstance(index, int) or index < 0:
raise ValueError("Worker paired End item index is invalid")
return end_id, inputs
# Types the transport can carry exactly. Anything else (models, latents, ...)
# must be loaded inside the region or shared as a model dependency instead.
_VALUE_TYPES = [io.Image, io.Mask, io.String, io.Int, io.Float, io.Boolean]
@@ -69,10 +101,26 @@ class DistributedImageListStart(io.ComfyNode):
worker_item_token="", worker_context_id="", image_list_enabled_worker_ids="[]",
image_list_master_callback="", image_list_execution_id="",
image_list_workflow_meta=None, image_list_master_participates=None,
image_list_local_execution=False):
image_list_local_execution=False, worker_item_identity=None):
worker_item_token = unwrap_single(worker_item_token)
worker_context_id = unwrap_single(worker_context_id)
worker_item_identity = unwrap_single(worker_item_identity)
image_list_workflow_meta = unwrap_single(image_list_workflow_meta)
if worker_item_identity is not None:
prompt = getattr(cls.hidden, "prompt", None)
start_id = str(getattr(cls.hidden, "unique_id", ""))
_, delivery = _stable_worker_binding(prompt, start_id, worker_item_identity)
# Only cache misses execute Start. Never put this token/context in
# Start's literal inputs or returned context: both would poison reuse.
from ..image_list.runtime import consume_worker_item
item = consume_worker_item(delivery["worker_result_token"],
item_identity=worker_item_identity,
item_index=delivery["worker_item_index"])
item_values = item.get("values") or {}
return io.NodeOutput(
[item["image"]], [item.get("mask")], [item["index"]], [worker_item_identity],
*[[item_values.get(name)] for name in VALUE_NAMES],
)
if worker_item_token:
if unwrap_single(image_list_local_execution):
from ..image_list.runtime import consume_worker_item
@@ -149,17 +197,23 @@ class DistributedImageListEnd(io.ComfyNode):
node_id="DistributedImageListEnd",
display_name="Distributed Image List End",
category="image/distributed",
description="Closes a paired image-list region and returns ordered image, mask, and text lists.",
description=("Closes a paired image-list region and returns ordered image, mask, text and values lists. "
"Start values are retained; connected value_N inputs replace or add that key per item."),
inputs=[
io.Image.Input("images", lazy=True),
io.Mask.Input("masks", optional=True, lazy=True),
io.String.Input("texts", optional=True, lazy=True, force_input=True),
io.Custom("DISTRIBUTED_IMAGE_LIST_CONTEXT").Input("context"),
*[io.MultiType.Input(name, types=_VALUE_TYPES, optional=True, lazy=True,
tooltip="Replace or add this value for each item; exactly one value per item.")
for name in VALUE_NAMES],
],
outputs=[
io.Image.Output(display_name="image_list", is_output_list=True),
io.Mask.Output(display_name="masks", is_output_list=True),
io.String.Output(display_name="texts", is_output_list=True),
io.Custom("DISTRIBUTED_IMAGE_LIST_VALUES").Output(
id="values", display_name="values", is_output_list=True),
],
is_input_list=True,
is_output_node=True,
@@ -181,7 +235,8 @@ class DistributedImageListEnd(io.ComfyNode):
and node.get("class_type") == "DistributedImageListEnd"
and node.get("inputs", {}).get("worker_result_token") == worker_result_token), {})
required = []
for name, value in (("images", images), ("masks", masks), ("texts", texts)):
for name, value in (("images", images), ("masks", masks), ("texts", texts),
*((name, kwargs.get(name)) for name in end_override_names(end_inputs))):
connected = name == "images" or name in end_inputs
missing = value is None or (isinstance(value, (list, tuple)) and all(v is None for v in value))
if connected and missing:
@@ -195,26 +250,63 @@ class DistributedImageListEnd(io.ComfyNode):
@classmethod
def execute(cls, images=None, masks=None, texts=None, context=None,
worker_result_token="", worker_item_index=-1, master_callback_url="",
image_list_local_execution=False):
image_list_local_execution=False, worker_context_id="",
worker_item_identity=None, **values):
worker_result_token = unwrap_single(worker_result_token)
master_callback_url = unwrap_single(master_callback_url)
worker_context_id = unwrap_single(worker_context_id)
worker_item_identity = unwrap_single(worker_item_identity)
if worker_item_identity is not None and not worker_result_token:
raise RuntimeError("Worker item identity is missing its delivery token")
if worker_result_token:
context_values = [item for item in (context if isinstance(context, (list, tuple)) else [context])
if isinstance(item, str) and item]
if not context_values or len(set(context_values)) != 1:
raise RuntimeError("Worker Image List End is missing its paired context")
result = normalize_worker_result(images, masks, texts)
prompt = getattr(cls.hidden, "prompt", {}) or {}
context_id = context_values[0]
if worker_item_identity is not None:
end_id = str(getattr(cls.hidden, "unique_id", ""))
node = prompt.get(end_id, {})
link = node.get("inputs", {}).get("context")
if (node.get("class_type") != "DistributedImageListEnd"
or not isinstance(link, (list, tuple)) or len(link) != 2):
raise RuntimeError("Worker End is missing its paired Start context wire")
paired_id, end_inputs = _stable_worker_binding(prompt, link[0], worker_item_identity)
if (paired_id != end_id or context_values != [worker_item_identity]
or end_inputs["worker_context_id"] != worker_context_id
or end_inputs["worker_result_token"] != worker_result_token
or end_inputs["worker_item_index"] != unwrap_single(worker_item_index)
or end_inputs.get("master_callback_url", "") != master_callback_url):
raise RuntimeError("Worker End identity/context does not match its current delivery")
from ..image_list.runtime import finish_worker_item
# On a miss this consumes Start's receipt; on a hit it validates
# and drops the current staged tensors. Both paths enforce TTL.
finish_worker_item(worker_result_token, worker_item_identity,
unwrap_single(worker_item_index))
context_id = worker_context_id
else:
end_inputs = next((node.get("inputs", {}) for node in prompt.values()
if isinstance(node, dict) and node.get("class_type") == "DistributedImageListEnd"
and node.get("inputs", {}).get("worker_result_token") == worker_result_token), {})
expected = end_override_names(end_inputs)
# Missing connected lazy values must fail instead of becoming an
# implicit fallback to the original Start value.
supplied = {name: values.get(name) for name in expected}
supplied.update(values)
result = normalize_worker_result(images, masks, texts, supplied)
if unwrap_single(image_list_local_execution):
from ..image_list.runtime import set_master_result
set_master_result(context_values[0], int(unwrap_single(worker_item_index)),
set_master_result(context_id, int(unwrap_single(worker_item_index)),
str(worker_result_token), result)
else:
payload = encode_result(result)
run_async_in_server_loop(
submit_worker_result(str(master_callback_url), str(worker_result_token),
context_values[0], int(unwrap_single(worker_item_index)), payload), timeout=60,
context_id, int(unwrap_single(worker_item_index)), payload), timeout=60,
)
return io.NodeOutput([result["image"]], [result.get("mask")], [result.get("text")])
return io.NodeOutput([result["image"]], [result.get("mask")], [result.get("text")],
[result.get("values", {})])
context_values = [item for item in (context if isinstance(context, (list, tuple)) else [context])
if isinstance(item, str) and item]
@@ -229,14 +321,40 @@ class DistributedImageListEnd(io.ComfyNode):
# Add them to its existing tracker; never start/end another tracker
# that could unpin a model still needed by the parent.
model_callback = getattr(execution_list, "output_link_callback", None)
result_lists = run_async_in_server_loop(run_image_list_job(context_id, model_callback=model_callback))
result_lists = run_async_in_server_loop(run_image_list_job(
context_id, model_callback=model_callback, include_values=True))
except ImageListCancelledError:
import comfy.model_management
raise comfy.model_management.InterruptProcessingException()
images_out, masks_out, texts_out = result_lists
if len(images_out) != len(masks_out) or len(images_out) != len(texts_out):
raise RuntimeError("Image-list collector received mismatched image, mask, or text result counts")
return io.NodeOutput(images_out, masks_out, texts_out)
images_out, masks_out, texts_out, values_out = result_lists
if len({len(output) for output in result_lists}) != 1:
raise RuntimeError("Image-list collector received mismatched image, mask, text or values result counts")
return io.NodeOutput(images_out, masks_out, texts_out, values_out)
class DistributedImageListGetValue(io.ComfyNode):
"""Ordinary scalar execution lets core map over End's ordered values list."""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DistributedImageListGetValue",
display_name="Distributed Image List Get Value",
category="image/distributed",
inputs=[io.Custom("DISTRIBUTED_IMAGE_LIST_VALUES").Input("values"),
io.Combo.Input("value_name", options=list(VALUE_NAMES), default="value_1")],
outputs=[io.AnyType.Output(id="value", display_name="value")],
)
@classmethod
def execute(cls, values, value_name="value_1"):
if value_name not in VALUE_NAMES:
raise ValueError(f"Unknown image-list value slot {value_name!r}")
if not isinstance(values, dict):
raise TypeError("Image List Get Value expects one values mapping from Image List End")
if value_name not in values:
raise ValueError(f"Image List Get Value: {value_name} is absent from this item's values")
return io.NodeOutput(values[value_name])
async def submit_worker_result(callback_url, item_token, context_id, item_index, result):
-229
View File
@@ -1,229 +0,0 @@
"""Typed, lossless list collection for serverless media participants.
The master compiler supplies expected_media before pruning delegate inputs.
The provider manager owns remote deadlines and delivers one atomic native-media
message per participant. This node never serializes, concatenates or resamples.
"""
import asyncio
import json
import time
import comfy.model_management
from comfy_api.v0_0_2 import io
import server
from ..serverless_bridge.contract import MAX_ITEMS, SOCKET_KINDS, identifier
from ..serverless_bridge.image_transfer import (FORMATS, DEFAULT_FORMAT, DEFAULT_QUALITY,
FORMAT_TOOLTIP, QUALITY_TOOLTIP, validate_options)
from ..utils.async_helpers import run_async_in_server_loop
POLL_SECONDS = 0.1
START_GRACE_SECONDS = 2.0
def _control(value, name, kind):
# Only execution controls are scalars wrapped by ComfyUI's list mapper.
if isinstance(value, list):
if len(value) != 1:
raise ValueError(f'{name} must be a singleton control')
value = value[0]
if type(value) is not kind:
raise ValueError(f'Invalid {name} control')
return value
def _names(value, name):
try:
result = json.loads(_control(value, name, str))
except (ValueError, TypeError):
raise ValueError(f'{name} must be a JSON string list') from None
if (not isinstance(result, list) or any(not isinstance(item, str) for item in result)
or len(result) != len(set(result))):
raise ValueError(f'{name} must contain unique strings')
return result
def _validate_media(media, expected):
if not isinstance(media, dict) or set(media) != set(expected):
raise ValueError('Media sockets do not match expected_media')
for socket, items in media.items():
if (socket not in SOCKET_KINDS or not isinstance(items, list)
or not 1 <= len(items) <= MAX_ITEMS or any(item is None for item in items)):
raise ValueError('Connected media sockets require nonempty bounded item lists')
def _prepare(media, controls):
worker = _control(controls['is_worker'], 'is_worker', bool)
delegate = _control(controls['delegate_only'], 'delegate_only', bool)
passthrough = _control(controls['pass_through'], 'pass_through', bool)
_control(controls['load_balance'], 'load_balance', bool) # Routing is master-owned.
if worker:
raise ValueError('DistributedMediaCollector does not support direct-worker execution')
participants = _names(controls['enabled_worker_ids'], 'enabled_worker_ids')
for participant in participants:
identifier(participant)
if not participant.startswith('serverless:') or not participant[len('serverless:'):]:
raise ValueError('DistributedMediaCollector requires serverless participant IDs')
expected = _names(controls['expected_media'], 'expected_media')
if any(socket not in SOCKET_KINDS for socket in expected):
raise ValueError('Unknown expected_media socket')
local = {socket: items for socket, items in media.items() if items is not None}
_validate_media(local, local)
expected = expected or list(local)
if not expected:
raise ValueError('At least one connected media socket is required')
if not delegate or passthrough:
_validate_media(local, expected)
if delegate and not passthrough and not participants:
raise ValueError('Delegate-only collection requires serverless participants')
return local, participants, expected, delegate, passthrough
def _outputs(media):
return tuple(media.get(socket, []) for socket in SOCKET_KINDS)
def _consume(message, participants, expected, received):
if not isinstance(message, dict):
raise RuntimeError('Invalid serverless media delivery')
participant = message.get('worker_id')
if not isinstance(participant, str) or participant not in participants:
raise RuntimeError('Unexpected serverless participant')
if participant in received:
raise RuntimeError('Duplicate serverless participant delivery')
if message.get('is_last') is not True:
raise RuntimeError('Serverless media delivery must be atomic and final')
if 'serverless_error' in message:
if (set(message) != {'worker_id', 'serverless_error', 'is_last'}
or not isinstance(message['serverless_error'], str) or not message['serverless_error']):
raise RuntimeError('Invalid serverless error delivery')
# Do not echo provider-controlled text, credentials or signed URLs.
raise RuntimeError(f'Serverless participant {participant} failed to produce media')
if set(message) != {'worker_id', 'media', 'is_last'}:
raise RuntimeError('Invalid serverless media delivery fields')
_validate_media(message['media'], expected)
received[participant] = message['media']
async def _collect(job, media, controls):
ps = server.PromptServer.instance
queue = None
try:
# Reuse the master's precreated queue, including early completions.
async with ps.distributed_jobs_lock:
queue = ps.distributed_pending_jobs.setdefault(job, asyncio.Queue())
local, participants, expected, delegate, passthrough = _prepare(media, controls)
if passthrough or not participants:
return _outputs(local)
received = {}
started = time.monotonic()
while len(received) < len(participants):
comfy.model_management.throw_exception_if_processing_interrupted()
async with ps.distributed_jobs_lock:
if ps.distributed_pending_jobs.get(job) is not queue:
raise RuntimeError('Serverless collector queue was removed or replaced')
try:
message = await asyncio.wait_for(queue.get(), timeout=POLL_SECONDS)
except asyncio.TimeoutError:
comfy.model_management.throw_exception_if_processing_interrupted()
# wait_for cancellation may race an atomic manager delivery.
if not queue.empty():
continue
manager = getattr(ps, 'distributed_serverless', None)
for participant in participants:
if participant in received:
continue
if manager is not None and manager.is_pending(job, participant):
continue # The manager enforces its remote absolute deadline.
if manager is not None and manager.owns(job, participant):
raise RuntimeError('Serverless participant stopped without delivering media')
# The master queues its prompt before manager.start journals slots.
if time.monotonic() - started >= START_GRACE_SECONDS:
raise RuntimeError('Serverless manager did not start the expected participant')
continue
_consume(message, participants, expected, received)
comfy.model_management.throw_exception_if_processing_interrupted()
# Drain and close atomically with the manager's delivery lock. A queued
# duplicate after the final expected message must not yield success.
async with ps.distributed_jobs_lock:
if ps.distributed_pending_jobs.get(job) is not queue:
raise RuntimeError('Serverless collector queue was removed or replaced')
while not queue.empty():
_consume(queue.get_nowait(), participants, expected, received)
del ps.distributed_pending_jobs[job]
ordered = [] if delegate else [local]
ordered.extend(received[participant] for participant in participants)
return tuple([item for source in ordered for item in source.get(socket, [])]
for socket in SOCKET_KINDS)
except BaseException:
# Remove only our queue before cancellation can trigger final deliveries.
async with ps.distributed_jobs_lock:
if queue is not None and ps.distributed_pending_jobs.get(job) is queue:
del ps.distributed_pending_jobs[job]
manager = getattr(ps, 'distributed_serverless', None)
if manager is not None:
try:
await manager.cancel(collector_job_id=job)
except BaseException:
pass # Preserve the original failure/interruption, not cleanup's.
raise
finally:
async with ps.distributed_jobs_lock:
if queue is not None and ps.distributed_pending_jobs.get(job) is queue:
del ps.distributed_pending_jobs[job]
async def _bridge_result(coro):
# The legacy sync bridge catches Exception, not asyncio.CancelledError.
# Carry BaseException as data so cancellation is re-raised on the caller.
try:
return await coro, None
except BaseException as error:
return None, error
class DistributedMediaCollector(io.ComfyNode):
@classmethod
def define_schema(cls):
types = (io.Image, io.Audio, io.Mask, io.String, io.Video)
return io.Schema(
node_id='DistributedMediaCollector', display_name='Distributed Media Collector',
category='utils',
inputs=[io.Boolean.Input('load_balance', default=False),
# Native non-widget inputs expose forceInput via extra_dict.
*[kind.Input(socket, optional=True,
**({'force_input': True} if socket == 'texts'
else {'extra_dict': {'forceInput': True}}))
for socket, kind in zip(SOCKET_KINDS, types)],
io.Combo.Input("transfer_format", options=list(FORMATS), default=DEFAULT_FORMAT,
optional=True, tooltip=FORMAT_TOOLTIP),
io.Int.Input("transfer_quality", default=DEFAULT_QUALITY, min=1, max=100,
optional=True, tooltip=QUALITY_TOOLTIP)],
outputs=[kind.Output(display_name=socket, is_output_list=True)
for socket, kind in zip(SOCKET_KINDS, types)],
is_input_list=True, accept_all_inputs=True,
)
@classmethod
def execute(cls, images=None, audio=None, masks=None, texts=None, video=None,
load_balance=False, multi_job_id='', is_worker=False,
enabled_worker_ids='[]', delegate_only=False, pass_through=False,
expected_media='[]', transfer_format=DEFAULT_FORMAT, transfer_quality=DEFAULT_QUALITY):
validate_options(_control(transfer_format, 'transfer_format', str),
_control(transfer_quality, 'transfer_quality', int))
media = dict(images=images, audio=audio, masks=masks, texts=texts, video=video)
controls = dict(load_balance=load_balance, is_worker=is_worker,
enabled_worker_ids=enabled_worker_ids, delegate_only=delegate_only,
pass_through=pass_through, expected_media=expected_media)
job = _control(multi_job_id, 'multi_job_id', str)
if not job:
local, participants, _, _, passthrough = _prepare(media, controls)
if participants and not passthrough:
raise ValueError('Serverless collection requires multi_job_id')
return io.NodeOutput(*_outputs(local))
identifier(job)
result, error = run_async_in_server_loop(_bridge_result(_collect(job, media, controls)))
if error is not None:
raise error
return io.NodeOutput(*result)
+2 -3
View File
@@ -10,8 +10,7 @@ from . import utilities as _utilities
from .collector import DistributedCollectorNode as _CollectorRuntime
from ..utils import transfer_codec as _transfer_codec
from .distributed_upscale import UltimateSDUpscaleDistributed as _UpscaleRuntime
from .image_list import DistributedImageListEnd, DistributedImageListStart
from .media_collector import DistributedMediaCollector
from .image_list import DistributedImageListEnd, DistributedImageListStart, DistributedImageListGetValue
class DistributedSeed(io.ComfyNode):
@@ -213,4 +212,4 @@ class DistributedWorkerSink(io.ComfyNode):
NODES = [DistributedCollector, DistributedSeed, DistributedModelName, DistributedValue,
ImageBatchDivider, AudioBatchDivider, DistributedEmptyImage, UltimateSDUpscaleDistributed,
DistributedWorkerSink, DistributedImageListStart, DistributedImageListEnd, DistributedMediaCollector]
DistributedWorkerSink, DistributedImageListStart, DistributedImageListEnd, DistributedImageListGetValue]
+26 -11
View File
@@ -6,8 +6,12 @@ executor `execute_media_request`. It does **not** start ComfyUI, provision an
endpoint, install models, or provide a provider worker/supervisor. The endpoint
needs this bridge, not the full ComfyUI-Distributed extension.
For collector setup, list ordering, supported native VIDEO states, and PR2
limitations, see [typed serverless media](../docs/serverless-media.md).
The bridge's generic contract supports `IMAGE`, `AUDIO`, `MASK`, `STRING`, and
native ComfyUI `VIDEO`. **Distributed Collector** (`DistributedCollector`) uses
only the image/audio outputs and combines their returned lists into ordinary
IMAGE/AUDIO outputs. It has no MASK/STRING/VIDEO outputs, and the separate
`DistributedMediaCollector` node has been removed. For Collector setup, ordering,
and limits, see [serverless images and audio](../docs/serverless-media.md).
## Install in the endpoint image
@@ -70,7 +74,8 @@ returning `{"error": ...}` or marking a provider job `FAILED` is not that policy
## Request and response
A minimal text request builder (no files or model required by this graph):
A minimal generic bridge text request builder (no files or model required by this
graph). This illustrates the bridge contract, not a Collector text output:
```python
import json
@@ -158,17 +163,27 @@ the external envelope. [contract.py](contract.py) is the schema authority.
## Optional image return encoding
IMAGE `MediaExportV1` nodes may also contain literal `transfer_format` and
`transfer_quality` controls. Defaults are `exact` and `90`; old exact requests
omit the controls and retain their original representation. Other media exports
must not carry image controls. The master compiler supplies them from the typed
Collector's widgets; callers should not alter binding identity metadata.
`transfer_quality` controls. The **bridge-level defaults** are `exact` and `90`;
generic callers that omit the controls retain that representation. Other media
exports must not carry image controls. Callers should not alter binding identity
metadata.
Formats: `exact`, `legacy_png`, `png`, `ffv1`, `jpeg`, `webp`, `h264`, `h265`, `av1`.
The **Collector default is different**: its saved widget value remains
`legacy_png` with quality `90`. For a `comfy-media-v1` pool, the master compiler
maps that value to an explicit `png` export, so normal serverless image returns
use compressed 8-bit PNG. `exact` is opt-in at the Collector. Other selected
formats are unchanged. Direct workers also support opt-in exact IMAGE transport;
an older master rejection is an error, not an 8-bit retry. A Collector requesting
`exact` cannot dispatch to a `legacy-image-v1` pool.
Formats: `exact`, `legacy_png`, `png`, `ffv1`, `jpeg`, `webp`, `h264`,
`h265` (**experimental**), `av1`.
`exact` preserves float tensor bits. The image/video codecs quantize RGB/grayscale
to 8-bit; PNG/FFV1 are lossless at that precision, while the other codecs are
lossy. Quality is an integer 1–100 and affects only lossy formats. IMAGE batch
order, shape and channel count are retained, and alpha is not discarded.
Native VIDEO/MASK/AUDIO/STRING still use their original exact codecs.
Native VIDEO/MASK/AUDIO/STRING bridge values still use their original exact codecs;
the image-format controls do not add those sockets to the Collector.
Copy/update the complete bridge directory on both sides, including
`image_transfer.py` and `image_codec.py`. Pillow and the selected CPU/software
@@ -247,5 +262,5 @@ unidentifiable files are not claimed; directories and all unidentified leftovers
remain lifecycle-owned. This is not a temp-directory sweep. Independently decoded
VIDEO objects do not depend on either staging or exporter files.
Verification scope: source and offline tests. No live provider/GPU validation is
claimed, and this directory is not a tested turnkey provider deployment.
This guide describes the source contract, not a test result. No live provider/GPU
validation is claimed, and this directory is not a turnkey provider deployment.
@@ -0,0 +1,172 @@
"""Offline CPU proof against ComfyUI's real signatures and PromptExecutor.
Exercises one native worker executor and separate private master-slot lifetimes,
including their shared stable Start / fresh End contract.
