Ci quality workflows (#1423)

* Add inference tests

* Clean up

* Rename test graph file

* Add readme for tests

* Separate server fixture

* test file name change

* Assert images are generated

* Clean up comments

* Add __init__.py so tests can run with command line `pytest`

* Fix command line args for pytest

* Loop all samplers/schedulers in test_inference.py

* Ci quality workflows compare (#1)

* Add image comparison tests

* Comparison tests do not pass with empty metadata

* Ensure tests are run in correct order

* Save image files  with test name

* Update tests readme

* Reduce step counts in tests to ~halve runtime

* Ci quality workflows build (#2)

* Add build test github workflow

.github/workflows/test-build.yml

@@ -0,0 +1,31 @@
+name: Build package
+
+#
+# This workflow is a test of the python package build.
+# Install Python dependencies across different Python versions.
+#
+
+on:
+  push:
+    paths:
+      - "requirements.txt"
+      - ".github/workflows/test-build.yml"
+
+jobs:
+  build:
+    name: Build Test
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt

pytest.ini

@@ -0,0 +1,5 @@
+[pytest]
+markers = 
+  inference: mark as inference test (deselect with '-m "not inference"')
+testpaths = tests
+addopts = -s

tests/README.md

@@ -0,0 +1,29 @@
+# Automated Testing
+
+## Running tests locally
+
+Additional requirements for running tests:
+```
+pip install pytest
+pip install websocket-client==1.6.1
+opencv-python==4.6.0.66
+scikit-image==0.21.0
+```
+Run inference tests:
+```
+pytest tests/inference
+```
+
+## Quality regression test
+Compares images in 2 directories to ensure they are the same
+
+1) Run an inference test to save a directory of "ground truth" images
+```
+    pytest tests/inference --output_dir tests/inference/baseline
+```
+2) Make code edits
+
+3) Run inference and quality comparison tests
+```
+pytest
+```

tests/compare/conftest.py

@@ -0,0 +1,41 @@
+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=True)
+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 = []
+    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

@@ -0,0 +1,195 @@
+import datetime
+import numpy as np
+import os
+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", autouse=True)
+    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):
+        yield baseline_fname
+        del baseline_fname
+    
+    def test_directories_not_empty(self, args_pytest):
+        baseline_dir = args_pytest['baseline_dir']
+        test_dir = args_pytest['test_dir']
+        assert len(os.listdir(baseline_dir)) != 0, f"Baseline directory {baseline_dir} is empty"
+        assert len(os.listdir(test_dir)) != 0, f"Test directory {test_dir} is empty"
+
+    def test_dir_has_all_matching_metadata(self, fname, test_file_names, args_pytest):
+        # 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}"
+
+    # 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())
+    def test_pipeline_compare(
+        self,
+        args_pytest,
+        fname,
+        test_file_names,
+        metric,
+    ):
+        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']
+        
+        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

@@ -0,0 +1,36 @@
+import os
+import pytest
+
+# Command line arguments for pytest
+def pytest_addoption(parser):
+    parser.addoption('--output_dir', action="store", default='tests/inference/samples', help='Output directory for generated images')
+    parser.addoption("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
+    parser.addoption("--port", type=int, default=8188, help="Set the listen port.")
+
+# This initializes args at the beginning of the test session
+@pytest.fixture(scope="session", autouse=True)
+def args_pytest(pytestconfig):
+    args = {}
+    args['output_dir'] = pytestconfig.getoption('output_dir')
+    args['listen'] = pytestconfig.getoption('listen')
+    args['port'] = pytestconfig.getoption('port')
+
+    os.makedirs(args['output_dir'], exist_ok=True)
+
+    return args
+
+def pytest_collection_modifyitems(items):
+    # Modifies items so tests run in the correct order
+    
+    LAST_TESTS = ['test_quality']
+
+    # Move the last items to the end
+    last_items = []
+    for test_name in LAST_TESTS:
+        for item in items.copy():
+            print(item.module.__name__, item)
+            if item.module.__name__  == test_name:
+                last_items.append(item)
+                items.remove(item)
+
+    items.extend(last_items)

tests/inference/graphs/default_graph_sdxl1_0.json

@@ -0,0 +1,144 @@
+{
+    "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

@@ -0,0 +1,247 @@
+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
+
+# Currently causes an error when running pytest with built-in pytest args
+# TODO: modify cli_args.py to not parse args on import
+# We will hard-code sampler and scheduler lists for now
+# from comfy.samplers import KSampler
+
+"""
+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',
+]
+#TODO use sampler and scheduler list from comfy.samplers.KSampler
+# sampler_list = KSampler.SAMPLERS
+# scheduler_list = KSampler.SCHEDULERS
+# Hard coded sampler and scheduler lists for now
+SCHEDULERS = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+SAMPLERS = ["euler", "euler_ancestral", "heun", "dpm_2", "dpm_2_ancestral",
+                "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
+                "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddim", "uni_pc", "uni_pc_bh2"]
+sampler_list = SAMPLERS
+scheduler_list = SCHEDULERS
+@pytest.mark.inference
+@pytest.mark.parametrize("sampler", sampler_list)
+@pytest.mark.parametrize("scheduler", scheduler_list)
+@pytest.mark.parametrize("prompt", prompt_list)
+class TestInference:
+    #
+    # Initialize server and client
+    #
+    @fixture(scope="class", autouse=True)
+    def _server(self, args_pytest):
+        # Start server
+        p = subprocess.Popen([
+                'python','main.py', 
+                '--output-directory', args_pytest["output_dir"],
+                '--listen', args_pytest["listen"],
+                '--port', str(args_pytest["port"]),
+                ])
+        yield
+        p.kill()
+        torch.cuda.empty_cache()
+
+    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="class", params=comfy_graph_list, ids=comfy_graph_ids, autouse=True)
+    def _client_graph(self, request, args_pytest, _server) -> (ComfyClient, ComfyGraph):
+        comfy_graph = request.param
+        
+        # Start client
+        comfy_client = self.start_client(args_pytest["listen"], args_pytest["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"
+
+