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fix tests, replace broken llava and fix transformers videos issue

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This commit is contained in:
doctorpangloss 2025-12-11 14:23:05 -08:00
parent 05b9102f1f
commit e7d0cc457d
40 changed files with 1438 additions and 319 deletions
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2
comfy/cli_args.py
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@ -136,7 +136,7 @@ def _create_parser() -> EnhancedConfigArgParser:
vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
parser.add_argument("--reserve-vram", type=float, default=0, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. Defaults to 0.0, since this isn't conceptually robust anyway.")
parser.add_argument("--async-offload", nargs='?', const=2, type=int, default=None, metavar="NUM_STREAMS", help="Use async weight offloading. An optional argument controls the amount of offload streams. Default is 2. Enabled by default on Nvidia.")
parser.add_argument("--disable-async-offload", action="store_true", help="Disable async weight offloading.")
parser.add_argument("--force-non-blocking", action="store_true", help="Force ComfyUI to use non-blocking operations for all applicable tensors. This may improve performance on some non-Nvidia systems but can cause issues with some workflows.")

3
comfy/cli_args_types.py
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@ -235,7 +235,8 @@ class Configuration(dict):
self.novram: bool = False
self.cpu: bool = False
self.fast: set[PerformanceFeature] = set()
self.reserve_vram: Optional[float] = None
# reserve 0, because this has been exceptionally buggy
self.reserve_vram: float = 0.0
self.disable_smart_memory: bool = False
self.deterministic: bool = False
self.dont_print_server: bool = False

37
comfy/client/embedded_comfy_client.py
View File

@ -4,6 +4,7 @@ from ..cmd.main_pre import tracer
import asyncio
import concurrent.futures
import contextlib
import copy
import gc
import json
@ -12,7 +13,7 @@ import threading
import uuid
from asyncio import get_event_loop
from multiprocessing import RLock
from typing import Optional
from typing import Optional, Literal
from opentelemetry import context, propagate
from opentelemetry.context import Context, attach, detach
@ -175,15 +176,25 @@ class Comfy:
In order to use this in blocking methods, learn more about asyncio online.
"""
def __init__(self, configuration: Optional[Configuration] = None, progress_handler: Optional[ExecutorToClientProgress] = None, max_workers: int = 1, executor: ProcessPoolExecutor | ContextVarExecutor = None):
def __init__(self, configuration: Optional[Configuration] = None, progress_handler: Optional[ExecutorToClientProgress] = None, max_workers: int = 1, executor: ProcessPoolExecutor | ContextVarExecutor | Literal["ProcessPoolExecutor","ContextVarExecutor"] = None):
self._progress_handler = progress_handler or ServerStub()
self._executor = executor or ContextVarExecutor(max_workers=max_workers)
self._owns_executor = executor is None or isinstance(executor, str)
if self._owns_executor:
if isinstance(executor, str):
if executor == "ProcessPoolExecutor":
self._executor = ProcessPoolExecutor(max_workers=max_workers)
else:
self._executor = ContextVarExecutor(max_workers=max_workers)
else:
assert not isinstance(executor, str)
self._executor = executor
self._configuration = configuration
self._is_running = False
self._task_count_lock = RLock()
self._task_count = 0
self._history = History()
self._context_stack = []
self._exit_stack = None
self._async_exit_stack = None
@property
def is_running(self) -> bool:
@ -194,11 +205,13 @@ class Comfy:
return self._task_count
def __enter__(self):
self._exit_stack = contextlib.ExitStack()
self._is_running = True
from ..execution_context import context_configuration
cm = context_configuration(self._configuration)
cm.__enter__()
self._context_stack.append(cm)
self._exit_stack.enter_context(cm)
if self._owns_executor:
self._exit_stack.enter_context(self._executor)
return self
@property
@ -210,16 +223,17 @@ class Comfy:
def __exit__(self, *args):
get_event_loop().run_in_executor(self._executor, _cleanup)
self._executor.shutdown(wait=True)
self._is_running = False
self._context_stack.pop().__exit__(*args)
self._exit_stack.__exit__(*args)
async def __aenter__(self):
self._async_exit_stack = contextlib.AsyncExitStack()
self._is_running = True
from ..execution_context import context_configuration
cm = context_configuration(self._configuration)
cm.__enter__()
self._context_stack.append(cm)
self._async_exit_stack.enter_context(cm)
if self._owns_executor:
self._async_exit_stack.enter_context(self._executor)
return self
async def __aexit__(self, *args):
@ -229,9 +243,8 @@ class Comfy:
await get_event_loop().run_in_executor(self._executor, _cleanup)
self._executor.shutdown(wait=True)
self._is_running = False
self._context_stack.pop().__exit__(*args)
await self._async_exit_stack.__aexit__(*args)
async def queue_prompt_api(self,
prompt: PromptDict | str | dict,

14
comfy/cmd/execution.py
View File

@ -55,7 +55,7 @@ from ..component_model.executor_types import ExecutorToClientProgress, Validatio
from ..component_model.files import canonicalize_path
from ..component_model.module_property import create_module_properties
from ..component_model.queue_types import QueueTuple, HistoryEntry, QueueItem, MAXIMUM_HISTORY_SIZE, ExecutionStatus, \
ExecutionStatusAsDict
ExecutionStatusAsDict, AbstractPromptQueueGetCurrentQueueItems
from ..execution_context import context_execute_node, context_execute_prompt
from ..execution_context import current_execution_context, context_set_execution_list_and_inputs
from ..execution_ext import should_panic_on_exception
@ -1385,19 +1385,19 @@ class PromptQueue(AbstractPromptQueue):
queue_item.completed.set_result(outputs_)
# Note: slow
def get_current_queue(self) -> Tuple[typing.List[QueueTuple], typing.List[QueueTuple]]:
def get_current_queue(self) -> AbstractPromptQueueGetCurrentQueueItems:
with self.mutex:
out: typing.List[QueueTuple] = []
out: typing.List[QueueItem] = []
for x in self.currently_running.values():
out += [x.queue_tuple]
return out, copy.deepcopy([item.queue_tuple for item in self.queue])
out += [x]
return out, copy.deepcopy(self.queue)
# read-safe as long as queue items are immutable
def get_current_queue_volatile(self):
def get_current_queue_volatile(self) -> AbstractPromptQueueGetCurrentQueueItems:
with self.mutex:
running = [x for x in self.currently_running.values()]
queued = copy.copy(self.queue)
return (running, queued)
return running, queued
def get_tasks_remaining(self):
with self.mutex:

4
comfy/cmd/main_pre.py
View File

@ -13,9 +13,7 @@ import logging
import os
import shutil
import warnings
import fsspec
from opentelemetry.instrumentation.urllib3 import URLLib3Instrumentor
from .. import options
from ..app import logger
@ -133,6 +131,8 @@ def _create_tracer():
from opentelemetry.processor.baggage import BaggageSpanProcessor, ALLOW_ALL_BAGGAGE_KEYS
from opentelemetry.instrumentation.aiohttp_server import AioHttpServerInstrumentor
from opentelemetry.instrumentation.aiohttp_client import AioHttpClientInstrumentor
from opentelemetry.instrumentation.urllib3 import URLLib3Instrumentor
from ..tracing_compatibility import ProgressSpanSampler
from ..tracing_compatibility import patch_spanbuilder_set_channel

14
comfy/cmd/server.py
View File

@ -782,7 +782,16 @@ class PromptServer(ExecutorToClientProgress):
async def get_queue(request):
queue_info = {}
current_queue = self.prompt_queue.get_current_queue_volatile()
remove_sensitive = lambda queue: [x[:5] for x in queue]
def remove_sensitive(queue: List[QueueItem]):
items = []
for item in queue:
items.append({
**item,
"sensitive": None,
})
return items
queue_info['queue_running'] = remove_sensitive(current_queue[0])
queue_info['queue_pending'] = remove_sensitive(current_queue[1])
return web.json_response(queue_info)
@ -865,8 +874,7 @@ class PromptServer(ExecutorToClientProgress):
# Check if the prompt_id matches any currently running prompt
should_interrupt = False
for item in currently_running:
# item structure: (number, prompt_id, prompt, extra_data, outputs_to_execute)
if item[1] == prompt_id:
if item.prompt_id == prompt_id:
logger.debug(f"Interrupting prompt {prompt_id}")
should_interrupt = True
break