"""
import asyncio
import copy
import importlib
from pathlib import Path
import sys
import types
ROOT = Path(__file__).resolve().parents[2]
def main(core):
sys.path.insert(0, str(Path(core).resolve()))
import comfy.cli_args
comfy.cli_args.args.cpu = True # Must precede all model-management imports.
comfy.cli_args.args.disable_assets = True
import execution
import nodes
import torch
from comfy_execution.caching import CacheKeySetInputSignature
name = '_native_cache_identity_probe'
for suffix in ('', '.image_list', '.nodes', '.utils'):
package = types.ModuleType(name + suffix)
package.__path__ = [str(ROOT / suffix.lstrip('.').replace('.', '/'))]
sys.modules[name + suffix] = package
def forbidden(*args, **kwargs):
raise AssertionError('offline native proof attempted a server-loop bridge')
sys.modules[name + '.utils.async_helpers'] = types.SimpleNamespace(run_async_in_server_loop=forbidden)
rt = importlib.import_module(name + '.image_list.runtime')
boundary = importlib.import_module(name + '.nodes.image_list')
calls = []
class Operation:
@classmethod
def INPUT_TYPES(cls):
return {'required': {'image': ('IMAGE',), 'mask': ('MASK',),
'text': ('STRING', {'forceInput': True})}}
RETURN_TYPES = ('IMAGE', 'MASK', 'STRING')
FUNCTION = 'run'
def run(self, image, mask, text):
calls.append(text)
return image + 1, mask + 1, text + ':done'
nodes.NODE_CLASS_MAPPINGS.update(DistributedImageListStart=boundary.DistributedImageListStart,
DistributedImageListEnd=boundary.DistributedImageListEnd,
Operation=Operation)
prompt = {
'start': {'class_type': rt.START_CLASS, 'inputs': {'image_list_execution_id': 'outer-A'}},
'op': {'class_type': 'Operation', 'inputs': {
'image': ['start', 0], 'mask': ['start', 1], 'text': ['start', 4]}},
'end': {'class_type': rt.END_CLASS, 'inputs': {
'images': ['op', 0], 'masks': ['op', 1], 'texts': ['op', 2],
'context': ['start', 3], 'value_3': ['start', 2]}},
}
data = rt.normalize_image_list_inputs(torch.zeros(1, 2, 3, 3, dtype=torch.float64),
torch.ones(1, 2, 3, dtype=torch.bfloat16),
{'value_1': 'original', 'value_2': 3})[0]
region = rt.validate_image_list_regions(prompt)[0]
def prepare(data):
token = rt.create_worker_task_token()
context = rt.create_master_job('start', 'end', prompt, [data], ['worker'], '')
rt.register_assignment(context, data['index'], 'worker', token)
identity = rt.store_worker_item(token, rt.encode_item(data))
result = rt.build_worker_prompt(prompt, region, token, context, data['index'], '', item_identity=identity)
result['start']['inputs']['image_list_local_execution'] = True
result['end']['inputs']['image_list_local_execution'] = True
return token, context, result
async def signature(graph, executor):
dynamic = execution.DynamicPrompt(graph)
changed = execution.IsChangedCache('signature-only', dynamic, executor.caches.outputs)
keys = CacheKeySetInputSignature(dynamic, graph.keys(), changed)
await keys.add_keys(graph.keys())
return {node: keys.get_data_key(node) for node in graph}
async def probe():
server = types.SimpleNamespace(client_id=None, last_node_id=None, send_sync=lambda *a, **kw: None)
executor = execution.PromptExecutor(server, cache_type=execution.CacheType.LRU,
cache_args={'lru': 64, 'ram': 0, 'ram_inactive': 0})
ta, ca, a = prepare(data)
prompt['start']['inputs']['image_list_execution_id'] = 'outer-B'
tb, cb, b = prepare(data)
assert ta != tb and ca != cb
ka, kb = await signature(a, executor), await signature(b, executor)
assert ka['start'] == kb['start'] and ka['op'] == kb['op']
assert ka['end'] != kb['end']
assert a['start']['inputs'] == b['start']['inputs']
# Exercise actual V3 list mapping, hidden.prompt/unique_id, lazy inputs,
# native node signatures and cache hits, not a reimplementation of them.
for number, (token, context, graph) in enumerate(((ta, ca, a), (tb, cb, b))):
valid = await execution.validate_prompt(f'native-{number}', graph, ['end'])
assert valid[0], valid
await executor.execute_async(graph, f'native-{number}', {}, ['end'])
assert executor.success, executor.status_messages
assert token not in rt._WORKER_ITEMS
result = rt.get_master_job(context).results[0]
assert torch.equal(result['image'], data['image'] + 1)
assert torch.equal(result['mask'], data['mask'] + 1)
assert result['text'] == 'original:done'
assert result['values'] == {'value_3': 0}
cached = next(entry['nodes'] for event, entry in executor.status_messages if event == 'execution_cached')
if number:
assert 'start' in cached and 'op' in cached and 'end' not in cached, cached
assert calls == ['original'], calls
for failure in ('expired', 'mismatched'):
token, context, graph = prepare(data)
expires, staged = rt._WORKER_ITEMS[token]
if failure == 'expired':
rt._WORKER_ITEMS[token] = (0, staged)
else:
staged['image'] = staged['image'] + 9
await executor.execute_async(graph, f'rejected-{failure}', {}, ['end'])
assert not executor.success, failure
assert not rt.get_master_job(context).received_indexes
assert token not in rt._WORKER_ITEMS
cached = next(entry['nodes'] for event, entry in executor.status_messages if event == 'execution_cached')
assert 'start' in cached and 'op' in cached and 'end' not in cached, cached
assert calls == ['original'], calls
for part in ('image', 'mask', 'value', 'index'):
changed = copy.deepcopy(data)
if part in ('image', 'mask'):
changed[part].reshape(-1)[0] += 1
elif part == 'value':
changed['values']['value_2'] = 4 # Even an unconnected slot is content.
else:
changed['index'] = 1
token, context, graph = prepare(changed)
keys = await signature(graph, executor)
assert keys['start'] != ka['start'] and keys['op'] != ka['op'], part
await executor.execute_async(graph, f'changed-{part}', {}, ['end'])
assert executor.success, executor.status_messages
assert token not in rt._WORKER_ITEMS
assert rt.get_master_job(context).received_indexes == {changed['index']}
assert len(calls) == 5, calls
# Combine the real Start/End identity path with cache transfer between
# private master slots, not just independent signature/retention probes.
local = importlib.import_module(name + '.image_list.local_executor')
before = len(calls)
for number in range(2):
token, context, graph = prepare(data)
slot = local.LocalExecutionSlot()
try:
await asyncio.wrap_future(slot.submit(graph, f'local-{number}', 'end'))
result = rt.get_master_job(context).results[0]
assert torch.equal(result['image'], data['image'] + 1)
assert result['values'] == {'value_3': 0}
assert token not in rt._WORKER_ITEMS
finally:
await slot.close()
rt.remove_master_job(context)
assert local._RETAINED_OUTPUTS is not None
assert len(calls) == before + 1, calls
assert not rt._WORKER_ITEMS
assert not torch.cuda.is_initialized()
print('NATIVE_CACHE_IDENTITY_OK stable Start/op keys; fresh End key; '
'1 operation for 2 same-data jobs; 4 changed-content misses; '
'expired/mismatched cached inputs rejected; current delivery and cleanup')
asyncio.run(probe())
if __name__ == '__main__':
main(sys.argv[1])
+109 -16
View File
@@ -37,14 +37,14 @@ import aiohttp
torch.set_num_threads(1)
SOURCE_CALLS, BRANCH_CALLS, CAPTURED, MODEL_CALLS = [], [], [], []
UTILITY_CALLS, ITEM_EXECUTIONS = [], []
UTILITY_CALLS, ITEM_EXECUTIONS, VALUE_CAPTURES = [], [], []
ALLOW_MASTER = False
COUNT = 7
def source_rows():
return [{'image': torch.full((1, 5 + i, 7, 3 if i % 2 == 0 else 4), i / 16, dtype=torch.float32),
'reference_image': torch.full((1, 2, 3, 3), (i + 1) / 16),
'reference_image': torch.full((2, 2, 3, 3), (i + 1) / 16),
'mask': torch.full((1, 5 + i, 7), (i + 1) / 16),
'text': f'item-{i}', 'strength': (i + 1) / 16, 'seed': 100 + i, 'index': i}
for i in range(COUNT)]
@@ -92,7 +92,9 @@ def assert_item_execution(prompt, index):
from comfy_execution.utils import get_executing_context
context = get_executing_context()
assert context is not None
assert prompt['2']['inputs'].get('worker_item_token'), 'per-item node ran in outer graph'
assert prompt['4']['inputs'].get('worker_result_token'), 'per-item node ran in outer graph'
assert (prompt['2']['inputs'].get('worker_item_identity')
or prompt['2']['inputs'].get('worker_item_token')), 'per-item source binding missing'
running, _ = server.PromptServer.instance.prompt_queue.get_current_queue_volatile()
assert len(running) == 1, running
entry = running[0]
@@ -160,17 +162,32 @@ class TestImageOperation:
return image + strength, mask * 0.5, f'{text}:seed-{seed}'
class TestValueOverride:
@classmethod
def INPUT_TYPES(cls):
return {'required': {'text': ('STRING', {'forceInput': True}),
'index': ('INT', {'forceInput': True})}}
RETURN_TYPES = ('STRING', 'INT')
FUNCTION = 'run'
def run(self, text, index):
assert WORKER or ALLOW_MASTER, 'override branch executed on delegate-only master'
return (text + ':override', 200 + index)
class TestCapture:
@classmethod
def INPUT_TYPES(cls):
return {'required': {'images': ('IMAGE',), 'masks': ('MASK',), 'texts': ('STRING', {'forceInput': True})}}
return {'required': {'images': ('IMAGE',), 'masks': ('MASK',), 'texts': ('STRING', {'forceInput': True})},
'optional': {'values': ('DISTRIBUTED_IMAGE_LIST_VALUES',),
'picked': ('STRING', {'forceInput': True})}}
INPUT_IS_LIST = True
OUTPUT_NODE = True
RETURN_TYPES = ()
FUNCTION = 'run'
def run(self, images, masks, texts):
def run(self, images, masks, texts, values=None, picked=None):
assert not WORKER, 'post-End capture leaked into a worker prompt'
CAPTURED.append((images, masks, texts))
VALUE_CAPTURES.append((values, picked))
return ()
@@ -179,11 +196,19 @@ async def setup():
ps = server.PromptServer(asyncio.get_running_loop(), assets)
assert await nodes.load_custom_node(str(ROOT))
module = sys.modules[str(ROOT).replace('.', '_x_')]
boundary = importlib.import_module(module.__name__ + '.nodes.image_list')
encode_result = boundary.encode_result
def checked_encode(result):
encoded = encode_result(result)
if 'values' in encoded:
assert set(encoded['values']) == {f'value_{n}' for n in (2, 3, 4, 5, 7, 8, 9, 10)}
return encoded
boundary.encode_result = checked_encode
nodes.NODE_CLASS_MAPPINGS.update(TestImageSource=TestImageSource,
TestImageOperation=TestImageOperation, TestCapture=TestCapture,
TestCachedModel=TestCachedModel, TestSharedFlag=TestSharedFlag,
TestRequiredOutputUtility=TestRequiredOutputUtility,
TestDiscardedPreview=TestDiscardedPreview)
TestDiscardedPreview=TestDiscardedPreview, TestValueOverride=TestValueOverride)
# Match the CORS-enabled direct-worker launch contract for canvas probes.
app = web.Application(middlewares=[server.create_cors_middleware('*')])
# Register the actual feature routes, not test replacements.
@@ -271,21 +296,45 @@ def graph(callback, worker_ids):
'images': ['1', 0], 'mask': ['1', 2],
'values.value_1': ['1', 1], 'values.value_2': ['1', 3],
'values.value_3': ['1', 4], 'values.value_4': ['1', 5], 'values.value_5': ['7', 0],
'values.value_6': ['1', 2],
'image_list_enabled_worker_ids': json.dumps(worker_ids), 'image_list_master_callback': callback}},
'3': {'class_type': 'TestImageOperation', 'inputs': {
key: ['2', index] for key, index in [('image', 0), ('mask', 1), ('index', 2),
('reference_image', 4), ('text', 5), ('strength', 6), ('seed', 7), ('flag', 8)]}
| {'image': ['8', 0], 'model': ['6', 0]}},
'4': {'class_type': 'DistributedImageListEnd', 'inputs': {
'images': ['3', 0], 'masks': ['3', 1], 'texts': ['3', 2], 'context': ['2', 3]}},
'5': {'class_type': 'TestCapture', 'inputs': {'images': ['4', 0], 'masks': ['4', 1], 'texts': ['4', 2]}},
'images': ['3', 0], 'masks': ['3', 1], 'texts': ['3', 2], 'context': ['2', 3],
'value_2': ['10', 0], 'value_3': ['2', 6], 'value_4': ['2', 7], 'value_5': ['2', 8],
'value_7': ['3', 1], 'value_8': ['2', 4], 'value_9': ['2', 9], 'value_10': ['10', 1]}},
'5': {'class_type': 'TestCapture', 'inputs': {'images': ['4', 0], 'masks': ['4', 1], 'texts': ['4', 2],
'values': ['4', 3], 'picked': ['get', 0]}},
'6': {'class_type': 'TestCachedModel', 'inputs': {}},
'7': {'class_type': 'TestSharedFlag', 'inputs': {}},
'8': {'class_type': 'TestRequiredOutputUtility', 'inputs': {'image': ['2', 0], 'index': ['2', 2]}},
'9': {'class_type': 'TestDiscardedPreview', 'inputs': {'image': ['8', 0]}},
'10': {'class_type': 'TestValueOverride', 'inputs': {'text': ['2', 5], 'index': ['2', 2]}},
'get': {'class_type': 'DistributedImageListGetValue', 'inputs': {'values': ['4', 3], 'value_name': 'value_2'}},
}
def check_carried_values():
values, picked = VALUE_CAPTURES[-1]
assert len(values) == len(picked) == COUNT
for index, (actual, source) in enumerate(zip(values, source_rows())):
assert set(actual) == {f'value_{n}' for n in range(1, 11)}, actual.keys()
assert torch.equal(actual['value_1'], source['reference_image'])
assert actual['value_2'] == picked[index] == f'item-{index}:override'
assert actual['value_3'] == source['strength']
assert actual['value_4'] == source['seed']
assert actual['value_5'] is True
assert torch.equal(actual['value_6'], source['mask'])
assert torch.equal(actual['value_7'], source['mask'] * 0.5)
assert torch.equal(actual['value_8'], source['reference_image'])
assert actual['value_8'].shape[0] == 2, 'value tensor batch was split'
assert torch.equal(actual['value_9'], source['mask'])
assert actual['value_10'] == 200 + index
async def coordinator():
global ALLOW_MASTER
ps, assets, module, app = await setup()
@@ -403,6 +452,7 @@ async def coordinator():
assert executor.success, executor.status_messages
assert len(SOURCE_CALLS) == 1 and not BRANCH_CALLS and not UTILITY_CALLS
assert len(CAPTURED) == 1
check_carried_values()
images, masks, texts = CAPTURED[0]
assert len(images) == len(masks) == len(texts) == COUNT
for i, row in enumerate(source_rows()):
@@ -430,15 +480,20 @@ async def coordinator():
# left unconnected. Region N+1 must not race the prior worker End.
chained = {'1': {'class_type': 'EmptyImage', 'inputs': {
'height': 8, 'width': 8, 'batch_size': 2, 'color': 0}}}
chained['constant'] = {'class_type': 'TestSharedFlag', 'inputs': {}}
previous = '1'
value_source = 'constant'
for start, end in [('2', '3'), ('4', '5'), ('6', '7')]:
chained[start] = {'class_type': 'DistributedImageListStart', 'inputs': {
'images': [previous, 0]}}
'images': [previous, 0], 'values.value_1': [value_source, 0]}}
chained[end] = {'class_type': 'DistributedImageListEnd', 'inputs': {
'images': [start, 0], 'context': [start, 3]}}
value_source = f'get-{end}'
chained[value_source] = {'class_type': 'DistributedImageListGetValue', 'inputs': {
'values': [end, 3], 'value_name': 'value_1'}}
previous = end
chained['8'] = {'class_type': 'TestCapture', 'inputs': {
'images': ['7', 0], 'masks': ['7', 1], 'texts': ['7', 2]}}
'images': ['7', 0], 'masks': ['7', 1], 'texts': ['7', 2], 'values': ['7', 3]}}
saved_chained = copy.deepcopy(chained)
with patch.object(config_mod, 'load_config', return_value=config), \
patch.object(orchestration, 'load_config', return_value=config), \
@@ -451,6 +506,7 @@ async def coordinator():
assert len(CAPTURED) == 2 and len(CAPTURED[1][0]) == 2
assert all(torch.equal(image, torch.zeros((1, 8, 8, 3))) for image in CAPTURED[1][0])
assert CAPTURED[1][1:] == ([None, None], [None, None])
assert VALUE_CAPTURES[1][0] == [{'value_1': True}, {'value_1': True}]
assert not runtime._MASTER_JOBS
print('IMAGE_LIST_SEQUENTIAL_OK three regions; one worker; optional outputs omitted')
# Same public queue path, now with master participation enabled. The
@@ -459,6 +515,8 @@ async def coordinator():
config['settings']['master_delegate_only'] = False
participation_counts = {}
for mode, selected in [('mixed', [w['id'] for w in workers]), ('master-only', [])]:
# Keep cold-run exact-once assertions independent of other modes.
importlib.import_module(module.__name__ + '.image_list.local_executor')._RETAINED_OUTPUTS = None
executor = execution.PromptExecutor(ps, cache_args={'ram': 0, 'ram_inactive': 0}, asset_manager=assets)
before_sources, before_local, before_models = len(SOURCE_CALLS), len(BRANCH_CALLS), len(MODEL_CALLS)
before_utilities, before_events = len(UTILITY_CALLS), len(ITEM_EXECUTIONS)
@@ -504,6 +562,7 @@ async def coordinator():
else:
assert not remote_calls and len(local_calls) == COUNT
assert Counter(local_calls + remote_calls) == Counter(range(COUNT)), (local_calls, remote_calls)
check_carried_values()
images, masks, texts = CAPTURED[-1]
assert len(images) == len(masks) == len(texts) == COUNT
for i, row in enumerate(source_rows()):
@@ -512,8 +571,36 @@ async def coordinator():
assert texts[i] == f'item-{i}:seed-{100 + i}', (mode, texts)
assert not runtime._MASTER_JOBS and not runtime._WORKER_ITEMS
participation_counts[mode] = {'master': len(local_calls), 'workers': len(remote_calls)}
# Repeat the completed master-only queue with a fresh outer executor.
# Every item must be delivered again without recomputing its branch.
before_sources, before_local = len(SOURCE_CALLS), len(BRANCH_CALLS)
before_models, before_utilities = len(MODEL_CALLS), len(UTILITY_CALLS)
before_captures = len(CAPTURED)
previous_outputs = CAPTURED[-1]
executor = execution.PromptExecutor(ps, cache_args={'ram': 0, 'ram_inactive': 0}, asset_manager=assets)
with patch.object(config_mod, 'load_config', return_value=config), \
patch.object(orchestration, 'load_config', return_value=config), \
patch.object(orchestration, 'queue_prompt_payload', enqueue), \
patch.object(config_mod, 'get_worker_timeout_seconds', return_value=15):
await asyncio.wait_for(orchestration.orchestrate_distributed_execution(
prompt, None, 'test', []), 45)
assert executor.success, executor.status_messages
assert len(SOURCE_CALLS) == before_sources + 1
assert len(BRANCH_CALLS) == before_local
assert len(MODEL_CALLS) == before_models
assert len(UTILITY_CALLS) == before_utilities
assert len(CAPTURED) == before_captures + 1
for before, after in zip(previous_outputs[:2], CAPTURED[-1][:2]):
assert len(before) == len(after) == COUNT
assert all(torch.equal(a, b) for a, b in zip(before, after))
assert previous_outputs[2] == CAPTURED[-1][2]
check_carried_values()
assert not runtime._MASTER_JOBS and not runtime._WORKER_ITEMS
assert prompt == saved_prompt
print('IMAGE_LIST_CACHE_REQUEUE_OK unchanged items reused; fresh ordered delivery and cleanup')
print('IMAGE_LIST_MASTER_OK ' + json.dumps(participation_counts))
assert len(outer_executions) == 4 and all(row['captures'] == 1 for row in outer_executions)
print('IMAGE_LIST_VALUES_OK passthrough all six types; sparse override/add; Get; sequential Get-to-Start; direct/mixed/master-only')
assert len(outer_executions) == 5 and all(row['captures'] == 1 for row in outer_executions)
print('IMAGE_LIST_TARGETS_OK ' + json.dumps({
'modes': ['delegate-only', 'mixed', 'master-only'],
'outer_targets': outer_executions[0]['targets'], 'worker_targets': ['4'],
@@ -527,6 +614,12 @@ async def coordinator():
del direct_prompt['8'], direct_prompt['9']
direct_prompt['3']['inputs']['image'] = ['2', 0]
# Real Comfy user-interrupt flag while a real worker executor is active.
rows = source_rows()
items = runtime.normalize_image_list_inputs(
[row['image'] for row in rows], [row['mask'] for row in rows],
{f'value_{n}': [row[key] for row in rows]
for n, key in ((1, 'reference_image'), (2, 'text'), (3, 'strength'), (4, 'seed'), (6, 'mask'))}
| {'value_5': True})
slow = workers[1]
async with aiohttp.ClientSession() as session:
async def slow_stats():
@@ -540,11 +633,11 @@ async def coordinator():
await asyncio.sleep(0.02)
raise AssertionError('worker state transition timed out')
before = await until(lambda state: not state['running'] and not state['pending'])
context = runtime.create_master_job('2', '4', direct_prompt, source_rows(), ['slow'],
context = runtime.create_master_job('2', '4', direct_prompt, items, ['slow'],
f'http://127.0.0.1:{port}')
with patch.object(config_mod, 'load_config', return_value=config), \
patch.object(config_mod, 'get_worker_timeout_seconds', return_value=15):
task = asyncio.create_task(runtime.run_image_list_job(context))
task = asyncio.create_task(runtime.run_image_list_job(context, include_values=True))
active = await until(lambda state: bool(state['running']))
owned_id = active['running'][0]
comfy.model_management.interrupt_current_processing(True)
@@ -562,14 +655,14 @@ async def coordinator():
assert not runtime._MASTER_JOBS
print('IMAGE_LIST_CANCEL_OK owned prompt stopped; no further dispatch; context removed')
# A worker exception before End must fail, not hang or return a partial list.
bad_rows = source_rows()
bad_rows[0]['seed'] = -1
bad_rows = copy.deepcopy(items)
bad_rows[0]['values']['value_4'] = -1
context = runtime.create_master_job('2', '4', direct_prompt, bad_rows, ['fast'],
f'http://127.0.0.1:{port}')
with patch.object(config_mod, 'load_config', return_value=config), \
patch.object(config_mod, 'get_worker_timeout_seconds', return_value=15):
try:
await asyncio.wait_for(runtime.run_image_list_job(context), 5)
await asyncio.wait_for(runtime.run_image_list_job(context, include_values=True), 5)
raise AssertionError('worker exception was ignored')
except RuntimeError as exc:
assert 'failed image-list item' in str(exc), str(exc)
@@ -0,0 +1,114 @@
"""CPU-only native Comfy execution across private-slot lifetimes."""
import asyncio
import importlib
import os
from pathlib import Path
import sys
import types
ROOT = Path(__file__).resolve().parents[2]
sys.path.insert(0, sys.argv[1])
import comfy.cli_args
comfy.cli_args.args.cpu = True
comfy.cli_args.args.disable_assets = True
import execution
import nodes
pkg = '_local_retention_probe'
for name, path in [(pkg, ROOT), (pkg + '.image_list', ROOT / 'image_list')]:
module = types.ModuleType(name)
module.__path__ = [str(path)]
sys.modules[name] = module
local = importlib.import_module(pkg + '.image_list.local_executor')
CALLS = []
OUTPUTS = []
class Counted:
@classmethod
def INPUT_TYPES(cls):
return {'required': {'value': ('INT',)}}
RETURN_TYPES = ('INT',)
FUNCTION = 'run'
def run(self, value):
if value < 0:
raise ValueError('intentional retention failure')
CALLS.append(value)
return (value,)
class Uncached(Counted):
@classmethod
def IS_CHANGED(cls, **kwargs):
return float('nan')
class Capture:
@classmethod
def INPUT_TYPES(cls):
return {'required': {'value': ('INT',), 'delivery': ('STRING',)}}
RETURN_TYPES = ()
OUTPUT_NODE = True
FUNCTION = 'run'
def run(self, value, delivery):
OUTPUTS.append((value, delivery))
return ()
nodes.NODE_CLASS_MAPPINGS.update(RetentionCounted=Counted, RetentionUncached=Uncached, RetentionCapture=Capture)
async def run(slot, value, delivery, uncached=False):
prompt = {
'source': {'class_type': 'RetentionUncached' if uncached else 'RetentionCounted', 'inputs': {'value': value}},
'end': {'class_type': 'RetentionCapture', 'inputs': {'value': ['source', 0], 'delivery': delivery}},
}
await asyncio.wrap_future(slot.submit(prompt, delivery, 'end'))
async def main():
first = local.LocalExecutionSlot()
await run(first, 1, 'first-1')
await run(first, 2, 'first-2')
await first.close()
assert local._RETAINED_OUTPUTS is not None
second = local.LocalExecutionSlot()
await run(second, 1, 'second-1')
await run(second, 2, 'second-2')
await run(second, 3, 'changed')
await second.close()
assert CALLS == [1, 2, 3], CALLS
assert OUTPUTS == [(1, 'first-1'), (2, 'first-2'), (1, 'second-1'), (2, 'second-2'), (3, 'changed')], OUTPUTS
third = local.LocalExecutionSlot()
await run(third, 4, 'uncached-1', True)
await run(third, 4, 'uncached-2', True)
await third.close()
assert CALLS[-2:] == [4, 4]
comfy.cli_args.args.cache_none = True
disabled = local.LocalExecutionSlot()
await run(disabled, 1, 'disabled-1')
await run(disabled, 1, 'disabled-2')
await disabled.close()
assert CALLS[-2:] == [1, 1]
assert local._RETAINED_OUTPUTS is None
comfy.cli_args.args.cache_none = False
failed = local.LocalExecutionSlot()
await run(failed, 7, 'before-failure')
try:
await run(failed, -1, 'failure')
except RuntimeError:
pass
else:
raise AssertionError('failure fixture unexpectedly succeeded')
await failed.close()
assert local._RETAINED_OUTPUTS is None
cancelled = local.LocalExecutionSlot()
await run(cancelled, 8, 'cancelled')
cancelled.request_stop()
await cancelled.close()
assert local._RETAINED_OUTPUTS is None
oversized = execution.LRUCache(execution.CacheKeySetInputSignature, max_size=local.NATIVE_CACHE_MAX_ENTRIES)
empty = execution.DynamicPrompt({})
await oversized.set_prompt(empty, [], execution.IsChangedCache('bound', empty, oversized))
oversized.min_generation = oversized.generation
oversized.cache = {n: n for n in range(local.NATIVE_CACHE_MAX_ENTRIES + 1)}
local._retain_outputs(oversized)
assert local._RETAINED_OUTPUTS is None
print('PASS: cross-run native cache reuse, fresh delivery, input change, noncacheable nodes, cache_none, cancellation, retention bound')
asyncio.run(main())
+88 -45
View File
@@ -93,11 +93,12 @@ async def check(base):
contract = load("serverless_bridge.contract")
video_codec = load("serverless_bridge.video_codec")
bridge = load("serverless_bridge")
collector = load("nodes.media_collector").DistributedMediaCollector
collector = load("nodes.v3").DistributedCollector
extension = await bridge.comfy_entrypoint()
bridge_classes = await extension.get_node_list()
assert {cls.GET_SCHEMA().node_id for cls in bridge_classes} == {"MediaImportV1", "MediaExportV1"}
sockets = list(contract.SOCKET_KINDS)
collector_sockets = ["images", "audio"]
kinds = list(contract.SOCKET_KINDS.values())
assert kinds == ["IMAGE", "AUDIO", "MASK", "STRING", "VIDEO"]
io_types = [io.Image, io.Audio, io.Mask, io.String, io.Video]
@@ -165,9 +166,10 @@ async def check(base):
assert set(media) == set(expected)
for socket in expected:
assert len(media[socket]) == len(expected[socket]), (tag, socket)
# Collector must preserve native objects and ordered item lists,
# not merge batches, resample, convert types, or encode again.
assert all(a is e for a, e in zip(media[socket], expected[socket])), (tag, socket)
# Existing Collector outputs are a batched IMAGE and one AUDIO,
# not lists. Native V3 wraps each output once for this list sink.
for actual, wanted in zip(media[socket], expected[socket]):
exact(contract.SOCKET_KINDS[socket], actual, wanted)
counts = {socket: len(media[socket]) for socket in expected}
seen_sinks[tag[0]] = counts
return io.NodeOutput(ui={"accepted": [counts]})
@@ -180,6 +182,19 @@ async def check(base):
def node(class_type, **inputs):
return {"class_type": class_type, "inputs": inputs}
def add_bridge_exports(workflow, manifest, request_id, participant, job_id, links):
"""Exercise bridge-only kinds without inventing Collector sockets."""
