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Code Issues Actions 10 Packages Projects Releases Wiki Activity

Test improvements and fixes

Browse Source 
- move workflows to distinct json files
 - add the comfy-org workflows for testing
 - fix issues where workflows from windows users would not be compatible
   with backends running on linux or macos in light of separator
   differences. Because this codebase uses get_or_download wherever
   checkpoints, models, etc. are used, this is the only place where the
   comparison is gracefully handled for downloading. Validation code
   will correctly convert backslashes to forward slashes, assuming that
   100% of the places they are used and when comparing with a list, they
   are intended to be paths and not strict symbols
This commit is contained in:
doctorpangloss 2024-08-05 15:53:47 -07:00
parent 3dbdfcef5e
commit 2bc95c1711
17 changed files with 1501 additions and 548 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
comfy/cmd/execution.py
View File

@ -9,6 +9,8 @@ import threading
import time
import traceback
import typing
from os import PathLike
from pathlib import PurePath
from typing import List, Optional, Tuple
import lazy_object_proxy
@ -23,6 +25,7 @@ from ..component_model.executor_types import ExecutorToClientProgress, Validatio
ValidationErrorDict, NodeErrorsDictValue, ValidationErrorExtraInfoDict, FormattedValue, RecursiveExecutionTuple, \
RecursiveExecutionErrorDetails, RecursiveExecutionErrorDetailsInterrupted, ExecutionResult, DuplicateNodeError, \
HistoryResultDict
from ..component_model.files import canonicalize_path
from ..component_model.queue_types import QueueTuple, HistoryEntry, QueueItem, MAXIMUM_HISTORY_SIZE, ExecutionStatus
from ..execution_context import new_execution_context, ExecutionContext
from ..nodes.package import import_all_nodes_in_workspace
@ -748,6 +751,9 @@ def validate_inputs(prompt, item, validated: typing.Dict[str, ValidateInputsTupl
continue
if isinstance(type_input, list):
if "\\" in val:
# try to normalize paths for comparison purposes
val = canonicalize_path(val)
if val not in type_input:
input_config = info
list_info = ""

7
comfy/component_model/files.py
View File

@ -3,6 +3,7 @@ from __future__ import annotations
import json
import os
from importlib import resources as resources
from pathlib import PurePath
from typing import Optional
@ -52,3 +53,9 @@ def get_package_as_path(package: str, subdir: Optional[str] = None) -> str:
if traversable.is_dir():
return str(traversable)
raise ValueError(f"Could not find path for package={package}, subdir={subdir}, traversable={str(traversable)}")
def canonicalize_path(path: os.PathLike | str | None) -> str | None:
if path is None:
return None
return PurePath(path).as_posix()

