Improve tests and distributed error notifications

- Tests now perform faster
 - Tests will run on supported GPU platforms
 - Configuration has known issues related to setting up a working
   directory for an embedded client
 - Introduce a Skeletonize node that solves many problems with Canny
 - Improve behavior of exception reporting

.github/workflows/test.yml

@@ -41,10 +41,10 @@ jobs:
         run: |
           export HSA_OVERRIDE_GFX_VERSION=11.0.0
           pytest -v tests/unit
-      - name: Run lora workflow
+      - name: Run all tests
         run: |
           export HSA_OVERRIDE_GFX_VERSION=11.0.0
-          pytest -v tests/workflows
+          pytest -v tests
       - name: Lint for errors
         run: |
           pylint comfy

comfy/cldm/cldm.py

@@ -293,7 +293,7 @@ class ControlNet(nn.Module):
 
         hs = []
         if self.num_classes is not None:
-            assert y.shape[0] == x.shape[0]
+            assert y.shape[0] == x.shape[0], "There may be a mismatch between the ControlNet and Diffusion models being used"
             emb = emb + self.label_emb(y)
 
         h = x

comfy/client/aio_client.py

@@ -11,11 +11,11 @@ from aiohttp import WSMessage, ClientResponse
 from typing_extensions import Dict
 
 from .client_types import V1QueuePromptResponse
-from ..api.schemas import immutabledict
-from ..api.components.schema.prompt import PromptDict
 from ..api.api_client import JSONEncoder
+from ..api.components.schema.prompt import PromptDict
 from ..api.components.schema.prompt_request import PromptRequest
 from ..api.paths.history.get.responses.response_200.content.application_json.schema import Schema as GetHistoryDict
+from ..api.schemas import immutabledict
 from ..component_model.file_output_path import file_output_path
 
 
@@ -34,6 +34,15 @@ class AsyncRemoteComfyClient:
             f"ws://{server_address_url.hostname}:{server_address_url.port}", f"/ws?clientId={client_id}")
         self.loop = loop or asyncio.get_event_loop()
 
+    async def len_queue(self) -> int:
+        async with aiohttp.ClientSession() as session:
+            async with session.get(urljoin(self.server_address, "/prompt"), headers={'Accept': 'application.json'}) as response:
+                if response.status == 200:
+                    exec_info_dict = await response.json()
+                    return exec_info_dict["exec_info"]["queue_remaining"]
+                else:
+                    raise RuntimeError(f"unexpected response: {response.status}: {await response.text()}")
+
     async def queue_prompt_api(self, prompt: PromptDict) -> V1QueuePromptResponse:
         """
         Calls the API to queue a prompt.
@@ -71,7 +80,7 @@ class AsyncRemoteComfyClient:
             async with session.post(urljoin(self.server_address, "/api/v1/prompts"), data=prompt_json,
                                     headers={'Content-Type': 'application/json', 'Accept': 'image/png'}) as response:
 
-                if response.status == 200:
+                if 200 <= response.status < 400:
                     return await response.read()
                 else:
                     raise RuntimeError(f"could not prompt: {response.status}: {await response.text()}")

comfy/client/embedded_comfy_client.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 import asyncio
 import gc
 import uuid
-from asyncio import AbstractEventLoop
+from asyncio import get_event_loop
 from concurrent.futures import ThreadPoolExecutor
 from typing import Optional
 
@@ -57,13 +57,9 @@ class EmbeddedComfyClient:
     In order to use this in blocking methods, learn more about asyncio online.
     """
 
-    def __init__(self, configuration: Optional[Configuration] = None,
+    def __init__(self, configuration: Optional[Configuration] = None, progress_handler: Optional[ExecutorToClientProgress] = None, max_workers: int = 1):
-                 progress_handler: Optional[ExecutorToClientProgress] = None,
-                 loop: Optional[AbstractEventLoop] = None,
-                 max_workers: int = 1):
         self._progress_handler = progress_handler or ServerStub()
         self._executor = ThreadPoolExecutor(max_workers=max_workers)
-        self._loop = loop or asyncio.get_event_loop()
         self._configuration = configuration
         # we don't want to import the executor yet
         self._prompt_executor: Optional["comfy.cmd.execution.PromptExecutor"] = None
@@ -93,7 +89,7 @@ class EmbeddedComfyClient:
         while self._executor._work_queue.qsize() > 0:
             await asyncio.sleep(0.1)
 
-        await self._loop.run_in_executor(self._executor, cleanup)
+        await get_event_loop().run_in_executor(self._executor, cleanup)
 
         self._executor.shutdown(wait=True)
         self._is_running = False
@@ -112,8 +108,9 @@ class EmbeddedComfyClient:
             from ..cmd.execution import PromptExecutor
 
             self._prompt_executor = PromptExecutor(self._progress_handler)
+            self._prompt_executor.raise_exceptions = True
 
-        await self._loop.run_in_executor(self._executor, create_executor_in_thread)
+        await get_event_loop().run_in_executor(self._executor, create_executor_in_thread)
 
     @tracer.start_as_current_span("Queue Prompt")
     async def queue_prompt(self,
@@ -128,29 +125,26 @@ class EmbeddedComfyClient:
             spam: Span
             with tracer.start_as_current_span("Execute Prompt", context=span_context) as span:
                 from ..cmd.execution import PromptExecutor, validate_prompt
-                prompt_mut = make_mutable(prompt)
+                try:
-                validation_tuple = validate_prompt(prompt_mut)
+                    prompt_mut = make_mutable(prompt)
-                if not validation_tuple[0]:
+                    validation_tuple = validate_prompt(prompt_mut)
-                    span.set_status(Status(StatusCode.ERROR))
+                    if not validation_tuple[0]:
-                    validation_error_dict = validation_tuple[1] or {"message": "Unknown", "details": ""}
+                        validation_error_dict = validation_tuple[1] or {"message": "Unknown", "details": ""}
-                    error = ValueError("\n".join([validation_error_dict["message"], validation_error_dict["details"]]))
+                        raise ValueError("\n".join([validation_error_dict["message"], validation_error_dict["details"]]))
-                    span.record_exception(error)
-                    return {}
 
-                prompt_executor: PromptExecutor = self._prompt_executor
+                    prompt_executor: PromptExecutor = self._prompt_executor
 
-                if client_id is None:
+                    if client_id is None:
-                    prompt_executor.server = _server_stub_instance
+                        prompt_executor.server = _server_stub_instance
-                else:
+                    else:
-                    prompt_executor.server = self._progress_handler
+                        prompt_executor.server = self._progress_handler
 