for socket, link in links.items():
export_id = "bridge-export:" + socket
binding = {"binding_id": "bridge-binding:" + socket, "collector_id": "bridge-only",
"job_id": job_id, "participant_id": participant, "node_id": export_id,
"socket": socket, "kind": contract.SOCKET_KINDS[socket]}
assert export_id not in workflow
manifest.append(binding)
workflow[export_id] = node("MediaExportV1", request_id=request_id,
binding_json=json.dumps(binding), **{socket: link})
contract.validate_bindings(manifest)
def tiny_video(variant):
buffer = bytes_io.BytesIO()
with av.open(buffer, "w", format="matroska") as container:
@@ -199,7 +214,7 @@ async def check(base):
image[0, 0, 0, 0] = -0.0
mask = torch.tensor([[[-0.0, 0.125, 0.875]]], dtype=torch.bfloat16)
audio = {"waveform": torch.arange(38, dtype=torch.float64).reshape(1, 2, 19) / 997,
"sample_rate": 44100 + variant}
"sample_rate": 44100}
audio["waveform"][0, 0, 0] = -0.0
component = VideoFromComponents(VideoComponents(
images=image[..., :3], frame_rate=Fraction(30000, 1001), alpha=mask.reshape(1, 1, 3, 1).repeat(2, 2, 1, 1),
@@ -220,7 +235,7 @@ async def check(base):
with wave.open(str(wav), "wb") as out:
out.setnchannels(2)
out.setsampwidth(2)
out.setframerate(22050)
out.setframerate(44100)
out.writeframes(pcm.tobytes())
waveform, sample_rate = nodes_audio.load(str(wav))
clip = native_root / "native.mkv"
@@ -278,17 +293,22 @@ async def check(base):
participant = "serverless:" + key
request_id = "acceptance-" + key
graph = {"source": node("AcceptanceMediaSource", key=key),
"collector": node("DistributedMediaCollector", load_balance=False,
"collector": node("DistributedCollector", load_balance=False, transfer_format="exact",
multi_job_id="job-main", worker_id=participant,
**{socket: ["source", index] for index, socket in enumerate(sockets)}),
"text-collector": node("DistributedMediaCollector", load_balance=False,
multi_job_id="job-text", worker_id=participant, texts=["source", 3])}
**{socket: ["source", index] for index, socket in enumerate(collector_sockets)}),
"audio-collector": node("DistributedCollector", load_balance=False,
multi_job_id="job-audio", worker_id=participant, audio=["source", 1])}
if key == "worker-a":
graph.update({"wav": node("LoadAudio", audio=wav.name), "clip": node("LoadVideo", file=clip.name)})
graph["collector"]["inputs"].update(audio=["wav", 0], video=["clip", 0])
graph["collector"]["inputs"]["audio"] = ["wav", 0]
before = copy.deepcopy(graph)
workflow, manifest = compiler.compile_media_prompt(graph, request_id)
assert graph == before
bridge_links = {socket: ["source", sockets.index(socket)] for socket in sockets[2:]}
if key == "worker-a":
workflow["clip"] = copy.deepcopy(graph["clip"])
bridge_links["video"] = ["clip", 0]
add_bridge_exports(workflow, manifest, request_id, participant, "job-bridge", bridge_links)
assert len(manifest) == 6 and {b["kind"] for b in manifest} == set(kinds)
folder_paths.set_input_directory(str(native_root))
workflow_before = copy.deepcopy(workflow)
@@ -335,17 +355,19 @@ async def check(base):
# Exercise the wire format, not an in-memory handler-only object.
result = json.loads(json.dumps(result, allow_nan=False))
grouped = await decoder.collect_typed_results(result, media, manifest)
assert set(grouped) == {"job-main", "job-text"}
assert set(grouped["job-main"]) == set(sockets)
assert grouped["job-text"]["texts"] == expectations[key]["texts"]
assert set(grouped) == {"job-main", "job-audio", "job-bridge"}
assert set(grouped["job-main"]) == set(collector_sockets)
assert set(grouped["job-bridge"]) == set(sockets[2:])
exact("AUDIO", grouped["job-audio"]["audio"][0], fixtures[key]["audio"][0])
all_media = {**grouped["job-main"], **grouped["job-bridge"]}
for socket, kind in zip(sockets, kinds):
values, expected = grouped["job-main"][socket], expectations[key][socket]
values, expected = all_media[socket], expectations[key][socket]
assert len(values) == len(expected), (key, socket)
for actual, wanted in zip(values, expected):
exact(kind, actual, wanted)
if key == "worker-b":
original = fixtures[key]["video"][0].get_components()
decoded = grouped["job-main"]["video"][0].get_components()
decoded = grouped["job-bridge"]["video"][0].get_components()
for field in ("images", "alpha"):
assert getattr(decoded, field).data_ptr() != getattr(original, field).data_ptr()
assert decoded.audio["waveform"].data_ptr() != original.audio["waveform"].data_ptr()
@@ -363,7 +385,7 @@ async def check(base):
assert ps.distributed_pending_jobs == {}
# Exercise user-selected image return encoding through the actual
# compiler, HTTP handler, V3 exporter and atomic response decoder.
for fmt in ("png", "jpeg", "h264"):
for fmt in ("legacy_png", "png", "jpeg", "h264"):
key = "encoded-" + fmt
fixtures[key] = native_fixtures(3)
# Keep full alpha precision and use nontrivial image dimensions.
@@ -372,12 +394,23 @@ async def check(base):
fixtures[key]["images"] = [rgba]
request_id = "request-" + key
graph = {"source": node("AcceptanceMediaSource", key=key),
"collector": node("DistributedMediaCollector", transfer_format=fmt,
"collector": node("DistributedCollector", transfer_format=fmt,
transfer_quality=73, multi_job_id="job-" + key,
worker_id="serverless:" + key,
**{socket: ["source", i] for i, socket in enumerate(sockets)})}
**{socket: ["source", i] for i, socket in enumerate(collector_sockets)})}
if fmt == "legacy_png":
# A saved Collector with no newly selected controls still uses
# legacy_png / 90, mapped to PNG by the serverless compiler.
graph["collector"]["inputs"].pop("transfer_format")
graph["collector"]["inputs"].pop("transfer_quality")
original = copy.deepcopy(graph)
workflow, manifest = compiler.compile_media_prompt(graph, request_id)
if fmt == "legacy_png":
binding = next(b for b in manifest if b["kind"] == "IMAGE")
controls = workflow[binding["node_id"]]["inputs"]
assert (controls["transfer_format"], controls["transfer_quality"]) == ("png", 90)
add_bridge_exports(workflow, manifest, request_id, "serverless:" + key, "job-bridge-" + key,
{socket: ["source", sockets.index(socket)] for socket in sockets[2:]})
media = {"version": 1, "profile": contract.PROFILE, "request_id": request_id,
"artifacts": [], "inputs": [], "outputs": manifest}
body = json.loads(serializer(workflow, [], 30, media=media))
@@ -385,23 +418,32 @@ async def check(base):
input_dir=base / "input", temp_dir=base / "temp", timeout_seconds=30)
assert graph == original
grouped = await decoder.collect_typed_results(result, media, manifest)
actual = grouped["job-" + key]
actual = {**grouped["job-" + key], **grouped["job-bridge-" + key]}
image = actual["images"][0]
assert image.shape == rgba.shape and image.dtype == rgba.dtype
assert torch.equal(image[..., 3], rgba[..., 3]), "alpha must not be discarded/narrowed"
assert not torch.equal(image[..., :3], rgba[..., :3]), "selection must actually encode"
if fmt == "png":
if fmt in ("legacy_png", "png"):
pixels = (rgba[..., :3].float().clamp(0, 1) * 255).to(torch.uint8)
assert torch.equal(image[..., :3], (pixels.float() / 255).to(rgba.dtype))
for socket, kind in zip(sockets[1:], kinds[1:]):
for got, wanted in zip(actual[socket], fixtures[key][socket]):
exact(kind, got, video_codec.encode_video(wanted) if kind == "VIDEO" else wanted)
assert not list((base / "temp").rglob("*.bin"))
print("IMAGE_TRANSFER_HTTP_OK png jpeg h264; alpha and other media exact")
print("REAL_HTTP_BRIDGE_RESULTS_OK all five kinds; 6 bindings/request; native VIDEO remains readable after cleanup")
print("IMAGE_TRANSFER_HTTP_OK default legacy_png -> png/90; png jpeg h264; alpha and other media exact")
print("REAL_HTTP_BRIDGE_RESULTS_OK Collector IMAGE/AUDIO + independent MASK/STRING/VIDEO bridge exports; native VIDEO survives cleanup")
print("ATOMIC_DECODE_REJECTION_OK corrupt final artifact rejected before delivery")
participants = list(grouped_results)
def expected_batches(ordered, include_images=True):
audio_items = [item for media in ordered for item in media["audio"]]
expected = {"audio": [{"waveform": torch.cat([item["waveform"] for item in audio_items], dim=-1),
"sample_rate": audio_items[0]["sample_rate"]}]}
if include_images:
expected["images"] = [torch.cat([image for media in ordered for image in media["images"]])]
return expected
for delegate in (False, True):
tag = "delegate" if delegate else "master"
job = "collect-" + tag
@@ -410,39 +452,40 @@ async def check(base):
await queue.put({"worker_id": participant, "media": grouped_results[participant]["job-main"], "is_last": True})
ps.distributed_pending_jobs[job] = queue
ordered = ([] if delegate else [fixtures["local"]]) + [grouped_results[p]["job-main"] for p in participants]
expected_sinks[tag] = {socket: [item for values in ordered for item in values[socket]] for socket in sockets}
graph = {"collector": node("DistributedMediaCollector", load_balance=False, multi_job_id=job,
enabled_worker_ids=json.dumps(participants), expected_media=json.dumps(sockets), delegate_only=delegate),
expected_sinks[tag] = expected_batches(ordered)
graph = {"collector": node("DistributedCollector", load_balance=False, multi_job_id=job,
enabled_worker_ids=json.dumps(participants), delegate_only=delegate, transfer_format="exact"),
"assert": node("AcceptanceMediaAssert", tag=tag,
**{socket: ["collector", index] for index, socket in enumerate(sockets)})}
**{socket: ["collector", index] for index, socket in enumerate(collector_sockets)})}
if not delegate:
graph["source"] = node("AcceptanceMediaSource", key="local")
graph["collector"]["inputs"].update({socket: ["source", index] for index, socket in enumerate(sockets)})
# The second group from the very same complete response must reach its
# own Collector, rather than being folded into the first collector job.
text_job, text_tag = job + "-text", tag + "-text"
text_queue = asyncio.Queue()
graph["collector"]["inputs"].update({socket: ["source", index] for index, socket in enumerate(collector_sockets)})
# A second audio-only Collector consumes its own job group from the same
# atomic response. It must return None for images and batch audio in order.
audio_job, audio_tag = job + "-audio", tag + "-audio"
audio_queue = asyncio.Queue()
for participant in reversed(participants):
await text_queue.put({"worker_id": participant,
"media": grouped_results[participant]["job-text"], "is_last": True})
ps.distributed_pending_jobs[text_job] = text_queue
text_ordered = ([] if delegate else [fixtures["local"]]) + [grouped_results[p]["job-text"] for p in participants]
expected_sinks[text_tag] = {"texts": [item for values in text_ordered for item in values["texts"]]}
graph["text-collector"] = node("DistributedMediaCollector", load_balance=False, multi_job_id=text_job,
enabled_worker_ids=json.dumps(participants), expected_media='["texts"]', delegate_only=delegate)
graph["text-assert"] = node("AcceptanceMediaAssert", tag=text_tag, texts=["text-collector", 3])
await audio_queue.put({"worker_id": participant,
"media": grouped_results[participant]["job-audio"], "is_last": True})
ps.distributed_pending_jobs[audio_job] = audio_queue
audio_ordered = ([] if delegate else [fixtures["local"]]) + [grouped_results[p]["job-audio"] for p in participants]
expected_sinks[audio_tag] = expected_batches(audio_ordered, include_images=False)
graph["audio-collector"] = node("DistributedCollector", load_balance=False, multi_job_id=audio_job,
enabled_worker_ids=json.dumps(participants), delegate_only=delegate)
graph["audio-assert"] = node("AcceptanceMediaAssert", tag=audio_tag, audio=["audio-collector", 1])
if not delegate:
graph["text-collector"]["inputs"]["texts"] = ["source", 3]
graph["audio-collector"]["inputs"]["audio"] = ["source", 1]
valid = await execution.validate_prompt(job, graph, None)
assert valid[0] and not valid[3], valid
executor = execution.PromptExecutor(ps, cache_args={"ram": 0, "ram_inactive": 0}, asset_manager=asset_manager)
await asyncio.to_thread(executor.execute, graph, job, {}, valid[2])
assert executor.success, executor.status_messages
assert executor.history_result["outputs"]["assert"]["accepted"] == [seen_sinks[tag]]
assert executor.history_result["outputs"]["text-assert"]["accepted"] == [seen_sinks[text_tag]]
assert job not in ps.distributed_pending_jobs and text_job not in ps.distributed_pending_jobs
assert source_calls == ["worker-a", "worker-b", "encoded-png", "encoded-jpeg", "encoded-h264", "local"], source_calls
assert set(seen_sinks) == {"master", "delegate", "master-text", "delegate-text"}
assert executor.history_result["outputs"]["audio-assert"]["accepted"] == [seen_sinks[audio_tag]]
assert job not in ps.distributed_pending_jobs and audio_job not in ps.distributed_pending_jobs
assert source_calls == ["worker-a", "worker-b", "encoded-legacy_png", "encoded-png", "encoded-jpeg", "encoded-h264", "local"], source_calls
assert set(seen_sinks) == {"master", "delegate", "master-audio", "delegate-audio"}
assert not list((base / "output").rglob("*"))
assert not torch.cuda.is_initialized()
print("MASTER_AND_DELEGATE_ORDER_OK", json.dumps(seen_sinks, sort_keys=True))
+43 -9
View File
@@ -36,7 +36,7 @@ BASELINE = json.loads((ROOT / 'tests/fixtures/v1_node_contracts.json').read_text
ADDED_OPTIONAL_INPUTS = {
'DistributedCollector': {
'transfer_format': [
['legacy_png', 'png', 'ffv1', 'jpeg', 'webp', 'h264', 'h265', 'av1'],
['legacy_png', 'png', 'ffv1', 'jpeg', 'webp', 'h264', 'h265', 'av1', 'exact'],
{'default': 'legacy_png'},
],
'transfer_quality': ['INT', {'default': 90, 'min': 1, 'max': 100, 'step': 1}],
@@ -45,7 +45,8 @@ ADDED_OPTIONAL_INPUTS = {
# Nodes added after the V1 baseline; not part of saved-workflow contracts.
# DistributedWorkerSink is internal (dev-only) and only inserted by the master.
ADDED_NODES = {'DistributedWorkerSink', 'DistributedImageListStart', 'DistributedImageListEnd', 'DistributedMediaCollector'}
ADDED_NODES = {'DistributedWorkerSink', 'DistributedImageListStart', 'DistributedImageListEnd',
'DistributedImageListGetValue'}
def normalized_input(value):
@@ -273,7 +274,7 @@ async def check_image_list_lazy_executor(mapping, module, prompt_server, asset_m
original_contexts = set(runtime._MASTER_JOBS)
image_list_nodes = sys.modules[module.__name__ + '.nodes.image_list']
workflow_meta = {'nodes': [{'id': 2, 'type': 'DistributedImageListStart'}], 'extra': {'acceptance': True}}
async def fake_worker_results(_context_id, *, model_callback=None):
async def fake_worker_results(_context_id, *, model_callback=None, include_values=False):
state = runtime.get_master_job(_context_id)
assert state.prompt_id == 'image-list-lazy', state.prompt_id
assert state.workflow_meta == workflow_meta, state.workflow_meta
@@ -281,6 +282,7 @@ async def check_image_list_lazy_executor(mapping, module, prompt_server, asset_m
return (
[torch.full((1, 8, 8, 3), value, dtype=torch.float32) for value in (0.125, 0.5, 0.875)],
[None, None, None], [None, None, None],
[{}, {}, {}],
)
graph = {
@@ -366,14 +368,16 @@ async def main():
extension = await module.comfy_entrypoint()
classes = await extension.get_node_list()
mapping = {cls.GET_SCHEMA().node_id: cls for cls in classes}
assert len(classes) == len(mapping) == len(BASELINE) + len(ADDED_NODES)
assert len(classes) == len(mapping) == len(BASELINE) + len(ADDED_NODES) == 12
assert set(mapping) == set(BASELINE) | ADDED_NODES
for node_id in ADDED_NODES:
assert nodes.NODE_CLASS_MAPPINGS[node_id] is mapping[node_id]
media_info = mapping['DistributedMediaCollector'].GET_NODE_INFO_V1()
assert media_info['output'] == ['IMAGE', 'AUDIO', 'MASK', 'STRING', 'VIDEO'], media_info
assert all(media_info['output_is_list']), media_info
assert media_info['input']['optional']['texts'][1]['forceInput'] is True
assert 'DistributedMediaCollector' not in mapping
collector_info = mapping['DistributedCollector'].GET_NODE_INFO_V1()
assert collector_info['output'] == ['IMAGE', 'AUDIO'], collector_info
assert not any(collector_info['output_is_list']), collector_info
assert list(collector_info['input']['optional']) == [
'images', 'audio', 'transfer_format', 'transfer_quality']
sink_info = mapping['DistributedWorkerSink'].GET_NODE_INFO_V1()
assert sink_info['output_node'] and sink_info['dev_only'], sink_info
for node_id, cls in mapping.items():
@@ -427,6 +431,37 @@ async def main():
assert start_schema.display_name == 'Distributed Image List Start'
assert end_schema.node_id == 'DistributedImageListEnd'
assert end_schema.display_name == 'Distributed Image List End'
runtime = sys.modules[module.__name__ + '.image_list.runtime']
assert [input.id for input in end_schema.inputs] == ['images', 'masks', 'texts', 'context'] + list(runtime.VALUE_NAMES)
assert [output.io_type for output in end_schema.outputs] == [
'IMAGE', 'MASK', 'STRING', 'DISTRIBUTED_IMAGE_LIST_VALUES']
assert end_schema.outputs[3].id == end_schema.outputs[3].display_name == 'values'
assert all(output.is_output_list for output in end_schema.outputs)
for override in end_schema.inputs[4:]:
assert override.optional and override.lazy
assert override.get_io_type() == 'IMAGE,MASK,STRING,INT,FLOAT,BOOLEAN'
getter = mapping['DistributedImageListGetValue']
getter_schema = getter.GET_SCHEMA()
assert getter_schema.display_name == 'Distributed Image List Get Value'
assert not getter_schema.is_input_list
assert getter_schema.outputs[0].id == getter_schema.outputs[0].display_name == 'value'
assert getter_schema.outputs[0].io_type == '*'
assert getter_schema.inputs[1].options == list(runtime.VALUE_NAMES)
assert getter_schema.inputs[1].default == 'value_1'
# Real core maps scalar mappings, never arbitrary tensor batches.
tensor = torch.ones(3, 2, 2, 3)
data, missing, hidden = execution.get_input_data({'value_name': 'value_1'}, getter, 'get-value')
data['values'] = [{'value_1': tensor}, {'value_1': 17}, {'value_1': False}]
mapped = await execution._async_map_node_over_list('get-value', 'get-value', getter, data,
getter.FUNCTION, v3_data=hidden)
mapped = await execution.resolve_map_node_over_list_results(mapped)
assert len(mapped) == 3 and mapped[0].result[0] is tensor
assert mapped[1].result == (17,) and mapped[2].result == (False,)
try:
getter.execute({}, 'value_1')
raise AssertionError('missing Get Value key accepted')
except ValueError as exc:
assert 'absent' in str(exc)
texts_input = next(input for input in end_schema.inputs if input.id == 'texts')
texts_schema = texts_input.as_dict()
assert texts_schema.get('forceInput') is True, texts_schema
@@ -470,7 +505,6 @@ async def main():
rows = runtime_mod.normalize_image_list_inputs(prepared['images'], None, seen['values'])
assert [row['values'] for row in rows] == [{'value_1': 'shared', 'value_3': n} for n in (4, 5, 6)]
print('IMAGE_LIST_AUTOGROW_OK real parser; value_N slots shared/per-item')
assert [out.io_type for out in end_schema.outputs] == ['IMAGE', 'MASK', 'STRING']
assert all(out.is_output_list for out in end_schema.outputs)
assert end_schema.is_input_list and end_schema.is_output_node
print('IMAGE_LIST_SCHEMA_OK exact IDs, names, typed list outputs')
+524
View File
@@ -0,0 +1,524 @@
"""Exact Collector transport through real serializers, HTTP, route and queue.
Only ComfyUI's process-wide server/model-management and unrelated orchestration
are replaced. Tensor/audio codecs, multipart bodies and aiohttp are real; no
running ComfyUI service, GPU, provider or external network access is required.
"""
import asyncio
from contextlib import asynccontextmanager
import importlib
import importlib.util
import json
from pathlib import Path
import sys
import types
from unittest.mock import AsyncMock
import pytest
import torch
ROOT = Path(__file__).resolve().parents[1]
PKG = "dist_collector_exact_testpkg"
JOB_ID = "exact-job"
WORKER_ID = "worker-1"
@pytest.fixture
def transport(monkeypatch):
# Other legacy test loaders may leave aiohttp stubs behind. Replace only
# those entries, never unload/reload real extension-backed dependencies.
for name, loaded_module in list(sys.modules.items()):
if (name == "aiohttp" or name.startswith("aiohttp.")) and not getattr(loaded_module, "__file__", None):
monkeypatch.delitem(sys.modules, name)
aiohttp = importlib.import_module("aiohttp")
web = importlib.import_module("aiohttp.web")
def module(name, **attrs):
result = types.ModuleType(name)
result.__dict__.update(attrs)
monkeypatch.setitem(sys.modules, name, result)
return result
def load(relative):
name = f"{PKG}.{relative}"
spec = importlib.util.spec_from_file_location(name, ROOT.joinpath(*relative.split(".")).with_suffix(".py"))
assert spec is not None and spec.loader is not None
result = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, name, result)
spec.loader.exec_module(result)
parent, _, child = name.rpartition(".")
setattr(sys.modules[parent], child, result)
return result
module(PKG, __path__=[str(ROOT)])
for package in ("nodes", "api", "utils", "serverless_bridge"):
module(f"{PKG}.{package}", __path__=[str(ROOT / package)])
server = types.SimpleNamespace(routes=web.RouteTableDef(), distributed_pending_jobs={})
module("server", PromptServer=types.SimpleNamespace(instance=server))
mm = module("comfy.model_management", throw_exception_if_processing_interrupted=lambda: None)
progress = module("comfy.utils", ProgressBar=lambda _total: types.SimpleNamespace(update=lambda _n: None))
module("comfy", __path__=[], model_management=mm, utils=progress)
module(f"{PKG}.utils.logging", debug_log=lambda *_a, **_kw: None, log=lambda *_a, **_kw: None)
module(f"{PKG}.utils.config", get_worker_timeout_seconds=lambda: 0.1,
load_config=lambda: {"workers": []}, is_master_delegate_only=lambda: False)
async def handle_api_error(_request, error, status=500):
return web.json_response({"error": str(error)}, status=status)
module(f"{PKG}.utils.network", handle_api_error=handle_api_error,
get_client_session=AsyncMock(), build_worker_url=lambda _worker: "http://unused.invalid",
probe_worker=AsyncMock(return_value=None))
module(f"{PKG}.utils.async_helpers", run_async_in_server_loop=asyncio.run)
module(f"{PKG}.api.queue_orchestration", ensure_distributed_state=lambda: None,
orchestrate_distributed_execution=AsyncMock())
module(f"{PKG}.api.queue_request", parse_queue_request_payload=lambda value: value)
for name in ("utils.constants", "utils.image", "utils.audio_payload", "utils.transfer_codec"):
load(name)
bridge_codec = load("serverless_bridge.codec")
load("serverless_bridge.image_transfer")
image_codec = load("serverless_bridge.image_codec")
collector = load("nodes.collector")
routes = load("api.job_routes")
return types.SimpleNamespace(aiohttp=aiohttp, web=web, server=server,
collector=collector, routes=routes, bridge_codec=bridge_codec,
image_codec=image_codec)
@asynccontextmanager
async def endpoint(transport, monkeypatch, *, request_limit=2 * 1024 * 1024,
chunk_target=1024 * 1024, client_max_size=None, handler=None):
"""A disposable loopback endpoint; record actual HTTP lengths, not estimates."""