20
comfy/model_downloader.py
View File

@ -23,6 +23,7 @@ from .cli_args import args
from .cmd import folder_paths
from .cmd.folder_paths import add_model_folder_path, supported_pt_extensions
from .component_model.deprecation import _deprecate_method
from .component_model.files import canonicalize_path
from .interruption import InterruptProcessingException
from .model_downloader_types import CivitFile, HuggingFile, CivitModelsGetResponse, CivitFile_, Downloadable, UrlFile
from .utils import ProgressBar, comfy_tqdm
@ -37,13 +38,14 @@ def get_filename_list_with_downloadable(folder_name: str, known_files: Optional[
existing = frozenset(folder_paths.get_filename_list(folder_name))
downloadable = frozenset() if args.disable_known_models else frozenset(str(f) for f in known_files)
return sorted(list(existing | downloadable))
return list(map(canonicalize_path, sorted(list(existing | downloadable))))
def get_or_download(folder_name: str, filename: str, known_files: Optional[List[Downloadable] | KnownDownloadables] = None) -> Optional[str]:
if known_files is None:
known_files = _get_known_models_for_folder_name(folder_name)
filename = canonicalize_path(filename)
path = folder_paths.get_full_path(folder_name, filename)
if path is None and not args.disable_known_models:
@ -52,7 +54,10 @@ def get_or_download(folder_name: str, filename: str, known_files: Optional[List[
this_model_directory = folder_paths.get_folder_paths(folder_name)[0]
known_file: Optional[HuggingFile | CivitFile] = None
for candidate in known_files:
if str(candidate) == filename or candidate.filename == filename or filename in candidate.alternate_filenames or filename == candidate.save_with_filename:
if (canonicalize_path(str(candidate)) == filename
or canonicalize_path(candidate.filename) == filename
or filename in list(map(canonicalize_path, candidate.alternate_filenames))
or filename == canonicalize_path(candidate.save_with_filename)):
known_file = candidate
break
if known_file is None:
@ -133,14 +138,12 @@ def get_or_download(folder_name: str, filename: str, known_files: Optional[List[
try:
os.makedirs(this_model_directory, exist_ok=True)
os.symlink(path, destination_link)
except Exception:
except Exception as exc_info:
try:
os.link(path, destination_link)
except Exception as exc_info:
link_exc_info = exc_info
if cache_hit:
shutil.copyfile(path, destination_link)
except Exception as exc_info:
link_exc_info = exc_info
if link_exc_info is not None:
logging.error(f"Failed to link file with alternative download save name in a way that is compatible with Hugging Face caching {repr(known_file)}. If cache_hit={cache_hit} is True, the file was copied into the destination.", exc_info=exc_info)
@ -155,7 +158,7 @@ def get_or_download(folder_name: str, filename: str, known_files: Optional[List[
civit_file: CivitFile_
for civit_file in chain.from_iterable(version['files'] for version in model_info['modelVersions']):
if civit_file['name'] == filename:
if canonicalize_path(civit_file['name']) == filename:
url = civit_file['downloadUrl']
break
elif isinstance(known_file, UrlFile):
@ -399,6 +402,7 @@ KNOWN_APPROX_VAES: Final[KnownDownloadables] = KnownDownloadables([
KNOWN_VAES: Final[KnownDownloadables] = KnownDownloadables([
HuggingFile("stabilityai/sdxl-vae", "sdxl_vae.safetensors"),
HuggingFile("stabilityai/sd-vae-ft-mse-original", "vae-ft-mse-840000-ema-pruned.safetensors"),
HuggingFile("black-forest-labs/FLUX.1-schnell", "ae.sft"),
], folder_name="vae")
KNOWN_HUGGINGFACE_MODEL_REPOS: Final[Set[str]] = {
@ -409,7 +413,9 @@ KNOWN_HUGGINGFACE_MODEL_REPOS: Final[Set[str]] = {
}
KNOWN_UNET_MODELS: Final[KnownDownloadables] = KnownDownloadables([
HuggingFile("ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet-Comfyui.fp16.safetensors")
HuggingFile("ByteDance/Hyper-SD", "Hyper-SDXL-1step-Unet-Comfyui.fp16.safetensors"),
HuggingFile("black-forest-labs/FLUX.1-schnell", "flux1-schnell.sft"),
HuggingFile("black-forest-labs/FLUX.1-dev", "flux1-dev.sft"),
], folder_name="unet")
KNOWN_CLIP_MODELS: Final[KnownDownloadables] = KnownDownloadables([

3
comfy_extras/nodes/nodes_flux.py
View File

@ -1,4 +1,5 @@
import node_helpers
from comfy import node_helpers
class CLIPTextEncodeFlux:
@classmethod

2
comfy_extras/nodes/nodes_language.py
View File

@ -249,7 +249,7 @@ class TransformersLoader(CustomNode):
try:
model = AutoModel.from_pretrained(**from_pretrained_kwargs)
except:
except Exception as exc_info:
# not yet supported by automodel
model = LlavaNextForConditionalGeneration.from_pretrained(**from_pretrained_kwargs)

1
requirements-dev.txt
View File

@ -1,5 +1,6 @@
pytest
pytest-asyncio
pytest-mock
websocket-client==1.6.1
PyInstaller
testcontainers