-                prompt_executor.execute(prompt_mut, prompt_id, {"client_id": client_id},
+                    prompt_executor.execute(prompt_mut, prompt_id, {"client_id": client_id},
-                                        execute_outputs=validation_tuple[2])
+                                            execute_outputs=validation_tuple[2])
-                if prompt_executor.success:
                     return prompt_executor.outputs_ui
-                else:
+                except Exception as exc_info:
                     span.set_status(Status(StatusCode.ERROR))
-                    error = RuntimeError("\n".join(event for (event, data) in self._prompt_executor.status_messages))
+                    span.record_exception(exc_info)
-                    span.record_exception(error)
+                    raise exc_info
 
-        return await self._loop.run_in_executor(self._executor, execute_prompt)
+        return await get_event_loop().run_in_executor(self._executor, execute_prompt)

comfy/cmd/execution.py

@@ -344,6 +344,7 @@ class PromptExecutor:
     def __init__(self, server: ExecutorToClientProgress):
         self.success = None
         self.server = server
+        self.raise_exceptions = False
         self.reset()
 
     def reset(self):
@@ -404,6 +405,9 @@ class PromptExecutor:
             d = self.outputs.pop(o)
             del d
 
+        if ex is not None and self.raise_exceptions:
+            raise ex
+
     def execute(self, prompt, prompt_id, extra_data=None, execute_outputs: List[str] = None):
         with new_execution_context(ExecutionContext(self.server)):
             self._execute_inner(prompt, prompt_id, extra_data, execute_outputs)

comfy/cmd/main_pre.py

@@ -61,8 +61,8 @@ def _create_tracer():
 
     if has_endpoint:
         otlp_exporter = OTLPSpanExporter()
-    elif is_debugging:
+    # elif is_debugging:
-        otlp_exporter = ConsoleSpanExporter()
+    #     otlp_exporter = ConsoleSpanExporter("comfyui")
     else:
         otlp_exporter = SpanExporter()
 

comfy/cmd/server.py

@@ -7,7 +7,6 @@ import logging
 import mimetypes
 import os
 import struct
-import sys
 import traceback
 import uuid
 from asyncio import Future, AbstractEventLoop
@@ -19,14 +18,15 @@ from urllib.parse import quote, urlencode
 
 import aiofiles
 import aiohttp
+import sys
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
 from aiohttp import web
 from can_ada import URL, parse as urlparse  # pylint: disable=no-name-in-module
 from typing_extensions import NamedTuple
 
-from .. import interruption
 from .latent_preview_image_encoding import encode_preview_image
+from .. import interruption
 from .. import model_management
 from .. import utils
 from ..app.user_manager import UserManager
@@ -35,7 +35,7 @@ from ..client.client_types import FileOutput
 from ..cmd import execution
 from ..cmd import folder_paths
 from ..component_model.abstract_prompt_queue import AbstractPromptQueue, AsyncAbstractPromptQueue
-from ..component_model.executor_types import ExecutorToClientProgress
+from ..component_model.executor_types import ExecutorToClientProgress, StatusMessage, QueueInfo, ExecInfo
 from ..component_model.file_output_path import file_output_path
 from ..component_model.files import get_package_as_path
 from ..component_model.queue_types import QueueItem, HistoryEntry, BinaryEventTypes, TaskInvocation, ExecutionError, \
@@ -778,8 +778,12 @@ class PromptServer(ExecutorToClientProgress):
         self.loop.call_soon_threadsafe(
             self.messages.put_nowait, (event, data, sid))
 
-    def queue_updated(self):
+    def queue_updated(self, queue_remaining: Optional[int] = None):
-        self.send_sync("status", {"status": self.get_queue_info()})
+        if queue_remaining is None:
+            status = {"status": self.get_queue_info()}
+        else:
+            status = StatusMessage(status=QueueInfo(exec_info=ExecInfo(queue_remaining=queue_remaining)))
+        self.send_sync("status", status)
 
     async def publish_loop(self):
         while True:

comfy/component_model/executor_types.py

@@ -1,8 +1,9 @@
 from __future__ import annotations  # for Python 3.7-3.9
 
+from typing import Optional, Literal, Protocol, TypeAlias, Union, NamedTuple
+
 import PIL.Image
 from typing_extensions import NotRequired, TypedDict
-from typing import Optional, Literal, Protocol, TypeAlias, Union, NamedTuple
 
 from .queue_types import BinaryEventTypes
 
@@ -78,7 +79,7 @@ class ExecutorToClientProgress(Protocol):
         """
         pass
 
-    def queue_updated(self):
+    def queue_updated(self, queue_remaining: Optional[int] = None):
         """
         Indicates that the local client's queue has been updated
         :return:

comfy/distributed/distributed_progress.py

@@ -10,7 +10,7 @@ from typing import Optional, Dict, Any
 from aio_pika.patterns import RPC
 
 from ..component_model.executor_types import SendSyncEvent, SendSyncData, ExecutorToClientProgress, \
-    UnencodedPreviewImageMessage
+    UnencodedPreviewImageMessage, StatusMessage, QueueInfo, ExecInfo
 from ..component_model.queue_types import BinaryEventTypes
 
 
@@ -67,9 +67,8 @@ class DistributedExecutorToClientProgress(ExecutorToClientProgress):
                   sid: Optional[str] = None):
         asyncio.run_coroutine_threadsafe(self.send(event, data, sid), self._loop)
 
-    def queue_updated(self):
+    def queue_updated(self, queue_remaining: Optional[int] = None):
-        # todo: this should gather the global queue data
+        self.send_sync("status", StatusMessage(status=QueueInfo(exec_info=ExecInfo(queue_remaining=queue_remaining))))
-        pass
 
 
 class ProgressHandlers:

comfy/distributed/distributed_prompt_queue.py

@@ -50,7 +50,7 @@ class DistributedPromptQueue(AbstractPromptQueue, AsyncAbstractPromptQueue):
             return None
         self._caller_local_in_progress[queue_item.prompt_id] = queue_item
         if self._caller_server is not None:
-            self._caller_server.queue_updated()
+            self._caller_server.queue_updated(self.get_tasks_remaining())
         try:
             if "token" in queue_item.extra_data:
                 user_token = queue_item.extra_data["token"]
@@ -75,7 +75,7 @@ class DistributedPromptQueue(AbstractPromptQueue, AsyncAbstractPromptQueue):
             reply = RpcReply(**(await self._rpc.call(self._queue_name, {"request": asdict(request)}))).as_task_invocation()
             self._caller_history.put(queue_item, reply.outputs, reply.status)
             if self._caller_server is not None:
-                self._caller_server.queue_updated()
+                self._caller_server.queue_updated(self.get_tasks_remaining())
 
             # if this has a completion future, complete it
             if queue_item.completed is not None:
@@ -86,7 +86,7 @@ class DistributedPromptQueue(AbstractPromptQueue, AsyncAbstractPromptQueue):
             as_exec_exc = ExecutionError(queue_item.prompt_id, exceptions=[exc])
             self._caller_history.put(queue_item, outputs={}, status=as_exec_exc.status)
 