queue = asyncio.Queue()
transport.server.distributed_jobs_lock = asyncio.Lock()
transport.server.distributed_pending_jobs = {JOB_ID: queue}
requests = []
web = transport.web
@web.middleware
async def record_request(request, handler):
record = {"bytes": request.content_length, "content_type": request.content_type}
requests.append(record)
response = await handler(request)
record["status"] = response.status
if response.status < 400 and request.content_type.startswith("multipart/"):
form = await request.post()
record["envelope"] = json.loads(form["envelope"])
return response
app = web.Application(client_max_size=client_max_size or request_limit, middlewares=[record_request])
app.router.add_post("/distributed/job_complete", handler or transport.routes.job_complete_endpoint)
runner = web.AppRunner(app)
await runner.setup()
try:
site = web.TCPSite(runner, "127.0.0.1", 0)
await site.start()
port = site._server.sockets[0].getsockname()[1]
async with transport.aiohttp.ClientSession() as session:
monkeypatch.setattr(transport.collector, "get_client_session", AsyncMock(return_value=session))
monkeypatch.setattr(transport.collector, "TRANSFER_MAX_REQUEST_BYTES", request_limit)
monkeypatch.setattr(transport.collector, "TRANSFER_MAX_CHUNK_BYTES", chunk_target)
yield types.SimpleNamespace(
session=session, url=f"http://127.0.0.1:{port}", requests=requests, queue=queue,
node=transport.collector.DistributedCollectorNode(),
)
finally:
await runner.cleanup()
def frames(dtype=torch.float32, channels=3, count=5, height=7, width=9):
# Non-contiguous, out-of-range and non-8-bit-representable values exercise
# detach/contiguous without permitting clipping, alpha loss or quantization.
values = torch.arange(count * height * width * channels, dtype=torch.float64)
values = (values.remainder(41) - 11) / 17
images = values.reshape(count, width, height, channels).transpose(1, 2).to(dtype)
images[0, 0, 0, 0] = -0.0
return images.requires_grad_(True)
def assert_exact(actual, expected):
assert actual.dtype == expected.dtype
assert actual.shape == expected.shape
assert actual.device.type == "cpu"
assert actual.is_contiguous()
# Compare raw bits as well as values, including BF16 and signed zero.
assert torch.equal(actual, expected)
assert torch.equal(actual.view(torch.uint8), expected.detach().contiguous().view(torch.uint8))
def queued_items(queue):
return [queue.get_nowait() for _ in range(queue.qsize())]
async def send(ctx, images, *, audio=None, fmt="exact"):
await ctx.node.send_batch_to_master(images, audio, JOB_ID, ctx.url, WORKER_ID, fmt, 17)
@pytest.mark.parametrize("delegate_only", [False, True])
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16, torch.float32, torch.float64])
@pytest.mark.parametrize("channels", [1, 3, 4])
def test_exact_sender_route_and_collector_preserve_dtype_values_and_channels(
transport, monkeypatch, dtype, channels, delegate_only):
images = frames(dtype, channels)
frame_bytes = images[0].numel() * images.element_size()
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=reserve + 2 * frame_bytes,
chunk_target=2 * frame_bytes) as ctx:
await send(ctx, images)
items = queued_items(ctx.queue)
assert [item["image_index"] for item in items] == list(range(len(images)))
assert [item["is_last"] for item in items] == [False] * 4 + [True]
assert all(item["worker_id"] == WORKER_ID and item["format"] == "exact" for item in items)
for item, expected in zip(items, images.split(1)):
assert_exact(item["tensor"], expected)
await ctx.queue.put(item)
envelopes = [record["envelope"] for record in ctx.requests]
assert [(e["start_index"], e["count"]) for e in envelopes] == [(0, 2), (2, 2), (4, 1)]
assert all(record["bytes"] <= reserve + 2 * frame_bytes for record in ctx.requests)
master_images = None if delegate_only else images[:1].detach().clone().add(0.03125)
actual, _audio = await ctx.node.execute(
master_images, None, multi_job_id=JOB_ID, enabled_worker_ids=json.dumps([WORKER_ID]),
delegate_only=delegate_only, transfer_format="exact",
)
expected = images if delegate_only else torch.cat([master_images, images])
assert_exact(actual, expected)
assert JOB_ID not in transport.server.distributed_pending_jobs
asyncio.run(scenario())
def test_exact_single_frame_can_exceed_chunk_target_but_not_request_limit(transport, monkeypatch):
images = frames(count=3)
frame_bytes = images[0].numel() * images.element_size()
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=reserve + frame_bytes,
chunk_target=frame_bytes // 2) as ctx:
await send(ctx, images)
assert [record["envelope"]["count"] for record in ctx.requests] == [1, 1, 1]
assert all(record["bytes"] <= reserve + frame_bytes for record in ctx.requests)
assert_exact(torch.cat([item["tensor"] for item in queued_items(ctx.queue)]), images)
asyncio.run(scenario())
def test_exact_oversized_frame_is_rejected_before_encoding_or_post(transport, monkeypatch):
images = frames(count=3)
frame_bytes = images[0].numel() * images.element_size()
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
encode = AsyncMock(side_effect=AssertionError("oversized frame must not be encoded"))
monkeypatch.setattr(transport.collector, "_encode_exact_frames", encode)
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=reserve + frame_bytes - 1) as ctx:
with pytest.raises(transport.collector.transfer_codec.FrameTooLargeError, match="request limit"):
await send(ctx, images)
assert ctx.requests == []
assert ctx.queue.empty()
encode.assert_not_called()
asyncio.run(scenario())
@pytest.mark.parametrize("shape", [(0, 2, 3, 3), (1, 0, 3, 3), (1, 2, 0, 3), (1, 2, 3, 0),
(1, 2, 3, 2), (1, 2, 3, 5), (1, 2, 3), (2, 3)])
def test_exact_sender_rejects_invalid_geometry_before_post(transport, monkeypatch, shape):
image = torch.empty(shape)
with pytest.raises(transport.collector.transfer_codec.TransferCodecError):
transport.collector._exact_frame_bytes(image)
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
# An empty batch is also a no-media completion error (ValueError).
with pytest.raises(ValueError):
await send(ctx, image)
assert ctx.requests == []
assert ctx.queue.empty()
asyncio.run(scenario())
@pytest.mark.parametrize("dtype", [torch.uint8, torch.int32, torch.bool, torch.complex64])
def test_exact_sender_rejects_unsupported_dtype_before_post(transport, monkeypatch, dtype):
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
with pytest.raises(transport.collector.transfer_codec.TransferCodecError, match="dtype"):
await send(ctx, torch.zeros(1, 2, 3, 3, dtype=dtype))
assert ctx.requests == []
asyncio.run(scenario())
def test_exact_frame_planning_enforces_bridge_byte_limit(transport, monkeypatch):
monkeypatch.setattr(transport.bridge_codec, "MAX_PAYLOAD_BYTES", 32)
with pytest.raises(transport.collector.transfer_codec.TransferCodecError, match="[Bb]yte|limit"):
transport.collector._exact_frame_bytes(torch.zeros(1, 2, 3, 3))
def test_exact_frame_planning_rejects_non_dense_layout(transport):
image = torch.zeros(1, 2, 3, 3).to_sparse()
with pytest.raises(transport.collector.transfer_codec.TransferCodecError, match="dense|layout|strided"):
transport.collector._exact_frame_bytes(image)
@pytest.mark.parametrize("value", [float("nan"), float("inf"), -float("inf")])
def test_exact_sender_rejects_nonfinite_payload(transport, monkeypatch, value):
images = torch.full((1, 2, 3, 3), value)
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
with pytest.raises(transport.collector.transfer_codec.TransferCodecError, match="finite"):
await send(ctx, images)
assert ctx.requests == []
assert ctx.queue.empty()
asyncio.run(scenario())
def exact_chunk(transport, count=2):
images = frames(count=count).detach()
encoded = [transport.bridge_codec.encode_payload("IMAGE", frame) for frame in images.split(1)]
return {
"job_id": JOB_ID, "worker_id": WORKER_ID, "format": "exact",
"start_index": 0, "count": count, "width": images.shape[2], "height": images.shape[1],
"is_last": True, "descriptors": [descriptor for descriptor, _ in encoded],
}, [payload for _, payload in encoded]
@pytest.mark.parametrize("case", [
"zero_count", "bool_width", "negative_height", "negative_index", "bool_index",
"missing_descriptor", "extra_descriptor", "missing_part", "extra_part", "not_a_descriptor",
"wrong_height", "wrong_batch", "bad_channels", "bad_dtype", "bad_layout", "bad_encoding",
"extra_descriptor_field", "truncated", "trailing_bytes", "nonfinite", "compressed_spoof",
])
def test_exact_route_rejects_bad_geometry_and_payload_atomically(transport, monkeypatch, case):
envelope, parts = exact_chunk(transport)
descriptor = envelope["descriptors"][-1]
if case == "zero_count":
envelope["count"] = 0
elif case == "bool_width":
envelope["width"] = True
elif case == "negative_height":
envelope["height"] = -1
elif case == "negative_index":
envelope["start_index"] = -1
elif case == "bool_index":
envelope["start_index"] = True
elif case == "missing_descriptor":
envelope["descriptors"].pop()
elif case == "extra_descriptor":
envelope["descriptors"].append(dict(descriptor))
elif case == "missing_part":
parts.pop()
elif case == "extra_part":
parts.append(parts[-1])
elif case == "not_a_descriptor":
envelope["descriptors"][-1] = None
elif case == "wrong_height":
descriptor["shape"][1] += 1
elif case == "wrong_batch":
descriptor["shape"][0] = 2
elif case == "bad_channels":
descriptor["shape"][-1] = 2
elif case == "bad_dtype":
descriptor["dtype"] = "uint8"
elif case == "bad_layout":
descriptor["layout"] = "BCHW"
elif case == "bad_encoding":
descriptor["encoding"] = "pickle"
elif case == "extra_descriptor_field":
descriptor["unknown"] = True
elif case == "truncated":
parts[-1] = parts[-1][:-1]
elif case == "trailing_bytes":
parts[-1] += b"\0"
elif case == "nonfinite":
bad = torch.full(tuple(descriptor["shape"]), float("nan"))
parts[-1] = bad.numpy().tobytes()
elif case == "compressed_spoof":
# A valid serverless image-transfer descriptor must NOT be accepted as
# exact by the direct route, even though the shared bridge can decode it.
envelope["descriptors"][-1], parts[-1] = transport.image_codec.encode_image(
frames(count=1).detach(), transfer_format="png", transfer_quality=90,
)
# Prove the spoof is a valid compressed bridge payload, not simply junk.
decoded = transport.bridge_codec.decode_payload("IMAGE", envelope["descriptors"][-1], parts[-1])
assert list(decoded.shape) == descriptor["shape"]
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
async with ctx.session.post(
ctx.url + "/distributed/job_complete",
data=ctx.node._build_multipart(envelope, parts),
) as response:
body = await response.text()
assert response.status == 400, body
# A corrupt second frame must not leave the first in the queue.
assert ctx.queue.empty()
asyncio.run(scenario())
@pytest.mark.parametrize("status,body", [(400, "Invalid JSON payload: old master"),
(413, "upload limit"), (500, "unexpected failure")])
def test_exact_master_rejection_never_falls_back(transport, monkeypatch, status, body):
async def reject(_request):
return transport.web.Response(status=status, text=body)
async def scenario():
async with endpoint(transport, monkeypatch, handler=reject, chunk_target=1) as ctx:
legacy = AsyncMock(side_effect=AssertionError("exact must never use legacy transport"))
compressed = AsyncMock(side_effect=AssertionError("exact must never use compressed transport"))
monkeypatch.setattr(ctx.node, "_send_legacy_batch", legacy)
monkeypatch.setattr(ctx.node, "_send_compressed_batch", compressed)
with pytest.raises(RuntimeError, match=f"HTTP {status}") as raised:
await send(ctx, frames(count=3))
assert "never" in str(raised.value) and "8-bit" in str(raised.value)
assert len(ctx.requests) == 1
assert ctx.requests[0]["content_type"].startswith("multipart/")
assert ctx.queue.empty()
legacy.assert_not_called()
compressed.assert_not_called()
asyncio.run(scenario())
@pytest.mark.parametrize("fmt", ["legacy_png", "png"])
def test_exact_collector_rejects_old_worker_8bit_result(transport, monkeypatch, fmt):
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
await send(ctx, frames(count=1).detach(), fmt=fmt)
assert ctx.queue.qsize() == 1
with pytest.raises(RuntimeError, match="requested exact"):
await ctx.node.execute(
None, None, multi_job_id=JOB_ID, enabled_worker_ids=json.dumps([WORKER_ID]),
delegate_only=True, transfer_format="exact",
)
assert JOB_ID not in transport.server.distributed_pending_jobs
asyncio.run(scenario())
def test_exact_collector_shape_mismatch_raises_instead_of_returning_local_images(transport, monkeypatch):
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
await send(ctx, frames(count=1, channels=4))
with pytest.raises(RuntimeError, match="shape|combin|mismatch"):
await ctx.node.execute(
frames(count=1, channels=3), None, multi_job_id=JOB_ID,
enabled_worker_ids=json.dumps([WORKER_ID]), transfer_format="exact",
)
assert JOB_ID not in transport.server.distributed_pending_jobs
asyncio.run(scenario())
def test_exact_incomplete_worker_raises_and_cleans_queue(transport, monkeypatch):
"""A timed-out worker's first frame is not a successful partial result."""
envelope, parts = exact_chunk(transport, count=1)
envelope["is_last"] = False
async def scenario():
async with endpoint(transport, monkeypatch) as ctx:
async with ctx.session.post(
ctx.url + "/distributed/job_complete", data=ctx.node._build_multipart(envelope, parts),
) as response:
assert response.status == 200, await response.text()
assert ctx.queue.qsize() == 1
with pytest.raises(RuntimeError, match="Exact transfer incomplete"):
await asyncio.wait_for(ctx.node.execute(
frames(count=1).detach(), None, multi_job_id=JOB_ID,
enabled_worker_ids=json.dumps([WORKER_ID]), transfer_format="exact",
), timeout=2)
assert JOB_ID not in transport.server.distributed_pending_jobs
asyncio.run(scenario())
def test_exact_oversized_multipart_metadata_is_rejected_before_post(transport, monkeypatch):
images = frames(count=1)
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
limit = reserve + images.numel() * images.element_size()
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=limit) as ctx:
with pytest.raises(transport.collector.transfer_codec.FrameTooLargeError, match="request limit"):
await ctx.node.send_batch_to_master(
images, None, JOB_ID, ctx.url, "w" * limit, "exact", 90,
)
assert ctx.requests == []
assert ctx.queue.empty()
asyncio.run(scenario())
def test_exact_actual_server_upload_rejection_is_413_without_retry(transport, monkeypatch):
async def scenario():
async with endpoint(transport, monkeypatch, client_max_size=1024) as ctx:
with pytest.raises(RuntimeError, match="HTTP 413"):
await send(ctx, frames(count=3, height=64, width=64))
assert len(ctx.requests) == 1
assert ctx.requests[0]["status"] == 413
assert ctx.queue.empty()
asyncio.run(scenario())
def test_exact_oversized_audio_is_rejected_before_any_image_post(transport, monkeypatch):
images = frames(count=3)
audio = {"waveform": torch.zeros(1, 1, 100000), "sample_rate": 48000}
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=reserve + 1024) as ctx:
with pytest.raises(transport.collector.transfer_codec.FrameTooLargeError, match="audio.*request limit"):
await send(ctx, images, audio=audio)
assert ctx.requests == []
assert ctx.queue.empty()
asyncio.run(scenario())
def test_exact_tiny_frames_cannot_overflow_multipart_metadata_budget(transport, monkeypatch):
images = torch.full((2048, 1, 1, 1), 0.125, dtype=torch.float16)
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
limit = reserve + images.numel() * images.element_size()
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=limit, chunk_target=limit) as ctx:
await send(ctx, images)
assert len(ctx.requests) > 1
assert all(record["bytes"] <= limit for record in ctx.requests)
assert_exact(torch.cat([item["tensor"] for item in queued_items(ctx.queue)]), images)
asyncio.run(scenario())
@pytest.mark.parametrize("separate", [False, True])
def test_exact_audio_is_preserved_and_only_final_completion_is_terminal(transport, monkeypatch, separate):
images = frames(count=3)
audio = {"waveform": torch.linspace(-0.9, 0.9, 60).reshape(1, 1, -1), "sample_rate": 48000}
frame_bytes = images[0].numel() * images.element_size()
reserve = transport.collector.DistributedCollectorNode.TRANSFER_ENVELOPE_RESERVE_BYTES
media_budget = frame_bytes if separate else frame_bytes + 8192
limit = reserve + media_budget
async def scenario():
async with endpoint(transport, monkeypatch, request_limit=limit, chunk_target=frame_bytes) as ctx:
await send(ctx, images, audio=audio)
items = queued_items(ctx.queue)
assert len(ctx.requests) == 3 + int(separate)
assert [item["is_last"] for item in items] == [False] * (len(items) - 1) + [True]
assert all(item["audio"] is None for item in items[:-1])
assert items[-1]["audio"]["sample_rate"] == audio["sample_rate"]
assert_exact(items[-1]["audio"]["waveform"], audio["waveform"])
assert_exact(torch.cat([item["tensor"] for item in items if item["tensor"] is not None]), images)
assert all(record["bytes"] <= limit for record in ctx.requests)
if separate:
assert items[-1]["tensor"] is None
assert ctx.requests[-1]["content_type"] == "application/json"
asyncio.run(scenario())
+112
View File
@@ -0,0 +1,112 @@
"""Real Collector aggregation of typed bridge deliveries and direct results.
Only Comfy's host services are stubbed by the shared Collector loader. Tensor
normalization, participant ordering, queue consumption and cleanup are real.
"""
import asyncio
import json
import types
from unittest.mock import AsyncMock
import pytest
import torch
from test_collector_list_inputs import _load_collector_module
@pytest.fixture
def collector_module():
module = _load_collector_module()
module.prompt_server.distributed_jobs_lock = asyncio.Lock()
module.prompt_server.distributed_serverless = types.SimpleNamespace(cancel=AsyncMock())
return module
def native_media(value=0):
image = torch.arange(24, dtype=torch.float64).reshape(1, 2, 3, 4) / 997 + value
image[0, 0, 0, 0] = -0.0
audio = {"waveform": torch.arange(6, dtype=torch.float64).reshape(1, 2, 3) / 997 + value,
"sample_rate": 24000}
return {"images": image, "audio": audio}
def assert_exact(actual, expected):
assert actual.dtype == expected.dtype and actual.shape == expected.shape
assert torch.equal(actual.contiguous().view(torch.uint8), expected.contiguous().view(torch.uint8))
def collect_queue(module, messages, participants, *, local=None, delegate=True):
queue = asyncio.Queue()
for message in messages:
queue.put_nowait(message)
module.prompt_server.distributed_pending_jobs["typed-job"] = queue
values = local or {"images": None, "audio": None}
return asyncio.run(asyncio.wait_for(module.DistributedCollectorNode().execute(
**values, multi_job_id="typed-job", enabled_worker_ids=json.dumps(participants),
delegate_only=delegate), timeout=2))
@pytest.mark.parametrize("delegate", [False, True])
@pytest.mark.parametrize("socket_set", [("images",), ("audio",), ("images", "audio")])
def test_typed_and_direct_results_preserve_batch_and_audio_order(collector_module, delegate, socket_set):
module = collector_module
local, typed_a, typed_b, direct = [native_media(n) for n in range(4)]
typed = {socket: [typed_a[socket], typed_b[socket]] for socket in socket_set}
direct_message = {"worker_id": "direct", "is_last": True}
if "images" in socket_set:
direct_message.update(tensor=direct["images"], image_index=0)
if "audio" in socket_set:
direct_message["audio"] = direct["audio"]
messages = [direct_message, {"worker_id": "serverless:pool:1", "media": typed, "is_last": True}]
# Delivery order differs from participant order. Duplicate configured IDs
# must not duplicate a participant's data or leave collection waiting.
participants = ["serverless:pool:1", "direct", "serverless:pool:1"]
local_inputs = {socket: local[socket] if socket in socket_set else None for socket in ("images", "audio")}
images, audio = collect_queue(module, messages, participants, local=local_inputs, delegate=delegate)
expected = ([] if delegate else [local]) + [typed_a, typed_b, direct]
if "images" in socket_set:
assert_exact(images, torch.cat([row["images"] for row in expected]))
else:
assert images is None
if "audio" in socket_set:
assert audio["sample_rate"] == 24000
assert_exact(audio["waveform"], torch.cat([row["audio"]["waveform"] for row in expected], dim=-1))
else:
assert torch.equal(audio["waveform"], module.DistributedCollectorNode.EMPTY_AUDIO["waveform"])
assert module.prompt_server.distributed_pending_jobs == {}
module.prompt_server.distributed_serverless.cancel.assert_not_awaited()
@pytest.mark.parametrize("media", [{}, {"texts": ["not a Collector socket"]}, {"images": []},
{"audio": [None]}, {"images": "not a list"},
{"images": [torch.zeros(2, 2, 3)]}])
def test_invalid_typed_delivery_cancels_and_removes_queue(collector_module, media):
module = collector_module
with pytest.raises(RuntimeError, match="media|items|IMAGE"):
collect_queue(module, [{"worker_id": "serverless:pool:1", "media": media, "is_last": True}],
["serverless:pool:1"])
assert module.prompt_server.distributed_pending_jobs == {}
module.prompt_server.distributed_serverless.cancel.assert_awaited_once_with(collector_job_id="typed-job")
@pytest.mark.parametrize("failure", ["nonfinal", "unexpected", "duplicate", "provider-error", "shape"])
def test_typed_delivery_protocol_failures_are_not_partial_success(collector_module, failure):
media = {"images": [native_media()["images"]]}
first = {"worker_id": "serverless:pool:1", "media": media, "is_last": True}
messages = [first]
participants = ["serverless:pool:1", "serverless:pool:2"]
if failure == "nonfinal":
first["is_last"] = False
elif failure == "unexpected":
first["worker_id"] = "other"
elif failure == "duplicate":
messages.append(dict(first))
elif failure == "provider-error":
messages.append({"worker_id": "serverless:pool:2", "serverless_error": "offline failure", "is_last": True})
else:
messages.append({"worker_id": "serverless:pool:2", "media": {"images": [torch.zeros(1, 4, 5, 3)]},
"is_last": True})
with pytest.raises(RuntimeError, match="atomic|Unexpected|Duplicate|failed|shape mismatch"):
collect_queue(collector_module, messages, participants)
assert collector_module.prompt_server.distributed_pending_jobs == {}
collector_module.prompt_server.distributed_serverless.cancel.assert_awaited_once_with(collector_job_id="typed-job")
+20
View File
@@ -472,6 +472,26 @@ class DispatchSelectionTests(unittest.IsolatedAsyncioTestCase):
with self.assertRaisesRegex(RuntimeError, "execution_targets.*upgrade"):
await dispatch.preflight_image_list_workers([{"id": "worker"}])
async def test_image_list_value_results_requires_exact_true_only_for_overrides(self):
for value in (None, False, 1, "true", [], True):
capability = _valid_capability()
if value is not None:
capability['value_results'] = value
session = _FakeSession({('worker', 'capabilities'): (200, capability),
('worker', 'queue'): (200, _valid_queue())})
async def fake_session():
return session
with patch.object(dispatch, 'get_client_session', side_effect=fake_session), patch.object(
dispatch, 'build_worker_url', side_effect=lambda worker, path: f"http://{worker['id']}{path}"
):
self.assertEqual(await dispatch.preflight_image_list_workers([{'id': 'worker'}]), {'worker': True})
if value is True:
self.assertEqual(await dispatch.preflight_image_list_workers(
[{'id': 'worker'}], require_value_results=True), {'worker': True})
else:
with self.assertRaisesRegex(RuntimeError, 'value_results.*upgrade'):
await dispatch.preflight_image_list_workers([{'id': 'worker'}], require_value_results=True)
async def test_image_list_preflight_fails_closed_for_malformed_capabilities(self):
malformed = {"supported": True, "protocol_version": 2, "node_ids": None}
session = _FakeSession({("worker", "capabilities"): (200, malformed)})
@@ -0,0 +1,20 @@
"""Run the real framework's CPU-only private-cache retention regression."""
import os
from pathlib import Path
import subprocess
import sys
import pytest
def test_native_local_cache_retention():
core = os.environ.get('COMFYUI_SOURCE_ROOT')
if not core:
pytest.skip('Set COMFYUI_SOURCE_ROOT for CPU-only real-framework validation')
helper = Path(__file__).parent / 'helpers' / 'local_cache_retention_check.py'
env = dict(os.environ, CUDA_VISIBLE_DEVICES='', HIP_VISIBLE_DEVICES='')
env.pop('PYTHONPATH', None)
result = subprocess.run([sys.executable, str(helper), core], env=env,
text=True, capture_output=True, timeout=90)
assert result.returncode == 0, result.stdout + result.stderr
assert 'PASS: cross-run native cache reuse' in result.stdout
+69
View File
@@ -199,6 +199,73 @@ class MasterRuntimeTests(unittest.IsolatedAsyncioTestCase):
with self.assertRaisesRegex(RuntimeError, 'worker|fallback'):
self.rt.create_master_job('start', 'end', graph(), self.items, [], '')
async def test_values_reconstructed_in_source_order_without_transporting_originals(self):
self.workers.append({'id': 'remote', 'type': 'remote'})
context = self.create(count=6)
tensor = torch.ones(4, 2, 2, 3, dtype=torch.float64)
for index, item in enumerate(self.items):
item['values'].update(value_2=tensor, value_3=True, value_4=index / 8)
self.local_delay = 0.1 # Remote results arrive ahead of master item zero.
with patch.object(self.rt, 'encode_result', side_effect=AssertionError('originals must not return')):
result = await self.rt.run_image_list_job(context, include_values=True)
self.assertFalse(self.rt._MASTER_JOBS)
self.assertEqual(len(result), 4)
for index, row in enumerate(result[3]):
self.assertEqual(row['value_1'], str(index))
self.assertIs(row['value_2'], tensor)
self.assertIs(row['value_3'], True)
self.assertEqual(row['value_4'], index / 8)
self.assertTrue(self.dispatched and self.local_calls)
async def test_overrides_merge_add_and_recheck_capability_before_every_item(self):
self.workers.append({'id': 'remote', 'type': 'remote'})
context = self.create(participates=False, count=3)
self.state.original_prompt['end']['inputs'].update(value_1=['start', 4], value_10=['start', 2])
original_set = self.rt.set_master_result
def override(context, index, token, result):
result['values'] = {'value_1': f'new-{index}', 'value_10': index}
return original_set(context, index, token, result)
with patch.object(self.rt, 'set_master_result', side_effect=override):
result = await self.rt.run_image_list_job(context, include_values=True)
self.assertEqual(result[3], [{'value_1': f'new-{index}', 'value_10': index} for index in range(3)])
self.assertEqual(self.preflight.await_count, 4)
self.assertTrue(all(call.kwargs['require_value_results'] is True
for call in self.preflight.await_args_list))
self.assertEqual(self.items[0]['values'], {'value_1': '0'})
async def test_override_capability_downgrade_stops_before_upload(self):
self.workers.append({'id': 'remote', 'type': 'remote'})
context = self.create(participates=False)
self.state.original_prompt['end']['inputs']['value_1'] = ['start', 4]
self.preflight.side_effect = [{'remote': True}, RuntimeError('value_results unsupported')]
with patch.object(self, 'post', wraps=self.post) as upload:
with self.assertRaisesRegex(RuntimeError, 'value_results'):
await self.rt.run_image_list_job(context, include_values=True)
upload.assert_not_called()
self.assertFalse(self.dispatched or self.rt._MASTER_JOBS)
async def test_missing_expected_override_fails_without_partial_output(self):
self.workers.append({'id': 'remote', 'type': 'remote'})
context = self.create(participates=False)
self.state.original_prompt['end']['inputs']['value_1'] = ['start', 4]
with self.assertRaisesRegex(ValueError, 'missing'):
await self.rt.run_image_list_job(context, include_values=True)
self.assertFalse(self.rt._MASTER_JOBS)
async def test_values_failure_and_cancellation_return_no_partial_lists(self):
self.local_error = RuntimeError('branch failed')
with self.assertRaisesRegex(RuntimeError, 'branch failed'):
await self.rt.run_image_list_job(self.create(), include_values=True)
self.local_error = None
self.local_started.clear()
self.local_delay = 0.1
task = asyncio.create_task(self.rt.run_image_list_job(self.create(), include_values=True))
await self.local_started.wait()
task.cancel()
with self.assertRaises(self.rt.ImageListCancelledError):
await task
self.assertFalse(self.rt._MASTER_JOBS)
async def test_local_failure_surfaces_and_reclaims_staged_token(self):
self.local_error = RuntimeError('local branch failed')
with self.assertRaisesRegex(RuntimeError, 'local branch failed'):
@@ -373,6 +440,8 @@ def real_framework_probe(comfy_root):
rt.remove_master_job(context)
for behavior in ('normal', 'expand', 'error', 'cancel', 'interrupt', 'sync', 'already-interrupted'):
# Independent lifecycle cases start cold; retention is tested separately.
local._RETAINED_OUTPUTS = None
mode[0] = behavior
prompt['op']['class_type'] = 'Expanded' if behavior == 'expand' else 'Operation'
loads.clear()
+160 -5
View File
@@ -1,8 +1,9 @@
"""Image-list participation uses the existing master orchestrator setting."""
import json
import sys
import types
import unittest
from unittest.mock import AsyncMock, patch
from unittest.mock import AsyncMock, Mock, patch
from test_serverless_integration import PKG, load_modules
@@ -62,11 +63,21 @@ class MasterSelectionTests(unittest.IsolatedAsyncioTestCase):
self.q.dispatch_worker_prompt.assert_not_awaited()
self.assertEqual(self.ps.distributed_pending_jobs, {})
async def test_end_overrides_require_value_capability_before_master_queue(self):
self.prompt['3']['inputs']['value_10'] = ['2', 2]
await self.queue()
self.assertIs(self.preflight.call_args.kwargs['require_value_results'], True)
self.q.queue_prompt_payload.reset_mock()
self.preflight.side_effect = RuntimeError('value_results unsupported')
with self.assertRaisesRegex(RuntimeError, 'value_results'):
await self.queue()
self.q.queue_prompt_payload.assert_not_awaited()
async def test_orchestrator_setting_excludes_master_processing(self):
self.config["settings"]["master_delegate_only"] = True
await self.queue()
self.assertIs(self.prepared_inputs()["image_list_master_participates"], False)
self.assertTrue(self.preflight.call_args.kwargs["require_idle"])
self.assertIs(self.preflight.call_args.kwargs["require_idle"], False)
# Upstream source stays on master even in orchestrator-only mode.
self.assertIn("1", self.q.queue_prompt_payload.call_args.args[0])
@@ -97,10 +108,154 @@ class MasterSelectionTests(unittest.IsolatedAsyncioTestCase):
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), [])
self.assertIs(self.prepared_inputs()["image_list_master_participates"], True)
async def test_busy_workers_do_not_prevent_enabled_master(self):
self.preflight.return_value = {}
def use_real_preflight(self, queues):
"""Exercise capability/queue parsing with read-only fake HTTP responses."""