554
tests/inference/test_workflows.py
View File

@ -1,545 +1,14 @@
import importlib.resources
import json
from importlib.abc import Traversable
import pytest
from comfy.api.components.schema.prompt import Prompt
from comfy.client.embedded_comfy_client import EmbeddedComfyClient
from comfy.model_downloader import add_known_models, KNOWN_LORAS
from comfy.model_downloader_types import CivitFile
_workflows = {
"hunyuandit_1": {
"3": {
"inputs": {
"seed": 377072733774956,
"steps": 1,
"cfg": 6,
"sampler_name": "euler",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "hunyuan_dit_1.2.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "a bottle with a 彩虹星系 inside it on top of a wooden table on a snowy mountain top with the ocean and clouds in the background\n",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"7": {
"inputs": {
"text": "",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
},
"audio_1": {
"14": {
"inputs": {
"ckpt_name": "stable_audio_open_1.0.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"15": {
"inputs": {
"clip_name": "t5_base.safetensors",
"type": "stable_audio"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"16": {
"inputs": {
"text": "hard bop, upright bass, slappy bass, low frequencies, drum kit brushed hi-hat, snare with ghost notes, syncopated, groove",
"clip": [
"15",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"17": {
"inputs": {
"text": "",
"clip": [
"15",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"19": {
"inputs": {
"cfg": 7,
"model": [
"14",
0
],
"positive": [
"16",
0
],
"negative": [
"17",
0
]
},
"class_type": "CFGGuider",
"_meta": {
"title": "CFGGuider"
}
},
"21": {
"inputs": {
"noise_seed": 600769511872395
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"22": {
"inputs": {
"noise": [
"21",
0
],
"guider": [
"19",
0
],
"sampler": [
"29",
0
],
"sigmas": [
"24",
0
],
"latent_image": [
"30",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"24": {
"inputs": {
"scheduler": "exponential",
"steps": 20,
"denoise": 1,
"model": [
"14",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"26": {
"inputs": {
"samples": [
"22",
1
],
"vae": [
"14",
2
]
},
"class_type": "VAEDecodeAudio",
"_meta": {
"title": "VAEDecodeAudio"
}
},
"27": {
"inputs": {
"filename_prefix": "audio/objectobject",
"audio": [
"26",
0
]
},
"class_type": "SaveAudio",
"_meta": {
"title": "SaveAudio"
}
},
"29": {
"inputs": {
"version": "regular"
},
"class_type": "SamplerEulerCFGpp",
"_meta": {
"title": "SamplerEulerCFG++"
}
},
"30": {
"inputs": {
"seconds": 47.6
},
"class_type": "EmptyLatentAudio",
"_meta": {
"title": "EmptyLatentAudio"
}
},
"31": {
"inputs": {
"filename_prefix": "latents/ComfyUI",
"samples": [
"22",
1
]
},
"class_type": "SaveLatent",
"_meta": {
"title": "SaveLatent"
}
}
},
"auraflow_1": {
"1": {
"inputs": {
"ckpt_name": "aura_flow_0.1.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"2": {
"inputs": {
"shift": 1.73,
"model": [
"1",
0
]
},
"class_type": "ModelSamplingAuraFlow",
"_meta": {
"title": "ModelSamplingAuraFlow"
}
},
"3": {
"inputs": {
"seed": 232240565010917,
"steps": 25,
"cfg": 3.5,
"sampler_name": "uni_pc",
"scheduler": "normal",
"denoise": 1,
"model": [
"2",
0
],
"positive": [
"4",
0
],
"negative": [
"5",
0
],
"latent_image": [
"6",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"text": "close-up portrait of cat",
"clip": [
"1",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"5": {
"inputs": {
"text": "",
"clip": [
"1",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"6": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"7": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"1",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"8": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"7",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
},
"lora_1": {
"3": {
"inputs": {
"seed": 851616030078638,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"10",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "masterpiece best quality girl",
"clip": [
"10",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "bad hands",
"clip": [
"10",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"lora_name": "epi_noiseoffset2.safetensors",
"strength_model": 1,
"strength_clip": 1,
"model": [
"4",
0
],
"clip": [
"4",
1
]
},
"class_type": "LoraLoader",
"_meta": {
"title": "Load LoRA"
}
}
}
}
from . import workflows
@pytest.fixture(scope="module", autouse=False)
@ -549,9 +18,15 @@ async def client(tmp_path_factory) -> EmbeddedComfyClient:
yield client
def _prepare_for_workflows() -> dict[str, Traversable]:
add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
return {f.name: f for f in importlib.resources.files(workflows).iterdir() if f.is_file() and f.name.endswith(".json")}
@pytest.mark.asyncio
@pytest.mark.parametrize("workflow_name, workflow", _workflows.items())
async def test_workflow(workflow_name: str, workflow: dict, has_gpu: bool, client: EmbeddedComfyClient):
@pytest.mark.parametrize("workflow_name, workflow_file", _prepare_for_workflows().items())
async def test_workflow(workflow_name: str, workflow_file: Traversable, has_gpu: bool, client: EmbeddedComfyClient):
if not has_gpu:
pytest.skip("requires gpu")
@ -561,8 +36,9 @@ async def test_workflow(workflow_name: str, workflow: dict, has_gpu: bool, clien
except (ImportError, ModuleNotFoundError):
pytest.skip("requires torchaudio")
workflow = json.loads(workflow_file.read_text())
prompt = Prompt.validate(workflow)
add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
# todo: add all the models we want to test a bit more elegantly
outputs = await client.queue_prompt(prompt)