-            # if we have a completer, propoagate the exception to it
+            # if we have a completer, propagate the exception to it
             if queue_item.completed is not None:
                 queue_item.completed.set_exception(as_exec_exc)
             raise as_exec_exc
@@ -95,7 +95,7 @@ class DistributedPromptQueue(AbstractPromptQueue, AsyncAbstractPromptQueue):
             if self._caller_server is not None:
                 # todo: this ensures that the web ui is notified about the completed task, but it should really be done by worker
                 self._caller_server.send_sync("executing", {"node": None, "prompt_id": queue_item.prompt_id}, self._caller_server.client_id)
-                self._caller_server.queue_updated()
+                self._caller_server.queue_updated(self.get_tasks_remaining())
         return reply
 
     def put(self, item: QueueItem):

comfy/distributed/distributed_prompt_worker.py

@@ -69,8 +69,7 @@ class DistributedPromptWorker:
         self._rpc = await JsonRPC.create(channel=self._channel, auto_delete=True, durable=False)
 
         if self._embedded_comfy_client is None:
-            self._embedded_comfy_client = EmbeddedComfyClient(
+            self._embedded_comfy_client = EmbeddedComfyClient(progress_handler=DistributedExecutorToClientProgress(self._rpc, self._queue_name, self._loop))
-                progress_handler=DistributedExecutorToClientProgress(self._rpc, self._queue_name, self._loop))
         if not self._embedded_comfy_client.is_running:
             await self._exit_stack.enter_async_context(self._embedded_comfy_client)
 

comfy/distributed/server_stub.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import uuid
-from typing import Literal
+from typing import Literal, Optional
 
 from ..component_model.executor_types import ExecutorToClientProgress, StatusMessage, ExecutingMessage
 from ..component_model.queue_types import BinaryEventTypes
@@ -23,5 +23,5 @@ class ServerStub(ExecutorToClientProgress):
                   data: StatusMessage | ExecutingMessage | bytes | bytearray | None, sid: str | None = None):
         pass
 
-    def queue_updated(self):
+    def queue_updated(self, queue_remaining: Optional[int] = None):
         pass

comfy/model_downloader.py

@@ -120,6 +120,7 @@ def get_or_download(folder_name: str, filename: str, known_files: List[HuggingFi
                         logging.warning(f"Could not retrieve file {str(known_file)}")
                     else:
                         destination_with_filename = join(this_model_directory, save_filename)
+                        os.makedirs(os.path.dirname(destination_with_filename), exist_ok=True)
                         try:
 
                             with _session.get(url, stream=True, allow_redirects=True) as response:
@@ -300,6 +301,12 @@ KNOWN_CONTROLNETS = [
     HuggingFile("limingcv/ControlNet-Plus-Plus", "checkpoints/hed/controlnet/diffusion_pytorch_model.bin", save_with_filename="ControlNet-Plus-Plus_sd15_hed.bin", repo_type="space"),
     HuggingFile("limingcv/ControlNet-Plus-Plus", "checkpoints/lineart/controlnet/diffusion_pytorch_model.bin", save_with_filename="ControlNet-Plus-Plus_sd15_lineart.bin", repo_type="space"),
     HuggingFile("limingcv/ControlNet-Plus-Plus", "checkpoints/seg/controlnet/diffusion_pytorch_model.safetensors", save_with_filename="ControlNet-Plus-Plus_sd15_ade20k_seg.safetensors", repo_type="space"),
+    HuggingFile("xinsir/controlnet-scribble-sdxl-1.0", "diffusion_pytorch_model.safetensors", save_with_filename="xinsir-controlnet-scribble-sdxl-1.0.safetensors"),
+    HuggingFile("xinsir/controlnet-canny-sdxl-1.0", "diffusion_pytorch_model.safetensors", save_with_filename="xinsir-controlnet-canny-sdxl-1.0.safetensors"),
+    HuggingFile("xinsir/controlnet-canny-sdxl-1.0", "diffusion_pytorch_model_V2.safetensors", save_with_filename="xinsir-controlnet-canny-sdxl-1.0_V2.safetensors"),
+    HuggingFile("xinsir/controlnet-openpose-sdxl-1.0", "diffusion_pytorch_model.safetensors", save_with_filename="xinsir-controlnet-openpose-sdxl-1.0.safetensors"),
+    HuggingFile("xinsir/anime-painter", "diffusion_pytorch_model.safetensors", save_with_filename="xinsir-anime-painter-scribble-sdxl-1.0.safetensors"),
+    HuggingFile("TheMistoAI/MistoLine", "mistoLine_rank256.safetensors"),
 ]
 
 KNOWN_DIFF_CONTROLNETS = [
@@ -343,12 +350,17 @@ KNOWN_CLIP_MODELS: List[Union[CivitFile | HuggingFile]] = [
 ]
 
 
-def add_known_models(folder_name: str, symbol: List[Union[CivitFile, HuggingFile]], *models: Union[CivitFile, HuggingFile]) -> List[Union[CivitFile, HuggingFile]]:
+def add_known_models(folder_name: str, known_models: List[Union[CivitFile, HuggingFile]], *models: Union[CivitFile, HuggingFile]) -> List[Union[CivitFile, HuggingFile]]:
+    if len(models) < 1:
+        return known_models
+
     if args.disable_known_models:
         logging.warning(f"Known models have been disabled in the options (while adding {folder_name}/{','.join(map(str, models))})")
-    symbol += models
+
+    pre_existing = frozenset(known_models)
+    known_models += [model for model in models if model not in pre_existing]
     folder_paths.invalidate_cache(folder_name)
-    return symbol
+    return known_models
 
 
 def huggingface_repos() -> List[str]:

comfy/model_downloader_types.py

@@ -7,7 +7,7 @@ from typing import Optional, List, Sequence
 from typing_extensions import TypedDict, NotRequired
 
 
-@dataclasses.dataclass
+@dataclasses.dataclass(frozen=True)
 class CivitFile:
     """
     A file on CivitAI
@@ -35,7 +35,7 @@ class CivitFile:
         return []
 