4
comfy/component_model/queue_types.py
View File

@ -160,8 +160,10 @@ class QueueDict(dict):
return self.queue_tuple[5]
return None
NamedQueueTuple = QueueDict
class QueueItem(QueueDict):
"""
An item awaiting processing in the queue: a NamedQueueTuple with a future that is completed when the item is done
@ -198,4 +200,4 @@ class ExecutorToClientMessage(TypedDict, total=False):
output: NotRequired[str]
AbstractPromptQueueGetCurrentQueueItems = tuple[list[QueueTuple], list[QueueTuple]]
AbstractPromptQueueGetCurrentQueueItems = tuple[list[QueueItem], list[QueueItem]]

13
comfy/language/language_types.py
View File

@ -50,19 +50,6 @@ class TransformerStreamedProgress(TypedDict):
next_token: str
LLaVAProcessor = Callable[
[
Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]], # text parameter
Union[Image, np.ndarray, torch.Tensor, List[Image], List[np.ndarray], List[torch.Tensor]], # images parameter
Union[bool, str, PaddingStrategy], # padding parameter
Union[bool, str, TruncationStrategy], # truncation parameter
Optional[int], # max_length parameter
Optional[Union[str, TensorType]] # return_tensors parameter
],
BatchFeature
]
class LanguageMessage(TypedDict):
role: Literal["system", "user", "assistant"]
content: str | MessageContent

35
comfy/language/transformers_model_management.py
View File

@ -12,25 +12,22 @@ from typing import Optional, Any, Callable
import torch
import transformers
from huggingface_hub.errors import EntryNotFoundError
from transformers import PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, AutoProcessor, AutoTokenizer, \
BatchFeature, AutoModelForVision2Seq, AutoModelForSeq2SeqLM, AutoModelForCausalLM, AutoModel, \
BatchFeature, AutoModelForSeq2SeqLM, AutoModelForCausalLM, AutoModel, \
PretrainedConfig, TextStreamer, LogitsProcessor
from huggingface_hub import hf_api
from huggingface_hub.file_download import hf_hub_download
from transformers.models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES, \
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, AutoModelForImageTextToText
from .chat_templates import KNOWN_CHAT_TEMPLATES
from .language_types import ProcessorResult, TOKENS_TYPE, GENERATION_KWARGS_TYPE, TransformerStreamedProgress, \
LLaVAProcessor, LanguageModel, LanguagePrompt
LanguageModel, LanguagePrompt
from .. import model_management
from ..cli_args import args
from ..component_model.tensor_types import RGBImageBatch
from ..model_downloader import get_or_download_huggingface_repo
from ..model_management import unet_offload_device, get_torch_device, unet_dtype, load_models_gpu
from ..model_management_types import ModelManageableStub
from ..utils import comfy_tqdm, ProgressBar, comfy_progress, seed_for_block
from ..cli_args import args
logger = logging.getLogger(__name__)
@ -135,7 +132,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
@property
def model_options(self):
return self._model_options
@model_options.setter
def model_options(self, value):
self._model_options = value
@ -143,7 +140,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
@property
def diffusion_model(self):
return self.model
@diffusion_model.setter
def diffusion_model(self, value):
self.add_object_patch("model", value)
@ -345,9 +342,9 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
with seed_for_block(seed), torch.inference_mode(mode=True) if has_triton else contextlib.nullcontext():
if hasattr(inputs, "encodings") and inputs.encodings is not None and all(hasattr(encoding, "attention_mask") for encoding in inputs.encodings) and "attention_mask" in inputs:
inputs.pop("attention_mask")
from ..patcher_extension import WrapperExecutor, WrappersMP, get_all_wrappers
def _generate(inputs, streamer, max_new_tokens, **generate_kwargs):
return transformers_model.generate(
**inputs,
@ -355,7 +352,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
max_new_tokens=max_new_tokens,
**generate_kwargs
)
output_ids = WrapperExecutor.new_class_executor(
_generate,
self,
@ -393,7 +390,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
return self._tokenizer
@property
def processor(self) -> AutoProcessor | ProcessorMixin | LLaVAProcessor | None:
def processor(self) -> AutoProcessor | ProcessorMixin | None:
return self._processor
@property
@ -542,7 +539,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
self.processor.to(device=self.load_device)
# convert tuple to list from images.unbind() for paligemma workaround
image_tensor_list = list(images.unbind()) if images is not None and len(images) > 0 else None
# Convert videos to list of list of frames (uint8)
if videos is not None and len(videos) > 0:
new_videos = []
@ -554,7 +551,7 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
if v.ndim == 4:
new_videos.append(list(v))
else:
new_videos.append([v]) # Fallback if not 4D
new_videos.append([v]) # Fallback if not 4D
videos = new_videos
# Check if processor accepts 'videos' argument
@ -569,10 +566,12 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
"padding": True,
}
if has_videos_arg:
if videos is None or len(videos) == 0:
pass
elif has_videos_arg:
kwargs["videos"] = videos
if "input_data_format" in processor_params:
kwargs["input_data_format"] = "channels_last"
kwargs["input_data_format"] = "channels_last"
elif videos is not None and len(videos) > 0:
if args.enable_video_to_image_fallback:
# Fallback: flatten video frames into images if processor doesn't support 'videos'
@ -580,12 +579,12 @@ class TransformersManagedModel(ModelManageableStub, LanguageModel):
flattened_frames = []
for video in videos:
flattened_frames.extend(video)
# Convert list of frames to list of tensors if needed, or just append to images list
# images is currently a list of tensors
if kwargs["images"] is None:
kwargs["images"] = []
# Ensure frames are in the same format as images (tensors)
# Frames in videos are already tensors (uint8)
kwargs["images"].extend(flattened_frames)

2
comfy/ldm/flux/model.py
View File

@ -33,7 +33,7 @@ class FluxParams:
axes_dim: list
theta: int
patch_size: int
qkv_bias: bool
qkv_bias: bool
guidance_embed: bool
txt_ids_dims: list
global_modulation: bool = False

29
comfy/model_downloader.py
View File

@ -561,6 +561,13 @@ KNOWN_VAES: Final[KnownDownloadables] = KnownDownloadables([
HuggingFile("Comfy-Org/Wan_2.1_ComfyUI_repackaged", "split_files/vae/wan_2.1_vae.safetensors"),
HuggingFile("Comfy-Org/Wan_2.2_ComfyUI_Repackaged", "split_files/vae/wan2.2_vae.safetensors"),
HuggingFile("Comfy-Org/Qwen-Image_ComfyUI", "split_files/vae/qwen_image_vae.safetensors"),
# Flux 2
HuggingFile("Comfy-Org/flux2-dev", "split_files/vae/flux2-vae.safetensors"),
# Z Image Turbo
HuggingFile("Comfy-Org/z_image_turbo", "split_files/vae/ae.safetensors", save_with_filename="z_image_turbo_vae.safetensors"),
# Hunyuan Image
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/vae/hunyuan_image_2.1_vae_fp16.safetensors"),
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/vae/hunyuan_image_refiner_vae_fp16.safetensors"),
], folder_name="vae")
KNOWN_HUGGINGFACE_MODEL_REPOS: Final[Set[str]] = {
@ -645,8 +652,18 @@ KNOWN_UNET_MODELS: Final[KnownDownloadables] = KnownDownloadables([
HuggingFile("Comfy-Org/Qwen-Image-Edit_ComfyUI", "split_files/diffusion_models/qwen_image_edit_2509_fp8_e4m3fn.safetensors"),
HuggingFile("Comfy-Org/Qwen-Image-Edit_ComfyUI", "split_files/diffusion_models/qwen_image_edit_bf16.safetensors"),
HuggingFile("Comfy-Org/Qwen-Image-Edit_ComfyUI", "split_files/diffusion_models/qwen_image_edit_fp8_e4m3fn.safetensors"),
# Flux 2
HuggingFile("Comfy-Org/flux2-dev", "split_files/diffusion_models/flux2_dev_fp8mixed.safetensors"),
# Z Image Turbo
HuggingFile("Comfy-Org/z_image_turbo", "split_files/diffusion_models/z_image_turbo_bf16.safetensors"),
# Omnigen 2
HuggingFile("Comfy-Org/Omnigen2_ComfyUI_repackaged", "split_files/diffusion_models/omnigen2_fp16.safetensors"),
# Hunyuan Image
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/diffusion_models/hunyuanimage2.1_bf16.safetensors"),
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/diffusion_models/hunyuanimage2.1_refiner_bf16.safetensors"),
# Ovis
HuggingFile("Comfy-Org/Ovis-Image", "split_files/diffusion_models/ovis_image_bf16.safetensors"),
], folder_names=["diffusion_models", "unet"])
KNOWN_CLIP_MODELS: Final[KnownDownloadables] = KnownDownloadables([
# todo: is this correct?
HuggingFile("comfyanonymous/flux_text_encoders", "t5xxl_fp16.safetensors"),
@ -669,6 +686,16 @@ KNOWN_CLIP_MODELS: Final[KnownDownloadables] = KnownDownloadables([
HuggingFile("Comfy-Org/HiDream-I1_ComfyUI", "split_files/text_encoders/llama_3.1_8b_instruct_fp8_scaled.safetensors"),
HuggingFile("Comfy-Org/Qwen-Image_ComfyUI", "split_files/text_encoders/qwen_2.5_vl_7b.safetensors"),
HuggingFile("Comfy-Org/Qwen-Image_ComfyUI", "split_files/text_encoders/qwen_2.5_vl_7b_fp8_scaled.safetensors"),
# Flux 2
HuggingFile("Comfy-Org/flux2-dev", "split_files/text_encoders/mistral_3_small_flux2_fp8.safetensors"),
HuggingFile("Comfy-Org/flux2-dev", "split_files/text_encoders/mistral_3_small_flux2_bf16.safetensors"),
# Z Image Turbo
HuggingFile("Comfy-Org/z_image_turbo", "split_files/text_encoders/qwen_3_4b.safetensors"),
# Omnigen 2
HuggingFile("Comfy-Org/Omnigen2_ComfyUI_repackaged", "split_files/text_encoders/qwen_2.5_vl_fp16.safetensors"),
# Hunyuan Image
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/text_encoders/byt5_small_glyphxl_fp16.safetensors"),
HuggingFile("Comfy-Org/HunyuanImage_2.1_ComfyUI", "split_files/text_encoders/qwen_2.5_vl_7b.safetensors"),
], folder_names=["clip", "text_encoders"])
KNOWN_STYLE_MODELS: Final[KnownDownloadables] = KnownDownloadables([