0
tests/inference/workflows/__init__.py Normal file
View File

180
tests/inference/workflows/audio-0.json Normal file
View File

@ -0,0 +1,180 @@
{
"14": {
"inputs": {
"ckpt_name": "stable_audio_open_1.0.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"15": {
"inputs": {
"clip_name": "t5_base.safetensors",
"type": "stable_audio"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
},
"16": {
"inputs": {
"text": "hard bop, upright bass, slappy bass, low frequencies, drum kit brushed hi-hat, snare with ghost notes, syncopated, groove",
"clip": [
"15",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"17": {
"inputs": {
"text": "",
"clip": [
"15",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"19": {
"inputs": {
"cfg": 7,
"model": [
"14",
0
],
"positive": [
"16",
0
],
"negative": [
"17",
0
]
},
"class_type": "CFGGuider",
"_meta": {
"title": "CFGGuider"
}
},
"21": {
"inputs": {
"noise_seed": 600769511872395
},
"class_type": "RandomNoise",
"_meta": {
"title": "RandomNoise"
}
},
"22": {
"inputs": {
"noise": [
"21",
0
],
"guider": [
"19",
0
],
"sampler": [
"29",
0
],
"sigmas": [
"24",
0
],
"latent_image": [
"30",
0
]
},
"class_type": "SamplerCustomAdvanced",
"_meta": {
"title": "SamplerCustomAdvanced"
}
},
"24": {
"inputs": {
"scheduler": "exponential",
"steps": 20,
"denoise": 1,
"model": [
"14",
0
]
},
"class_type": "BasicScheduler",
"_meta": {
"title": "BasicScheduler"
}
},
"26": {
"inputs": {
"samples": [
"22",
1
],
"vae": [
"14",
2
]
},
"class_type": "VAEDecodeAudio",
"_meta": {
"title": "VAEDecodeAudio"
}
},
"27": {
"inputs": {
"filename_prefix": "audio/objectobject",
"audio": [
"26",
0
]
},
"class_type": "SaveAudio",
"_meta": {
"title": "SaveAudio"
}
},
"29": {
"inputs": {
"version": "regular"
},
"class_type": "SamplerEulerCFGpp",
"_meta": {
"title": "SamplerEulerCFG++"
}
},
"30": {
"inputs": {
"seconds": 47.6
},
"class_type": "EmptyLatentAudio",
"_meta": {
"title": "EmptyLatentAudio"
}
},
"31": {
"inputs": {
"filename_prefix": "latents/ComfyUI",
"samples": [
"22",
1
]
},
"class_type": "SaveLatent",
"_meta": {
"title": "SaveLatent"
}
}
}