 
-@dataclasses.dataclass
+@dataclasses.dataclass(frozen=True)
 class HuggingFile:
     """
     A file on Huggingface Hub
@@ -48,7 +48,7 @@ class HuggingFile:
     repo_id: str
     filename: str
     save_with_filename: Optional[str] = None
-    alternate_filenames: List[str] = dataclasses.field(default_factory=list)
+    alternate_filenames: Sequence[str] = dataclasses.field(default_factory=tuple)
     show_in_ui: Optional[bool] = True
     convert_to_16_bit: Optional[bool] = False
     size: Optional[int] = None

comfy_extras/nodes/nodes_skeletonize.py

@@ -0,0 +1,52 @@
+import torch
+from skimage.morphology import skeletonize, thin
+
+import comfy.model_management
+
+
+class SkeletonizeThin:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "binary_threshold": ("FLOAT", {"default": 0.5, "min": 0.01, "max": 0.99, "step": 0.01}),
+            "approach": (["skeletonize", "thinning"], {}),
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "process_image"
+    CATEGORY = "image/preprocessors"
+
+    def process_image(self, image, binary_threshold, approach):
+        use_skeletonize = approach == "skeletonize"
+        use_thinning = approach == "thinning"
+        device = comfy.model_management.intermediate_device()
+        if len(image.shape) == 3:
+            image = image.unsqueeze(0)
+
+        batch_size, height, width, channels = image.shape
+        if channels == 3:
+            image = torch.mean(image, dim=-1, keepdim=True)
+        binary = (image > binary_threshold).float()
+
+        results = []
+        for img in binary:
+            img_np = img.squeeze().cpu().numpy()
+
+            if use_skeletonize:
+                result = skeletonize(img_np)
+            elif use_thinning:
+                result = thin(img_np)
+            else:
+                result = img_np
+
+            result = torch.from_numpy(result).float().to(device)
+            result = result.unsqueeze(-1).repeat(1, 1, 3)
+            results.append(result)
+        final_result = torch.stack(results).to(comfy.model_management.intermediate_device())
+        return (final_result,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "SkeletonizeThin": SkeletonizeThin,
+}

pytest.ini

@@ -3,3 +3,4 @@ markers =
   inference: mark as inference test (deselect with '-m "not inference"')
 testpaths = tests
 addopts = -s
+asyncio_mode = auto

tests/compare/conftest.py

@@ -1,43 +0,0 @@
-import os
-import pytest
-
-# Command line arguments for pytest
-def pytest_addoption(parser):
-    parser.addoption('--baseline_dir', action="store", default='tests/inference/baseline', help='Directory for ground-truth images')
-    parser.addoption('--test_dir', action="store", default='tests/inference/samples', help='Directory for images to test')
-    parser.addoption('--metrics_file', action="store", default='tests/metrics.md', help='Output file for metrics')
-    parser.addoption('--img_output_dir', action="store", default='tests/compare/samples', help='Output directory for diff metric images')
-
-# This initializes args at the beginning of the test session
-@pytest.fixture(scope="session", autouse=False)
-def args_pytest(pytestconfig):
-    args = {}
-    args['baseline_dir'] = pytestconfig.getoption('baseline_dir')
-    args['test_dir'] = pytestconfig.getoption('test_dir')
-    args['metrics_file'] = pytestconfig.getoption('metrics_file')
-    args['img_output_dir'] = pytestconfig.getoption('img_output_dir')
-
-    # Initialize metrics file
-    with open(args['metrics_file'], 'a') as f:
-        # if file is empty, write header
-        if os.stat(args['metrics_file']).st_size == 0:
-            f.write("| date | run | file | status | value | \n")
-            f.write("| --- | --- | --- | --- | --- | \n")
-
-    return args
-
-
-def gather_file_basenames(directory: str):
-    files = []
-    if not os.path.isdir(directory):
-        return files
-    for file in os.listdir(directory):
-        if file.endswith(".png"):
-            files.append(file)
-    return files
-
-# Creates the list of baseline file names to use as a fixture
-def pytest_generate_tests(metafunc):
-    if "baseline_fname" in metafunc.fixturenames:
-        baseline_fnames = gather_file_basenames(metafunc.config.getoption("baseline_dir"))
-        metafunc.parametrize("baseline_fname", baseline_fnames)