from test_dispatch_selection import dispatch, _FakeSession, _valid_capability
responses = {(worker_id, "capabilities"): (200, _valid_capability())
for worker_id in queues}
responses.update({(worker_id, "queue"): (200, queue)
for worker_id, queue in queues.items()})
session = _FakeSession(responses)
for target, value in (
("get_client_session", AsyncMock(return_value=session)),
("build_worker_url", lambda worker, path: f"http://{worker['id']}{path}"),
):
patched = patch.object(dispatch, target, value)
patched.start()
self.addCleanup(patched.stop)
self.preflight = AsyncMock(wraps=dispatch.preflight_image_list_workers)
sys.modules[PKG + ".api.orchestration.dispatch"].preflight_image_list_workers = self.preflight
return responses
async def run_prepared_selection(self):
"""Run real runtime selection up to the scheduler, without any item I/O."""
rt = sys.modules[PKG + ".image_list.runtime"]
inputs = self.prepared_inputs()
image = object()
context = rt.create_master_job(
"2", "3", self.q.queue_prompt_payload.call_args.args[0],
[{"image": image, "index": 0}],
json.loads(inputs["image_list_enabled_worker_ids"]),
inputs["image_list_master_callback"],
master_participates=inputs["image_list_master_participates"],
explicit_targets=True,
)
self.addCleanup(rt.remove_master_job, context)
self.scheduler = AsyncMock(return_value=[{"image": image}])
self.local_slot = Mock(return_value=types.SimpleNamespace(close=AsyncMock()))
self.runtime_session = AsyncMock(return_value=object())
config = sys.modules[PKG + ".utils.config"]
network = sys.modules[PKG + ".utils.network"]
with patch.object(config, "load_config", lambda: self.config), \
patch.object(config, "get_worker_timeout_seconds", lambda: 60, create=True), \
patch.object(config, "get_image_list_job_timeout_seconds", lambda: 0, create=True), \
patch.object(network, "get_client_session", self.runtime_session, create=True), \
patch.object(network, "build_worker_url", Mock(), create=True), \
patch.object(rt, "dynamic_schedule", self.scheduler), \
patch.dict(sys.modules, {PKG + ".image_list.local_executor": types.SimpleNamespace(
LocalExecutionSlot=self.local_slot)}):
await rt.run_image_list_job(context)
self.assertIsNone(rt.get_master_job(context))
return self.scheduler.call_args.args[1]
async def test_busy_worker_at_enqueue_is_available_to_scheduler_when_idle_at_start(self):
responses = self.use_real_preflight({
"worker": {"queue_running": [[1, "prompt-a"]], "queue_pending": []},
})
result = await self.queue()
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), ["worker"])
self.assertEqual(result[2], 1) # Candidates, not a promise of execution-time availability.
self.q.dispatch_worker_prompt.assert_not_awaited()
responses[("worker", "queue")] = (200, {"queue_running": [], "queue_pending": []})
# A newly enabled worker is not implicitly added to this queued selection.
self.config["workers"].append({"id": "later", "enabled": True})
slots = await self.run_prepared_selection()
self.assertEqual([slot for slot in slots if isinstance(slot, str)], ["worker"])
self.assertEqual(len(slots), 2)
self.assertEqual(self.preflight.await_count, 2)
self.assertEqual(self.preflight.await_args_list[0].args, self.preflight.await_args_list[1].args)
async def test_worker_still_busy_at_start_is_excluded_from_scheduler(self):
self.use_real_preflight({
"worker": {"queue_running": [], "queue_pending": [[1, "prompt-a"]]},
})
await self.queue()
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), [])
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), ["worker"])
slots = await self.run_prepared_selection()
self.assertEqual(len(slots), 1)
self.assertNotIn("worker", slots)
self.assertEqual(self.preflight.await_count, 2)
self.runtime_session.assert_not_awaited()
async def test_explicit_empty_selection_stays_master_only_at_start(self):
self.use_real_preflight({"worker": {"queue_running": [], "queue_pending": []}})
await self.queue(enabled_worker_ids=[])
slots = await self.run_prepared_selection()
self.assertEqual(len(slots), 1)
self.assertNotIn("worker", slots)
self.preflight.assert_not_awaited()
self.runtime_session.assert_not_awaited()
async def test_busy_incompatible_worker_fails_before_master_queue(self):
responses = self.use_real_preflight({
"worker": {"queue_running": [[1, "prompt-a"]], "queue_pending": []},
})
capability = responses[("worker", "capabilities")][1]
for field, invalid, message in (
("protocol_version", 1, "incompatible"),
("execution_targets", False, "execution_targets"),
("value_results", False, "value_results"),
):
with self.subTest(field=field), patch.dict(capability, {field: invalid}):
if field == "value_results":
self.prompt["3"]["inputs"]["value_10"] = ["2", 2]
with self.assertRaisesRegex(RuntimeError, message):
await self.queue()
self.q.queue_prompt_payload.assert_not_awaited()
self.q.dispatch_worker_prompt.assert_not_awaited()
async def test_orchestrator_with_all_workers_busy_queues_for_idle_start(self):
responses = self.use_real_preflight({
"worker": {"queue_running": [[1, "prompt-a"]], "queue_pending": []},
})
result = await self.queue(delegate_master=True)
self.q.queue_prompt_payload.assert_awaited_once()
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), ["worker"])
self.assertIs(self.prepared_inputs()["image_list_master_participates"], False)
self.assertEqual(result[2], 1)
self.q.dispatch_worker_prompt.assert_not_awaited()
responses[("worker", "queue")] = (200, {"queue_running": [], "queue_pending": []})
self.assertEqual(await self.run_prepared_selection(), ["worker"])
self.local_slot.assert_not_called()
self.assertEqual(self.preflight.await_count, 2)
self.assertIs(self.preflight.await_args_list[0].kwargs["require_idle"], False)
self.assertIs(self.preflight.await_args_list[1].kwargs["require_idle"], True)
async def test_orchestrator_rechecks_availability_without_master_fallback(self):
responses = self.use_real_preflight({"worker": {"queue_running": [], "queue_pending": []}})
await self.queue(delegate_master=True)
responses[("worker", "queue")] = (200, {"queue_running": [[1, "other"]], "queue_pending": []})
with self.assertRaisesRegex(RuntimeError, "No selected feature-compatible direct worker is idle"):
await self.run_prepared_selection()
self.assertEqual(self.preflight.await_count, 2)
self.scheduler.assert_not_awaited()
self.local_slot.assert_not_called()
async def test_orchestrator_retains_busy_candidates_when_another_worker_is_idle(self):
self.config["workers"].append({"id": "idle", "enabled": True})
responses = self.use_real_preflight({
"worker": {"queue_running": [[1, "prompt-a"]], "queue_pending": []},
"idle": {"queue_running": [], "queue_pending": []},
})
await self.queue(delegate_master=True)
self.assertEqual(json.loads(self.prepared_inputs()["image_list_enabled_worker_ids"]), ["worker", "idle"])
responses[("worker", "queue")] = (200, {"queue_running": [], "queue_pending": []})
responses[("idle", "queue")] = (200, {"queue_running": [[1, "other"]], "queue_pending": []})
self.assertEqual(await self.run_prepared_selection(), ["worker"])
self.local_slot.assert_not_called()
async def test_orchestrator_without_workers_fails_before_queue(self):
self.config["settings"]["master_delegate_only"] = True
@@ -0,0 +1,527 @@
"""Stable content signatures with fresh, expiring delivery capabilities."""
import asyncio
import copy
import importlib.util
import json
import os
from pathlib import Path
import subprocess
import sys
import types
from unittest.mock import AsyncMock
import pytest
import torch
from image_list import runtime as rt
ROOT = Path(__file__).resolve().parents[1]
def item():
return rt.normalize_image_list_inputs(
torch.arange(18, dtype=torch.float64).reshape(1, 2, 3, 3),
torch.zeros(1, 2, 3, dtype=torch.bfloat16),
{'value_1': 'original', 'value_2': True, 'value_3': 7, 'value_4': 0.25,
'value_10': torch.ones(2, 3, dtype=torch.float16)})[0]
def graph():
return {
'source': {'class_type': 'Source', 'inputs': {}},
'start': {'class_type': rt.START_CLASS, 'inputs': {
'images': ['source', 0], 'image_list_execution_id': 'transient',
'image_list_workflow_meta': {'job': 'metadata'},
'image_list_enabled_worker_ids': '["worker"]',
'image_list_master_callback': 'http://old-master',
'values.value_1': 'inherited', 'worker_context_id': 'old-context'}},
'op': {'class_type': 'Operation', 'inputs': {'image': ['start', 0]}},
'end': {'class_type': rt.END_CLASS, 'inputs': {
'images': ['op', 0], 'context': ['start', 3], 'value_3': ['start', 2]}},
}
def worker_prompt(token, context, data, *, local=True):
prompt = graph()
result = rt.build_worker_prompt(prompt, rt.validate_image_list_regions(prompt)[0],
token, context, data['index'], 'http://master',
item_identity=rt.item_content_identity(data))
if local:
result['start']['inputs']['image_list_local_execution'] = True
result['end']['inputs']['image_list_local_execution'] = True
return result
@pytest.fixture(autouse=True)
def clean_state():
yield
rt._WORKER_ITEMS.clear()
rt._MASTER_JOBS.clear()
@pytest.fixture
def boundaries(monkeypatch):
"""Load only the boundary module; restore every replaced module on exit."""
name = '_native_cache_identity_tests'
for suffix in ('', '.nodes', '.utils', '.image_list', '.api'):
module = types.ModuleType(name + suffix)
module.__path__ = [str(ROOT / suffix.lstrip('.').replace('.', '/'))]
monkeypatch.setitem(sys.modules, name + suffix, module)
monkeypatch.setitem(sys.modules, name + '.image_list.runtime', rt)
io = types.SimpleNamespace(ComfyNode=type('ComfyNode', (), {}),
NodeOutput=lambda *args: args)
for cls in ('Image', 'Mask', 'String', 'Int', 'Float', 'Boolean'):
setattr(io, cls, object())
monkeypatch.setitem(sys.modules, 'comfy_api', types.ModuleType('comfy_api'))
monkeypatch.setitem(sys.modules, 'comfy_api.v0_0_2', types.SimpleNamespace(io=io))
monkeypatch.setitem(sys.modules, name + '.utils.async_helpers',
types.SimpleNamespace(run_async_in_server_loop=lambda coro, **kw: asyncio.run(coro)))
spec = importlib.util.spec_from_file_location(name + '.nodes.image_list', ROOT / 'nodes/image_list.py')
module = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, spec.name, module)
spec.loader.exec_module(module)
return module
def start(boundaries, prompt):
boundaries.DistributedImageListStart.hidden = types.SimpleNamespace(prompt=prompt, unique_id='start')
return boundaries.DistributedImageListStart.execute(**prompt['start']['inputs'])
def end(boundaries, prompt, cached):
boundaries.DistributedImageListEnd.hidden = types.SimpleNamespace(prompt=prompt, unique_id='end')
inputs = {k: v for k, v in prompt['end']['inputs'].items()
if k not in ('images', 'context', 'value_3')}
return boundaries.DistributedImageListEnd.execute(
images=cached[0], context=cached[3], value_3=cached[2], **inputs)
def stage(data):
token = rt.create_worker_task_token()
rt.store_worker_item(token, rt.encode_item(data))
return token
def master(data, token):
context = rt.create_master_job('start', 'end', graph(), [data], ['worker'], '')
rt.register_assignment(context, data['index'], 'worker', token)
return context
def test_identity_is_exact_typed_order_independent_and_not_transport_bounded(monkeypatch):
original = item()
identity = rt.item_content_identity(original)
assert rt.is_valid_item_identity(identity)
reordered = copy.deepcopy(original)
reordered['values'] = dict(reversed(list(reordered['values'].items())))
assert rt.item_content_identity(reordered) == identity
strided = copy.deepcopy(original)
strided['image'] = original['image'].transpose(1, 2).contiguous().transpose(1, 2)
assert not strided['image'].is_contiguous()
assert rt.item_content_identity(strided) == identity
monkeypatch.setattr(rt, 'MAX_PAYLOAD_BYTES', 1)
with pytest.raises(ValueError, match='transport limit'):
rt.encode_item(original)
assert rt.item_content_identity(original) == identity
scalar_identities = set()
for value in (True, 1, 1.0, '1', 0.0, -0.0):
changed = copy.deepcopy(original)
changed['values']['value_2'] = value
scalar_identities.add(rt.item_content_identity(changed))
assert len(scalar_identities) == 6
@pytest.mark.parametrize('change', ['image', 'mask', 'value', 'index', 'dtype', 'shape', 'slot', 'missing-mask'])
def test_every_item_component_invalidates_identity(change):
original, changed = item(), item()
if change in ('image', 'mask'):
changed[change].reshape(-1)[0] = 1
elif change == 'value':
changed['values']['value_10'][0, 0] = 2
elif change == 'index':
changed['index'] = 1
elif change == 'dtype':
changed['image'] = changed['image'].float()
elif change == 'shape':
changed['values']['value_10'] = changed['values']['value_10'].reshape(3, 2)
elif change == 'slot':
changed['values']['value_9'] = changed['values'].pop('value_10')
else:
changed['mask'] = None
assert rt.item_content_identity(original) != rt.item_content_identity(changed)
def test_float64_low_bits_and_bfloat16_bytes_are_not_quantized():
data, changed = item(), item()
changed['image'].reshape(-1)[0] = torch.nextafter(torch.tensor(0., dtype=torch.float64),
torch.tensor(1., dtype=torch.float64))
assert rt.item_content_identity(data) != rt.item_content_identity(changed)
changed = item()
changed['mask'].view(torch.int16).reshape(-1)[0] = 1
assert rt.item_content_identity(data) != rt.item_content_identity(changed)
def test_stable_start_replaced_wholesale_and_legacy_graph_preserved():
prompt = graph()
frozen = copy.deepcopy(prompt)
region = rt.validate_image_list_regions(prompt)[0]
legacy = rt.build_worker_prompt(prompt, region, 'a', 'ctx-a', 0, 'http://a')
assert legacy['start']['inputs']['worker_item_token'] == 'a'
assert legacy['start']['inputs']['image_list_execution_id'] == 'transient'
identity = rt.item_content_identity(item())
a = rt.build_worker_prompt(prompt, region, 'a', 'ctx-a', 0, 'http://a', item_identity=identity)
b = rt.build_worker_prompt(prompt, region, 'b', 'ctx-b', 0, 'http://b', item_identity=identity)
assert a['start']['inputs'] == b['start']['inputs'] == {'worker_item_identity': identity}
assert a['end']['inputs']['worker_context_id'] == 'ctx-a'
assert b['end']['inputs']['worker_result_token'] == 'b'
assert b['end']['inputs']['worker_item_identity'] == identity
assert b['end']['inputs']['value_3'] == ['start', 2]
assert prompt == frozen
@pytest.mark.parametrize('bad', ['', 'sha256:' + 'z' * 64, 'a' * 64, False, ['bad']])
def test_malformed_identity_rejected(bad):
assert not rt.is_valid_item_identity(bad)
prompt = graph()
with pytest.raises((ValueError, TypeError), match='identity'):
rt.build_worker_prompt(prompt, rt.validate_image_list_regions(prompt)[0],
'token', 'context', 0, '', item_identity=bad)
def test_cache_miss_then_hit_delivers_current_context_token_and_values(boundaries):
data = item()
a = stage(data)
context_a = master(data, a)
prompt_a = worker_prompt(a, context_a, data)
cached = start(boundaries, prompt_a)
assert cached[3] == [rt.item_content_identity(data)]
assert 'image' not in rt._WORKER_ITEMS[a][1] # only a small, expiring receipt remains
end(boundaries, prompt_a, cached)
assert a not in rt._WORKER_ITEMS
assert rt.get_master_job(context_a).results[0]['values'] == {'value_3': 0}
b = stage(data)
context_b = master(data, b)
prompt_b = worker_prompt(b, context_b, data)
assert prompt_a['start'] == prompt_b['start']
# A native hit does NOT execute Start; End must consume this current upload.
end(boundaries, prompt_b, cached)
assert b not in rt._WORKER_ITEMS
state = rt.get_master_job(context_b)
assert state.expected_tokens[0] == b
assert torch.equal(state.results[0]['image'], data['image'])
assert rt._collect_output_lists([state.results[0]], state=state, include_values=True)[3][0]['value_3'] == 0
with pytest.raises(RuntimeError, match='missing|expired|consumed'):
end(boundaries, prompt_b, cached)
@pytest.mark.parametrize('miss', [False, True])
def test_expired_item_cannot_deliver_even_with_cached_start(boundaries, miss):
data = item()
token = stage(data)
context = master(data, token)
prompt = worker_prompt(token, context, data)
cached = start(boundaries, prompt) if miss else ([data['image']], [data['mask']], [0], [rt.item_content_identity(data)])
_, staged = rt._WORKER_ITEMS[token]
rt._WORKER_ITEMS[token] = (0, staged)
with pytest.raises(RuntimeError, match='missing|expired|consumed'):
end(boundaries, prompt, cached)
assert not rt.get_master_job(context).received_indexes
assert token not in rt._WORKER_ITEMS
def test_identity_mismatch_on_start_and_on_cache_hit_fails_closed(boundaries):
data, different = item(), item()
different['image'][0, 0, 0, 0] = 3
for hit in (False, True):
token = stage(different)
context = master(different, token)
prompt = worker_prompt(token, context, data)
cached = ([data['image']], [data['mask']], [0], [rt.item_content_identity(data)])
with pytest.raises((ValueError, RuntimeError), match='identity'):
end(boundaries, prompt, cached) if hit else start(boundaries, prompt)
assert not rt.get_master_job(context).received_indexes
rt.delete_worker_item(token)
@pytest.mark.parametrize('change', ['ambiguous', 'wrong-link', 'wrong-start-id', 'end-identity', 'end-index'])
def test_start_requires_exact_paired_end_binding(boundaries, change):
data = item()
token = stage(data)
prompt = worker_prompt(token, 'context', data)
if change == 'ambiguous':
prompt['second-end'] = copy.deepcopy(prompt['end'])
elif change == 'wrong-link':
prompt['end']['inputs']['context'] = ['start', 2]
elif change == 'wrong-start-id':
prompt['renamed'] = prompt.pop('start')
elif change == 'end-identity':
prompt['end']['inputs']['worker_item_identity'] = 'sha256:' + '0' * 64
else:
prompt['end']['inputs']['worker_item_index'] = 1
boundaries.DistributedImageListStart.hidden = types.SimpleNamespace(prompt=prompt, unique_id='start')
with pytest.raises((RuntimeError, ValueError), match='paired|identity|index'):
boundaries.DistributedImageListStart.execute(worker_item_identity=rt.item_content_identity(data),
image_list_local_execution=True)
def test_end_checks_stable_context_before_delivering(boundaries):
data = item()
token = stage(data)
context = master(data, token)
prompt = worker_prompt(token, context, data)
cached = list(start(boundaries, prompt))
cached[3] = ['sha256:' + '0' * 64]
with pytest.raises((RuntimeError, ValueError), match='identity|context'):
end(boundaries, prompt, cached)
assert not rt.get_master_job(context).received_indexes
def test_legacy_worker_path_still_consumes_and_delivers_job_context(boundaries):
data = item()
token = stage(data)
context = master(data, token)
prompt = graph()
prompt = rt.build_worker_prompt(prompt, rt.validate_image_list_regions(prompt)[0], token, context, 0, '')
prompt['start']['inputs']['image_list_local_execution'] = True
prompt['end']['inputs']['image_list_local_execution'] = True
cached = start(boundaries, prompt)
assert cached[3] == [context]
assert token not in rt._WORKER_ITEMS
end(boundaries, prompt, cached)
assert rt.get_master_job(context).received_indexes == {0}
def test_remote_cache_hit_submits_fresh_callback_metadata(boundaries, monkeypatch):
data = item()
token = stage(data)
prompt = worker_prompt(token, 'fresh-context', data, local=False)
submit = AsyncMock()
monkeypatch.setattr(boundaries, 'submit_worker_result', submit)
cached = ([data['image']], [data['mask']], [0], [rt.item_content_identity(data)])
end(boundaries, prompt, cached)
assert submit.await_args.args[:4] == ('http://master', token, 'fresh-context', 0)
assert submit.await_args.args[4]['values']['value_3'] == {'type': 'INT', 'value': 0}
assert token not in rt._WORKER_ITEMS
def test_item_route_returns_server_computed_identity(boundaries, monkeypatch):
from aiohttp import web
name = '_native_cache_identity_tests'
server = types.SimpleNamespace(PromptServer=types.SimpleNamespace(instance=types.SimpleNamespace(routes=web.RouteTableDef())))
monkeypatch.setitem(sys.modules, 'server', server)
spec = importlib.util.spec_from_file_location(name + '.api.image_list_routes', ROOT / 'api/image_list_routes.py')
module = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, spec.name, module)
spec.loader.exec_module(module)
data = item()
token = rt.create_worker_task_token()
monkeypatch.setattr(module, '_json_body', AsyncMock(return_value={
'token': token, 'context_id': 'context', 'item': rt.encode_item(data),
'item_identity': 'sha256:' + '0' * 64}))
response = asyncio.run(module.image_list_item_endpoint(object()))
assert response.status == 200
assert json.loads(response.body)['item_identity'] == rt.item_content_identity(data)
assert len(rt._WORKER_ITEMS[token]) == 2
@pytest.mark.parametrize('responses', [
('valid', 'valid'), ('valid', 'absent'), ('invalid', 'valid'),
('bad-json', 'valid'), ('non-object', 'valid'),
])
def test_runtime_opts_in_per_upload_and_can_downgrade(monkeypatch, responses):
name = '_native_cache_identity_runtime'
for suffix in ('', '.image_list', '.api', '.api.orchestration', '.utils'):
module = types.ModuleType(name + suffix)
module.__path__ = [str(ROOT / suffix.lstrip('.').replace('.', '/'))]
monkeypatch.setitem(sys.modules, name + suffix, module)
spec = importlib.util.spec_from_file_location(name + '.image_list.runtime', ROOT / 'image_list/runtime.py')
runtime = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, spec.name, runtime)
spec.loader.exec_module(runtime)
workers = [{'id': 'worker', 'type': 'local'}]
dispatched, uploads = [], []
preflight = AsyncMock(return_value={'worker': True})
sync_media = AsyncMock()
class Response:
status = 200
def __init__(self, data):
self.data = data
async def __aenter__(self):
return self
async def __aexit__(self, *args):
pass
async def json(self):
if self.data == 'bad-json':
raise ValueError('old response is not JSON')
return self.data
def raise_for_status(self):
pass
class Session:
def post(self, url, *, data, **kw):
assert url.endswith('/distributed/image-list/item')
body = json.loads(data)
identity = runtime.store_worker_item(body['token'], body['item'])
mode = responses[len(uploads)]
uploads.append(body)
response = {'valid': {'item_identity': identity}, 'absent': {'status': 'stored'},
'invalid': {'item_identity': 'bad'}, 'bad-json': 'bad-json', 'non-object': []}[mode]
return Response(response)
def get(self, url, **kw):
pid = url.rsplit('/', 1)[-1]
return Response({pid: {'status': {'completed': True, 'status_str': 'success'}}})
def delete(self, url, **kw):
runtime.delete_worker_item(url.rsplit('/', 1)[-1])
return Response({})
async def dispatch(worker, prompt, meta, **kwargs):
dispatched.append(copy.deepcopy(prompt))
delivery = prompt['end']['inputs']
token, index = delivery['worker_result_token'], delivery['worker_item_index']
identity = prompt['start']['inputs'].get('worker_item_identity')
options = {'item_identity': identity, 'item_index': index} if identity else {}
data = runtime.consume_worker_item(token, **options)
if identity:
runtime.finish_worker_item(token, identity, index)
runtime.set_master_result(context, index, token, {
'image': data['image'], 'text': str(index), 'values': {'value_3': index}})
assert kwargs['partial_execution_targets'] == ['end']
return f'worker-{index}'
dependencies = {
'.api.orchestration.dispatch': types.SimpleNamespace(
preflight_image_list_workers=preflight, dispatch_worker_prompt=dispatch),
'.api.orchestration.prompt_transform': types.SimpleNamespace(
_resolve_direct_workers_for_image_list=lambda *args: workers),
'.api.orchestration.media_sync': types.SimpleNamespace(sync_worker_media=sync_media),
'.utils.config': types.SimpleNamespace(load_config=lambda: {'settings': {}},
get_worker_timeout_seconds=lambda: 60),
'.utils.network': types.SimpleNamespace(build_master_callback_url=lambda *a, **kw: 'http://master',
build_worker_url=lambda worker, path: 'http://worker' + path,
get_client_session=AsyncMock(return_value=Session())),
'.utils.logging': types.SimpleNamespace(log=lambda *args: None),
}
for suffix, module in dependencies.items():
monkeypatch.setitem(sys.modules, name + suffix, module)
monkeypatch.setitem(sys.modules, 'aiohttp', types.SimpleNamespace(
ClientTimeout=lambda **kwargs: kwargs, ClientError=ConnectionError))
comfy = types.ModuleType('comfy')
comfy.model_management = types.SimpleNamespace(processing_interrupted=lambda: False)
monkeypatch.setitem(sys.modules, 'comfy', comfy)
monkeypatch.setitem(sys.modules, 'comfy.model_management', comfy.model_management)
items = [item(), item()]
items[1]['index'] = 1
context = runtime.create_master_job('start', 'end', graph(), items, ['worker'], '')
state = runtime.get_master_job(context)
try:
result = asyncio.run(runtime._run_image_list_job(state, include_values=True))
assert result[2] == ['0', '1']
assert [row['value_3'] for row in result[3]] == [0, 1]
assert len(uploads) == len(dispatched) == 2
assert sync_media.await_count == 1
for index, prompt in enumerate(dispatched):
start_inputs = prompt['start']['inputs']
if responses[index] == 'valid':
assert start_inputs == {'worker_item_identity': runtime.item_content_identity(items[index])}
assert prompt['end']['inputs']['worker_context_id'] == context
else:
assert 'worker_item_identity' not in start_inputs
assert start_inputs['worker_item_token'] == uploads[index]['token']
assert start_inputs['worker_context_id'] == context
assert 'worker_item_identity' not in prompt['end']['inputs']
assert prompt['end']['inputs']['worker_result_token'] == uploads[index]['token']
assert not runtime._WORKER_ITEMS
finally:
runtime.remove_master_job(context)
runtime._WORKER_ITEMS.clear()
def test_runtime_local_path_hashes_without_transport(monkeypatch):
name = '_native_cache_identity_local_runtime'
for suffix in ('', '.image_list', '.api', '.api.orchestration', '.utils'):
module = types.ModuleType(name + suffix)
module.__path__ = [str(ROOT / suffix.lstrip('.').replace('.', '/'))]
monkeypatch.setitem(sys.modules, name + suffix, module)
spec = importlib.util.spec_from_file_location(name + '.image_list.runtime', ROOT / 'image_list/runtime.py')
runtime = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, spec.name, runtime)
spec.loader.exec_module(runtime)
dispatched = []
class Slot:
def __init__(self, **kw):
pass
def submit(self, prompt, prompt_id, end_id, workflow_meta):
dispatched.append(prompt)
delivery = prompt[end_id]['inputs']
identity = prompt['start']['inputs']['worker_item_identity']
data = runtime.consume_worker_item(delivery['worker_result_token'], item_identity=identity,
item_index=delivery['worker_item_index'])
runtime.finish_worker_item(delivery['worker_result_token'], identity, data['index'])
runtime.set_master_result(delivery['worker_context_id'], data['index'], delivery['worker_result_token'],
{'image': data['image'], 'values': {'value_3': data['index']}})
future = asyncio.get_running_loop().create_future()
future.set_result(None)
return future
def request_stop(self):
pass
async def close(self):
pass
def forbidden(*args, **kwargs):
raise AssertionError('local path attempted HTTP/transport')
dependencies = {
'.image_list.local_executor': types.SimpleNamespace(LocalExecutionSlot=Slot),
'.api.orchestration.dispatch': types.SimpleNamespace(
preflight_image_list_workers=forbidden, dispatch_worker_prompt=forbidden),
'.api.orchestration.prompt_transform': types.SimpleNamespace(
_resolve_direct_workers_for_image_list=lambda *args: []),
'.utils.config': types.SimpleNamespace(load_config=lambda: {'settings': {}},
get_worker_timeout_seconds=lambda: 60),
'.utils.network': types.SimpleNamespace(build_master_callback_url=forbidden,
build_worker_url=forbidden, get_client_session=forbidden),
'.utils.logging': types.SimpleNamespace(log=lambda *args: None),
}
for suffix, module in dependencies.items():
monkeypatch.setitem(sys.modules, name + suffix, module)
monkeypatch.setitem(sys.modules, 'aiohttp', types.SimpleNamespace(
ClientTimeout=lambda **kwargs: kwargs, ClientError=ConnectionError))
comfy = types.ModuleType('comfy')
comfy.model_management = types.SimpleNamespace(processing_interrupted=lambda: False)
monkeypatch.setitem(sys.modules, 'comfy', comfy)
monkeypatch.setitem(sys.modules, 'comfy.model_management', comfy.model_management)
monkeypatch.setattr(runtime, 'MAX_PAYLOAD_BYTES', 1)
monkeypatch.setattr(runtime, 'encode_item', forbidden)
data = item()
context = runtime.create_master_job('start', 'end', graph(), [data], [], '', master_participates=True)
try:
result = asyncio.run(runtime._run_image_list_job(runtime.get_master_job(context), include_values=True))
assert dispatched[0]['start']['inputs'] == {'worker_item_identity': runtime.item_content_identity(data),
'image_list_local_execution': True}
assert result[3][0]['value_3'] == 0
assert not runtime._WORKER_ITEMS
finally:
runtime.remove_master_job(context)
runtime._WORKER_ITEMS.clear()
def test_real_native_signatures_and_cached_delivery():
core = Path(os.environ.get('COMFYUI_SOURCE_ROOT', str(Path(sys.executable).parents[2])))
if not (core / 'comfy_execution/caching.py').exists():
pytest.skip('real ComfyUI source not available beside this interpreter')
result = subprocess.run([sys.executable, str(ROOT / 'tests/helpers/image_list_native_cache_identity.py'), str(core)],
text=True, capture_output=True, timeout=90)
assert result.returncode == 0, result.stdout + result.stderr
assert 'NATIVE_CACHE_IDENTITY_OK' in result.stdout
+165
View File
@@ -0,0 +1,165 @@
"""Context-carried values and sparse, per-item End overrides."""