120
tests/inference/workflows/auraflow-0.json Normal file
View File

@ -0,0 +1,120 @@
{
"1": {
"inputs": {
"ckpt_name": "aura_flow_0.1.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"2": {
"inputs": {
"shift": 1.73,
"model": [
"1",
0
]
},
"class_type": "ModelSamplingAuraFlow",
"_meta": {
"title": "ModelSamplingAuraFlow"
}
},
"3": {
"inputs": {
"seed": 232240565010917,
"steps": 25,
"cfg": 3.5,
"sampler_name": "uni_pc",
"scheduler": "normal",
"denoise": 1,
"model": [
"2",
0
],
"positive": [
"4",
0
],
"negative": [
"5",
0
],
"latent_image": [
"6",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"text": "close-up portrait of cat",
"clip": [
"1",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"5": {
"inputs": {
"text": "",
"clip": [
"1",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"6": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"7": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"1",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"8": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"7",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

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{
"3": {
"inputs": {
"seed": 156680208700286,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "text, watermark",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

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{
"3": {
"inputs": {
"seed": 377072733774956,
"steps": 1,
"cfg": 6,
"sampler_name": "euler",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": [
"4",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "hunyuan_dit_1.2.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "a bottle with a 彩虹星系 inside it on top of a wooden table on a snowy mountain top with the ocean and clouds in the background\n",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"7": {
"inputs": {
"text": "",
"clip": [
"4",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
}
}

126
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@ -0,0 +1,126 @@
{
"3": {
"inputs": {
"seed": 851616030078638,
"steps": 20,
"cfg": 8,
"sampler_name": "euler",
"scheduler": "normal",
"denoise": 1,
"model": [
"10",
0
],
"positive": [
"6",
0
],
"negative": [
"7",
0
],
"latent_image": [
"5",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"4": {
"inputs": {
"ckpt_name": "v1-5-pruned-emaonly.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"5": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1
},
"class_type": "EmptyLatentImage",
"_meta": {
"title": "Empty Latent Image"
}
},
"6": {
"inputs": {
"text": "masterpiece best quality girl",
"clip": [
"10",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"7": {
"inputs": {
"text": "bad hands",
"clip": [
"10",
1
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"8": {
"inputs": {
"samples": [
"3",
0
],
"vae": [
"4",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"9": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"8",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"10": {
"inputs": {
"lora_name": "epi_noiseoffset2.safetensors",
"strength_model": 1,
"strength_clip": 1,
"model": [
"4",
0
],
"clip": [
"4",
1
]
},
"class_type": "LoraLoader",
"_meta": {
"title": "Load LoRA"
}
}
}

187
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{
"6": {
"inputs": {
"text": "a female character with long, flowing hair that appears to be made of ethereal, swirling patterns resembling the Northern Lights or Aurora Borealis. The background is dominated by deep blues and purples, creating a mysterious and dramatic atmosphere. The character's face is serene, with pale skin and striking features. She wears a dark-colored outfit with subtle patterns. The overall style of the artwork is reminiscent of fantasy or supernatural genres",
"clip": [
"11",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"11": {
"inputs": {
"clip_name1": "clip_g.safetensors",
"clip_name2": "clip_l.safetensors",
"clip_name3": "t5xxl_fp8_e4m3fn.safetensors"
},
"class_type": "TripleCLIPLoader",
"_meta": {
"title": "TripleCLIPLoader"
}
},
"13": {
"inputs": {
"shift": 3,
"model": [
"252",
0
]
},
"class_type": "ModelSamplingSD3",
"_meta": {
"title": "ModelSamplingSD3"
}
},
"67": {
"inputs": {
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"68": {
"inputs": {
"start": 0.1,
"end": 1,
"conditioning": [
"67",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"69": {
"inputs": {
"conditioning_1": [
"68",
0
],
"conditioning_2": [
"70",
0
]
},
"class_type": "ConditioningCombine",
"_meta": {
"title": "Conditioning (Combine)"
}
},
"70": {
"inputs": {
"start": 0,
"end": 0.1,
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"71": {
"inputs": {
"text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
"clip": [
"11",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"135": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"231": {
"inputs": {
"samples": [
"271",
0
],
"vae": [
"252",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"233": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"231",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"252": {
"inputs": {
"ckpt_name": "sd3_medium.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"271": {
"inputs": {
"seed": 945512652412924,
"steps": 28,
"cfg": 4.5,
"sampler_name": "dpmpp_2m",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": [
"13",
0
],
"positive": [
"6",
0
],
"negative": [
"69",
0
],
"latent_image": [
"135",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
}
}