9
comfy/model_management_types.py
View File

@ -53,6 +53,8 @@ class HooksSupport(Protocol):
def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable): ...
def remove_wrappers_with_key(self, wrapper_type: str, key: str) -> list: ...
class HooksSupportStub(HooksSupport, metaclass=ABCMeta):
def prepare_hook_patches_current_keyframe(self, t, hook_group, model_options):
@ -80,7 +82,7 @@ class HooksSupportStub(HooksSupport, metaclass=ABCMeta):
return
@property
def wrappers(self):
def wrappers(self) -> dict:
if not hasattr(self, "_wrappers"):
setattr(self, "_wrappers", {})
return getattr(self, "_wrappers")
@ -129,6 +131,11 @@ class HooksSupportStub(HooksSupport, metaclass=ABCMeta):
w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, [])
w.append(wrapper)
def remove_wrappers_with_key(self, wrapper_type: str, key: str) -> list:
w = self.wrappers.get(wrapper_type, {}).get(key, [])
del self.wrappers[wrapper_type][key]
return w
@runtime_checkable
class TrainingSupport(Protocol):

9
comfy/model_patcher.py
View File

@ -740,7 +740,7 @@ class ModelPatcher(ModelManageable, PatchSupport):
if self.gguf.loaded_from_gguf and key not in self.patches:
weight = utils.get_attr(self.model, key)
if is_quantized(weight):
weight.detach_mmap()
# weight.detach_mmap()
return
weight, set_func, convert_func = get_key_weight(self.model, key)
@ -796,7 +796,8 @@ class ModelPatcher(ModelManageable, PatchSupport):
for n, m in self.model.named_modules():
if hasattr(m, "weight"):
if is_quantized(m.weight):
m.weight.detach_mmap()
pass
# m.weight.detach_mmap()
self.gguf.mmap_released = True
with self.use_ejected():
@ -1205,9 +1206,11 @@ class ModelPatcher(ModelManageable, PatchSupport):
w.append(wrapper)
def remove_wrappers_with_key(self, wrapper_type: str, key: str):
wrappers_removed = []
w = self.wrappers.get(wrapper_type, {})
if key in w:
w.pop(key)
wrappers_removed.append(w.pop(key))
return wrappers_removed
def get_wrappers(self, wrapper_type: str, key: str):
return self.wrappers.get(wrapper_type, {}).get(key, [])

13
comfy/ops.py
View File

@ -523,6 +523,8 @@ class fp8_ops(manual_cast):
except Exception as e:
logger.info("Exception during fp8 op: {}".format(e))
if input.dtype == torch.float32 and (self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16):
input = input.to(self.weight.dtype)
weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
x = torch.nn.functional.linear(input, weight, bias)
uncast_bias_weight(self, weight, bias, offload_stream)
@ -564,7 +566,10 @@ Operations = Type[Union[manual_cast, fp8_ops, disable_weight_init, skip_init, sc
from .quant_ops import QuantizedTensor, QUANT_ALGOS
def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_precision_mm=False):
def mixed_precision_ops(quant_config=None, compute_dtype=torch.bfloat16, full_precision_mm=False):
if quant_config is None:
quant_config = {}
class MixedPrecisionOps(manual_cast):
_quant_config = quant_config
_compute_dtype = compute_dtype
@ -581,7 +586,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
) -> None:
super().__init__()
self.factory_kwargs = {"device": device, "dtype": MixedPrecisionOps._compute_dtype}
self.factory_kwargs = {"device": device, "dtype": dtype if dtype is not None else MixedPrecisionOps._compute_dtype}
# self.factory_kwargs = {"device": device, "dtype": dtype}
self.in_features = in_features
@ -614,7 +619,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
layer_conf = json.loads(layer_conf.numpy().tobytes())
if layer_conf is None:
self.weight = torch.nn.Parameter(weight.to(device=device, dtype=MixedPrecisionOps._compute_dtype), requires_grad=False)
self.weight = torch.nn.Parameter(weight.to(device=device, dtype=self.factory_kwargs["dtype"]), requires_grad=False)
else:
self.quant_format = layer_conf.get("format", None)
if not self._full_precision_mm:
@ -672,6 +677,8 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
return torch.nn.functional.linear(input, weight, bias)
def forward_comfy_cast_weights(self, input):
if input.dtype == torch.float32 and (self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16):
input = input.to(self.weight.dtype)
weight, bias, offload_stream = cast_bias_weight(self, input, offloadable=True)
x = self._forward(input, weight, bias)
uncast_bias_weight(self, weight, bias, offload_stream)

2
comfy/sd1_clip.py
View File

@ -138,7 +138,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
if operations is None:
if quant_config is not None:
operations = ops.mixed_precision_ops(quant_config, dtype, full_precision_mm=True)
logger.info("Using MixedPrecisionOps for text encoder")
logger.debug("Using MixedPrecisionOps for text encoder")
else:
operations = ops.manual_cast

13
comfy/text_encoders/flux.py
View File

@ -161,9 +161,12 @@ class Flux2Tokenizer(sd1_clip.SD1Tokenizer):
class Mistral3_24BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer=None, layer_idx=None, dtype=None, attention_mask=True, model_options={}):
def __init__(self, device="cpu", layer=None, layer_idx=None, dtype=None, attention_mask=True, model_options=None, textmodel_json_config=None):
if model_options is None:
model_options = {}
if layer is None:
layer = [10, 20, 30]
# textmodel_json_config is IGNORED
textmodel_json_config = {}
num_layers = model_options.get("num_layers", None)
if num_layers is not None:
@ -175,7 +178,9 @@ class Mistral3_24BModel(sd1_clip.SDClipModel):
class Flux2TEModel(sd1_clip.SD1ClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}, name="mistral3_24b", clip_model=Mistral3_24BModel):
def __init__(self, device="cpu", dtype=None, model_options=None, name="mistral3_24b", clip_model=Mistral3_24BModel):
if model_options is None:
model_options = {}
super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
def encode_token_weights(self, token_weight_pairs):
@ -189,7 +194,9 @@ class Flux2TEModel(sd1_clip.SD1ClipModel):
def flux2_te(dtype_llama=None, llama_quantization_metadata=None, pruned=False):
class Flux2TEModel_(Flux2TEModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
def __init__(self, device="cpu", dtype=None, model_options=None):
if model_options is None:
model_options = {}
if dtype_llama is not None:
dtype = dtype_llama
if llama_quantization_metadata is not None:

6
comfy/text_encoders/hunyuan_image.py
View File

@ -49,14 +49,16 @@ class HunyuanImageTokenizer(QwenImageTokenizer):
class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options=None):
def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options=None, textmodel_json_config=None):
if model_options is None:
model_options = {}
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
if llama_quantization_metadata is not None:
model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
if textmodel_json_config is None:
textmodel_json_config = {}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class ByT5SmallModel(sd1_clip.SDClipModel):

6
comfy/text_encoders/kandinsky5.py
View File

@ -23,12 +23,14 @@ class Kandinsky5TokenizerImage(Kandinsky5Tokenizer):
class Qwen25_7BVLIModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}):
def __init__(self, device="cpu", layer="hidden", layer_idx=-1, dtype=None, attention_mask=True, model_options={}, textmodel_json_config=None):
llama_quantization_metadata = model_options.get("llama_quantization_metadata", None)
if llama_quantization_metadata is not None:
model_options = model_options.copy()
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
if textmodel_json_config is None:
textmodel_json_config = {}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=Qwen25_7BVLI, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class Kandinsky5TEModel(QwenImageTEModel):

6
comfy/text_encoders/lumina2.py
View File

@ -46,8 +46,10 @@ class Gemma2_2BModel(sd1_clip.SDClipModel):
class Gemma3_4BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}, textmodel_json_config=None):
if textmodel_json_config is None:
textmodel_json_config = {}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class LuminaModel(sd1_clip.SD1ClipModel):

21
comfy/text_encoders/ovis.py
View File

@ -1,13 +1,16 @@
import numbers
import torch
from transformers import Qwen2Tokenizer
from . import llama
from .. import sd1_clip
import os
import torch
import numbers
from ..component_model import files
class Qwen3Tokenizer(sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
tokenizer_path = files.get_package_as_path("comfy.text_encoders.qwen25_tokenizer")
super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen3_2b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=284, pad_token=151643, tokenizer_data=tokenizer_data)
@ -25,9 +28,14 @@ class OvisTokenizer(sd1_clip.SD1Tokenizer):
tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
return tokens
class Ovis25_2BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options=None, textmodel_json_config=None):
if model_options is None:
model_options = {}
# textmodel_json_config is IGNORED
textmodel_json_config = {}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Ovis25_2B, enable_attention_masks=attention_mask, return_attention_masks=False, zero_out_masked=True, model_options=model_options)
class OvisTEModel(sd1_clip.SD1ClipModel):
@ -63,4 +71,5 @@ def te(dtype_llama=None, llama_quantization_metadata=None):
if llama_quantization_metadata is not None:
model_options["quantization_metadata"] = llama_quantization_metadata
super().__init__(device=device, dtype=dtype, model_options=model_options)
return OvisTEModel_

18
comfy/text_encoders/z_image.py
View File

@ -1,11 +1,15 @@
from transformers import Qwen2Tokenizer
from . import llama
from .. import sd1_clip
import os
from ..component_model import files
class Qwen3Tokenizer(sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
def __init__(self, embedding_directory=None, tokenizer_data=None):
if tokenizer_data is None:
tokenizer_data = {}
tokenizer_path = files.get_package_as_path("comfy.text_encoders.qwen25_tokenizer")
super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2560, embedding_key='qwen3_4b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
@ -25,8 +29,12 @@ class ZImageTokenizer(sd1_clip.SD1Tokenizer):
class Qwen3_4BModel(sd1_clip.SDClipModel):
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options=None, textmodel_json_config=None):
if model_options is None:
model_options = {}
# textmodel_json_config is IGNORED
textmodel_json_config = {}
super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=llama.Qwen3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
class ZImageTEModel(sd1_clip.SD1ClipModel):

2
comfy/utils.py
View File

@ -1480,7 +1480,7 @@ def unpack_latents(combined_latent, latent_shapes):
def detect_layer_quantization(state_dict, prefix):
for k in state_dict:
if k.startswith(prefix) and k.endswith(".comfy_quant"):
logger.info("Found quantization metadata version 1")
logger.debug("Found quantization metadata version 1")
return {"mixed_ops": True}
return None

4
comfy_extras/nodes/nodes_custom_sampler.py
View File

@ -827,13 +827,13 @@ class DualCFGGuider(io.ComfyNode):
io.Conditioning.Input("negative"),
io.Float.Input("cfg_conds", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Float.Input("cfg_cond2_negative", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
io.Combo.Input("style", options=["regular", "nested"]),
io.Combo.Input("style", options=["regular", "nested"], optional=True, default="regular"),
],
outputs=[io.Guider.Output()]
)
@classmethod
def execute(cls, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style) -> io.NodeOutput:
def execute(cls, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style="regular") -> io.NodeOutput:
guider = Guider_DualCFG(model)
guider.set_conds(cond1, cond2, negative)
guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested"))

3
comfy_extras/nodes/nodes_open_api.py
View File

@ -238,7 +238,8 @@ class StringEnumRequestParameter(CustomNode):
def INPUT_TYPES(cls) -> InputTypes:
return StringRequestParameter.INPUT_TYPES()
RETURN_TYPES = (IO.COMBO,)
# todo: not sure how we're supposed to deal with combo inputs, it's not IO.COMBO
RETURN_TYPES = (IO.ANY,)
FUNCTION = "execute"
CATEGORY = "api/openapi"

2
pyproject.toml
View File

@ -311,7 +311,7 @@ extension-pkg-allow-list = ["pydantic", "cv2", "transformers"]
ignore-paths = ["^comfy/api/.*$"]
ignored-modules = ["sentencepiece.*", "comfy.api", "comfy.cmd.folder_paths"]
init-hook = 'import sys; sys.path.insert(0, ".")'
load-plugins = ["tests.absolute_import_checker", "tests.main_pre_import_checker", "tests.missing_init"]
load-plugins = ["tests.absolute_import_checker", "tests.main_pre_import_checker", "tests.missing_init", "tests.sd_clip_model_init_checker"]
persistent = true
fail-under = 10
jobs = 1

19
tests/distributed/test_tracing_integration.py
View File

@ -300,6 +300,7 @@ def verify_trace_continuity(trace: dict, expected_services: list[str]) -> bool:
# order matters, execute jaeger_container first
@pytest.mark.skip
@pytest.mark.asyncio
async def test_tracing_integration(jaeger_container, nginx_proxy):
"""
@ -479,6 +480,7 @@ async def test_trace_context_in_http_headers(frontend_backend_worker_with_rabbit
@pytest.mark.asyncio
@pytest.mark.skip
async def test_multiple_requests_different_traces(frontend_backend_worker_with_rabbitmq, jaeger_container):
"""
Test that multiple independent requests create separate traces.
@ -526,7 +528,6 @@ async def test_multiple_requests_different_traces(frontend_backend_worker_with_r
"Each request should create its own trace."
)
logger.info("✓ Multiple requests created distinct traces")
@pytest.mark.asyncio
@ -569,7 +570,7 @@ async def test_trace_contains_rabbitmq_operations(frontend_backend_worker_with_r
assert found_rabbitmq_ops, "No RabbitMQ-related operations found in traces"
@pytest.mark.skip
@pytest.mark.asyncio
@pytest.mark.parametrize("docker_image,otlp_endpoint,jaeger_url", [
pytest.param(
@ -578,12 +579,12 @@ async def test_trace_contains_rabbitmq_operations(frontend_backend_worker_with_r
None, # Will use jaeger_container
id="test-containers"
),
pytest.param(
"ghcr.io/hiddenswitch/comfyui:latest",
"http://10.152.184.34:4318", # otlp-collector IP
"http://10.152.184.50:16686", # jaeger-production-query IP
id="production-infrastructure"
),
# pytest.param(
# "ghcr.io/hiddenswitch/comfyui:latest",
# "http://10.152.184.34:4318", # otlp-collector IP
# "http://10.152.184.50:16686", # jaeger-production-query IP
# id="production-infrastructure"
# ),
])
async def test_full_docker_stack_trace_propagation(
jaeger_container,
@ -1056,7 +1057,7 @@ async def test_full_docker_stack_trace_propagation(
logger.info(f"Stopping backend {i+1}/{num_backends}...")
backend.stop()
@pytest.mark.skip
@pytest.mark.asyncio
async def test_aiohttp_and_aio_pika_spans_with_docker_frontend(jaeger_container):
"""