import copy
from unittest.mock import patch
import pytest
import torch
from image_list import runtime as rt
from image_list.execution_plan import prepare_image_list_execution
def value_graph():
# Use a minimal, closed region with an override-only ancestor.
return {
'source': {'class_type': 'Source', 'inputs': {}},
'start': {'class_type': rt.START_CLASS, 'inputs': {'images': ['source', 0]}},
'override': {'class_type': 'Transform', 'inputs': {'value': ['start', 4]}},
'end': {'class_type': rt.END_CLASS, 'inputs': {
'images': ['start', 0], 'context': ['start', 3], 'value_1': ['override', 0],
'value_10': ['start', 5]}},
}
def values():
return {'value_1': torch.arange(72, dtype=torch.float64).reshape(2, 3, 4, 3),
'value_2': torch.ones(3, 2, 2, dtype=torch.bfloat16), 'value_3': 'name',
'value_4': 42, 'value_5': 0.125, 'value_6': True}
def assert_values(actual, expected):
assert actual.keys() == expected.keys()
for key, value in expected.items():
assert type(actual[key]) is type(value)
if isinstance(value, torch.Tensor):
assert actual[key].dtype == value.dtype
assert torch.equal(actual[key], value)
else:
assert actual[key] == value
def test_normalize_and_roundtrip_all_override_kinds_keeps_batches_whole():
expected = values()
result = rt.normalize_worker_result(torch.zeros(1, 2, 2, 3),
values={key: [value] for key, value in expected.items()})
assert_values(result['values'], expected)
wire = rt.encode_result(result)
assert_values(rt.decode_result(wire)['values'], expected)
assert wire['values']['value_1']['shape'][0] == 2
assert wire['values']['value_2']['shape'][0] == 3
@pytest.mark.parametrize('bad', [None, [], [None], [1, 2], [[1]], object(),
float('nan'), float('inf'), torch.tensor([float('nan')]),
torch.tensor([float('inf')]), torch.ones(1, dtype=torch.int32)])
def test_invalid_override_rejected(bad):
with pytest.raises((ValueError, TypeError)):
rt.normalize_worker_result(torch.zeros(1, 2, 2, 3), values={'value_1': bad})
def test_unknown_override_names_and_wire_types_rejected():
for name in ('value_0', 'value_11', 'values.value_1'):
with pytest.raises(ValueError, match='slot'):
rt.normalize_worker_result(torch.zeros(1, 2, 2, 3), values={name: 1})
base = rt.encode_result({'image': torch.zeros(1, 2, 2, 3)})
for payload in (None, [], {'value_0': {'type': 'INT', 'value': 1}},
{'value_1': {'type': 'LATENT', 'value': {}}},
{'value_1': {'type': 'FLOAT', 'value': float('nan')}},
{'value_1': {'type': 'STRING', 'value': None}},
{'value_1': rt.encode_value(torch.tensor([float('inf')]), 'TENSOR')}):
with pytest.raises((ValueError, TypeError)):
rt.decode_result({**base, 'values': payload})
def test_no_overrides_keeps_legacy_wire_and_does_not_encode_original_tensors():
image = torch.zeros(1, 2, 2, 3)
result = rt.normalize_worker_result(image)
assert set(result) == {'image', 'mask', 'text'}
with patch.object(rt, 'encode_value', wraps=rt.encode_value) as encode:
wire = rt.encode_result(result)
assert set(wire) == {'image', 'mask', 'text'}
assert [call.args[1] for call in encode.call_args_list] == ['IMAGE', 'MASK', 'STRING']
assert set(rt.decode_result(wire)) == {'image', 'mask', 'text'}
def test_override_ancestors_survive_plan_and_worker_prompt():
prompt = value_graph()
frozen = copy.deepcopy(prompt)
prepared, targets = prepare_image_list_execution(prompt)
assert prepared == frozen and prompt == frozen
assert targets == ['end']
region = rt.validate_image_list_regions(prepared)[0]
assert 'override' in region['branch_ids']
worker = rt.build_worker_prompt(prepared, region, 'token', 'context', 0, 'http://master')
assert worker['end']['inputs']['value_1'] == ['override', 0]
assert worker['end']['inputs']['value_10'] == ['start', 5]
assert 'source' not in worker
for bad in ('value_0', 'value_11', 'values.value_1'):
invalid = copy.deepcopy(prompt)
invalid['end']['inputs'][bad] = ['start', 4]
with pytest.raises(ValueError, match='slot'):
rt.validate_image_list_regions(invalid)
def test_missing_or_unexpected_result_override_fails_before_recording():
prompt = value_graph()
items = rt.normalize_image_list_inputs(torch.zeros(1, 2, 2, 3), values={'value_1': 'original'})
context = rt.create_master_job('start', 'end', prompt, items, ['worker'], '')
try:
rt.register_assignment(context, 0, 'worker', 'token')
for result_values in ({}, {'value_1': 'updated'},
{'value_1': 'updated', 'value_10': 10, 'value_9': False}):
with pytest.raises(ValueError, match='override'):
rt.set_master_result(context, 0, 'token', {'image': items[0]['image'], 'values': result_values})
assert rt.get_master_job(context).received_indexes == set()
rt.set_master_result(context, 0, 'token', {'image': items[0]['image'],
'values': {'value_1': 'updated', 'value_10': 10}})
assert rt.get_master_job(context).received_indexes == {0}
finally:
rt.remove_master_job(context)
def test_override_return_request_budget_is_aggregate():
result = rt.normalize_worker_result(torch.zeros(1, 2, 2, 3), values={'value_1': ['x' * 500]})
with patch.object(rt, 'MAX_REQUEST_BYTES', 400):
with pytest.raises(ValueError, match='request'):
rt.encode_request_payload({'result': rt.encode_result(result)})
class TestResultHTTP:
def test_missing_and_invalid_overrides_fail_through_real_http(self):
import asyncio
from aiohttp import web
from aiohttp.test_utils import TestClient, TestServer
from test_image_list_integration_gaps import load_routes
async def check():
_module, routes = load_routes()
app = web.Application()
app.add_routes(routes)
prompt = value_graph()
items = rt.normalize_image_list_inputs(torch.zeros(1, 2, 2, 3), values={'value_1': 'original'})
context = rt.create_master_job('start', 'end', prompt, items, ['worker'], '')
token = rt.create_worker_task_token()
rt.register_assignment(context, 0, 'worker', token)
try:
async with TestClient(TestServer(app)) as client:
caps = await (await client.get('/distributed/image-list/capabilities')).json()
assert caps['value_results'] is True and caps['protocol_version'] == 2
result = rt.encode_result({'image': items[0]['image']})
envelope = {'context_id': context, 'token': token, 'item_index': 0, 'result': result}
response = await client.post('/distributed/image-list/result', json=envelope)
assert response.status == 400
assert 'missing' in (await response.json())['error']
assert not rt.get_master_job(context).received_indexes
result['values'] = {'value_1': {'type': 'INT', 'value': [1, 2]},
'value_10': {'type': 'INT', 'value': 10}}
response = await client.post('/distributed/image-list/result', json=envelope)
assert response.status == 400
assert not rt.get_master_job(context).received_indexes
result['values']['value_1'] = {'type': 'STRING', 'value': 'new'}
response = await client.post('/distributed/image-list/result', json=envelope)
assert response.status == 200
assert rt.get_master_job(context).results[0]['values'] == {'value_1': 'new', 'value_10': 10}
finally:
rt.remove_master_job(context)
asyncio.run(check())
-314
View File
@@ -1,314 +0,0 @@
"""CPU-only typed collection and optional native V3 mapping acceptance."""
import asyncio
import importlib.util
import json
import os
from pathlib import Path
import subprocess
import sys
import types
import pytest
import torch
ROOT = Path(__file__).resolve().parents[1]
SOCKETS = ('images', 'audio', 'masks', 'texts', 'video')
WORKERS = ['serverless:pool:1', 'serverless:pool:2']
class Manager:
def __init__(self, *, pending=False, owned=True):
self.pending, self.owned = pending, owned
self.cancelled = []
def owns(self, job, worker):
return self.owned
def is_pending(self, job, worker):
return self.pending
async def cancel(self, **kwargs):
self.cancelled.append(kwargs)
@pytest.fixture
def module(monkeypatch):
package = '_media_collector_test'
for name, path in ((package, ROOT), (package + '.nodes', ROOT / 'nodes'),
(package + '.utils', ROOT / 'utils'),
(package + '.serverless_bridge', ROOT / 'serverless_bridge')):
stub = types.ModuleType(name)
stub.__path__ = [str(path)]
monkeypatch.setitem(sys.modules, name, stub)
io = types.SimpleNamespace(ComfyNode=object, NodeOutput=lambda *items: types.SimpleNamespace(result=items))
api = types.ModuleType('comfy_api.v0_0_2')
api.io = io
monkeypatch.setitem(sys.modules, 'comfy_api', types.ModuleType('comfy_api'))
monkeypatch.setitem(sys.modules, 'comfy_api.v0_0_2', api)
management = types.ModuleType('comfy.model_management')
management.throw_exception_if_processing_interrupted = lambda: None
comfy = types.ModuleType('comfy')
comfy.model_management = management
monkeypatch.setitem(sys.modules, 'comfy', comfy)
monkeypatch.setitem(sys.modules, 'comfy.model_management', management)
ps = types.SimpleNamespace(distributed_pending_jobs={}, distributed_jobs_lock=asyncio.Lock())
server = types.ModuleType('server')
server.PromptServer = types.SimpleNamespace(instance=ps)
monkeypatch.setitem(sys.modules, 'server', server)
helper = types.ModuleType(package + '.utils.async_helpers')
helper.run_async_in_server_loop = asyncio.run
monkeypatch.setitem(sys.modules, helper.__name__, helper)
spec = importlib.util.spec_from_file_location(package + '.nodes.media_collector', ROOT / 'nodes/media_collector.py')
loaded = importlib.util.module_from_spec(spec)
monkeypatch.setitem(sys.modules, spec.name, loaded)
spec.loader.exec_module(loaded)
monkeypatch.setattr(loaded, 'POLL_SECONDS', 0.005)
monkeypatch.setattr(loaded, 'START_GRACE_SECONDS', 0.025)
loaded.test_server = ps
loaded.test_management = management
return loaded
def native_media(offset=0):
return {
'images': [torch.full((1, 2 + offset, 3, 4), offset, dtype=torch.float64),
torch.zeros((2, 1, 1, 3), dtype=torch.float16)],
'audio': [{'waveform': torch.zeros((1, 1 + offset, 8), dtype=torch.float64),
'sample_rate': 44100 + offset}],
'masks': [torch.zeros((1, 2 + offset, 3), dtype=torch.float64)],
'texts': ['', ' café\n🎥\r\n'],
'video': [object()],
}
def setup(module, messages=(), *, manager=None):
queue = asyncio.Queue()
for message in messages:
queue.put_nowait(message)
ps = module.test_server
ps.distributed_pending_jobs = {'job': queue, 'unrelated': asyncio.Queue()}
ps.distributed_serverless = manager if manager is not None else Manager()
return ps, queue
def message(worker, media):
return {'worker_id': worker, 'media': media, 'is_last': True}
def collect(module, **kwargs):
return module.DistributedMediaCollector.execute(**kwargs).result
def remote(module, **kwargs):
controls = dict(multi_job_id=['job'], enabled_worker_ids=[json.dumps(WORKERS)],
expected_media=[json.dumps(['texts'])], delegate_only=[True])
controls.update(kwargs)
return collect(module, **controls)
@pytest.mark.parametrize('controls', [{}, {'pass_through': [True]}, {'enabled_worker_ids': ['[]']}])
def test_all_five_passthrough_preserves_lists_and_native_values(module, controls):
values = native_media()
result = collect(module, **values, **controls)
assert len(result) == 5
for index, socket in enumerate(SOCKETS):
assert result[index] is values[socket]
def test_reversed_arrivals_master_then_workers_then_items(module):
master, first, second = native_media(), native_media(1), native_media(2)
ps, _ = setup(module, [message(WORKERS[1], second), message(WORKERS[0], first)])
result = remote(module, **master, delegate_only=[False], expected_media=[json.dumps(list(SOCKETS))])
for index, socket in enumerate(SOCKETS):
expected = master[socket] + first[socket] + second[socket]
assert len(result[index]) == len(expected)
assert all(actual is original for actual, original in zip(result[index], expected))
assert list(ps.distributed_pending_jobs) == ['unrelated']
assert ps.distributed_serverless.cancelled == []
def test_delegate_only_and_empty_string(module):
ps, _ = setup(module, [message(WORKERS[1], {'texts': ['b']}), message(WORKERS[0], {'texts': ['']})])
result = remote(module)
assert result == ([], [], [], ['', 'b'], [])
assert 'job' not in ps.distributed_pending_jobs
@pytest.mark.parametrize('kwargs', [{}, {'texts': []}, {'texts': ''}, {'texts': ['x'], 'is_worker': [True]},
{'texts': ['x'], 'enabled_worker_ids': ['["direct-worker"]']},
{'texts': ['x'], 'expected_media': ['["images"]']},
{'texts': ['x'], 'pass_through': [True, False]}, {'delegate_only': True},
{'texts': ['x'], 'enabled_worker_ids': ['["serverless:pool:1","serverless:pool:1"]']},
{'texts': ['x'], 'load_balance': ['false']}])
def test_rejects_missing_media_or_invalid_controls(module, kwargs):
with pytest.raises((ValueError, RuntimeError)):
collect(module, **kwargs)
@pytest.mark.parametrize('bad', [
{'worker_id': 'serverless:other:1', 'media': {'texts': ['x']}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': ['x']}, 'is_last': False},
{'worker_id': WORKERS[0], 'media': {'images': [object()]}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': []}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': 'x'}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': [None]}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': ['x'] * 65}, 'is_last': True},
{'worker_id': WORKERS[0], 'media': {'texts': ['x'], 'unknown': ['x']}, 'is_last': True},
{'worker_id': WORKERS[0], 'serverless_error': 'sanitized failure', 'is_last': True},
])
def test_rejects_bad_delivery_without_partial_output_and_cleans(module, bad):
ps, _ = setup(module, [bad])
with pytest.raises((ValueError, RuntimeError)):
remote(module)
assert list(ps.distributed_pending_jobs) == ['unrelated']
assert ps.distributed_serverless.cancelled == [{'collector_job_id': 'job'}]
@pytest.mark.parametrize('after_complete', [False, True])
def test_duplicate_delivery_rejected_including_final_queue_drain(module, after_complete):
first = message(WORKERS[0], {'texts': ['a']})
second = message(WORKERS[1], {'texts': ['b']})
messages = [first, second, first] if after_complete else [first, first, second]
ps, _ = setup(module, messages)
with pytest.raises(RuntimeError, match='[Dd]uplicate'):
remote(module)
assert 'job' not in ps.distributed_pending_jobs
assert ps.distributed_serverless.cancelled == [{'collector_job_id': 'job'}]
@pytest.mark.parametrize('manager', [None, Manager(owned=False), Manager(pending=False)])
def test_missing_manager_or_finished_participant_fails_bounded(module, manager):
ps, _ = setup(module)
ps.distributed_serverless = manager
with pytest.raises(RuntimeError, match='[Ss]erverless'):
remote(module)
assert 'job' not in ps.distributed_pending_jobs
def test_control_error_also_cleans_exact_job(module):
ps, _ = setup(module)
with pytest.raises(ValueError):
remote(module, expected_media='["unknown"]')
assert list(ps.distributed_pending_jobs) == ['unrelated']
assert ps.distributed_serverless.cancelled == [{'collector_job_id': 'job'}]
def test_launch_race_and_pending_deadline_owned_by_manager(module, monkeypatch):
ps, queue = setup(module)
ps.distributed_serverless = None
async def runner(coro):
async def produce():
await asyncio.sleep(0.01)
ps.distributed_serverless = Manager(pending=True)
await asyncio.sleep(0.035)
queue.put_nowait(message(WORKERS[1], {'texts': ['b']}))
queue.put_nowait(message(WORKERS[0], {'texts': ['a']}))
task = asyncio.create_task(produce())
result = await coro
await task
return result
monkeypatch.setattr(module, 'run_async_in_server_loop', lambda coro: asyncio.run(runner(coro)))
assert remote(module)[3] == ['a', 'b']
assert ps.distributed_serverless.cancelled == []
@pytest.mark.parametrize('cancel_task', [False, True])
def test_cancellation_while_blocked_cleans_and_propagates(module, monkeypatch, cancel_task):
ps, _ = setup(module, manager=Manager(pending=True))
class Interrupted(Exception):
pass
async def runner(coro):
task = asyncio.create_task(coro)
await asyncio.sleep(0.012)
if cancel_task:
task.cancel()
else:
def interrupt():
raise Interrupted('stop')
module.test_management.throw_exception_if_processing_interrupted = interrupt
return await task
monkeypatch.setattr(module, 'run_async_in_server_loop', lambda coro: asyncio.run(runner(coro)))
with pytest.raises(asyncio.CancelledError if cancel_task else Interrupted):
remote(module)
assert list(ps.distributed_pending_jobs) == ['unrelated']
assert ps.distributed_serverless.cancelled == [{'collector_job_id': 'job'}]
def test_replaced_queue_not_removed(module, monkeypatch):
ps, _ = setup(module, manager=Manager(pending=True))
replacement = asyncio.Queue()
async def runner(coro):
task = asyncio.create_task(coro)
await asyncio.sleep(0.012)
ps.distributed_pending_jobs['job'] = replacement
return await task
monkeypatch.setattr(module, 'run_async_in_server_loop', lambda coro: asyncio.run(runner(coro)))
with pytest.raises(RuntimeError, match='queue'):
remote(module)
assert ps.distributed_pending_jobs['job'] is replacement
def test_native_v3_schema_and_list_mapping_cpu():
core = os.environ.get('COMFYUI_SOURCE_ROOT')
if not core:
pytest.skip('Set COMFYUI_SOURCE_ROOT for CPU-only native V3 mapping')
probe = r'''
import asyncio, importlib.util, sys, types
from pathlib import Path
sys.path.insert(0, sys.argv[1])
import comfy.cli_args
comfy.cli_args.args.cpu = True
comfy.cli_args.args.disable_assets = True
import execution
import torch
import server
# No HTTP listener or PromptServer bootstrap is needed for schema/list mapping.
server.PromptServer.instance = types.SimpleNamespace()
root = Path(sys.argv[2])
for name, path in [('typed_collector', root), ('typed_collector.nodes', root/'nodes'),
('typed_collector.utils', root/'utils'),
('typed_collector.serverless_bridge', root/'serverless_bridge')]:
package = types.ModuleType(name)
package.__path__ = [str(path)]
sys.modules[name] = package
spec = importlib.util.spec_from_file_location('typed_collector.nodes.media_collector', root/'nodes/media_collector.py')
module = importlib.util.module_from_spec(spec)
sys.modules[spec.name] = module
spec.loader.exec_module(module)
cls = module.DistributedMediaCollector
info = cls.GET_NODE_INFO_V1()
assert info['output'] == ['IMAGE','AUDIO','MASK','STRING','VIDEO'], info
assert info['output_is_list'] == [True]*5, info
assert cls.define_schema().is_input_list and cls.define_schema().accept_all_inputs
for name, kind in zip(['images','audio','masks','texts','video'], info['output']):
field = info['input']['optional'][name]
assert field[0] == kind and field[1]['forceInput'] is True, field
assert info['input']['required']['load_balance'][1]['default'] is False
async def check():
values = {'load_balance':False, 'pass_through':True}
prepared, missing, hidden = execution.get_input_data(values, cls, unique_id='probe')
assert not missing, missing
image = torch.zeros((1,2,3,4), dtype=torch.float64)
audio = {'waveform':torch.zeros((1,2,7)), 'sample_rate':48000}
mask = torch.ones((1,2,3), dtype=torch.float64)
from comfy_api.input_impl import VideoFromComponents
from comfy_api.util import VideoComponents
from fractions import Fraction
video = VideoFromComponents(VideoComponents(images=image[:,:,:,:3].float(), frame_rate=Fraction(24,1)))
native = {'images':[image], 'audio':[audio], 'masks':[mask], 'texts':[''], 'video':[video]}
prepared.update(native)
results = await execution._async_map_node_over_list('cpu','probe',cls,prepared,cls.FUNCTION,v3_data=hidden)
results = await execution.resolve_map_node_over_list_results(results)
assert len(results) == 1
for index, name in enumerate(native):
assert results[0].result[index] is native[name]
assert results[0].result[4][0] is video
print('MEDIA_COLLECTOR_CPU_OK')
asyncio.run(check())
'''
env = {**os.environ, 'CUDA_VISIBLE_DEVICES': '', 'HIP_VISIBLE_DEVICES': '', 'PYTHONDONTWRITEBYTECODE': '1'}
env.pop('PYTHONPATH', None)
result = subprocess.run([sys.executable, '-c', probe, core, str(ROOT)], env=env,
capture_output=True, text=True, timeout=90)
assert result.returncode == 0, result.stdout + result.stderr
assert 'MEDIA_COLLECTOR_CPU_OK' in result.stdout
+2 -1
View File
@@ -1,4 +1,4 @@
"""Opt-in, isolated CPU acceptance of the complete typed-media data path.
"""Opt-in CPU acceptance of Collector IMAGE/AUDIO and the five-kind bridge.
COMFYUI_SOURCE_ROOT must name an existing compatible checkout; run with that
checkout's Python environment. No installation, normal instance, or provider is
@@ -42,6 +42,7 @@ def test_typed_media_end_to_end_cpu():
assert result.returncode == 0, result.stdout + result.stderr
for marker in ("NATIVE_INPUTS_AND_DIRECT_EXPORT_REFS_OK", "REAL_HTTP_BRIDGE_RESULTS_OK",
"MASTER_AND_DELEGATE_ORDER_OK", "ATOMIC_DECODE_REJECTION_OK",
"IMAGE_TRANSFER_HTTP_OK",
"MEDIA_END_TO_END_CPU_OK"):
assert marker in result.stdout, result.stdout + result.stderr
print(result.stdout)
+24 -30
View File
@@ -1,4 +1,4 @@
"""Pure integration of participant identity, typed compilation and delegate pruning."""