189
tests/inference/workflows/sd3-multiprompt-0.json Normal file
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{
"11": {
"inputs": {
"clip_name1": "clip_g.safetensors",
"clip_name2": "clip_l.safetensors",
"clip_name3": "t5xxl_fp8_e4m3fn.safetensors"
},
"class_type": "TripleCLIPLoader",
"_meta": {
"title": "TripleCLIPLoader"
}
},
"13": {
"inputs": {
"shift": 3,
"model": [
"252",
0
]
},
"class_type": "ModelSamplingSD3",
"_meta": {
"title": "ModelSamplingSD3"
}
},
"67": {
"inputs": {
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"68": {
"inputs": {
"start": 0.1,
"end": 1,
"conditioning": [
"67",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"69": {
"inputs": {
"conditioning_1": [
"68",
0
],
"conditioning_2": [
"70",
0
]
},
"class_type": "ConditioningCombine",
"_meta": {
"title": "Conditioning (Combine)"
}
},
"70": {
"inputs": {
"start": 0,
"end": 0.1,
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"71": {
"inputs": {
"text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
"clip": [
"11",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"135": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"231": {
"inputs": {
"samples": [
"271",
0
],
"vae": [
"252",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"233": {
"inputs": {
"images": [
"231",
0
]
},
"class_type": "PreviewImage",
"_meta": {
"title": "Preview Image"
}
},
"252": {
"inputs": {
"ckpt_name": "sd3_medium.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"271": {
"inputs": {
"seed": 945512652412924,
"steps": 28,
"cfg": 4.5,
"sampler_name": "dpmpp_2m",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": [
"13",
0
],
"positive": [
"273",
0
],
"negative": [
"69",
0
],
"latent_image": [
"135",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"273": {
"inputs": {
"clip_l": "the background is dominated by deep red and purples, creating a mysterious and dramatic atmosphere similar to a volcanic explosion",
"clip_g": "the background is dominated by deep red and purples, creating a mysterious and dramatic atmosphere similar to a volcanic explosion",
"t5xxl": "portrait of a female character with long, flowing hair that appears to be made of ethereal, swirling patterns resembling the Northern Lights or Aurora Borealis. Her face is serene, with pale skin and striking features. She wears a dark-colored outfit with subtle patterns. The overall style of the artwork is reminiscent of fantasy or supernatural genres\n",
"empty_padding": "none",
"clip": [
"11",
0
]
},
"class_type": "CLIPTextEncodeSD3",
"_meta": {
"title": "CLIPTextEncodeSD3"
}
}
}

186
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{
"6": {
"inputs": {
"text": "a female character with long, flowing hair that appears to be made of ethereal, swirling patterns resembling the Northern Lights or Aurora Borealis. The background is dominated by deep blues and purples, creating a mysterious and dramatic atmosphere. The character's face is serene, with pale skin and striking features. She wears a dark-colored outfit with subtle patterns. The overall style of the artwork is reminiscent of fantasy or supernatural genres",
"clip": [
"272",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Prompt)"
}
},
"13": {
"inputs": {
"shift": 3,
"model": [
"252",
0
]
},
"class_type": "ModelSamplingSD3",
"_meta": {
"title": "ModelSamplingSD3"
}
},
"67": {
"inputs": {
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningZeroOut",
"_meta": {
"title": "ConditioningZeroOut"
}
},
"68": {
"inputs": {
"start": 0.1,
"end": 1,
"conditioning": [
"67",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"69": {
"inputs": {
"conditioning_1": [
"68",
0
],
"conditioning_2": [
"70",
0
]
},
"class_type": "ConditioningCombine",
"_meta": {
"title": "Conditioning (Combine)"
}
},
"70": {
"inputs": {
"start": 0,
"end": 0.1,
"conditioning": [
"71",
0
]
},
"class_type": "ConditioningSetTimestepRange",
"_meta": {
"title": "ConditioningSetTimestepRange"
}
},
"71": {
"inputs": {
"text": "bad quality, poor quality, doll, disfigured, jpg, toy, bad anatomy, missing limbs, missing fingers, 3d, cgi",
"clip": [
"272",
0
]
},
"class_type": "CLIPTextEncode",
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"135": {
"inputs": {
"width": 1024,
"height": 1024,
"batch_size": 1
},
"class_type": "EmptySD3LatentImage",
"_meta": {
"title": "EmptySD3LatentImage"
}
},
"231": {
"inputs": {
"samples": [
"271",
0
],
"vae": [
"252",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE Decode"
}
},
"233": {
"inputs": {
"filename_prefix": "ComfyUI",
"images": [
"231",
0
]
},
"class_type": "SaveImage",
"_meta": {
"title": "Save Image"
}
},
"252": {
"inputs": {
"ckpt_name": "sd3_medium.safetensors"
},
"class_type": "CheckpointLoaderSimple",
"_meta": {
"title": "Load Checkpoint"
}
},
"271": {
"inputs": {
"seed": 780289980632866,
"steps": 28,
"cfg": 4.5,
"sampler_name": "dpmpp_2m",
"scheduler": "sgm_uniform",
"denoise": 1,
"model": [
"13",
0
],
"positive": [
"6",
0
],
"negative": [
"69",
0
],
"latent_image": [
"135",
0
]
},
"class_type": "KSampler",
"_meta": {
"title": "KSampler"
}
},
"272": {
"inputs": {
"clip_name": "t5xxl_fp8_e4m3fn.safetensors",
"type": "sd3"
},
"class_type": "CLIPLoader",
"_meta": {
"title": "Load CLIP"
}
}
}