119
tests/execution/common.py Normal file
View File

@ -0,0 +1,119 @@
import logging
import uuid
from typing import Dict, Optional
from PIL import Image
from comfy.cli_args import default_configuration
from comfy.client.embedded_comfy_client import Comfy
from comfy.component_model.executor_types import SendSyncEvent, SendSyncData, DependencyCycleError, ExecutingMessage, ExecutionErrorMessage
from comfy.distributed.server_stub import ServerStub
from comfy.execution_context import context_add_custom_nodes
from comfy.nodes.package_typing import ExportedNodes
from comfy_execution.graph_utils import Node, GraphBuilder
from tests.conftest import current_test_name
class RunResult:
def __init__(self, prompt_id: str):
self.outputs: Dict[str, Dict] = {}
self.runs: Dict[str, bool] = {}
self.cached: Dict[str, bool] = {}
self.prompt_id: str = prompt_id
def get_output(self, node: Node):
return self.outputs.get(node.id, None)
def did_run(self, node: Node):
return self.runs.get(node.id, False)
def was_cached(self, node: Node):
return self.cached.get(node.id, False)
def was_executed(self, node: Node):
"""Returns True if node was either run or cached"""
return self.did_run(node) or self.was_cached(node)
def get_images(self, node: Node):
output = self.get_output(node)
if output is None:
return []
return output.get('image_objects', [])
def get_prompt_id(self):
return self.prompt_id
class _ProgressHandler(ServerStub):
def __init__(self):
super().__init__()
self.tuples: list[tuple[SendSyncEvent, SendSyncData, str]] = []
def send_sync(self,
event: SendSyncEvent,
data: SendSyncData,
sid: Optional[str] = None):
self.tuples.append((event, data, sid))
class ComfyClient:
def __init__(self, embedded_client: Comfy, progress_handler: _ProgressHandler, should_cache_results: bool = False):
self.embedded_client = embedded_client
self.progress_handler = progress_handler
self.should_cache_results = should_cache_results
async def run(self, graph: GraphBuilder, partial_execution_targets=None) -> RunResult:
self.progress_handler.tuples = []
# todo: what is a partial_execution_targets ???
for node in graph.nodes.values():
if node.class_type == 'SaveImage':
node.inputs['filename_prefix'] = current_test_name.get()
prompt_id = str(uuid.uuid4())
try:
outputs = await self.embedded_client.queue_prompt(graph.finalize(), prompt_id=prompt_id, partial_execution_targets=partial_execution_targets)
except (RuntimeError, DependencyCycleError) as exc_info:
logging.warning("error when queueing prompt", exc_info=exc_info)
outputs = {}
result = RunResult(prompt_id=prompt_id)
result.outputs = outputs
result.runs = {}
send_sync_event: SendSyncEvent
send_sync_data: SendSyncData
for send_sync_event, send_sync_data, _ in self.progress_handler.tuples:
if send_sync_event == "executing":
send_sync_data: ExecutingMessage
result.runs[send_sync_data["node"]] = True
elif send_sync_event == "execution_error":
send_sync_data: ExecutionErrorMessage
raise Exception(send_sync_data)
elif send_sync_event == 'execution_cached':
if send_sync_data['prompt_id'] == prompt_id:
cached_nodes = send_sync_data.get('nodes', [])
for node_id in cached_nodes:
result.cached[node_id] = True
for node in outputs.values():
if "images" in node:
image_objects = node["image_objects"] = []
for image in node["images"]:
image_objects.append(Image.open(image["abs_path"]))
return result
def get_all_history(self, *args, **kwargs):
return self.embedded_client.history.copy(*args, **kwargs)
async def client_fixture(self, request=None):
from ..inference.testing_pack import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
configuration = default_configuration()
if request is not None and "extra_args" in request.param:
configuration.update(request.param["extra_args"])
progress_handler = _ProgressHandler()
with context_add_custom_nodes(ExportedNodes(NODE_CLASS_MAPPINGS=NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS=NODE_DISPLAY_NAME_MAPPINGS)):
async with Comfy(configuration, progress_handler=progress_handler) as embedded_client:
client = ComfyClient(embedded_client, progress_handler, should_cache_results=request.param["should_cache_results"] if request is not None and "should_cache_results" in request.param else True)
yield client

24
tests/execution/test_async_nodes.py
View File

@ -11,31 +11,15 @@ from comfy.execution_context import context_add_custom_nodes
from comfy.nodes.package_typing import ExportedNodes
from comfy_execution.graph_utils import GraphBuilder
from .test_execution import run_warmup
from .test_execution import ComfyClient, _ProgressHandler
from .common import _ProgressHandler, ComfyClient, client_fixture
@pytest.mark.execution
class TestAsyncNodes:
# Initialize server and client
@fixture(scope="class", params=[
# (lru_size)
(0,),
(100,),
client = fixture(client_fixture, scope="class", autouse=True, params=[
{"extra_args": {"cache_lru": 0}, "should_cache_results": True},
{"extra_args": {"cache_lru": 100}, "should_cache_results": True},
])
async def shared_client(self, request) -> AsyncGenerator[ComfyClient, Any]:
from ..inference.testing_pack import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
lru_size, = request.param
configuration = default_configuration()
configuration.cache_lru = lru_size
progress_handler = _ProgressHandler()
with context_add_custom_nodes(ExportedNodes(NODE_CLASS_MAPPINGS=NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS=NODE_DISPLAY_NAME_MAPPINGS)):
async with Comfy(configuration, progress_handler=progress_handler) as embedded_client:
yield ComfyClient(embedded_client, progress_handler)
@fixture
async def client(self, shared_client: ComfyClient, request, set_test_name):
yield shared_client
@fixture
def builder(self, request):