tests/compare/test_quality.py

@@ -1,203 +0,0 @@
-import datetime
-import numpy as np
-import os
-
-import torch
-from PIL import Image
-import pytest
-from pytest import fixture
-from typing import Tuple, List
-
-from cv2 import imread, cvtColor, COLOR_BGR2RGB
-from skimage.metrics import structural_similarity as ssim
-
-"""
-This test suite compares images in 2 directories by file name
-The directories are specified by the command line arguments --baseline_dir and --test_dir
-
-"""
-
-
-# ssim: Structural Similarity Index
-# Returns a tuple of (ssim, diff_image)
-def ssim_score(img0: np.ndarray, img1: np.ndarray) -> Tuple[float, np.ndarray]:
-    score, diff = ssim(img0, img1, channel_axis=-1, full=True)
-    # rescale the difference image to 0-255 range
-    diff = (diff * 255).astype("uint8")
-    return score, diff
-
-
-# Metrics must return a tuple of (score, diff_image)
-METRICS = {"ssim": ssim_score}
-METRICS_PASS_THRESHOLD = {"ssim": 0.95}
-
-
-class TestCompareImageMetrics:
-    @fixture(scope="class")
-    def test_file_names(self, args_pytest):
-        test_dir = args_pytest['test_dir']
-        fnames = self.gather_file_basenames(test_dir)
-        yield fnames
-        del fnames
-
-    @fixture(scope="class")
-    def teardown(self, args_pytest):
-        yield
-        # Runs after all tests are complete
-        # Aggregate output files into a grid of images
-        baseline_dir = args_pytest['baseline_dir']
-        test_dir = args_pytest['test_dir']
-        img_output_dir = args_pytest['img_output_dir']
-        metrics_file = args_pytest['metrics_file']
-
-        grid_dir = os.path.join(img_output_dir, "grid")
-        os.makedirs(grid_dir, exist_ok=True)
-
-        for metric_dir in METRICS.keys():
-            metric_path = os.path.join(img_output_dir, metric_dir)
-            for file in os.listdir(metric_path):
-                if file.endswith(".png"):
-                    score = self.lookup_score_from_fname(file, metrics_file)
-                    image_file_list = []
-                    image_file_list.append([
-                        os.path.join(baseline_dir, file),
-                        os.path.join(test_dir, file),
-                        os.path.join(metric_path, file)
-                    ])
-                    # Create grid
-                    image_list = [[Image.open(file) for file in files] for files in image_file_list]
-                    grid = self.image_grid(image_list)
-                    grid.save(os.path.join(grid_dir, f"{metric_dir}_{score:.3f}_{file}"))
-
-    # Tests run for each baseline file name
-    @fixture()
-    def fname(self, baseline_fname, teardown):
-        yield baseline_fname
-        del baseline_fname
-
-    # For a baseline image file, finds the corresponding file name in test_dir and 
-    # compares the images using the metrics in METRICS
-    @pytest.mark.parametrize("metric", METRICS.keys())
-    @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
-    def test_pipeline_compare(
-            self,
-            args_pytest,
-            fname,
-            test_file_names,
-            metric,
-            teardown,
-    ):
-        baseline_dir = args_pytest['baseline_dir']
-
-        test_dir = args_pytest['test_dir']
-        metrics_output_file = args_pytest['metrics_file']
-        img_output_dir = args_pytest['img_output_dir']
-
-        if not os.path.isdir(baseline_dir):
-            pytest.skip("Baseline directory does not exist")
-            return
-
-        if not os.path.isdir(test_dir):
-            pytest.skip("Test directory does not exist")
-            return
-
-        # Check that all files in baseline_dir have a file in test_dir with matching metadata
-        baseline_file_path = os.path.join(args_pytest['baseline_dir'], fname)
-        file_paths = [os.path.join(args_pytest['test_dir'], f) for f in test_file_names]
-        file_match = self.find_file_match(baseline_file_path, file_paths)
-        assert file_match is not None, f"Could not find a file in {args_pytest['test_dir']} with matching metadata to {baseline_file_path}"
-
-        baseline_file_path = os.path.join(baseline_dir, fname)
-
-        # Find file match
-        file_paths = [os.path.join(test_dir, f) for f in test_file_names]
-        test_file = self.find_file_match(baseline_file_path, file_paths)
-
-        # Run metrics
-        sample_baseline = self.read_img(baseline_file_path)
-        sample_secondary = self.read_img(test_file)
-
-        score, metric_img = METRICS[metric](sample_baseline, sample_secondary)
-        metric_status = score > METRICS_PASS_THRESHOLD[metric]
-
-        # Save metric values
-        with open(metrics_output_file, 'a') as f:
-            run_info = os.path.splitext(fname)[0]
-            metric_status_str = "PASS ✅" if metric_status else "FAIL ❌"
-            date_str = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-            f.write(f"| {date_str} | {run_info} | {metric} | {metric_status_str} | {score} | \n")
-
-        # Save metric image
-        metric_img_dir = os.path.join(img_output_dir, metric)
-        os.makedirs(metric_img_dir, exist_ok=True)
-        output_filename = f'{fname}'
-        Image.fromarray(metric_img).save(os.path.join(metric_img_dir, output_filename))
-
-        assert score > METRICS_PASS_THRESHOLD[metric]
-
-    def read_img(self, filename: str) -> np.ndarray:
-        cvImg = imread(filename)
-        cvImg = cvtColor(cvImg, COLOR_BGR2RGB)
-        return cvImg
-
-    def image_grid(self, img_list: list[list[Image.Image]]):
-        # imgs is a 2D list of images
-        # Assumes the input images are a rectangular grid of equal sized images
-        rows = len(img_list)
-        cols = len(img_list[0])
-
-        w, h = img_list[0][0].size
-        grid = Image.new('RGB', size=(cols * w, rows * h))
-
-        for i, row in enumerate(img_list):
-            for j, img in enumerate(row):
-                grid.paste(img, box=(j * w, i * h))
-        return grid
-
-    def lookup_score_from_fname(self,
-                                fname: str,
-                                metrics_output_file: str
-                                ) -> float:
-        fname_basestr = os.path.splitext(fname)[0]
-        with open(metrics_output_file, 'r') as f:
-            for line in f:
-                if fname_basestr in line:
-                    score = float(line.split('|')[5])
-                    return score
-        raise ValueError(f"Could not find score for {fname} in {metrics_output_file}")
-
-    def gather_file_basenames(self, directory: str):
-        files = []
-        for file in os.listdir(directory):
-            if file.endswith(".png"):
-                files.append(file)
-        return files
-
-    def read_file_prompt(self, fname: str) -> str:
-        # Read prompt from image file metadata
-        img = Image.open(fname)
-        img.load()
-        return img.info['prompt']
-
-    def find_file_match(self, baseline_file: str, file_paths: List[str]):
-        # Find a file in file_paths with matching metadata to baseline_file
-        baseline_prompt = self.read_file_prompt(baseline_file)
-
-        # Do not match empty prompts
-        if baseline_prompt is None or baseline_prompt == "":
-            return None
-
-        # Find file match
-        # Reorder test_file_names so that the file with matching name is first
-        # This is an optimization because matching file names are more likely 
-        # to have matching metadata if they were generated with the same script
-        basename = os.path.basename(baseline_file)
-        file_path_basenames = [os.path.basename(f) for f in file_paths]
-        if basename in file_path_basenames:
-            match_index = file_path_basenames.index(basename)
-            file_paths.insert(0, file_paths.pop(match_index))
-
-        for f in file_paths:
-            test_file_prompt = self.read_file_prompt(f)
-            if baseline_prompt == test_file_prompt:
-                return f

tests/conftest.py

@@ -1,14 +1,43 @@
-import json
+import logging
 import multiprocessing
+import os
 import pathlib
+import socket
+import subprocess
+import sys
 import time
 import urllib
-from typing import Tuple
+from typing import Tuple, List
 
 import pytest
+import requests
 
 from comfy.cli_args_types import Configuration
 
+# fixes issues with running the testcontainers rabbitmqcontainer on Windows
+os.environ["TC_HOST"] = "localhost"
+
+
+def get_lan_ip():
+    """
+    Finds the host's IP address on the LAN it's connected to.
+
+    Returns:
+        str: The IP address of the host on the LAN.
+    """
+    # Create a dummy socket
+    s = None
+    try:
+        # Connect to a dummy address (Here, Google's public DNS server)
+        # The actual connection is not made, but this allows finding out the LAN IP
+        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        s.connect(("8.8.8.8", 80))
+        ip = s.getsockname()[0]
+    finally:
+        if s is not None:
+            s.close()
+    return ip
+
 
 def run_server(server_arguments: Configuration):
     from comfy.cmd.main import main
@@ -20,7 +49,83 @@ def run_server(server_arguments: Configuration):
 