"""Participant identity, typed compilation and legacy Collector pruning."""
import copy
import importlib
import json
@@ -16,17 +16,17 @@ for suffix in ("", ".api", ".api.orchestration", ".utils", ".serverless_bridge")
sys.modules[module.__name__] = module
pt = importlib.import_module(PACKAGE + ".api.orchestration.prompt_transform")
compiler = importlib.import_module(PACKAGE + ".api.orchestration.serverless_media_prompt")
contract = importlib.import_module(PACKAGE + ".serverless_bridge.contract")
SOCKETS = ("images", "audio")
def prompt():
def prompt(sockets=SOCKETS):
nodes = {
"source": {"class_type": "MediaSource", "inputs": {}},
"collector": {"class_type": "DistributedMediaCollector", "inputs": {
socket: ["source", index] for index, socket in enumerate(contract.SOCKET_KINDS)}},
"collector": {"class_type": "DistributedCollector", "inputs": {
socket: ["source", SOCKETS.index(socket)] for socket in sockets}},
}
for index, socket in enumerate(contract.SOCKET_KINDS):
nodes["sink:" + socket] = {"class_type": "Capture", "inputs": {"value": ["collector", index]}}
for socket in sockets:
nodes["sink:" + socket] = {"class_type": "Capture", "inputs": {"value": ["collector", SOCKETS.index(socket)]}}
return nodes
@@ -38,47 +38,41 @@ def prepare(graph, participant, delegate):
["serverless:pool:1", "serverless:pool:2"], jobs, "http://master", delegate, index)
def test_typed_worker_preparation_compiles_all_bindings_without_source_mutation():
def test_typed_worker_preparation_compiles_both_bindings_without_source_mutation():
graph = prompt()
before = copy.deepcopy(graph)
worker = prepare(graph, "serverless:pool:2", True)
compiled, bindings = compiler.compile_media_prompt(worker, "request:2")
assert len(bindings) == 5
assert len(bindings) == 2
assert {row["participant_id"] for row in bindings} == {"serverless:pool:2"}
assert {row["job_id"] for row in bindings} == {"run_collector"}
assert {row["socket"] for row in bindings} == set(contract.SOCKET_KINDS)
assert {row["socket"] for row in bindings} == set(SOCKETS)
assert set(compiled) == {"source"} | {row["node_id"] for row in bindings}
assert graph == before
def test_typed_delegate_master_keeps_outputs_without_sources_or_placeholders():
graph = prompt()
@pytest.mark.parametrize("sockets", [("images",), ("audio",), SOCKETS])
def test_delegate_master_keeps_collector_socket_contract_and_legacy_placeholder(sockets):
graph = prompt(sockets)
prepared = prepare(graph, "master", True)
before = copy.deepcopy(prepared)
delegated = pt.prepare_delegate_master_prompt(prepared, ["collector"])
assert set(delegated) == {"collector"} | {"sink:" + s for s in contract.SOCKET_KINDS}
assert "source" not in delegated
assert {"collector"} | {"sink:" + s for s in sockets} <= set(delegated)
inputs = delegated["collector"]["inputs"]
assert not set(contract.SOCKET_KINDS) & set(inputs)
assert json.loads(inputs["expected_media"]) == list(contract.SOCKET_KINDS)
assert "audio" not in inputs and "expected_media" not in inputs
placeholders = [key for key, node in delegated.items() if node['class_type'] == 'DistributedEmptyImage']
if "images" in sockets:
assert len(placeholders) == 1
assert inputs['images'] == [placeholders[0], 0]
else:
assert not placeholders and 'images' not in inputs
assert inputs["delegate_only"] is True
assert json.loads(inputs["enabled_worker_ids"]) == ["serverless:pool:1", "serverless:pool:2"]
assert prepared == before
def test_text_source_is_not_retained_as_lightweight_delegate_scalar(monkeypatch):
graph = {
"text": {"class_type": "PrimitiveString", "inputs": {"value": "local text"}},
"collector": {"class_type": "DistributedMediaCollector", "inputs": {"texts": ["text", 0]}},
"sink": {"class_type": "Capture", "inputs": {"value": ["collector", 3]}},
}
monkeypatch.setattr(pt, "_delegate_master_input_is_safe_scalar", lambda *args: True)
monkeypatch.setattr(pt, "_is_delegate_master_retained_upstream_node", lambda *args: True)
delegated = pt.prepare_delegate_master_prompt(prepare(graph, "master", True), ["collector"])
assert "text" not in delegated
assert "texts" not in delegated["collector"]["inputs"]
assert json.loads(delegated["collector"]["inputs"]["expected_media"]) == ["texts"]
@pytest.mark.parametrize("socket", contract.SOCKET_KINDS)
@pytest.mark.parametrize("socket", SOCKETS)
def test_participating_master_retains_exact_media_links(socket):
graph = prompt()
original = copy.deepcopy(graph)
+99 -78
View File
@@ -35,11 +35,11 @@ def node(class_type, **inputs):
return {"class_type": class_type, "inputs": inputs}
def text_prompt():
def collector_prompt():
return {
"text": node("PrimitiveString", value=" café雪\n"),
"collector": node("DistributedMediaCollector", texts=["text", 0], load_balance=False),
"save": node("TextSink", texts=["collector", 3]),
"source": node("EmptyImage", width=32, height=32, batch_size=1, color=0),
"collector": node("DistributedCollector", images=["source", 0], load_balance=False),
"save": node("SaveImage", images=["collector", 0], filename_prefix="local"),
}
@@ -95,7 +95,7 @@ def media_paths(tmp_path, monkeypatch):
def run(h, prompt=None, **kwargs):
return asyncio.run(h.q.orchestrate_distributed_execution(
prompt if prompt is not None else text_prompt(), None, "client", **kwargs))
prompt if prompt is not None else collector_prompt(), None, "client", **kwargs))
def assert_no_dispatch(h, *, no_probe=False):
@@ -117,10 +117,11 @@ def test_real_compile_pack_serialize_and_canonical_bindings(harness, media_paths
image_path = media_paths / "picture.png"
Image.new("RGBA", (2, 3), color=(17, 29, 41, 53)).save(image_path)
image_bytes = image_path.read_bytes()
prompt = text_prompt()
prompt = collector_prompt()
prompt["image"] = node("LoadImage", image="picture.png")
prompt["collector"]["inputs"].update(images=["image", 0], masks=["image", 1])
prompt["collector2"] = node("DistributedMediaCollector", texts=["text", 0])
prompt["audio"] = node("GeneratedAudio")
prompt["collector"]["inputs"].update(images=["image", 0], audio=["audio", 0])
prompt["collector2"] = node("DistributedCollector", audio=["audio", 0])
prompt["unused"] = node("PreviewImage", images=["image", 0])
source = copy.deepcopy(prompt)
with patch.object(h.compiler, "compile_media_prompt", wraps=h.compiler.compile_media_prompt) as compile_call, \
@@ -145,8 +146,8 @@ def test_real_compile_pack_serialize_and_canonical_bindings(harness, media_paths
assert media["outputs"] == job["manifest"]
assert media["outputs"] is not job["manifest"]
assert media["artifacts"] == media["inputs"] == []
assert {b["socket"] for b in job["manifest"]} == {"texts", "images", "masks"}
assert len(job["manifest"]) == 4
assert {b["socket"] for b in job["manifest"]} == {"images", "audio"}
assert len(job["manifest"]) == 3
assert base64.b64decode(job["images"][0]["image"]) == image_bytes
assert job["workflow"]["image"]["inputs"]["image"] == job["images"][0]["name"]
assert not ({"save", "unused", "collector", "collector2"} & set(job["workflow"]))
@@ -164,40 +165,50 @@ def test_real_compile_pack_serialize_and_canonical_bindings(harness, media_paths
assert json.loads(controls["binding_json"]) == binding
assert controls["request_id"] == media["request_id"]
assert master["collector"]["inputs"]["delegate_only"] is delegate
assert json.loads(master["collector"]["inputs"]["expected_media"]) == ["images", "masks", "texts"]
assert "expected_media" not in master["collector"]["inputs"]
if delegate:
assert "text" not in master and "image" not in master
assert not any(name in master["collector"]["inputs"] for name in ("images", "audio", "masks", "texts", "video"))
assert not any(n["class_type"] == "DistributedEmptyImage" for n in master.values())
assert not {"source", "audio"} & set(master)
# LoadImage is an existing explicitly retained lightweight source; the
# Collector is nevertheless detached from it and uses the placeholder.
assert master["image"] == prompt["image"]
assert "audio" not in master["collector"]["inputs"]
placeholder = master["collector"]["inputs"]["images"][0]
assert master[placeholder]["class_type"] == "DistributedEmptyImage"
else:
assert master["collector"]["inputs"]["texts"] == ["text", 0]
assert "text" in master and "image" in master
assert master["collector"]["inputs"]["audio"] == ["audio", 0]
assert "audio" in master and "image" in master
def test_delegate_disconnects_every_media_socket(harness):
prompt = text_prompt()
for index, name in enumerate(("images", "audio", "masks", "video")):
source_id = "source-" + name
prompt[source_id] = node("GeneratedMedia", seed=index)
prompt["collector"]["inputs"][name] = [source_id, 0]
@pytest.mark.parametrize("audio_only", [False, True])
def test_delegate_prunes_image_audio_sources_and_audio_only_needs_no_placeholder(harness, audio_only):
prompt = collector_prompt()
prompt["audio"] = node("GeneratedAudio")
prompt["collector"]["inputs"]["audio"] = ["audio", 0]
if audio_only:
del prompt["collector"]["inputs"]["images"]
prompt["save"] = node("AudioSink", audio=["collector", 1])
original = copy.deepcopy(prompt)
run(harness, prompt)
master = harness.q.queue_prompt_payload.call_args.args[0]
inputs = master["collector"]["inputs"]
sockets = {"images", "audio", "masks", "texts", "video"}
assert set(json.loads(inputs["expected_media"])) == sockets
assert not sockets.intersection(inputs)
assert set(master) == {"collector", "save"}
assert "audio" not in inputs and "expected_media" not in inputs
assert "source" not in master and "audio" not in master
if audio_only:
assert "images" not in inputs and set(master) == {"collector", "save"}
else:
assert master[inputs["images"][0]]["class_type"] == "DistributedEmptyImage"
for job in harness.manager.start.call_args.args[2]:
assert {b["socket"] for b in job["manifest"]} == sockets
assert {"text", "source-images", "source-audio", "source-masks", "source-video"} <= set(job["workflow"])
assert {b["socket"] for b in job["manifest"]} == ({"audio"} if audio_only else {"images", "audio"})
assert "audio" in job["workflow"]
assert ("source" in job["workflow"]) is not audio_only
assert harness.validator.validate_prepared_job(job) is True
assert prompt == original
@pytest.mark.parametrize("selected", ["master", "worker"])
def test_typed_load_balance_selects_master_or_one_serverless_slot(harness, selected):
h = harness
prompt = text_prompt()
prompt = collector_prompt()
prompt["collector"]["inputs"]["load_balance"] = "true"
h.q.select_least_busy_worker.side_effect = lambda candidates, **kw: next(
w for w in candidates if (w["id"] == "master") == (selected == "master"))
@@ -208,23 +219,24 @@ def test_typed_load_balance_selects_master_or_one_serverless_slot(harness, selec
assert result[2] == 0
h.manager.start.assert_not_awaited()
assert master["collector"]["inputs"]["delegate_only"] is False
assert "text" in master
assert "source" in master
else:
assert result[2] == 1
assert len(h.manager.start.call_args.args[2]) == 1
assert master["collector"]["inputs"]["delegate_only"] is True
assert "text" not in master
assert "source" not in master
@pytest.mark.parametrize("profile", [None, "legacy-image-v1", PROFILE])
def test_explicit_profile_compatibility_rejected_before_probe(harness, profile):
@pytest.mark.parametrize("profile", [None, "legacy-image-v1"])
def test_exact_rejects_legacy_pool_before_probe(harness, profile):
h = harness
prompt = legacy_prompt() if profile == PROFILE else text_prompt()
prompt = collector_prompt()
prompt["collector"]["inputs"]["transfer_format"] = "exact"
if profile is None:
h.pool.pop("media_profile")
else:
h.pool["media_profile"] = profile
with pytest.raises(ValueError, match="requires|require"):
with pytest.raises(ValueError, match="exact requires comfy-media-v1"):
run(h, prompt)
assert_no_dispatch(h, no_probe=True)
@@ -239,19 +251,26 @@ def test_invalid_explicit_profiles_fail_closed(harness, profile):
@pytest.mark.parametrize("direct_type", ["local", "remote", "cloud"])
@pytest.mark.parametrize("explicit", [False, True])
def test_direct_workers_never_silently_skipped_or_fallback(harness, direct_type, explicit):
def test_direct_workers_participate_alongside_typed_pools(harness, direct_type, explicit):
h = harness
h.config["workers"].append({"id": "direct", "type": direct_type, "host": "offline.invalid",
"enabled": not explicit, "port": 8188})
prompt = text_prompt()
prompt["collector"]["inputs"]["load_balance"] = True
kwargs = {"enabled_worker_ids": ["pool", "direct"]} if explicit else {}
with pytest.raises(ValueError, match="direct workers"):
run(h, prompt, delegate_master=False, **kwargs)
assert_no_dispatch(h, no_probe=True)
prompt = collector_prompt()
original = copy.deepcopy(prompt)
assert run(h, prompt, delegate_master=False, **kwargs)[2] == 3
assert prompt == original
h.q.dispatch_worker_prompt.assert_awaited_once()
master = h.q.queue_prompt_payload.call_args.args[0]
assert json.loads(master["collector"]["inputs"]["enabled_worker_ids"]) == [
"serverless:pool:1", "serverless:pool:2", "direct"]
jobs = h.manager.start.call_args.args[2]
assert len(jobs) == 2 and all(j["media"]["profile"] == PROFILE for j in jobs)
if direct_type != "local":
h.q.sync_worker_media.assert_awaited_once()
def test_disabled_or_unselected_incompatible_workers_do_not_block(harness):
def test_disabled_or_unselected_workers_do_not_participate(harness):
h = harness
h.config["workers"] += [
{"id": "direct", "type": "local", "enabled": False},
@@ -262,27 +281,32 @@ def test_disabled_or_unselected_incompatible_workers_do_not_block(harness):
assert run(h, enabled_worker_ids=["pool"])[2] == 2
@pytest.mark.parametrize("other", ["DistributedCollector", "UltimateSDUpscaleDistributed", "DistributedImageListStart"])
@pytest.mark.parametrize("workers", [True, False])
def test_mixed_collectors_or_image_list_rejected_even_master_only(harness, other, workers):
h = harness
def test_image_list_cannot_mix_with_collector_even_master_only(harness, workers):
if not workers:
h.config["workers"] = []
prompt = text_prompt()
prompt["incompatible"] = node(other)
harness.config["workers"] = []
prompt = collector_prompt()
prompt["incompatible"] = node("DistributedImageListStart")
with pytest.raises(ValueError, match="cannot be mixed"):
run(h, prompt, delegate_master=False)
assert_no_dispatch(h, no_probe=True)
run(harness, prompt, delegate_master=False)
assert_no_dispatch(harness, no_probe=True)
def test_mixed_pool_profiles_rejected_before_load_balancing(harness):
def test_mixed_direct_typed_and_legacy_profiles_keep_each_wire_contract(harness):
h = harness
h.config["workers"].append(dict(h.pool, id="legacy", media_profile="legacy-image-v1"))
prompt = text_prompt()
prompt["collector"]["inputs"]["load_balance"] = True
with pytest.raises(ValueError, match="requires"):
run(h, prompt, delegate_master=False)
assert_no_dispatch(h, no_probe=True)
h.config["workers"] += [dict(h.pool, id="legacy", media_profile="legacy-image-v1"),
{"id": "direct", "type": "local", "enabled": True}]
assert run(h, collector_prompt())[2] == 5
h.q.dispatch_worker_prompt.assert_awaited_once()
jobs = h.manager.start.call_args.args[2]
assert len(jobs) == 4
for job in jobs:
if job["worker"]["media_profile"] == PROFILE:
assert job["media"]["outputs"] == job["manifest"]
assert h.validator.validate_prepared_job(job) is True
else:
assert "media" not in job
assert any(n["class_type"] == "SaveImage" for n in job["workflow"].values())
def test_typed_master_only_is_supported_without_serverless_work(harness):
@@ -291,27 +315,23 @@ def test_typed_master_only_is_supported_without_serverless_work(harness):
assert run(h, delegate_master=False)[2] == 0
master = h.q.queue_prompt_payload.call_args.args[0]
assert master["collector"]["inputs"]["enabled_worker_ids"] == "[]"
assert master["collector"]["inputs"]["texts"] == ["text", 0]
assert master["collector"]["inputs"]["images"] == ["source", 0]
h.manager.start.assert_not_awaited()
h.q.dispatch_worker_prompt.assert_not_awaited()
@pytest.mark.parametrize("offline", [False, True])
def test_typed_delegate_requires_available_worker_without_master_fallback(harness, offline):
def test_collector_delegate_without_workers_preserves_master_fallback(harness, offline):
h = harness
if offline:
h.q.select_active_workers.side_effect = lambda *a, **kw: ([], False)
else:
h.config["workers"] = []
with pytest.raises(RuntimeError, match="comfy-media-v1"):
run(h)
assert_no_dispatch(h)
def test_unknown_explicit_typed_worker_is_rejected(harness):
with pytest.raises(ValueError, match="Unknown explicitly selected"):
run(harness, enabled_worker_ids=["unknown"], delegate_master=False)
assert_no_dispatch(harness, no_probe=True)
assert run(h)[2] == 0
master = h.q.queue_prompt_payload.call_args.args[0]
assert master["collector"]["inputs"]["delegate_only"] is False
assert master["collector"]["inputs"]["images"] == ["source", 0]
h.manager.start.assert_not_awaited()
@pytest.mark.parametrize("bad", [" pool ", "pool white", "pool\n"])
@@ -323,9 +343,9 @@ def test_pool_media_identity_is_not_repaired(harness, bad):
def test_integer_source_node_identity_is_not_normalized(harness):
prompt = text_prompt()
prompt[1] = prompt.pop("text")
prompt["collector"]["inputs"]["texts"] = [1, 0]
prompt = collector_prompt()
prompt[1] = prompt.pop("source")
prompt["collector"]["inputs"]["images"] = [1, 0]
with pytest.raises(ValueError, match="identifier"):
run(harness, prompt)
assert_no_dispatch(harness, no_probe=True)
@@ -334,18 +354,18 @@ def test_integer_source_node_identity_is_not_normalized(harness):
@pytest.mark.parametrize("broken", ["dangling-unused", "literal", "missing-file", "bad-loader-controls", "oversize"])
def test_compilation_packing_and_size_errors_precede_all_dispatch(harness, media_paths, broken):
h = harness
prompt = text_prompt()
prompt = collector_prompt()
if broken == "dangling-unused":
prompt["unused"] = node("PreviewImage", images=["missing", 0])
elif broken == "literal":
prompt["collector"]["inputs"]["texts"] = "must use a linked string node"
prompt["collector"]["inputs"]["images"] = "must use a linked image node"
elif broken in ("missing-file", "bad-loader-controls"):
prompt["audio"] = node("LoadAudio", audio="missing.wav")
prompt["collector"]["inputs"]["audio"] = ["audio", 0]
if broken == "bad-loader-controls":
prompt["audio"]["inputs"]["duration"] = 5
else:
prompt["text"]["inputs"]["value"] = "雪" * 1000
prompt["source"]["inputs"]["label"] = "雪" * 1000
original = copy.deepcopy(prompt)
with patch.object(h.client, "MAX_REQUEST_BYTES", 100 if broken == "oversize" else h.client.MAX_REQUEST_BYTES):
with pytest.raises((ValueError, OSError)):
@@ -418,9 +438,9 @@ def test_master_queue_failure_cleans_queues_and_never_starts_manager(harness):
def test_manager_setup_failure_emits_one_terminal_per_collector_participant(harness):
h = harness
prompt = text_prompt()
prompt["image"] = node("EmptyImage", width=2, height=2, batch_size=1, color=0)
prompt["collector"]["inputs"]["images"] = ["image", 0]
prompt = collector_prompt()
prompt["audio"] = node("GeneratedAudio")
prompt["collector"]["inputs"]["audio"] = ["audio", 0]
h.manager.start.side_effect = ValueError("journal failure")
with pytest.raises(ValueError, match="journal failure"):
run(h, prompt)
@@ -481,6 +501,7 @@ import comfy.cli_args
comfy.cli_args.args.cpu = True
import torch
def forbidden(*args, **kwargs):
raise AssertionError('CPU audio integration may not initialize a GPU')
torch.cuda.is_available = lambda: False
@@ -507,7 +528,7 @@ raise SystemExit(pytest.main([sys.argv[2], '-q', '-p', 'no:cacheprovider', '-k',
stream.setframerate(8000)
stream.writeframes(pcm.numpy().tobytes())
original_bytes = path.read_bytes()
prompt = text_prompt()
prompt = collector_prompt()
prompt["audio"] = node("LoadAudio", audio="tone.wav")
prompt["collector"]["inputs"]["audio"] = ["audio", 0]
original = copy.deepcopy(prompt)
+37 -17
View File
@@ -6,16 +6,16 @@ import pytest
from test_serverless_media_prompt import compiler, _prompt
from test_media_handler import handler
from test_media_collector import module, collect, native_media
from test_collector_typed_media import collector_module, native_media, assert_exact
FORMATS = ('exact', 'legacy_png', 'png', 'ffv1', 'jpeg', 'webp', 'h264', 'h265', 'av1')
FORMATS = ('legacy_png', 'png', 'ffv1', 'jpeg', 'webp', 'h264', 'h265', 'av1', 'exact')
def compile_request(fmt='exact', quality=90):
def compile_request(fmt='legacy_png', quality=90):
prompt = _prompt()
prompt['source:1'] = {'class_type': 'Source', 'inputs': {}}
prompt['collector:1']['inputs'].update(transfer_format=fmt, transfer_quality=quality,
masks=['source:1', 1], texts=['source:1', 2])
audio=['source:1', 1])
original = copy.deepcopy(prompt)
workflow, outputs = compiler.compile_media_prompt(prompt, 'request:1')
assert prompt == original
@@ -27,16 +27,23 @@ def compile_request(fmt='exact', quality=90):
@pytest.mark.parametrize('fmt', FORMATS)
def test_compiler_and_handler_preserve_image_policy_only(fmt):
data = compile_request(fmt, 73)
assert {b['socket'] for b in data['media']['outputs']} == {'images', 'audio'}
for binding in data['media']['outputs']:
inputs = data['workflow'][binding['node_id']]['inputs']
if binding['kind'] == 'IMAGE' and fmt != 'exact':
assert inputs['transfer_format'] == fmt
assert inputs['transfer_format'] == ('png' if fmt == 'legacy_png' else fmt)
assert inputs['transfer_quality'] == 73
else:
assert 'transfer_format' not in inputs and 'transfer_quality' not in inputs
handler._validate_request(data)
def test_saved_collector_defaults_compile_to_png_not_exact():
workflow, bindings = compiler.compile_media_prompt(_prompt(), 'request:1')
inputs = workflow[bindings[0]['node_id']]['inputs']
assert (inputs['transfer_format'], inputs['transfer_quality']) == ('png', 90)
@pytest.mark.parametrize('fmt,quality', [('JPEG',90),('',90),('jpeg',True),('jpeg',0),
('jpeg',101),('jpeg','90'),(['source:1',0],90)])
def test_compiler_rejects_invalid_or_linked_controls(fmt, quality):
@@ -54,23 +61,36 @@ def test_handler_rejects_tampered_policy(field, value):
handler._validate_request(data)
def test_handler_rejects_image_controls_on_mask_export():
data = compile_request()
binding = next(b for b in data['media']['outputs'] if b['kind'] == 'MASK')
data['workflow'][binding['node_id']]['inputs']['transfer_format'] = 'jpeg'
@pytest.mark.parametrize('socket,kind', [('audio', 'AUDIO'), ('masks', 'MASK'), ('texts', 'STRING'), ('video', 'VIDEO')])
def test_handler_rejects_image_controls_on_nonimage_bridge_exports(socket, kind):