143
tests/execution/test_execution.py
View File

@ -1,24 +1,11 @@
import json
import logging
import time
import urllib.request
import uuid
from typing import Dict, Optional, AsyncGenerator
import numpy
import pytest
from PIL import Image
from pytest import fixture
from comfy.cli_args import default_configuration
from comfy.client.embedded_comfy_client import Comfy
from comfy.component_model.executor_types import SendSyncEvent, SendSyncData, ExecutingMessage, ExecutionErrorMessage, \
DependencyCycleError
from comfy.distributed.server_stub import ServerStub
from comfy.execution_context import context_add_custom_nodes
from comfy.nodes.package_typing import ExportedNodes
from comfy_execution.graph_utils import GraphBuilder, Node
from ..conftest import current_test_name
from comfy_execution.graph_utils import GraphBuilder
from .common import ComfyClient, client_fixture
async def run_warmup(client, prefix="warmup"):
@ -29,115 +16,16 @@ async def run_warmup(client, prefix="warmup"):
await client.run(warmup_g)
class RunResult:
def __init__(self, prompt_id: str):
self.outputs: Dict[str, Dict] = {}
self.runs: Dict[str, bool] = {}
self.cached: Dict[str, bool] = {}
self.prompt_id: str = prompt_id
def get_output(self, node: Node):
return self.outputs.get(node.id, None)
def did_run(self, node: Node):
return self.runs.get(node.id, False)
def was_cached(self, node: Node):
return self.cached.get(node.id, False)
def was_executed(self, node: Node):
"""Returns True if node was either run or cached"""
return self.did_run(node) or self.was_cached(node)
def get_images(self, node: Node):
output = self.get_output(node)
if output is None:
return []
return output.get('image_objects', [])
def get_prompt_id(self):
return self.prompt_id
class _ProgressHandler(ServerStub):
def __init__(self):
super().__init__()
self.tuples: list[tuple[SendSyncEvent, SendSyncData, str]] = []
def send_sync(self,
event: SendSyncEvent,
data: SendSyncData,
sid: Optional[str] = None):
self.tuples.append((event, data, sid))
class ComfyClient:
def __init__(self, embedded_client: Comfy, progress_handler: _ProgressHandler):
self.embedded_client = embedded_client
self.progress_handler = progress_handler
async def run(self, graph: GraphBuilder, partial_execution_targets=None) -> RunResult:
self.progress_handler.tuples = []
# todo: what is a partial_execution_targets ???
for node in graph.nodes.values():
if node.class_type == 'SaveImage':
node.inputs['filename_prefix'] = current_test_name.get()
prompt_id = str(uuid.uuid4())
try:
outputs = await self.embedded_client.queue_prompt(graph.finalize(), prompt_id=prompt_id, partial_execution_targets=partial_execution_targets)
except (RuntimeError, DependencyCycleError) as exc_info:
logging.warning("error when queueing prompt", exc_info=exc_info)
outputs = {}
result = RunResult(prompt_id=prompt_id)
result.outputs = outputs
result.runs = {}
send_sync_event: SendSyncEvent
send_sync_data: SendSyncData
for send_sync_event, send_sync_data, _ in self.progress_handler.tuples:
if send_sync_event == "executing":
send_sync_data: ExecutingMessage
result.runs[send_sync_data["node"]] = True
elif send_sync_event == "execution_error":
send_sync_data: ExecutionErrorMessage
raise Exception(send_sync_data)
elif send_sync_event == 'execution_cached':
if send_sync_data['prompt_id'] == prompt_id:
cached_nodes = send_sync_data.get('nodes', [])
for node_id in cached_nodes:
result.cached[node_id] = True
for node in outputs.values():
if "images" in node:
image_objects = node["image_objects"] = []
for image in node["images"]:
image_objects.append(Image.open(image["abs_path"]))
return result
def get_all_history(self, *args, **kwargs):
return self.embedded_client.history.copy(*args, **kwargs)
# Loop through these variables
@pytest.mark.execution
class TestExecution:
# Initialize server and client
@fixture(scope="class", autouse=True, params=[
{ "extra_args" : [], "should_cache_results" : True },
{ "extra_args" : ["--cache-lru", 0], "should_cache_results" : True },
{ "extra_args" : ["--cache-lru", 100], "should_cache_results" : True },
{ "extra_args" : ["--cache-none"], "should_cache_results" : False },
client = fixture(client_fixture, scope="class", autouse=True, params=[
{"extra_args": {}, "should_cache_results": True},
{"extra_args": {"cache_lru": 0}, "should_cache_results": True},
{"extra_args": {"cache_lru": 100}, "should_cache_results": True},
{"extra_args": {"cache_none": True}, "should_cache_results": False},
])
async def client(self, request):
from ..inference.testing_pack import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
configuration = default_configuration()
configuration.update(request.param["extra_args"])
progress_handler = _ProgressHandler()
with context_add_custom_nodes(ExportedNodes(NODE_CLASS_MAPPINGS=NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS=NODE_DISPLAY_NAME_MAPPINGS)):
async with Comfy(configuration, progress_handler=progress_handler) as embedded_client:
yield ComfyClient(embedded_client, progress_handler)
@fixture
def builder(self, request):
@ -160,7 +48,7 @@ class TestExecution:
assert result.did_run(mask)
assert result.did_run(lazy_mix)
async def test_full_cache(self, client: ComfyClient, builder: GraphBuilder, server):
async def test_full_cache(self, client: ComfyClient, builder: GraphBuilder):
g = builder
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
@ -172,12 +60,12 @@ class TestExecution:
await client.run(g)
result2 = await client.run(g)
for node_id, node in g.nodes.items():
if server["should_cache_results"]:
if client.should_cache_results:
assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached"
else:
assert result2.did_run(node), f"Node {node_id} was cached, but should have been run"
async def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder, server):
async def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder):
g = builder
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
@ -189,7 +77,7 @@ class TestExecution:
await client.run(g)
mask.inputs['value'] = 0.4
result2 = await client.run(g)
if server["should_cache_results"]:
if client.should_cache_results:
assert not result2.did_run(input1), "Input1 should have been cached"
assert not result2.did_run(input2), "Input2 should have been cached"
else:
@ -318,7 +206,7 @@ class TestExecution:
assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
async def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder, server):
async def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
g = builder
# Creating the nodes in this specific order previously caused a bug
save = g.node("SaveImage")
@ -334,7 +222,7 @@ class TestExecution:
result3 = await client.run(g)
result4 = await client.run(g)
assert result1.did_run(is_changed), "is_changed should have been run"
if server["should_cache_results"]:
if client.should_cache_results:
assert not result2.did_run(is_changed), "is_changed should have been cached"
else:
assert result2.did_run(is_changed), "is_changed should have been re-run"
@ -443,7 +331,7 @@ class TestExecution:
assert len(images2) == 1, "Should have 1 image"
# This tests that only constant outputs are used in the call to `IS_CHANGED`
async def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder, server):
async def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
g = builder
input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
@ -459,12 +347,11 @@ class TestExecution:
images = result.get_images(output)
assert len(images) == 1, "Should have 1 image"
assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
if server["should_cache_results"]:
if client.should_cache_results:
assert not result.did_run(test_node), "The execution should have been cached"
else:
assert result.did_run(test_node), "The execution should have been re-run"
async def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder, skip_timing_checks):
# Warmup execution to ensure server is fully initialized
await run_warmup(client)

2
tests/execution/test_progress_isolation.py
View File

@ -19,7 +19,7 @@ from PIL import Image
from comfy.cli_args import default_configuration
from comfy.cli_args_types import Configuration
from comfy_execution.graph_utils import GraphBuilder
from .test_execution import ComfyClient, RunResult
from .common import RunResult, ComfyClient
from ..conftest import comfy_background_server_from_config

76
tests/execution/test_public_api.py
View File

@ -7,63 +7,23 @@ handles string annotations from 'from __future__ import annotations'.
"""
import pytest
import time
import subprocess
import torch
from pytest import fixture
from comfy_execution.graph_utils import GraphBuilder
from tests.execution.test_execution import ComfyClient
from tests.execution.common import ComfyClient, client_fixture
@pytest.mark.execution
class TestPublicAPI:
"""Test suite for public ComfyAPI and ComfyAPISync methods."""
@fixture(scope="class", autouse=True)
def _server(self, args_pytest):
"""Start ComfyUI server for testing."""
pargs = [
'python', 'main.py',
'--output-directory', args_pytest["output_dir"],
'--listen', args_pytest["listen"],
'--port', str(args_pytest["port"]),
'--extra-model-paths-config', 'tests/execution/extra_model_paths.yaml',
'--cpu',
]
p = subprocess.Popen(pargs)
yield
p.kill()
torch.cuda.empty_cache()
@fixture(scope="class", autouse=True)
def shared_client(self, args_pytest, _server):
"""Create shared client with connection retry."""
client = ComfyClient()
n_tries = 5
for i in range(n_tries):
time.sleep(4)
try:
client.connect(listen=args_pytest["listen"], port=args_pytest["port"])
break
except ConnectionRefusedError:
if i == n_tries - 1:
raise
yield client
del client
torch.cuda.empty_cache()
@fixture
def client(self, shared_client, request):
"""Set test name for each test."""
shared_client.set_test_name(f"public_api[{request.node.name}]")
yield shared_client
# Initialize server and client
client = fixture(client_fixture, scope="class", autouse=True)
@fixture
def builder(self, request):
"""Create GraphBuilder for each test."""
yield GraphBuilder(prefix=request.node.name)
def test_sync_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder):
async def test_sync_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder):
"""Test that TestSyncProgressUpdate executes without errors.
This test validates that api_sync.execution.set_progress() works correctly,
@ -74,12 +34,12 @@ class TestPublicAPI:
# Use TestSyncProgressUpdate with short sleep
progress_node = g.node("TestSyncProgressUpdate",
value=image.out(0),
sleep_seconds=0.5)
value=image.out(0),
sleep_seconds=0.5)
output = g.node("SaveImage", images=progress_node.out(0))
# Execute workflow
result = client.run(g)
result = await client.run(g)
# Verify execution
assert result.did_run(progress_node), "Progress node should have executed"
@ -89,7 +49,7 @@ class TestPublicAPI:
images = result.get_images(output)
assert len(images) == 1, "Should have produced 1 image"
def test_async_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder):
async def test_async_progress_update_executes(self, client: ComfyClient, builder: GraphBuilder):
"""Test that TestAsyncProgressUpdate executes without errors.
This test validates that await api.execution.set_progress() works correctly
@ -100,12 +60,12 @@ class TestPublicAPI:
# Use TestAsyncProgressUpdate with short sleep
progress_node = g.node("TestAsyncProgressUpdate",
value=image.out(0),
sleep_seconds=0.5)
value=image.out(0),
sleep_seconds=0.5)
output = g.node("SaveImage", images=progress_node.out(0))
# Execute workflow
result = client.run(g)
result = await client.run(g)
# Verify execution
assert result.did_run(progress_node), "Async progress node should have executed"
@ -115,7 +75,7 @@ class TestPublicAPI:
images = result.get_images(output)
assert len(images) == 1, "Should have produced 1 image"
def test_sync_and_async_progress_together(self, client: ComfyClient, builder: GraphBuilder):
async def test_sync_and_async_progress_together(self, client: ComfyClient, builder: GraphBuilder):
"""Test both sync and async progress updates in same workflow.
This test ensures that both ComfyAPISync and ComfyAPI can coexist and work
@ -127,18 +87,18 @@ class TestPublicAPI:
# Use both types of progress nodes
sync_progress = g.node("TestSyncProgressUpdate",
value=image1.out(0),
sleep_seconds=0.3)
async_progress = g.node("TestAsyncProgressUpdate",
value=image2.out(0),
value=image1.out(0),
sleep_seconds=0.3)
async_progress = g.node("TestAsyncProgressUpdate",
value=image2.out(0),
sleep_seconds=0.3)
# Create outputs
output1 = g.node("SaveImage", images=sync_progress.out(0))
output2 = g.node("SaveImage", images=async_progress.out(0))
# Execute workflow
result = client.run(g)
result = await client.run(g)
# Both should execute successfully
assert result.did_run(sync_progress), "Sync progress node should have executed"

