mirror of
https://github.com/comfyanonymous/ComfyUI.git
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2b653e8c18
* Support for async execution functions This commit adds support for node execution functions defined as async. When a node's execution function is defined as async, we can continue executing other nodes while it is processing. Standard uses of `await` should "just work", but people will still have to be careful if they spawn actual threads. Because torch doesn't really have async/await versions of functions, this won't particularly help with most locally-executing nodes, but it does work for e.g. web requests to other machines. In addition to the execute function, the `VALIDATE_INPUTS` and `check_lazy_status` functions can also be defined as async, though we'll only resolve one node at a time right now for those. * Add the execution model tests to CI * Add a missing file It looks like this got caught by .gitignore? There's probably a better place to put it, but I'm not sure what that is. * Add the websocket library for automated tests * Add additional tests for async error cases Also fixes one bug that was found when an async function throws an error after being scheduled on a task. * Add a feature flags message to reduce bandwidth We now only send 1 preview message of the latest type the client can support. We'll add a console warning when the client fails to send a feature flags message at some point in the future. * Add async tests to CI * Don't actually add new tests in this PR Will do it in a separate PR * Resolve unit test in GPU-less runner * Just remove the tests that GHA can't handle * Change line endings to UNIX-style * Avoid loading model_management.py so early Because model_management.py has a top-level `logging.info`, we have to be careful not to import that file before we call `setup_logging`. If we do, we end up having the default logging handler registered in addition to our custom one.
588 lines
27 KiB
Python
588 lines
27 KiB
Python
from io import BytesIO
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import numpy
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from PIL import Image
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import pytest
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from pytest import fixture
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import time
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import torch
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from typing import Union, Dict
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import json
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import subprocess
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import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
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import uuid
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import urllib.request
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import urllib.parse
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import urllib.error
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from comfy_execution.graph_utils import GraphBuilder, Node
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class RunResult:
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def __init__(self, prompt_id: str):
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self.outputs: Dict[str,Dict] = {}
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self.runs: Dict[str,bool] = {}
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self.prompt_id: str = prompt_id
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def get_output(self, node: Node):
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return self.outputs.get(node.id, None)
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def did_run(self, node: Node):
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return self.runs.get(node.id, False)
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def get_images(self, node: Node):
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output = self.get_output(node)
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if output is None:
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return []
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return output.get('image_objects', [])
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def get_prompt_id(self):
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return self.prompt_id
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class ComfyClient:
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def __init__(self):
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self.test_name = ""
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def connect(self,
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listen:str = '127.0.0.1',
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port:Union[str,int] = 8188,
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client_id: str = str(uuid.uuid4())
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):
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self.client_id = client_id
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self.server_address = f"{listen}:{port}"
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ws = websocket.WebSocket()
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ws.connect("ws://{}/ws?clientId={}".format(self.server_address, self.client_id))
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self.ws = ws
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def queue_prompt(self, prompt):
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p = {"prompt": prompt, "client_id": self.client_id}
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data = json.dumps(p).encode('utf-8')
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req = urllib.request.Request("http://{}/prompt".format(self.server_address), data=data)
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return json.loads(urllib.request.urlopen(req).read())
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def get_image(self, filename, subfolder, folder_type):
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data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
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url_values = urllib.parse.urlencode(data)
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with urllib.request.urlopen("http://{}/view?{}".format(self.server_address, url_values)) as response:
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return response.read()
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def get_history(self, prompt_id):
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with urllib.request.urlopen("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
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return json.loads(response.read())
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def set_test_name(self, name):
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self.test_name = name
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def run(self, graph):
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prompt = graph.finalize()
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for node in graph.nodes.values():
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if node.class_type == 'SaveImage':
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node.inputs['filename_prefix'] = self.test_name
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prompt_id = self.queue_prompt(prompt)['prompt_id']
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result = RunResult(prompt_id)
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while True:
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out = self.ws.recv()
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if isinstance(out, str):
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message = json.loads(out)
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if message['type'] == 'executing':
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data = message['data']
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if data['prompt_id'] != prompt_id:
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continue
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if data['node'] is None:
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break
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result.runs[data['node']] = True
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elif message['type'] == 'execution_error':
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raise Exception(message['data'])
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elif message['type'] == 'execution_cached':
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pass # Probably want to store this off for testing
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history = self.get_history(prompt_id)[prompt_id]
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for node_id in history['outputs']:
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node_output = history['outputs'][node_id]
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result.outputs[node_id] = node_output
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images_output = []
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if 'images' in node_output:
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for image in node_output['images']:
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image_data = self.get_image(image['filename'], image['subfolder'], image['type'])
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image_obj = Image.open(BytesIO(image_data))
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images_output.append(image_obj)
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node_output['image_objects'] = images_output
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return result
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#
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# Loop through these variables
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#
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@pytest.mark.execution
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class TestExecution:
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#
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# Initialize server and client
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#
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@fixture(scope="class", autouse=True, params=[
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# (use_lru, lru_size)
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(False, 0),
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(True, 0),
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(True, 100),
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])
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def _server(self, args_pytest, request):
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# Start server
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pargs = [
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'python','main.py',
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'--output-directory', args_pytest["output_dir"],
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'--listen', args_pytest["listen"],
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'--port', str(args_pytest["port"]),
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'--extra-model-paths-config', 'tests/inference/extra_model_paths.yaml',
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]
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use_lru, lru_size = request.param
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if use_lru:
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pargs += ['--cache-lru', str(lru_size)]
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print("Running server with args:", pargs) # noqa: T201
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p = subprocess.Popen(pargs)
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yield
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p.kill()
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torch.cuda.empty_cache()
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def start_client(self, listen:str, port:int):
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# Start client
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comfy_client = ComfyClient()
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# Connect to server (with retries)
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n_tries = 5
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for i in range(n_tries):
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time.sleep(4)
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try:
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comfy_client.connect(listen=listen, port=port)
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except ConnectionRefusedError as e:
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print(e) # noqa: T201
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print(f"({i+1}/{n_tries}) Retrying...") # noqa: T201
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else:
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break
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return comfy_client
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@fixture(scope="class", autouse=True)
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def shared_client(self, args_pytest, _server):
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client = self.start_client(args_pytest["listen"], args_pytest["port"])
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yield client
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del client
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torch.cuda.empty_cache()
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@fixture
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def client(self, shared_client, request):
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shared_client.set_test_name(f"execution[{request.node.name}]")
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yield shared_client
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@fixture
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def builder(self, request):
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yield GraphBuilder(prefix=request.node.name)
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def test_lazy_input(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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mask = g.node("StubMask", value=0.0, height=512, width=512, batch_size=1)
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lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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output = g.node("SaveImage", images=lazy_mix.out(0))
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result = client.run(g)
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result_image = result.get_images(output)[0]
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assert numpy.array(result_image).any() == 0, "Image should be black"
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assert result.did_run(input1)