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from contextvars import ContextVar
from typing import Final
import pytest
from pytest_mock import MockerFixture
from comfy.cli_args import args
from comfy.cmd.execution import validate_prompt, nodes
valid_prompt: Final[dict] = {
"1": {
"inputs": {
"ckpt_name": "model1.safetensors",
},
"class_type": "CheckpointLoaderSimple",
},
"2": {
"inputs": {
"text": "a beautiful landscape",
"clip": ["1", 1],
},
"class_type": "CLIPTextEncode",
},
"3": {
"inputs": {
"text": "ugly, deformed",
"clip": ["1", 1],
},
"class_type": "CLIPTextEncode",
},
"4": {
"inputs": {
"width": 512,
"height": 512,
"batch_size": 1,
},
"class_type": "EmptyLatentImage",
},
"5": {
"inputs": {
"model": ["1", 0],
"seed": 42,
"steps": 20,
"cfg": 7.0,
"sampler_name": "euler",
"scheduler": "normal",
"positive": ["2", 0],
"negative": ["3", 0],
"latent_image": ["4", 0],
"denoise": 1.0,
},
"class_type": "KSampler",
},
"6": {
"inputs": {
"samples": ["5", 0],
"vae": ["1", 2],
},
"class_type": "VAEDecode",
},
"7": {
"inputs": {
"images": ["6", 0],
"filename_prefix": "test_output",
},
"class_type": "SaveImage",
},
}
known_models: ContextVar[list[str]] = ContextVar('known_models', default=[])
@pytest.fixture
def mock_nodes(mocker: MockerFixture):
class MockCheckpointLoaderSimple:
@staticmethod
def INPUT_TYPES():
models = known_models.get()
return {
"required": {
"ckpt_name": (models if models else ["model1.safetensors", "model2.safetensors"],),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
mocker.patch.dict(nodes.NODE_CLASS_MAPPINGS, {
"CheckpointLoaderSimple": MockCheckpointLoaderSimple,
"KSampler": type("KSampler", (), {
"INPUT_TYPES": staticmethod(lambda: {
"required": {
"model": ("MODEL",),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
"sampler_name": (["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_2m", "ddim", "uni_pc", "uni_pc_bh2"],),
"scheduler": (["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"],),
"positive": ("CONDITIONING",),
"negative": ("CONDITIONING",),
"latent_image": ("LATENT",),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}),
"RETURN_TYPES": ("LATENT",),
}),
"CLIPTextEncode": type("CLIPTextEncode", (), {
"INPUT_TYPES": staticmethod(lambda: {
"required": {
"text": ("STRING", {"multiline": True}),
"clip": ("CLIP",),
}
}),
"RETURN_TYPES": ("CONDITIONING",),
}),
"VAEDecode": type("VAEDecode", (), {
"INPUT_TYPES": staticmethod(lambda: {
"required": {
"samples": ("LATENT",),
"vae": ("VAE",),
}
}),
"RETURN_TYPES": ("IMAGE",),
}),
"SaveImage": type("SaveImage", (), {
"INPUT_TYPES": staticmethod(lambda: {
"required": {
"images": ("IMAGE",),
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
}
}),
"RETURN_TYPES": (),
"OUTPUT_NODE": True,
}),
"EmptyLatentImage": type("EmptyLatentImage", (), {
"INPUT_TYPES": staticmethod(lambda: {
"required": {
"width": ("INT", {"default": 512, "min": 16, "max": 8192}),
"height": ("INT", {"default": 512, "min": 16, "max": 8192}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