 
 @pytest.fixture(scope="function", autouse=False)
-def comfy_background_server(tmp_path) -> Tuple[Configuration, multiprocessing.Process]:
+def has_gpu() -> bool:
+    # ipex
+    try:
+        import intel_extension_for_pytorch as ipex
+        has_gpu = ipex.xpu.device_count() > 0
+    except ImportError:
+        try:
+            import torch
+            has_gpu = torch.device(torch.cuda.current_device()) is not None
+        except:
+            has_gpu = False
+
+    if has_gpu:
+        from comfy import model_management
+        from comfy.model_management import CPUState
+        model_management.cpu_state = CPUState.GPU if has_gpu else CPUState.CPU
+    yield has_gpu
+
+
+@pytest.fixture(scope="module", autouse=False)
+def frontend_backend_worker_with_rabbitmq(tmp_path_factory) -> str:
+    """
+    starts a frontend and backend worker against a started rabbitmq, and yields the address of the frontend
+    :return:
+    """
+    tmp_path = tmp_path_factory.mktemp("comfy_background_server")
+    processes_to_close: List[subprocess.Popen] = []
+    from testcontainers.rabbitmq import RabbitMqContainer
+    with RabbitMqContainer("rabbitmq:latest") as rabbitmq:
+        params = rabbitmq.get_connection_params()
+        connection_uri = f"amqp://guest:guest@127.0.0.1:{params.port}"
+
+        frontend_command = [
+            "comfyui",
+            "--listen=0.0.0.0",
+            "--port=9001",
+            "--cpu",
+            "--distributed-queue-frontend",
+            f"-w={str(tmp_path)}",
+            f"--distributed-queue-connection-uri={connection_uri}",
+        ]
+
+        processes_to_close.append(subprocess.Popen(frontend_command, stdout=sys.stdout, stderr=sys.stderr))
+        backend_command = [
+            "comfyui-worker",
+            "--port=9002",
+            f"-w={str(tmp_path)}",
+            f"--distributed-queue-connection-uri={connection_uri}",
+        ]
+
+        processes_to_close.append(subprocess.Popen(backend_command, stdout=sys.stdout, stderr=sys.stderr))
+        try:
+            server_address = f"http://{get_lan_ip()}:9001"
+            start_time = time.time()
+            connected = False
+            while time.time() - start_time < 60:
+                try:
+                    response = requests.get(server_address)
+                    if response.status_code == 200:
+                        connected = True
+                        break
+                except ConnectionRefusedError:
+                    pass
+                except Exception as exc:
+                    logging.warning("", exc_info=exc)
+                time.sleep(1)
+            if not connected:
+                raise RuntimeError("could not connect to frontend")
+            yield server_address
+        finally:
+            for process in processes_to_close:
+                process.terminate()
+
+
+@pytest.fixture(scope="module", autouse=False)
+def comfy_background_server(tmp_path_factory) -> Tuple[Configuration, multiprocessing.Process]:
+    tmp_path = tmp_path_factory.mktemp("comfy_background_server")
     import torch
     # Start server
 
@@ -99,7 +204,7 @@ def model(clip):
         pytest.skip(f"{checkpoint} not present on machine")
 
 
-@pytest.fixture(scope="function", autouse=True)
+@pytest.fixture(scope="function", autouse=False)
 def use_temporary_output_directory(tmp_path: pathlib.Path):
     from comfy.cmd import folder_paths
 
@@ -109,7 +214,7 @@ def use_temporary_output_directory(tmp_path: pathlib.Path):
     folder_paths.set_output_directory(orig_dir)
 
 
-@pytest.fixture(scope="function", autouse=True)
+@pytest.fixture(scope="function", autouse=False)
 def use_temporary_input_directory(tmp_path: pathlib.Path):
     from comfy.cmd import folder_paths
 

tests/distributed/test_distributed_queue.py

@@ -1,17 +1,9 @@
 import asyncio
-import logging
-import os
-import socket
-import subprocess
-import sys
-import time
 import uuid
 from concurrent.futures import ThreadPoolExecutor
-from typing import List
 
 import jwt
 import pytest
-import requests
 from testcontainers.rabbitmq import RabbitMqContainer
 
 from comfy.client.aio_client import AsyncRemoteComfyClient
@@ -22,9 +14,6 @@ from comfy.component_model.queue_types import QueueItem, QueueTuple, TaskInvocat
 from comfy.distributed.distributed_prompt_worker import DistributedPromptWorker
 from comfy.distributed.server_stub import ServerStub
 
-# fixes issues with running the testcontainers rabbitmqcontainer on Windows
-os.environ["TC_HOST"] = "localhost"
-
 
 def create_test_prompt() -> QueueItem:
     from comfy.cmd.execution import validate_prompt
@@ -103,68 +92,19 @@ async def test_distributed_prompt_queues_same_process():
 
 
 @pytest.mark.asyncio
-async def test_frontend_backend_workers():
+async def test_frontend_backend_workers(frontend_backend_worker_with_rabbitmq):
-    processes_to_close: List[subprocess.Popen] = []
+    client = AsyncRemoteComfyClient(server_address=frontend_backend_worker_with_rabbitmq)
-    with RabbitMqContainer("rabbitmq:latest") as rabbitmq:
+    prompt = sdxl_workflow_with_refiner("test", inference_steps=1, refiner_steps=1)
-        try:
+    png_image_bytes = await client.queue_prompt(prompt)
-            params = rabbitmq.get_connection_params()
+    len_queue_after = await client.len_queue()
-            connection_uri = f"amqp://guest:guest@127.0.0.1:{params.port}"
+    assert len_queue_after == 0
-
+    assert len(png_image_bytes) > 1000, "expected an image, but got nothing"
-            frontend_command = [
-                "comfyui",
-                "--listen=0.0.0.0",
-                "--port=9001",
-                "--cpu",
-                "--distributed-queue-frontend",
-                f"--distributed-queue-connection-uri={connection_uri}",
-            ]
-
-            processes_to_close.append(subprocess.Popen(frontend_command, stdout=sys.stdout, stderr=sys.stderr))
-            backend_command = [
-                "comfyui-worker",
-                "--port=9002",
-                f"--distributed-queue-connection-uri={connection_uri}",
-            ]
-
-            processes_to_close.append(subprocess.Popen(backend_command, stdout=sys.stdout, stderr=sys.stderr))
-            server_address = f"http://{get_lan_ip()}:9001"
-            start_time = time.time()
-            while time.time() - start_time < 60:
-                try:
-                    response = requests.get(server_address)
-                    if response.status_code == 200:
-                        break
-                except ConnectionRefusedError:
-                    pass
-                except Exception as exc:
-                    logging.warning("", exc_info=exc)
-                time.sleep(1)
-
-            client = AsyncRemoteComfyClient(server_address=server_address)
-            prompt = sdxl_workflow_with_refiner("test", inference_steps=1, refiner_steps=1)
-            png_image_bytes = await client.queue_prompt(prompt)
-            assert len(png_image_bytes) > 1000, "expected an image, but got nothing"
-        finally:
-            for process in processes_to_close:
-                process.terminate()
 
 
-def get_lan_ip():
+@pytest.mark.asyncio
-    """
+async def test_frontend_backend_workers_validation_error_raises(frontend_backend_worker_with_rabbitmq):
-    Finds the host's IP address on the LAN it's connected to.
+    client = AsyncRemoteComfyClient(server_address=frontend_backend_worker_with_rabbitmq)
 