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assert not result.did_run(input2)
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assert result.did_run(mask)
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assert result.did_run(lazy_mix)
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def test_full_cache(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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g.node("SaveImage", images=lazy_mix.out(0))
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client.run(g)
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result2 = client.run(g)
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for node_id, node in g.nodes.items():
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assert not result2.did_run(node), f"Node {node_id} ran, but should have been cached"
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def test_partial_cache(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="NOISE", height=512, width=512, batch_size=1)
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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g.node("SaveImage", images=lazy_mix.out(0))
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client.run(g)
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mask.inputs['value'] = 0.4
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result2 = client.run(g)
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assert not result2.did_run(input1), "Input1 should have been cached"
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assert not result2.did_run(input2), "Input2 should have been cached"
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def test_error(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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# Different size of the two images
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input2 = g.node("StubImage", content="NOISE", height=256, width=256, batch_size=1)
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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lazy_mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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g.node("SaveImage", images=lazy_mix.out(0))
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try:
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client.run(g)
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assert False, "Should have raised an error"
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except Exception as e:
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assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
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@pytest.mark.parametrize("test_value, expect_error", [
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(5, True),
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("foo", True),
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(5.0, False),
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])
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def test_validation_error_literal(self, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
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g = builder
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validation1 = g.node("TestCustomValidation1", input1=test_value, input2=3.0)
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g.node("SaveImage", images=validation1.out(0))
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if expect_error:
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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else:
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client.run(g)
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@pytest.mark.parametrize("test_type, test_value", [
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("StubInt", 5),
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("StubMask", 5.0)
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])
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def test_validation_error_edge1(self, test_type, test_value, client: ComfyClient, builder: GraphBuilder):
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g = builder
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stub = g.node(test_type, value=test_value)
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validation1 = g.node("TestCustomValidation1", input1=stub.out(0), input2=3.0)
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g.node("SaveImage", images=validation1.out(0))
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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@pytest.mark.parametrize("test_type, test_value, expect_error", [
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("StubInt", 5, True),
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("StubFloat", 5.0, False)
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])
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def test_validation_error_edge2(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
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g = builder
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stub = g.node(test_type, value=test_value)
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validation2 = g.node("TestCustomValidation2", input1=stub.out(0), input2=3.0)
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g.node("SaveImage", images=validation2.out(0))
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if expect_error:
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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else:
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client.run(g)
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@pytest.mark.parametrize("test_type, test_value, expect_error", [
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("StubInt", 5, True),
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("StubFloat", 5.0, False)
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])
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def test_validation_error_edge3(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
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g = builder
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stub = g.node(test_type, value=test_value)
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validation3 = g.node("TestCustomValidation3", input1=stub.out(0), input2=3.0)
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g.node("SaveImage", images=validation3.out(0))
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if expect_error:
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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else:
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client.run(g)
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@pytest.mark.parametrize("test_type, test_value, expect_error", [
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("StubInt", 5, True),
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("StubFloat", 5.0, False)
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])
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def test_validation_error_edge4(self, test_type, test_value, expect_error, client: ComfyClient, builder: GraphBuilder):
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g = builder
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stub = g.node(test_type, value=test_value)
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validation4 = g.node("TestCustomValidation4", input1=stub.out(0), input2=3.0)
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g.node("SaveImage", images=validation4.out(0))
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if expect_error:
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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else:
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client.run(g)
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@pytest.mark.parametrize("test_value1, test_value2, expect_error", [
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(0.0, 0.5, False),
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(0.0, 5.0, False),
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(0.0, 7.0, True)
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])
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def test_validation_error_kwargs(self, test_value1, test_value2, expect_error, client: ComfyClient, builder: GraphBuilder):
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g = builder
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validation5 = g.node("TestCustomValidation5", input1=test_value1, input2=test_value2)
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g.node("SaveImage", images=validation5.out(0))
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if expect_error:
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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else:
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client.run(g)
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def test_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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lazy_mix1 = g.node("TestLazyMixImages", image1=input1.out(0), mask=mask.out(0))
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lazy_mix2 = g.node("TestLazyMixImages", image1=lazy_mix1.out(0), image2=input2.out(0), mask=mask.out(0))
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g.node("SaveImage", images=lazy_mix2.out(0))
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# When the cycle exists on initial submission, it should raise a validation error
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with pytest.raises(urllib.error.HTTPError):
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client.run(g)
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def test_dynamic_cycle_error(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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generator = g.node("TestDynamicDependencyCycle", input1=input1.out(0), input2=input2.out(0))
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g.node("SaveImage", images=generator.out(0))
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# When the cycle is in a graph that is generated dynamically, it should raise a runtime error
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try:
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client.run(g)
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assert False, "Should have raised an error"
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except Exception as e:
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assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
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assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
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def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
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input3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
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mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
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mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
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mix2 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input3.out(0), mask=mask.out(0))
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# We have multiple outputs. The first is invalid, but the second is valid
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g.node("SaveImage", images=mix1.out(0))
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g.node("SaveImage", images=mix2.out(0))
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g.remove_node("removeme")
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client.run(g)
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# Add back in the missing node to make sure the error doesn't break the server
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input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
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client.run(g)
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def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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# Creating the nodes in this specific order previously caused a bug
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save = g.node("SaveImage")
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is_changed = g.node("TestCustomIsChanged", should_change=False)
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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save.set_input('images', is_changed.out(0))
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is_changed.set_input('image', input1.out(0))
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result1 = client.run(g)
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result2 = client.run(g)
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is_changed.set_input('should_change', True)
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result3 = client.run(g)
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result4 = client.run(g)
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assert result1.did_run(is_changed), "is_changed should have been run"
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assert not result2.did_run(is_changed), "is_changed should have been cached"
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assert result3.did_run(is_changed), "is_changed should have been re-run"
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assert result4.did_run(is_changed), "is_changed should not have been cached"
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def test_undeclared_inputs(self, client: ComfyClient, builder: GraphBuilder):
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g = builder
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input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
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input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
input3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
input4 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
average = g.node("TestVariadicAverage", input1=input1.out(0), input2=input2.out(0), input3=input3.out(0), input4=input4.out(0))
|
|
output = g.node("SaveImage", images=average.out(0))
|
|
|
|
result = client.run(g)
|
|
result_image = result.get_images(output)[0]
|
|
expected = 255 // 4
|
|
assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
|
|
|
|
def test_for_loop(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
iterations = 4
|
|
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
is_changed = g.node("TestCustomIsChanged", should_change=True, image=input2.out(0))
|
|
for_open = g.node("TestForLoopOpen", remaining=iterations, initial_value1=is_changed.out(0))
|
|
average = g.node("TestVariadicAverage", input1=input1.out(0), input2=for_open.out(2))
|
|
for_close = g.node("TestForLoopClose", flow_control=for_open.out(0), initial_value1=average.out(0))
|
|
output = g.node("SaveImage", images=for_close.out(0))
|
|
|
|
for iterations in range(1, 5):
|
|
for_open.set_input('remaining', iterations)
|
|
result = client.run(g)
|
|
result_image = result.get_images(output)[0]
|
|
expected = 255 // (2 ** iterations)