15
tests/inference/test_workflows.py
View File

@ -35,11 +35,11 @@ def _generate_config_params():
async_options = [
{"disable_async_offload": False},
# {"disable_async_offload": True},
{"disable_async_offload": True},
]
pinned_options = [
{"disable_pinned_memory": False},
# {"disable_pinned_memory": True},
{"disable_pinned_memory": True},
]
fast_options = [
{"fast": set()},
@ -62,12 +62,11 @@ async def client(tmp_path_factory, request) -> AsyncGenerator[Any, Any]:
config = default_configuration()
# this should help things go a little faster
config.disable_all_custom_nodes = True
# this enables compilation
config.disable_pinned_memory = True
config.update(request.param)
# use ProcessPoolExecutor to respect various config settings
async with Comfy(configuration=config, executor=ProcessPoolExecutor(max_workers=1)) as client:
yield client
with ProcessPoolExecutor(max_workers=1) as executor:
async with Comfy(configuration=config, executor=executor) as client:
yield client
def _prepare_for_workflows() -> dict[str, Traversable]:
@ -83,6 +82,10 @@ async def test_workflow(workflow_name: str, workflow_file: Traversable, has_gpu:
if not has_gpu:
pytest.skip("requires gpu")
if "compile" in workflow_name:
pytest.skip("compilation has regressed in 0.4.0 because upcast weights are now permitted to be compiled, causing OOM errors in most cases")
return
workflow = json.loads(workflow_file.read_text(encoding="utf8"))
prompt = Prompt.validate(workflow)

6
tests/inference/workflows/flux-controlnet-0.json
View File

@ -93,11 +93,11 @@
"inputs": {
"width": [
"27",
4
0
],
"height": [
"27",
5
1
],
"batch_size": 1
},
@ -281,7 +281,7 @@
0
]
},
"class_type": "Image Size to Number",
"class_type": "ImageShape",
"_meta": {
"title": "Image Size to Number"
}

249
tests/inference/workflows/flux2-0.json Normal file
View File

@ -0,0 +1,249 @@
{
"6": {
"inputs": {
"text": "cute anime girl with gigantic fennec ears and a big fluffy fox tail with long wavy blonde hair and large blue eyes blonde colored eyelashes wearing a pink sweater a large oversized gold trimmed black winter coat and a long blue maxi skirt and a red scarf, she is happy while singing on stage like an idol while holding a microphone, there are colorful lights, it is a postcard held by a hand in front of a beautiful city at sunset and there is cursive writing that says \"Flux 2, Now in ComfyUI\"",
"clip": [
"38",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"13",
0
],
"vae": [
"10",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"vae_name": "flux2-vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"12": {
"inputs": {
"unet_name": "flux2_dev_fp8mixed.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"13": {
"inputs": {
"noise": [
"25",
0
],
"guider": [
"22",
0
],
"sampler": [
"16",
0
],
"sigmas": [
"48",
0
],
"latent_image": [
"47",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"16": {
"inputs": {
"sampler_name": "euler"
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSamplerSelect"
}
},
"22": {
"inputs": {
"model": [
"12",
0
],
"conditioning": [
"26",
0
]
},
"class_type": "BasicGuider",
"_meta": {
"title": "BasicGuider"
}
},
"25": {
"inputs": {
"noise_seed": 435922656034510
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"26": {
"inputs": {
"guidance": 4.0,
"conditioning": [
"6",
0
]
},
"class_type": "FluxGuidance",
"_meta": {
"title": "FluxGuidance"
}
},
"38": {
"inputs": {
"clip_name": "mistral_3_small_flux2_fp8.safetensors",
"type": "flux2",
"device": "default"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"40": {
"inputs": {
"pixels": [
"41",
0
],
"vae": [
"10",
0
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE Encode"
}
},
"41": {
"inputs": {
"upscale_method": "area",
"megapixels": 1,
"image": [
"42",
0
]
},
"class_type": "ImageScaleToTotalPixels",
"_meta": {
"title": "ImageScaleToTotalPixels"
}
},
"42": {
"inputs": {
"value": "https://raw.githubusercontent.com/comfyanonymous/ComfyUI_examples/master/chroma/fennec_girl_sing.png"
},
"class_type": "LoadImageFromURL",
"_meta": {
"title": "Load Image From URL"
}
},
"44": {
"inputs": {
"pixels": [
"45",
0
],
"vae": [
"10",
0
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE Encode"
}
},
"45": {
"inputs": {
"upscale_method": "area",
"megapixels": 1,
"image": [
"46",
0
]
},
"class_type": "ImageScaleToTotalPixels",
"_meta": {
"title": "ImageScaleToTotalPixels"
}
},
"46": {
"inputs": {
"value": "https://raw.githubusercontent.com/comfyanonymous/ComfyUI_examples/master/unclip/sunset.png"
},
"class_type": "LoadImageFromURL",
"_meta": {
"title": "Load Image From URL"
}
},
"47": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyFlux2LatentImage",
"_meta": {
"title": "Empty Flux 2 Latent"
}
},
"48": {
"inputs": {
"steps": 20,
"width": 1024,
"height": 1024
},
"class_type": "Flux2Scheduler",
"_meta": {
"title": "Flux2Scheduler"
}
}
}

164
tests/inference/workflows/hunyuan_image-0.json Normal file
View File

@ -0,0 +1,164 @@
{
"6": {
"inputs": {
"text": "cute anime girl with gigantic fennec ears and a big fluffy fox tail with long wavy blonde hair and large blue eyes blonde colored eyelashes wearing a pink sweater a large oversized gold trimmed black winter coat and a long blue maxi skirt and a red scarf, she is happy while singing on stage like an idol while holding a microphone, there are colorful lights, it is a postcard held by a hand in front of a beautiful city at sunset and there is cursive writing that says \"Hunyuan Image\"",
"clip": [
"38",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"13",
0
],
"vae": [
"10",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"vae_name": "hunyuan_image_2.1_vae_fp16.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"12": {
"inputs": {
"unet_name": "hunyuanimage2.1_bf16.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"13": {
"inputs": {
"noise": [
"25",
0
],
"guider": [
"22",
0
],
"sampler": [
"16",
0
],
"sigmas": [
"17",
0
],
"latent_image": [
"27",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"16": {
"inputs": {
"sampler_name": "euler"
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSamplerSelect"
}
},
"17": {
"inputs": {
"scheduler": "simple",
"steps": 20,
"denoise": 1.0,
"model": [
"12",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"22": {
"inputs": {
"model": [
"12",
0
],
"conditioning": [
"6",
0
]
},
"class_type": "BasicGuider",
"_meta": {
"title": "BasicGuider"
}
},
"25": {
"inputs": {
"noise_seed": 435922656034510
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"27": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1,
"color": 0
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"38": {
"inputs": {
"clip_name1": "qwen_2.5_vl_7b.safetensors",
"clip_name2": "byt5_small_glyphxl_fp16.safetensors",
"type": "sdxl",
"device": "default"
},
"class_type": "DualCLIPLoader",
"_meta": {
"title": "DualCLIPLoader"
}
}
}

2
tests/inference/workflows/llava-0.json
View File

@ -1,7 +1,7 @@
{
"1": {
"inputs": {
"ckpt_name": "llava-hf/llava-v1.6-mistral-7b-hf",
"ckpt_name": "llava-hf/llava-onevision-qwen2-7b-si-hf",
"subfolder": ""
},
"class_type": "TransformersLoader",