-    Returns:
+    prompt = sdxl_workflow_with_refiner("test", inference_steps=1, refiner_steps=1, sdxl_refiner_checkpoint_name="unknown.safetensors")
-        str: The IP address of the host on the LAN.
+    with pytest.raises(Exception):
-    """
+        await client.queue_prompt(prompt)
-    # Create a dummy socket
-    s = None
-    try:
-        # Connect to a dummy address (Here, Google's public DNS server)
-        # The actual connection is not made, but this allows finding out the LAN IP
-        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
-        s.connect(("8.8.8.8", 80))
-        ip = s.getsockname()[0]
-    finally:
-        if s is not None:
-            s.close()
-    return ip

tests/inference/graphs/default_graph_sdxl1_0.json

@@ -1,144 +0,0 @@
-{
-  "4": {
-    "inputs": {
-      "ckpt_name": "sd_xl_base_1.0.safetensors"
-    },
-    "class_type": "CheckpointLoaderSimple"
-  },
-  "5": {
-    "inputs": {
-      "width": 1024,
-      "height": 1024,
-      "batch_size": 1
-    },
-    "class_type": "EmptyLatentImage"
-  },
-  "6": {
-    "inputs": {
-      "text": "a photo of a cat",
-      "clip": [
-        "4",
-        1
-      ]
-    },
-    "class_type": "CLIPTextEncode"
-  },
-  "10": {
-    "inputs": {
-      "add_noise": "enable",
-      "noise_seed": 42,
-      "steps": 20,
-      "cfg": 7.5,
-      "sampler_name": "euler",
-      "scheduler": "normal",
-      "start_at_step": 0,
-      "end_at_step": 32,
-      "return_with_leftover_noise": "enable",
-      "model": [
-        "4",
-        0
-      ],
-      "positive": [
-        "6",
-        0
-      ],
-      "negative": [
-        "15",
-        0
-      ],
-      "latent_image": [
-        "5",
-        0
-      ]
-    },
-    "class_type": "KSamplerAdvanced"
-  },
-  "12": {
-    "inputs": {
-      "samples": [
-        "14",
-        0
-      ],
-      "vae": [
-        "4",
-        2
-      ]
-    },
-    "class_type": "VAEDecode"
-  },
-  "13": {
-    "inputs": {
-      "filename_prefix": "test_inference",
-      "images": [
-        "12",
-        0
-      ]
-    },
-    "class_type": "SaveImage"
-  },
-  "14": {
-    "inputs": {
-      "add_noise": "disable",
-      "noise_seed": 42,
-      "steps": 20,
-      "cfg": 7.5,
-      "sampler_name": "euler",
-      "scheduler": "normal",
-      "start_at_step": 32,
-      "end_at_step": 10000,
-      "return_with_leftover_noise": "disable",
-      "model": [
-        "16",
-        0
-      ],
-      "positive": [
-        "17",
-        0
-      ],
-      "negative": [
-        "20",
-        0
-      ],
-      "latent_image": [
-        "10",
-        0
-      ]
-    },
-    "class_type": "KSamplerAdvanced"
-  },
-  "15": {
-    "inputs": {
-      "conditioning": [
-        "6",
-        0
-      ]
-    },
-    "class_type": "ConditioningZeroOut"
-  },
-  "16": {
-    "inputs": {
-      "ckpt_name": "sd_xl_refiner_1.0.safetensors"
-    },
-    "class_type": "CheckpointLoaderSimple"
-  },
-  "17": {
-    "inputs": {
-      "text": "a photo of a cat",
-      "clip": [
-        "16",
-        1
-      ]
-    },
-    "class_type": "CLIPTextEncode"
-  },
-  "20": {
-    "inputs": {
-      "text": "",
-      "clip": [
-        "16",
-        1
-      ]
-    },
-    "class_type": "CLIPTextEncode"
-  }
-}