|
|
assert numpy.array(result_image).min() == expected and numpy.array(result_image).max() == expected, "Image should be grey"
|
|
assert result.did_run(is_changed)
|
|
|
|
def test_mixed_expansion_returns(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
val_list = g.node("TestMakeListNode", value1=0.1, value2=0.2, value3=0.3)
|
|
mixed = g.node("TestMixedExpansionReturns", input1=val_list.out(0))
|
|
output_dynamic = g.node("SaveImage", images=mixed.out(0))
|
|
output_literal = g.node("SaveImage", images=mixed.out(1))
|
|
|
|
result = client.run(g)
|
|
images_dynamic = result.get_images(output_dynamic)
|
|
assert len(images_dynamic) == 3, "Should have 2 images"
|
|
assert numpy.array(images_dynamic[0]).min() == 25 and numpy.array(images_dynamic[0]).max() == 25, "First image should be 0.1"
|
|
assert numpy.array(images_dynamic[1]).min() == 51 and numpy.array(images_dynamic[1]).max() == 51, "Second image should be 0.2"
|
|
assert numpy.array(images_dynamic[2]).min() == 76 and numpy.array(images_dynamic[2]).max() == 76, "Third image should be 0.3"
|
|
|
|
images_literal = result.get_images(output_literal)
|
|
assert len(images_literal) == 3, "Should have 2 images"
|
|
for i in range(3):
|
|
assert numpy.array(images_literal[i]).min() == 255 and numpy.array(images_literal[i]).max() == 255, "All images should be white"
|
|
|
|
def test_mixed_lazy_results(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
val_list = g.node("TestMakeListNode", value1=0.0, value2=0.5, value3=1.0)
|
|
mask = g.node("StubMask", value=val_list.out(0), height=512, width=512, batch_size=1)
|
|
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
input2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
mix = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
|
|
rebatch = g.node("RebatchImages", images=mix.out(0), batch_size=3)
|
|
output = g.node("SaveImage", images=rebatch.out(0))
|
|
|
|
result = client.run(g)
|
|
images = result.get_images(output)
|
|
assert len(images) == 3, "Should have 3 image"
|
|
assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be 0.0"
|
|
assert numpy.array(images[1]).min() == 127 and numpy.array(images[1]).max() == 127, "Second image should be 0.5"
|
|
assert numpy.array(images[2]).min() == 255 and numpy.array(images[2]).max() == 255, "Third image should be 1.0"
|
|
|
|
def test_output_reuse(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
|
|
output1 = g.node("SaveImage", images=input1.out(0))
|
|
output2 = g.node("SaveImage", images=input1.out(0))
|
|
|
|
result = client.run(g)
|
|
images1 = result.get_images(output1)
|
|
images2 = result.get_images(output2)
|
|
assert len(images1) == 1, "Should have 1 image"
|
|
assert len(images2) == 1, "Should have 1 image"
|
|
|
|
|
|
# This tests that only constant outputs are used in the call to `IS_CHANGED`
|
|
def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
|
|
test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
|
|
|
|
output = g.node("PreviewImage", images=test_node.out(0))
|
|
|
|
result = client.run(g)
|
|
images = result.get_images(output)
|
|
assert len(images) == 1, "Should have 1 image"
|
|
assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
|
|
|
|
result = client.run(g)
|
|
images = result.get_images(output)
|
|
assert len(images) == 1, "Should have 1 image"
|
|
assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
|
|
assert not result.did_run(test_node), "The execution should have been cached"
|
|
|
|
def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
|
|
# Create sleep nodes for each duration
|
|
sleep_node1 = g.node("TestSleep", value=image.out(0), seconds=2.8)
|
|
sleep_node2 = g.node("TestSleep", value=image.out(0), seconds=2.9)
|
|
sleep_node3 = g.node("TestSleep", value=image.out(0), seconds=3.0)
|
|
|
|
# Add outputs to verify the execution
|
|
_output1 = g.node("PreviewImage", images=sleep_node1.out(0))
|
|
_output2 = g.node("PreviewImage", images=sleep_node2.out(0))
|
|
_output3 = g.node("PreviewImage", images=sleep_node3.out(0))
|
|
|
|
start_time = time.time()
|
|
result = client.run(g)
|
|
elapsed_time = time.time() - start_time
|
|
|
|
# The test should take around 0.4 seconds (the longest sleep duration)
|
|
# plus some overhead, but definitely less than the sum of all sleeps (0.9s)
|
|
# We'll allow for up to 0.8s total to account for overhead
|
|
assert elapsed_time < 4.0, f"Parallel execution took {elapsed_time}s, expected less than 0.8s"
|
|
|
|
# Verify that all nodes executed
|
|
assert result.did_run(sleep_node1), "Sleep node 1 should have run"
|
|
assert result.did_run(sleep_node2), "Sleep node 2 should have run"
|
|
assert result.did_run(sleep_node3), "Sleep node 3 should have run"
|
|
|
|
def test_parallel_sleep_expansion(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
# Create input images with different values
|
|
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
image3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
|
|
# Create a TestParallelSleep node that expands into multiple TestSleep nodes
|
|
parallel_sleep = g.node("TestParallelSleep",
|
|
image1=image1.out(0),
|
|
image2=image2.out(0),
|
|
image3=image3.out(0),
|
|
sleep1=0.4,
|
|
sleep2=0.5,
|
|
sleep3=0.6)
|
|
output = g.node("SaveImage", images=parallel_sleep.out(0))
|
|
|
|
start_time = time.time()
|
|
result = client.run(g)
|
|
elapsed_time = time.time() - start_time
|
|
|
|
# Similar to the previous test, expect parallel execution of the sleep nodes
|
|
# which should complete in less than the sum of all sleeps
|
|
assert elapsed_time < 0.8, f"Expansion execution took {elapsed_time}s, expected less than 0.8s"
|
|
|
|
# Verify the parallel sleep node executed
|
|
assert result.did_run(parallel_sleep), "ParallelSleep node should have run"
|
|
|
|
# Verify we get an image as output (blend of the three input images)
|
|
result_images = result.get_images(output)
|
|
assert len(result_images) == 1, "Should have 1 image"
|
|
# Average pixel value should be around 170 (255 * 2 // 3)
|
|
avg_value = numpy.array(result_images[0]).mean()
|
|
assert avg_value == 170, f"Image average value {avg_value} should be 170"
|
|
|
|
# This tests that nodes with OUTPUT_IS_LIST function correctly when they receive an ExecutionBlocker
|
|
# as input. We also test that when that list (containing an ExecutionBlocker) is passed to a node,
|
|
# only that one entry in the list is blocked.
|
|
def test_execution_block_list_output(self, client: ComfyClient, builder: GraphBuilder):
|
|
g = builder
|
|
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
image2 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
|
|
image3 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
|
|
image_list = g.node("TestMakeListNode", value1=image1.out(0), value2=image2.out(0), value3=image3.out(0))
|
|
int1 = g.node("StubInt", value=1)
|
|
int2 = g.node("StubInt", value=2)
|
|
int3 = g.node("StubInt", value=3)
|
|
int_list = g.node("TestMakeListNode", value1=int1.out(0), value2=int2.out(0), value3=int3.out(0))
|
|
compare = g.node("TestIntConditions", a=int_list.out(0), b=2, operation="==")
|
|
blocker = g.node("TestExecutionBlocker", input=image_list.out(0), block=compare.out(0), verbose=False)
|
|
|
|
list_output = g.node("TestMakeListNode", value1=blocker.out(0))
|
|
output = g.node("PreviewImage", images=list_output.out(0))
|
|
|
|
result = client.run(g)
|
|
assert result.did_run(output), "The execution should have run"
|
|
images = result.get_images(output)
|
|
assert len(images) == 2, "Should have 2 images"
|
|
assert numpy.array(images[0]).min() == 0 and numpy.array(images[0]).max() == 0, "First image should be black"
|
|
assert numpy.array(images[1]).min() == 0 and numpy.array(images[1]).max() == 0, "Second image should also be black"
|