}
}),
"RETURN_TYPES": ("LATENT",),
}),
})
@pytest.fixture
def disable_known_models():
original_value = args.disable_known_models
args.disable_known_models = False
yield
args.disable_known_models = original_value
def test_validate_prompt_valid(mock_nodes):
prompt = valid_prompt
result = validate_prompt(prompt)
assert result.valid
assert result.error is None
assert set(result.good_output_node_ids) == {"7"}
def test_validate_prompt_invalid_node(mock_nodes):
prompt = {
"1": {
"inputs": {},
"class_type": "NonExistentNode",
},
}
result = validate_prompt(prompt)
assert not result.valid
assert result.error["type"] == "invalid_prompt"
assert "NonExistentNode" in result.error["message"]
def test_prompt_has_no_output(mock_nodes):
prompt = {
"1": {
"inputs": {},
"class_type": "CheckpointLoaderSimple",
},
}
result = validate_prompt(prompt)
assert not result.valid
assert result.error["type"] == "prompt_no_outputs"
def test_validate_prompt_invalid_input_type(mock_nodes):
prompt = valid_prompt.copy()
prompt["1"] = {
"inputs": {
"ckpt_name": 123,
},
"class_type": "CheckpointLoaderSimple",
}
result = validate_prompt(prompt)
assert not result.valid
assert result.error["type"] == "prompt_outputs_failed_validation"
assert result.node_errors["1"]["errors"][0]["type"] == "exception_during_inner_validation"
@pytest.mark.parametrize("ckpt_name, known_model", [
("model\\with\\backslash.safetensors", "model/with/backslash.safetensors"),
("model/with/forward/slash.safetensors", "model/with/forward/slash.safetensors"),
("mixed\\slash/path.safetensors", "mixed/slash/path.safetensors"),
("model with spaces.safetensors", "model with spaces.safetensors"),
("model_with_underscores.safetensors", "model_with_underscores.safetensors"),
("C:\\Windows\\Temp\\model.safetensors", "C:/Windows/Temp/model.safetensors"),
("/home/user/models/model.safetensors", "/home/user/models/model.safetensors"),
])
def test_validate_prompt_path_variations(mock_nodes, disable_known_models, ckpt_name, known_model):
token = known_models.set([known_model])
try:
prompt = valid_prompt.copy()
prompt["1"] = {
"inputs": {
"ckpt_name": ckpt_name,
},
"class_type": "CheckpointLoaderSimple",
}
result = validate_prompt(prompt)
assert result.valid, f"Failed for ckpt_name: {ckpt_name}, known_model: {known_model}"
assert result.error is None, f"Error for ckpt_name: {ckpt_name}, known_model: {known_model}"
finally:
known_models.reset(token)
def test_validate_prompt_default_models(mock_nodes, disable_known_models):
prompt = valid_prompt.copy()
prompt["1"]["inputs"]["ckpt_name"] = "model1.safetensors"
result = validate_prompt(prompt)
assert result.valid, "Failed for default model list"
assert result.error is None, "Error for default model list"
def test_validate_prompt_no_outputs(mock_nodes):
prompt = {
"1": {
"inputs": {
"ckpt_name": "model1.safetensors",
},
"class_type": "CheckpointLoaderSimple",
},
}
result = validate_prompt(prompt)
assert not result.valid
assert result.error["type"] == "prompt_no_outputs"