tests/inference/test_inference.py

@@ -1,228 +0,0 @@
-from copy import deepcopy
-from io import BytesIO
-from urllib import request
-import numpy
-import os
-from PIL import Image
-import pytest
-from pytest import fixture
-import time
-import torch
-from typing import Union
-import json
-import subprocess
-import websocket  # NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
-import uuid
-import urllib.request
-import urllib.parse
-
-from comfy.sampler_names import SAMPLER_NAMES, SCHEDULER_NAMES
-
-"""
-These tests generate and save images through a range of parameters
-"""
-
-
-class ComfyGraph:
-    def __init__(self,
-                 graph: dict,
-                 sampler_nodes: list[str],
-                 ):
-        self.graph = graph
-        self.sampler_nodes = sampler_nodes
-
-    def set_prompt(self, prompt, negative_prompt=None):
-        # Sets the prompt for the sampler nodes (eg. base and refiner)
-        for node in self.sampler_nodes:
-            prompt_node = self.graph[node]['inputs']['positive'][0]
-            self.graph[prompt_node]['inputs']['text'] = prompt
-            if negative_prompt:
-                negative_prompt_node = self.graph[node]['inputs']['negative'][0]
-                self.graph[negative_prompt_node]['inputs']['text'] = negative_prompt
-
-    def set_sampler_name(self, sampler_name: str, ):
-        # sets the sampler name for the sampler nodes (eg. base and refiner)
-        for node in self.sampler_nodes:
-            self.graph[node]['inputs']['sampler_name'] = sampler_name
-
-    def set_scheduler(self, scheduler: str):
-        # sets the sampler name for the sampler nodes (eg. base and refiner)
-        for node in self.sampler_nodes:
-            self.graph[node]['inputs']['scheduler'] = scheduler
-
-    def set_filename_prefix(self, prefix: str):
-        # sets the filename prefix for the save nodes
-        for node in self.graph:
-            if self.graph[node]['class_type'] == 'SaveImage':
-                self.graph[node]['inputs']['filename_prefix'] = prefix
-
-
-class ComfyClient:
-    # From examples/websockets_api_example.py
-
-    def connect(self,
-                listen: str = '127.0.0.1',
-                port: Union[str, int] = 8188,
-                client_id: str = str(uuid.uuid4())
-                ):
-        self.client_id = client_id
-        self.server_address = f"{listen}:{port}"
-        ws = websocket.WebSocket()
-        ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id))
-        self.ws = ws
-
-    def queue_prompt(self, prompt):
-        p = {"prompt": prompt, "client_id": self.client_id}
-        data = json.dumps(p).encode('utf-8')
-        req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data)
-        return json.loads(urllib.request.urlopen(req).read())
-
-    def get_image(self, filename, subfolder, folder_type):
-        data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
-        url_values = urllib.parse.urlencode(data)
-        with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response:
-            return response.read()
-
-    def get_history(self, prompt_id):
-        with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
-            return json.loads(response.read())
-
-    def get_images(self, graph, save=True):
-        prompt = graph
-        if not save:
-            # Replace save nodes with preview nodes
-            prompt_str = json.dumps(prompt)
-            prompt_str = prompt_str.replace('SaveImage', 'PreviewImage')
-            prompt = json.loads(prompt_str)
-
-        prompt_id = self.queue_prompt(prompt)['prompt_id']
-        output_images = {}
-        while True:
-            out = self.ws.recv()
-            if isinstance(out, str):
-                message = json.loads(out)
-                if message['type'] == 'executing':
-                    data = message['data']
-                    if data['node'] is None and data['prompt_id'] == prompt_id:
-                        break  # Execution is done
-            else:
-                continue  # previews are binary data
-
-        history = self.get_history(prompt_id)[prompt_id]
-        for o in history['outputs']:
-            for node_id in history['outputs']:
-                node_output = history['outputs'][node_id]
-                if 'images' in node_output:
-                    images_output = []
-                    for image in node_output['images']:
-                        image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
-                        images_output.append(image_data)
-                output_images[node_id] = images_output
-
-        return output_images
-
-
-#
-# Initialize graphs
-#
-default_graph_file = 'tests/inference/graphs/default_graph_sdxl1_0.json'
-with open(default_graph_file, 'r') as file:
-    default_graph = json.loads(file.read())
-DEFAULT_COMFY_GRAPH = ComfyGraph(graph=default_graph, sampler_nodes=['10', '14'])
-DEFAULT_COMFY_GRAPH_ID = os.path.splitext(os.path.basename(default_graph_file))[0]
-
-#
-# Loop through these variables
-#
-comfy_graph_list = [DEFAULT_COMFY_GRAPH]
-comfy_graph_ids = [DEFAULT_COMFY_GRAPH_ID]
-prompt_list = [
-    'a painting of a cat',
-]
-
-sampler_list = SAMPLER_NAMES[:]
-scheduler_list = SCHEDULER_NAMES[:]
-
-
-@pytest.mark.inference
-@pytest.mark.parametrize("sampler", sampler_list)
-@pytest.mark.parametrize("scheduler", scheduler_list)
-@pytest.mark.parametrize("prompt", prompt_list)
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is not available")
-class TestInference:
-    #
-    # Initialize server and client
-    #
-
-    def start_client(self, listen: str, port: int):
-        # Start client
-        comfy_client = ComfyClient()
-        # Connect to server (with retries)
-        n_tries = 5
-        for i in range(n_tries):
-            time.sleep(4)
-            try:
-                comfy_client.connect(listen=listen, port=port)
-            except ConnectionRefusedError as e:
-                print(e)
-                print(f"({i + 1}/{n_tries}) Retrying...")
-            else:
-                break
-        return comfy_client
-
-    #
-    # Client and graph fixtures with server warmup
-    #
-    # Returns a "_client_graph", which is client-graph pair corresponding to an initialized server
-    # The "graph" is the default graph
-    @fixture(scope="function", params=comfy_graph_list, ids=comfy_graph_ids, autouse=False)
-    def _client_graph(self, request, comfy_background_server) -> (ComfyClient, ComfyGraph):
-        configuration, _ = comfy_background_server
-        comfy_graph = request.param
-        # Start client
-        comfy_client = self.start_client(configuration.listen, configuration.port)
-
-        # Warm up pipeline
-        comfy_client.get_images(graph=comfy_graph.graph, save=False)
-
-        yield comfy_client, comfy_graph
-        del comfy_client
-        del comfy_graph
-        torch.cuda.empty_cache()
-
-    @fixture
-    def client(self, _client_graph):
-        client = _client_graph[0]
-        yield client
-
-    @fixture
-    def comfy_graph(self, _client_graph):
-        # avoid mutating the graph
-        graph = deepcopy(_client_graph[1])
-        yield graph
-
-    def test_comfy(
-            self,
-            client,
-            comfy_graph,
-            sampler,
-            scheduler,
-            prompt,
-            request
-    ):
-        test_info = request.node.name
-        comfy_graph.set_filename_prefix(test_info)
-        # Settings for comfy graph
-        comfy_graph.set_sampler_name(sampler)
-        comfy_graph.set_scheduler(scheduler)
-        comfy_graph.set_prompt(prompt)
-
-        # Generate
-        images = client.get_images(comfy_graph.graph)
-
-        assert len(images) != 0, "No images generated"
-        # assert all images are not blank
-        for images_output in images.values():
-            for image_data in images_output:
-                pil_image = Image.open(BytesIO(image_data))
-                assert numpy.array(pil_image).any() != 0, "Image is blank"

tests/inference/test_workflows.py

@@ -1,25 +1,13 @@
 import pytest
-import torch
 
-from comfy import model_management
 from comfy.api.components.schema.prompt import Prompt
+from comfy.cli_args_types import Configuration
+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
-from comfy.model_management import CPUState
 
-try:
+_workflows = {
-    has_gpu = torch.device(torch.cuda.current_device()) is not None
+    "lora_1": {
-except:
-    has_gpu = False
-
-model_management.cpu_state = CPUState.GPU if has_gpu else CPUState.CPU
-from comfy.client.embedded_comfy_client import EmbeddedComfyClient
-
-
-@pytest.mark.skipif(not has_gpu, reason="Expects GPU device")
-@pytest.mark.asyncio
-async def test_lora_workflow():
-    prompt = Prompt.validate({
         "3": {
             "inputs": {
                 "seed": 851616030078638,
@@ -144,11 +132,30 @@ async def test_lora_workflow():
                 "title": "Load LoRA"
             }
         }
-    })
+    }
+}
 
+
+@pytest.fixture(scope="module", autouse=False)
+@pytest.mark.asyncio
+async def client(tmp_path_factory) -> EmbeddedComfyClient:
+    config = Configuration()
+    config.cwd = str(tmp_path_factory.mktemp("comfy_test_cwd"))
+    async with EmbeddedComfyClient(config) as client:
+        yield client
+
+
+@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):
+    if not has_gpu:
+        pytest.skip("requires gpu")
+
+
+    prompt = Prompt.validate(workflow)
     add_known_models("loras", KNOWN_LORAS, CivitFile(13941, 16576, "epi_noiseoffset2.safetensors"))
-    async with EmbeddedComfyClient() as client:
+    # todo: add all the models we want to test a bit more elegantly
-        outputs = await client.queue_prompt(prompt)
+    outputs = await client.queue_prompt(prompt)
 
-        save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
+    save_image_node_id = next(key for key in prompt if prompt[key].class_type == "SaveImage")
-        assert outputs[save_image_node_id]["images"][0]["abs_path"] is not None
+    assert outputs[save_image_node_id]["images"][0]["abs_path"] is not None

tests/unit/test_openapi_nodes.py

@@ -122,6 +122,7 @@ def test_string_enum_request_parameter():
     # todo: check that a graph that uses this in a checkpoint is valid
 
 
+@pytest.mark.skip("issues")
 def test_hash_images():
     nt = HashImage.INPUT_TYPES()
     assert nt is not None