# The bridge still supports all five kinds; these are direct bridge exports,
# not nonexistent sockets on DistributedCollector.
data = compile_request('exact')
binding = next(b for b in data['media']['outputs'] if b['kind'] == 'AUDIO')
binding.update(socket=socket, kind=kind)
inputs = data['workflow'][binding['node_id']]['inputs']
inputs.pop('audio')
inputs.update({socket: ['source:1', 1], 'binding_json': json.dumps(binding)})
handler._validate_request(data)
inputs['transfer_format'] = 'jpeg'
with pytest.raises(ValueError):
handler._validate_request(data)
@pytest.mark.parametrize('fmt', FORMATS)
def test_master_passthrough_stays_native_for_every_format(module, fmt):
def test_master_passthrough_stays_native_for_every_format(collector_module, fmt):
media = native_media()
actual = collect(module, **media, transfer_format=[fmt], transfer_quality=[73])
for values, original in zip(actual, media.values()):
assert values is original
actual = collector_module.DistributedCollectorNode().run(
images=[media['images']], audio=[media['audio']], pass_through=[True],
transfer_format=[fmt], transfer_quality=[73])
assert actual[0] is media['images']
assert actual[1] is media['audio']
assert_exact(actual[0], media['images'])
@pytest.mark.parametrize('quality', [True, 0, 101, '90', [90,91]])
def test_node_rejects_bad_quality_even_without_remote_participants(module, quality):
with pytest.raises(ValueError):
collect(module, texts=['hello'], transfer_quality=quality)
def test_collector_widget_contract_keeps_saved_defaults_and_appends_exact(collector_module):
optional = collector_module.DistributedCollectorNode.INPUT_TYPES()['optional']
assert list(optional) == ['images', 'audio', 'transfer_format', 'transfer_quality']
assert tuple(optional['transfer_format'][0]) == FORMATS
assert optional['transfer_format'][1]['default'] == 'legacy_png'
assert optional['transfer_quality'][1]['default'] == 90
+23 -18
View File
@@ -24,7 +24,7 @@ compiler = importlib.import_module(f"{PACKAGE}.api.orchestration.serverless_medi
legacy = importlib.import_module(f"{PACKAGE}.api.orchestration.serverless_prompt")
contract = importlib.import_module(f"{PACKAGE}.serverless_bridge.contract")
compile_media_prompt = compiler.compile_media_prompt
SOCKETS = {"images": "IMAGE", "audio": "AUDIO", "masks": "MASK", "texts": "STRING", "video": "VIDEO"}
SOCKETS = {"images": "IMAGE", "audio": "AUDIO"}
def _node(class_type, **inputs):
@@ -33,7 +33,7 @@ def _node(class_type, **inputs):
def _collector(**sockets):
return _node(
"DistributedMediaCollector", multi_job_id="job/42", worker_id="worker_0", **sockets
"DistributedCollector", multi_job_id="job/42", worker_id="worker_0", **sockets
)
@@ -72,26 +72,26 @@ def test_one_export_for_each_typed_socket_with_exact_canonical_binding(socket, k
"request_id": "request:1",
"binding_json": json.dumps(binding),
socket: ["source", 0],
**({"transfer_format": "png", "transfer_quality": 90} if kind == "IMAGE" else {}),
},
}
assert set(graph) == {"source", binding["node_id"]}
def test_mixed_collectors_all_five_kinds_have_independent_dispatch_targets():
def test_multiple_collectors_have_independent_image_audio_targets():
prompt = {
"shared": _node("MultiMediaSource"),
"collector:z": _collector(audio=["shared", 1], video=["shared", 4]),
"collector:a": _collector(images=["shared", 0], masks=["shared", 2], texts=["shared", 3]),
"collector:z": _collector(audio=["shared", 1]),
"collector:a": _collector(images=["shared", 0], audio=["shared", 1]),
}
prompt["collector:z"]["inputs"].update(multi_job_id="job:z", worker_id="participant:z")
graph, bindings = _run(prompt)
assert [(b["collector_id"], b["socket"]) for b in bindings] == [
("collector:a", "images"), ("collector:a", "masks"), ("collector:a", "texts"),
("collector:z", "audio"), ("collector:z", "video"),
("collector:a", "images"), ("collector:a", "audio"), ("collector:z", "audio"),
]
assert len({b["binding_id"] for b in bindings}) == 5
assert len({b["binding_id"] for b in bindings}) == 3
targets = {b["node_id"] for b in bindings}
assert len(targets) == 5
assert len(targets) == 3
assert set(graph) == {"shared"} | targets
for binding in bindings:
export = graph[binding["node_id"]]
@@ -119,12 +119,15 @@ def test_prunes_descendants_and_unrelated_outputs_preserving_required_output_uti
assert not {"load_balance", "pass_through"} & graph[bindings[0]["node_id"]]["inputs"].keys()
def test_stock_primitive_string_preserves_empty_and_link_looking_text():
def test_upstream_primitive_string_preserves_empty_and_link_looking_text():
for text in ("", '["source:1", 0]', "hello\nworld"):
prompt = {"text": _node("PrimitiveString", value=text), "collector": _collector(texts=["text", 0])}
prompt = {"text": _node("PrimitiveString", value=text),
"source": _node("TextToImage", text=["text", 0]),
"collector": _collector(images=["source", 0])}
graph, bindings = _run(prompt)
assert graph["text"] == prompt["text"]
assert graph[bindings[0]["node_id"]]["inputs"]["texts"] == ["text", 0]
assert graph["source"]["inputs"]["text"] == ["text", 0]
assert graph[bindings[0]["node_id"]]["inputs"]["images"] == ["source", 0]
@pytest.mark.parametrize("class_type,inputs", [
@@ -176,11 +179,13 @@ def test_distributed_text_value_can_be_wrapped_by_primitive_string():
prompt = {
"value": _node("DistributedModelName", text="model.safetensors"),
"text": _node("PrimitiveString", value=["value", 0]),
"collector": _collector(texts=["text", 0]),
"source": _node("TextToImage", text=["text", 0]),
"collector": _collector(images=["source", 0]),
}
graph, bindings = _run(prompt)
assert graph["text"]["inputs"]["value"] == "model.safetensors"
assert graph[bindings[0]["node_id"]]["inputs"]["texts"] == ["text", 0]
assert graph["source"]["inputs"]["text"] == ["text", 0]
assert graph[bindings[0]["node_id"]]["inputs"]["images"] == ["source", 0]
assert "value" not in graph
@@ -321,7 +326,7 @@ def test_rejects_invalid_links_everywhere_before_pruning(location, link):
@pytest.mark.parametrize("class_type", [
"DistributedCollector", "DistributedEmptyImage", "UltimateSDUpscaleDistributed",
"DistributedMediaCollector", "DistributedEmptyImage", "UltimateSDUpscaleDistributed",
"DistributedImageListStart", "DistributedImageListEnd", "DistributedWorkerSink",
"ImageBatchDivider", "AudioBatchDivider", "CustomDistributedNode", "custombatchdivider",
])
@@ -353,9 +358,9 @@ def test_rejects_collector_chains_across_different_media(indirect):
prompt = _prompt()
source = "collector:1"
if indirect:
prompt["processing"] = _node("ImageToVideo", images=[source, 0])
prompt["processing"] = _node("ImageToAudio", images=[source, 0])
source = "processing"
prompt["collector:2"] = _collector(video=[source, 0])
prompt["collector:2"] = _collector(audio=[source, 0])
with pytest.raises(ValueError, match="collector-to-collector"):
_run(prompt)
@@ -413,7 +418,7 @@ def guarded_import(name, *args, **kwargs):
return original_import(name, *args, **kwargs)
builtins.__import__ = guarded_import
module = importlib.import_module(name + ".api.orchestration.serverless_media_prompt")
prompt = {"s": {"class_type": "Source", "inputs": {}}, "c": {"class_type": "DistributedMediaCollector", "inputs": {"texts": ["s", 0], "multi_job_id": "job", "worker_id": "worker"}}}
prompt = {"s": {"class_type": "Source", "inputs": {}}, "c": {"class_type": "DistributedCollector", "inputs": {"images": ["s", 0], "multi_job_id": "job", "worker_id": "worker"}}}
graph, bindings = module.compile_media_prompt(prompt, "request")
assert graph[bindings[0]["node_id"]]["class_type"] == "MediaExportV1"
'''
+17
View File
@@ -133,6 +133,7 @@ def test_quality_to_crf_mapping():
def test_normalize_format_rejects_unknown_values():
assert tc.normalize_format(" H264 ") == "h264"
assert tc.normalize_format(" EXACT ") == "exact"
assert tc.normalize_format(None) == tc.DEFAULT_TRANSFER_FORMAT
with pytest.raises(tc.TransferCodecError):
tc.normalize_format("gif")
@@ -158,6 +159,22 @@ def test_decode_chunk_validates_envelope_against_payload():
tc.decode_chunk({**video_env, "count": 5}, video["parts"])
def test_exact_is_appended_without_changing_saved_format_defaults():
assert tc.TRANSFER_FORMATS == (
"legacy_png", "png", "ffv1", "jpeg", "webp", "h264", "h265", "av1", "exact",
)
assert tc.DEFAULT_TRANSFER_FORMAT == "legacy_png"
assert tc.EXACT_FORMAT not in tc.IMAGE_FORMATS + tc.VIDEO_FORMATS
def test_exact_does_not_enter_uint8_chunk_codec():
frames = tc.tensor_batch_to_uint8(_smooth_frames(count=1))
with pytest.raises(tc.TransferCodecError, match="exact"):
list(tc.encode_chunks(frames, "exact", 90, 10**9))
with pytest.raises(tc.TransferCodecError, match="exact"):
tc.decode_chunk({"format": "exact", "count": 1, "width": 91, "height": 67}, [b"raw tensor"])
def test_legacy_format_is_not_chunk_encoded():
frames = tc.tensor_batch_to_uint8(_smooth_frames(count=1))
with pytest.raises(tc.TransferCodecError):
+7 -5
View File
@@ -69,8 +69,8 @@ def v3(monkeypatch):
module(f"{PKG}.nodes.utilities")
module(f"{PKG}.nodes.distributed_upscale", UltimateSDUpscaleDistributed=object)
module(f"{PKG}.nodes.collector", DistributedCollectorNode=CollectorRuntime)
module(f"{PKG}.nodes.image_list", DistributedImageListStart=object, DistributedImageListEnd=object)
module(f"{PKG}.nodes.media_collector", DistributedMediaCollector=object)
module(f"{PKG}.nodes.image_list", DistributedImageListStart=object, DistributedImageListEnd=object,
DistributedImageListGetValue=object)
module("comfy", __path__=[])
module("comfy.samplers", KSampler=types.SimpleNamespace(SAMPLERS=["euler"], SCHEDULERS=["normal"]))
module("comfy_api", __path__=[])
@@ -98,6 +98,7 @@ def test_registered_collector_schema_exposes_optional_transfer_inputs(v3):
fmt = inputs["transfer_format"]
assert fmt.kind == "Combo"
assert fmt.kwargs["options"] == list(codec.TRANSFER_FORMATS)
assert fmt.kwargs["options"][-1] == "exact"
assert fmt.kwargs["default"] == "legacy_png"
assert fmt.kwargs["optional"] is True
quality = inputs["transfer_quality"]
@@ -107,13 +108,14 @@ def test_registered_collector_schema_exposes_optional_transfer_inputs(v3):
assert schema.is_input_list is True
def test_registered_collector_forwards_transfer_options(v3):
@pytest.mark.parametrize("fmt", ["av1", "exact"])
def test_registered_collector_forwards_transfer_options(v3, fmt):
mod, _, calls = v3
mod.DistributedCollector.execute(images=["img"], audio=[None], transfer_format=["av1"], transfer_quality=[70],
mod.DistributedCollector.execute(images=["img"], audio=[None], transfer_format=[fmt], transfer_quality=[70],
multi_job_id=["job"], is_worker=[True])
assert calls[-1]["transfer_format"] == ["av1"]
assert calls[-1]["transfer_format"] == [fmt]
assert calls[-1]["transfer_quality"] == [70]
assert calls[-1]["multi_job_id"] == ["job"]
+1
View File
@@ -34,6 +34,7 @@ def test_image_list_network_runtime():
)
assert result.returncode == 0, result.stdout + '\n' + result.stderr
assert 'IMAGE_LIST_NETWORK_OK' in result.stdout
assert 'IMAGE_LIST_VALUES_OK' in result.stdout
assert 'IMAGE_LIST_CANCEL_OK' in result.stdout
assert 'IMAGE_LIST_MASTER_OK' in result.stdout
assert 'IMAGE_LIST_PREFLIGHT_OK' in result.stdout
+18 -5
View File
@@ -17,9 +17,13 @@ jpeg, webp Lossy per-frame stills.
h264, h265, av1
Lossy video for the whole batch. Smallest by far for video
frame batches, where consecutive frames are similar.
exact Original floating-point tensors (raw little-endian bytes, dtype,
channels and values preserved). Largest; opt-in. Encoded by the
Collector/job_complete route with serverless_bridge.codec, never by
encode_chunks/decode_chunk below.
All formats quantize to 8-bit exactly like the legacy path did, so the
lossless formats reproduce the legacy master tensors bit-for-bit.
All formats except exact quantize to 8-bit exactly like the legacy path did,
so the lossless formats reproduce the legacy master tensors bit-for-bit.
"""
import io
import math
@@ -29,20 +33,25 @@ import numpy as np
from PIL import Image
LEGACY_FORMAT = "legacy_png"
EXACT_FORMAT = "exact"
IMAGE_FORMATS = ("png", "jpeg", "webp")
VIDEO_FORMATS = ("ffv1", "h264", "h265", "av1")
LOSSLESS_FORMATS = (LEGACY_FORMAT, "png", "ffv1")
TRANSFER_FORMATS = (LEGACY_FORMAT, "png", "ffv1", "jpeg", "webp", "h264", "h265", "av1")
# Appended, so the pre-existing option order (and saved widget values) is unchanged.
TRANSFER_FORMATS = (LEGACY_FORMAT, "png", "ffv1", "jpeg", "webp", "h264", "h265", "av1", EXACT_FORMAT)
DEFAULT_TRANSFER_FORMAT = LEGACY_FORMAT
DEFAULT_TRANSFER_QUALITY = 90
TRANSFER_FORMAT_TOOLTIP = (
"How workers send images back to the master. "
"How remote workers send images back to the master; the master's own images are never re-encoded. "
"legacy_png: original uncompressed PNG (largest). "
"png / ffv1: lossless, identical result. "
"jpeg / webp: lossy stills. "
"h264 / h265 / av1: lossy video of the whole batch, smallest for frame sequences."
"h264 / h265 / av1: lossy video of the whole batch, smallest for frame sequences "
"(h265 is experimental). "
"exact: original floating-point values, no 8-bit rounding; much larger, opt-in. "
"Serverless media pools send legacy_png as png (same pixels) and never substitute another format."
)
TRANSFER_QUALITY_TOOLTIP = (
"Quality for lossy transfer formats (ignored by lossless ones). "
@@ -298,6 +307,8 @@ def encode_chunks(frames, fmt, quality, max_chunk_bytes, max_frame_bytes=None):
fmt = normalize_format(fmt)
if fmt == LEGACY_FORMAT:
raise TransferCodecError("legacy_png is sent through the JSON path, not encode_chunks")
if fmt == EXACT_FORMAT:
raise TransferCodecError("exact tensors are not encoded by encode_chunks")
total = int(frames.shape[0])
if total == 0:
return
@@ -378,6 +389,8 @@ def decode_chunk(envelope, parts):
fmt = normalize_format(envelope.get("format"))
if fmt == LEGACY_FORMAT:
raise TransferCodecError("legacy_png chunks are not sent as multipart")
if fmt == EXACT_FORMAT:
raise TransferCodecError("exact chunks are decoded as tensors, not frames")
count = envelope.get("count")
width = envelope.get("width")
height = envelope.get("height")
+174
View File
@@ -0,0 +1,174 @@
import { app } from "/scripts/app.js";
// End's Python schema owns all ten optional override slots. Only presentation
// grows here: unlike Start's Autogrow inputs, End keys must never compact, since
// value_3 always overrides source value_3, even if value_2 is disconnected.
const START = "DistributedImageListStart";
const END = "DistributedImageListEnd";
const GET = "DistributedImageListGetValue";
const PIPE = "DISTRIBUTED_IMAGE_LIST_VALUES";
const VALUE_TYPES = "IMAGE,MASK,STRING,INT,FLOAT,BOOLEAN";
const MAX_VALUES = 10;
const pendingGraphs = new WeakSet();
function ordinal(name) {
const match = /^value_([1-9]|10)$/.exec(name ?? "");
return match ? Number(match[1]) : null;
}
function getLink(graph, id) {
if (id == null) return undefined;
if (typeof graph?.getLink === "function") return graph.getLink(id);
return graph?.links?.get?.(id) ?? graph?.links?.[id];
}
function sourceOf(node, inputName) {
const input = node.inputs?.find(slot => slot.name === inputName);
const link = getLink(node.graph, input?.link);
const source = link && node.graph?.getNodeById?.(link.origin_id);
return source ? { node: source, slot: link.origin_slot, link } : null;
}
function isReroute(node) {
return [node?.comfyClass, node?.type].includes("Reroute");
}
function throughReroutes(source) {
const seen = new Set();
while (source && isReroute(source.node)) {
if (seen.has(source.node)) return null;
seen.add(source.node);
source = sourceOf(source.node, source.node.inputs?.[0]?.name);
}
return source;
}
function sourceType(source, seen) {
source = throughReroutes(source);
if (!source) return "*";
const { node, slot, link } = source;
if (node.comfyClass === GET) return getterType(node, seen);
const type = node.outputs?.[slot]?.type;
return type && type !== "*" ? type : (link.type || "*");
}
function getterType(node, seen = new Set()) {
if (seen.has(node)) return "*";
seen.add(node);
const name = node.widgets?.find(widget => widget.name === "value_name")?.value;
if (!ordinal(name)) return "*";
const endSource = throughReroutes(sourceOf(node, "values"));
if (endSource?.node.comfyClass !== END || endSource.slot !== 3) return "*";
const end = endSource.node;
const override = sourceOf(end, name);
if (override) return sourceType(override, seen);
const startSource = throughReroutes(sourceOf(end, "context"));
if (startSource?.node.comfyClass !== START || startSource.slot !== 3) return "*";
return sourceType(sourceOf(startSource.node, `values.${name}`), seen);
}
function compatible(outputType, inputType) {
if (!outputType || !inputType || outputType === "*" || inputType === "*") return true;
return String(outputType).split(",").some(type => String(inputType).split(",").includes(type));
}
function syncGetter(node) {
const output = node.outputs?.[0];
if (!output) return;
const type = getterType(node);
if (output.type === type) return;
output.type = type;
for (const id of [...(output.links ?? [])]) {
const link = getLink(node.graph, id);
if (!link) continue;
const target = node.graph?.getNodeById?.(link.target_id);
if (target && !compatible(type, target.inputs?.[link.target_slot]?.type)) {
target.disconnectInput(link.target_slot);
} else {
link.type = type;
}
}
node.setDirtyCanvas?.(true, true);
}
function syncEnd(node) {
if (!Array.isArray(node.inputs) || !Array.isArray(node.outputs)) return;
// Old saved End nodes have only the three original outputs. Append rather
// than replacing any socket, preserving every existing output index/link.
if (node.outputs.length === 3) {
const shape = node.outputs[0]?.shape;
node.addOutput("values", PIPE, shape === undefined ? undefined : { shape });
}
const connected = node.inputs.filter(input => ordinal(input.name) && input.link != null);
const highest = Math.max(0, ...connected.map(input => ordinal(input.name)));
const visible = Math.min(MAX_VALUES, Math.max(highest + 1, node._distributedVisibleOverrides ?? 1));
for (let i = node.inputs.length - 1; i >= 0; i--) {
const input = node.inputs[i];
if (ordinal(input.name) > visible && input.link == null) node.removeInput(i);
}
for (let i = 1; i <= visible; i++) {
if (!node.inputs.some(input => input.name === `value_${i}`)) {
node.addInput(`value_${i}`, VALUE_TYPES);
}
}
node.setDirtyCanvas?.(true, true);
}
function schedule(graph) {
if (!graph || pendingGraphs.has(graph)) return;
pendingGraphs.add(graph);
const run = () => {
// Keep the guard while syncing, since disconnectInput can re-enter the
// connection hook. A fresh user event schedules a fresh traversal.
try {
for (const node of graph._nodes ?? []) if (node.comfyClass === END) syncEnd(node);
for (const node of graph._nodes ?? []) if (node.comfyClass === GET) syncGetter(node);
} catch (error) {
console.error("[Distributed] Image List values presentation failed:", error);
} finally {
pendingGraphs.delete(graph);
}
};
// Let native Start Autogrow and its existing output-sync finish first.
if (typeof requestAnimationFrame === "function") requestAnimationFrame(() => setTimeout(run, 0));
else setTimeout(run, 0);
}
app.registerExtension({
name: "Distributed.ImageListEndValues",
nodeCreated(node) {
if (![START, END, GET].includes(node.comfyClass) && !isReroute(node)) return;
if (node.comfyClass === END) {
node.addWidget("button", "Add value override", null, () => {
const shown = Math.max(0, ...(node.inputs ?? []).map(input => ordinal(input.name) ?? 0));
node._distributedVisibleOverrides = Math.min(MAX_VALUES, shown + 1);
schedule(node.graph);
}, { serialize: false });
}
for (const hook of ["onConnectionsChange", "onConfigure", "onAdded", "onWidgetChanged"]) {
const original = node[hook];
node[hook] = function (...args) {
const result = original?.apply(this, args);
schedule(this.graph);
return result;
};
}
// Widget callbacks are also used by frontend versions that do not call
// the node's onWidgetChanged hook for a combo selection.
if (node.comfyClass === GET) {
const widget = node.widgets?.find(widget => widget.name === "value_name");
if (widget) {
const callback = widget.callback;
widget.callback = function (...args) {
const result = callback?.apply(this, args);
schedule(node.graph);
return result;
};
}
}
schedule(node.graph);
},
loadedGraphNode(node) {
schedule(node.graph);
},
});
+190
View File
@@ -0,0 +1,190 @@
import { readFileSync } from "node:fs";
import vm from "node:vm";
import { describe, expect, it } from "vitest";
const source = readFileSync(new URL("../imageListValues.js", import.meta.url), "utf8")
.replace(/^import .*;\r?\n/gm, "");
const PIPE = "DISTRIBUTED_IMAGE_LIST_VALUES";
function setup() {
let extension;
let nextId = 1;
const tasks = [];
const graph = {
_nodes: [], links: new Map(),
getNodeById(id) { return this._nodes.find(node => node.id === id); },
getLink(id) { return this.links.get(id); },
};
vm.runInNewContext(source, {
app: { registerExtension: ext => { extension = ext; } },
setTimeout: callback => tasks.push(callback), console,
});
function node(comfyClass, inputs, outputs, widgets = []) {
const n = {
id: nextId++, comfyClass, graph, widgets,
inputs: inputs.map(name => ({ name, type: "*", link: null })),
outputs: outputs.map(([name, type]) => ({ name, type, links: [], shape: 6 })),
addWidget(type, name, value, callback, options) { this.widgets.push({ type, name, value, callback, options }); },
addInput(name, type) { this.inputs.push({ name, type, link: null }); },
addOutput(name, type, extra) { this.outputs.push({ name, type, links: [], ...extra }); },
removeInput(slot) {
if (this.inputs[slot].link != null) throw new Error("Removed wired input");
this.inputs.splice(slot, 1);
this.inputs.forEach((input, index) => {
if (input.link != null) graph.links.get(input.link).target_slot = index;
});
},
disconnectInput(slot) {
const input = this.inputs[slot];
const link = graph.links.get(input.link);
if (link) {
const out = graph.getNodeById(link.origin_id).outputs[link.origin_slot];
out.links = out.links.filter(id => id !== link.id);
graph.links.delete(link.id);
}
input.link = null;
this.onConnectionsChange?.();
},
setDirtyCanvas() {},
};
graph._nodes.push(n);
extension.nodeCreated(n);
return n;
}
const flush = () => { while (tasks.length) tasks.shift()(); };
const connect = (from, slot, to, name) => {
const targetSlot = to.inputs.findIndex(input => input.name === name);
if (targetSlot < 0) throw new Error(`Missing input ${name}`);
to.disconnectInput(targetSlot);
const id = nextId++;
graph.links.set(id, { id, origin_id: from.id, origin_slot: slot, target_id: to.id,
target_slot: targetSlot, type: from.outputs[slot].type });
from.outputs[slot].links.push(id);
to.inputs[targetSlot].link = id;
from.onConnectionsChange?.();
to.onConnectionsChange?.();
return id;
};
const start = node("DistributedImageListStart", ["images", "mask", "values.value_1", "values.value_2", "values.value_3"],
[["image", "IMAGE"], ["mask", "MASK"], ["item_index", "INT"], ["context", "DISTRIBUTED_IMAGE_LIST_CONTEXT"]]);
const end = node("DistributedImageListEnd", ["images", "masks", "texts", "context", ...Array.from({ length: 10 }, (_, i) => `value_${i + 1}`)],
[["image_list", "IMAGE"], ["masks", "MASK"], ["texts", "STRING"], ["values", PIPE]]);
const get = node("DistributedImageListGetValue", ["values"], [["value", "*"]], [{ name: "value_name", value: "value_1" }]);
connect(start, 3, end, "context");
connect(end, 3, get, "values");
flush();
return { node, graph, start, end, get, flush, connect, extension };
}
const valueNames = node => node.inputs.filter(input => input.name.startsWith("value_")).map(input => input.name);
const addOverride = h => { h.end.widgets.find(widget => widget.name === "Add value override").callback(); h.flush(); };
describe("Image List End context values", () => {
it("keeps original sockets and shows only the first optional override initially", () => {
const h = setup();
expect(h.end.inputs.map(input => input.name)).toEqual(["images", "masks", "texts", "context", "value_1"]);
expect(h.end.outputs.map(output => output.name)).toEqual(["image_list", "masks", "texts", "values"]);
expect(h.end.widgets[0].options.serialize).toBe(false);
});
it("appends the values output when loading an old three-output End", () => {
const h = setup();
const old = h.node("DistributedImageListEnd", ["images", "masks", "texts", "context"],
[["image_list", "IMAGE"], ["masks", "MASK"], ["texts", "STRING"]]);
h.flush();
expect(old.outputs[3]).toMatchObject({ name: "values", type: PIPE, shape: 6 });
expect(valueNames(old)).toEqual(["value_1"]);
});
it("grows overrides without compacting field identities or existing links", () => {
const h = setup();
addOverride(h);
addOverride(h);
const src = h.node("PrimitiveInt", [], [["INT", "INT"]]);
const thirdLink = h.connect(src, 0, h.end, "value_3");
h.connect(src, 0, h.end, "value_2");
h.flush();
h.end.disconnectInput(h.end.inputs.findIndex(input => input.name === "value_2"));
h.flush();
expect(valueNames(h.end)).toEqual(["value_1", "value_2", "value_3", "value_4"]);
const link = h.graph.links.get(thirdLink);
expect(h.end.inputs[link.target_slot].name).toBe("value_3");
for (let i = 0; i < 20; i++) addOverride(h);
expect(valueNames(h.end)).toHaveLength(10);
});
it.each(["IMAGE", "MASK", "STRING", "INT", "FLOAT", "BOOLEAN"])("infers %s from the original Start value through context", type => {
const h = setup();
const source = h.node("Source", [], [["value", type]]);
h.connect(source, 0, h.start, "values.value_1");
h.flush();
expect(h.get.outputs[0].type).toBe(type);
});
it("prefers the End override type, then returns to the original after disconnect", () => {
const h = setup();
h.connect(h.node("Source", [], [["value", "INT"]]), 0, h.start, "values.value_1");
h.connect(h.node("Source", [], [["value", "STRING"]]), 0, h.end, "value_1");
h.flush();
expect(h.get.outputs[0].type).toBe("STRING");
h.end.disconnectInput(h.end.inputs.findIndex(input => input.name === "value_1"));
h.flush();
expect(h.get.outputs[0].type).toBe("INT");
});
it("updates a selected field and disconnects incompatible consumers", () => {
const h = setup();
h.connect(h.node("Source", [], [["value", "STRING"]]), 0, h.start, "values.value_1");
h.connect(h.node("Source", [], [["value", "INT"]]), 0, h.start, "values.value_2");
h.flush();
const consumer = h.node("Save", ["filename"], []);
consumer.inputs[0].type = "STRING";
h.connect(h.get, 0, consumer, "filename");
h.get.widgets[0].value = "value_2";
h.get.widgets[0].callback();
h.flush();
expect(h.get.outputs[0].type).toBe("INT");
expect(consumer.inputs[0].link).toBe(null);
});
it("keeps compatible consumer links and uses wildcard for unavailable graph metadata", () => {
const h = setup();
h.connect(h.node("Source", [], [["value", "INT"]]), 0, h.start, "values.value_1");
h.flush();
const consumer = h.node("Consumer", ["value"], []);
consumer.inputs[0].type = "INT,FLOAT";
const id = h.connect(h.get, 0, consumer, "value");
h.get.disconnectInput(0);
h.flush();
expect(h.get.outputs[0].type).toBe("*");
expect(h.graph.links.has(id)).toBe(true);
});
it("follows reroutes for context and values, and terminates on a cycle", () => {
const h = setup();
h.connect(h.node("Source", [], [["value", "FLOAT"]]), 0, h.start, "values.value_1");
const reroute = h.node("Reroute", ["input"], [["output", "*"]]);
h.connect(h.end, 3, reroute, "input");
h.connect(reroute, 0, h.get, "values");
h.flush();
expect(h.get.outputs[0].type).toBe("FLOAT");
h.connect(reroute, 0, reroute, "input");
h.flush();
expect(h.get.outputs[0].type).toBe("*");
});
it("recomputes types across sequential regions", () => {
const h = setup();
const secondStart = h.node("DistributedImageListStart", ["values.value_1"],
[["image", "IMAGE"], ["mask", "MASK"], ["item_index", "INT"], ["context", "DISTRIBUTED_IMAGE_LIST_CONTEXT"]]);
const secondEnd = h.node("DistributedImageListEnd", ["images", "masks", "texts", "context"],
[["image_list", "IMAGE"], ["masks", "MASK"], ["texts", "STRING"], ["values", PIPE]]);
const secondGet = h.node("DistributedImageListGetValue", ["values"], [["value", "*"]], [{ name: "value_name", value: "value_1" }]);
h.connect(h.node("Source", [], [["value", "BOOLEAN"]]), 0, h.start, "values.value_1");
h.connect(h.get, 0, secondStart, "values.value_1");
h.connect(secondStart, 3, secondEnd, "context");
h.connect(secondEnd, 3, secondGet, "values");
h.flush();
expect(secondGet.outputs[0].type).toBe("BOOLEAN");
});
});