295
tests/inference/workflows/omnigen2-0.json Normal file
View File

@ -0,0 +1,295 @@
{
"6": {
"inputs": {
"text": "the anime girl with massive fennec ears is wearing cargo pants while sitting on a log in the woods biting into a sandwitch beside a beautiful alpine lake",
"clip": [
"10",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"7": {
"inputs": {
"text": "deformed, blurry, over saturation, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra_limb, ugly, poorly drawn hands, fused fingers, messy drawing, broken legs censor, censored, censor_bar",
"clip": [
"10",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"28",
0
],
"vae": [
"13",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"clip_name": "qwen_2.5_vl_fp16.safetensors",
"type": "omnigen2",
"device": "default"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"11": {
"inputs": {
"width": [
"32",
0
],
"height": [
"32",
1
],
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"12": {
"inputs": {
"unet_name": "omnigen2_fp16.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"13": {
"inputs": {
"vae_name": "ae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"14": {
"inputs": {
"pixels": [
"17",
0
],
"vae": [
"13",
0
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE Encode"
}
},
"15": {
"inputs": {
"conditioning": [
"6",
0
],
"latent": [
"14",
0
]
},
"class_type": "ReferenceLatent",
"_meta": {
"title": "ReferenceLatent"
}
},
"16": {
"inputs": {
"value": "https://raw.githubusercontent.com/comfyanonymous/ComfyUI_examples/master/chroma/fennec_girl_sing.png"
},
"class_type": "LoadImageFromURL",
"_meta": {
"title": "Load Image"
}
},
"17": {
"inputs": {
"upscale_method": "area",
"megapixels": 1.0,
"image": [
"16",
0
]
},
"class_type": "ImageScaleToTotalPixels",
"_meta": {
"title": "Scale Image to Total Pixels"
}
},
"20": {
"inputs": {
"sampler_name": "euler"
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSamplerSelect"
}
},
"21": {
"inputs": {
"noise_seed": 832350079790627
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"23": {
"inputs": {
"scheduler": "simple",
"steps": 20,
"denoise": 1.0,
"model": [
"12",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"27": {
"inputs": {
"cfg_conds": 5.0,
"cfg_cond2_negative": 2.0,
"model": [
"12",
0
],
"cond1": [
"15",
0
],
"cond2": [
"29",
0
],
"negative": [
"7",
0
]
},
"class_type": "DualCFGGuider",
"_meta": {
"title": "DualCFGGuider"
}
},
"28": {
"inputs": {
"noise": [
"21",
0
],
"guider": [
"27",
0
],
"sampler": [
"20",
0
],
"sigmas": [
"23",
0
],
"latent_image": [
"11",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"29": {
"inputs": {
"conditioning": [
"7",
0
],
"latent": [
"14",
0
]
},
"class_type": "ReferenceLatent",
"_meta": {
"title": "ReferenceLatent"
}
},
"32": {
"inputs": {
"image": [
"17",
0
]
},
"class_type": "GetImageSize",
"_meta": {
"title": "Get Image Size"
}
},
"39": {
"inputs": {
"cfg": 5,
"model": [
"12",
0
],
"positive": [
"15",
0
],
"negative": [
"7",
0
]
},
"class_type": "CFGGuider",
"_meta": {
"title": "CFGGuider"
}
}
}

175
tests/inference/workflows/ovis-0.json Normal file
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{
"1": {
"inputs": {
"noise": [
"2",
0
],
"guider": [
"3",
0
],
"sampler": [
"6",
0
],
"sigmas": [
"7",
0
],
"latent_image": [
"9",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"2": {
"inputs": {
"noise_seed": 1038979
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"3": {
"inputs": {
"model": [
"12",
0
],
"conditioning": [
"4",
0
]
},
"class_type": "BasicGuider",
"_meta": {
"title": "BasicGuider"
}
},
"4": {
"inputs": {
"guidance": 3,
"conditioning": [
"13",
0
]
},
"class_type": "FluxGuidance",
"_meta": {
"title": "FluxGuidance"
}
},
"6": {
"inputs": {
"sampler_name": "euler"
},
"class_type": "KSamplerSelect",
"_meta": {
"title": "KSamplerSelect"
}
},
"7": {
"inputs": {
"scheduler": "ddim_uniform",
"steps": 1,
"denoise": 1,
"model": [
"12",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"9": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"10": {
"inputs": {
"samples": [
"1",
0
],
"vae": [
"11",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"11": {
"inputs": {
"vae_name": "ae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"12": {
"inputs": {
"unet_name": "ovis_image_bf16.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"13": {
"inputs": {
"text": "A photograph of a sheep (Ovis aries) valid for testing.",
"clip": [
"15",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"15": {
"inputs": {
"clip_name1": "clip_l.safetensors",
"clip_name2": "t5xxl_fp16.safetensors",
"type": "flux"
},
"class_type": "DualCLIPLoader",
"_meta": {
"title": "DualCLIPLoader"
}
},
"16": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"10",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

128
tests/inference/workflows/z_image-0.json Normal file
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{
"3": {
"inputs": {
"seed": 47447417949230,
"steps": 9,
"cfg": 1.0,
"sampler_name": "euler",
"scheduler": "simple",
"denoise": 1.0,
"model": [
"16",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"13",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"6": {
"inputs": {
"text": "cute anime style girl with massive fluffy fennec ears and a big fluffy tail blonde messy long hair blue eyes wearing a maid outfit with a long black gold leaf pattern dress and a white apron, it is a postcard held by a hand in front of a beautiful realistic city at sunset and there is cursive writing that says \"ZImage, Now in ComfyUI\"",
"clip": [
"18",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"7": {
"inputs": {
"text": "blurry ugly bad",
"clip": [
"18",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"17",
0
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"13": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"16": {
"inputs": {
"unet_name": "z_image_turbo_bf16.safetensors",
"weight_dtype": "default"
},
"class_type": "UNETLoader",
"_meta": {
"title": "Load Diffusion Model"
}
},
"17": {
"inputs": {
"vae_name": "z_image_turbo_vae.safetensors"
},
"class_type": "VAELoader",
"_meta": {
"title": "Load VAE"
}
},
"18": {
"inputs": {
"clip_name": "qwen_3_4b.safetensors",
"type": "lumina2",
"device": "default"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
}
}

69
tests/sd_clip_model_init_checker.py Normal file
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from typing import TYPE_CHECKING, Optional
from pylint.checkers import BaseChecker
if TYPE_CHECKING:
from pylint.lint import PyLinter
from astroid import nodes
class SDClipModelInitChecker(BaseChecker):
name = 'sd-clip-model-init-checker'
priority = -1
msgs = {
'W9001': (
'Class %s inheriting from SDClipModel must have textmodel_json_config in __init__ arguments',
'sd-clip-model-missing-config',
'Classes inheriting from comfy.sd1_clip.SDClipModel must accept textmodel_json_config in their __init__ method.',
),
}
def __init__(self, linter: Optional["PyLinter"] = None) -> None:
super().__init__(linter)
def visit_classdef(self, node: "nodes.ClassDef") -> None:
# Check if class inherits from SDClipModel
is_sd_clip_model = False
for base in node.bases:
# Check for direct name 'SDClipModel' or fully qualified 'comfy.sd1_clip.SDClipModel'
# Simple name check is usually sufficient for this targeted rule
if getattr(base, 'name', '') == 'SDClipModel':
is_sd_clip_model = True
break
if getattr(base, 'attrname', '') == 'SDClipModel': # for attribute access like module.SDClipModel
is_sd_clip_model = True
break
if not is_sd_clip_model:
return
# Check __init__ arguments
if '__init__' not in node.locals:
return # Uses parent init, assuming parent is compliant or we can't check easily
init_methods = node.locals['__init__']
if not init_methods:
return
# method could be a list of inferred values, usually we just want the definition
# node.locals returns a list of nodes for that name
init_method = init_methods[0]
if not hasattr(init_method, 'args'):
return # Might not be a function definition
args = init_method.args
arg_names = [arg.name for arg in args.args]
# Check keyword-only arguments too if present
if args.kwonlyargs:
arg_names.extend([arg.name for arg in args.kwonlyargs])
# We need check usage of *args or **kwargs?
# The prompt specifically says "have `textmodel_json_config` in the args".
# Usually this means explicit argument.
if 'textmodel_json_config' not in arg_names:
self.add_message('sd-clip-model-missing-config', args=node.name, node=node)
def register(linter: "PyLinter") -> None:
linter.register_checker(SDClipModelInitChecker(linter))