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Add the execution model tests to CI

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This commit is contained in:
Jacob Segal 2025-06-13 20:54:11 -07:00
parent 46c8311d14
commit 50bf72f852
3 changed files with 34 additions and 30 deletions
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4
.github/workflows/test-unit.yml vendored
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@ -28,3 +28,7 @@ jobs:
run: | run: |
pip install -r tests-unit/requirements.txt pip install -r tests-unit/requirements.txt
python -m pytest tests-unit python -m pytest tests-unit
- name: Run Execution Model Tests
run: |
python -m pytest tests/inference/test_execution.py

34
tests/inference/test_execution.py
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@ -500,59 +500,59 @@ class TestExecution:
def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder): def test_parallel_sleep_nodes(self, client: ComfyClient, builder: GraphBuilder):
g = builder g = builder
image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) image = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
# Create sleep nodes for each duration # Create sleep nodes for each duration
sleep_node1 = g.node("TestSleep", value=image.out(0), seconds=2.8) 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_node2 = g.node("TestSleep", value=image.out(0), seconds=2.9)
sleep_node3 = g.node("TestSleep", value=image.out(0), seconds=3.0) sleep_node3 = g.node("TestSleep", value=image.out(0), seconds=3.0)
# Add outputs to verify the execution # Add outputs to verify the execution
output1 = g.node("PreviewImage", images=sleep_node1.out(0)) _output1 = g.node("PreviewImage", images=sleep_node1.out(0))
output2 = g.node("PreviewImage", images=sleep_node2.out(0)) _output2 = g.node("PreviewImage", images=sleep_node2.out(0))
output3 = g.node("PreviewImage", images=sleep_node3.out(0)) _output3 = g.node("PreviewImage", images=sleep_node3.out(0))
start_time = time.time() start_time = time.time()
result = client.run(g) result = client.run(g)
elapsed_time = time.time() - start_time elapsed_time = time.time() - start_time
# The test should take around 0.4 seconds (the longest sleep duration) # 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) # 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 # 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" assert elapsed_time < 4.0, f"Parallel execution took {elapsed_time}s, expected less than 0.8s"
# Verify that all nodes executed # Verify that all nodes executed
assert result.did_run(sleep_node1), "Sleep node 1 should have run" 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_node2), "Sleep node 2 should have run"
assert result.did_run(sleep_node3), "Sleep node 3 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): def test_parallel_sleep_expansion(self, client: ComfyClient, builder: GraphBuilder):
g = builder g = builder
# Create input images with different values # Create input images with different values
image1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1) 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) 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) image3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
# Create a TestParallelSleep node that expands into multiple TestSleep nodes # Create a TestParallelSleep node that expands into multiple TestSleep nodes
parallel_sleep = g.node("TestParallelSleep", parallel_sleep = g.node("TestParallelSleep",
image1=image1.out(0), image1=image1.out(0),
image2=image2.out(0), image2=image2.out(0),
image3=image3.out(0), image3=image3.out(0),
sleep1=0.4, sleep1=0.4,
sleep2=0.5, sleep2=0.5,
sleep3=0.6) sleep3=0.6)
output = g.node("SaveImage", images=parallel_sleep.out(0)) output = g.node("SaveImage", images=parallel_sleep.out(0))
start_time = time.time() start_time = time.time()
result = client.run(g) result = client.run(g)
elapsed_time = time.time() - start_time elapsed_time = time.time() - start_time
# Similar to the previous test, expect parallel execution of the sleep nodes # Similar to the previous test, expect parallel execution of the sleep nodes
# which should complete in less than the sum of all sleeps # 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" assert elapsed_time < 0.8, f"Expansion execution took {elapsed_time}s, expected less than 0.8s"
# Verify the parallel sleep node executed # Verify the parallel sleep node executed
assert result.did_run(parallel_sleep), "ParallelSleep node should have run" assert result.did_run(parallel_sleep), "ParallelSleep node should have run"
# Verify we get an image as output (blend of the three input images) # Verify we get an image as output (blend of the three input images)
result_images = result.get_images(output) result_images = result.get_images(output)
assert len(result_images) == 1, "Should have 1 image" assert len(result_images) == 1, "Should have 1 image"

26
tests/inference/testing_nodes/testing-pack/specific_tests.py
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@ -361,19 +361,19 @@ class TestSamplingInExpansion:
def sampling_in_expansion(self, model, clip, vae, seed, steps, cfg, prompt, negative_prompt): def sampling_in_expansion(self, model, clip, vae, seed, steps, cfg, prompt, negative_prompt):
g = GraphBuilder() g = GraphBuilder()
# Create a basic image generation workflow using the input model, clip and vae # Create a basic image generation workflow using the input model, clip and vae
# 1. Setup text prompts using the provided CLIP model # 1. Setup text prompts using the provided CLIP model
positive_prompt = g.node("CLIPTextEncode", positive_prompt = g.node("CLIPTextEncode",
text=prompt, text=prompt,
clip=clip) clip=clip)
negative_prompt = g.node("CLIPTextEncode", negative_prompt = g.node("CLIPTextEncode",
text=negative_prompt, text=negative_prompt,
clip=clip) clip=clip)
# 2. Create empty latent with specified size # 2. Create empty latent with specified size
empty_latent = g.node("EmptyLatentImage", width=512, height=512, batch_size=1) empty_latent = g.node("EmptyLatentImage", width=512, height=512, batch_size=1)
# 3. Setup sampler and generate image latent # 3. Setup sampler and generate image latent
sampler = g.node("KSampler", sampler = g.node("KSampler",
model=model, model=model,
@ -385,10 +385,10 @@ class TestSamplingInExpansion:
cfg=cfg, cfg=cfg,
sampler_name="euler_ancestral", sampler_name="euler_ancestral",
scheduler="normal") scheduler="normal")
# 4. Decode latent to image using VAE # 4. Decode latent to image using VAE
output = g.node("VAEDecode", samples=sampler.out(0), vae=vae) output = g.node("VAEDecode", samples=sampler.out(0), vae=vae)
return { return {
"result": (output.out(0),), "result": (output.out(0),),
"expand": g.finalize(), "expand": g.finalize(),
@ -446,18 +446,18 @@ class TestParallelSleep(ComfyNodeABC):
def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id): def parallel_sleep(self, image1, image2, image3, sleep1, sleep2, sleep3, unique_id):
# Create a graph dynamically with three TestSleep nodes # Create a graph dynamically with three TestSleep nodes
g = GraphBuilder() g = GraphBuilder()
# Create sleep nodes for each duration and image # Create sleep nodes for each duration and image
sleep_node1 = g.node("TestSleep", value=image1, seconds=sleep1) sleep_node1 = g.node("TestSleep", value=image1, seconds=sleep1)
sleep_node2 = g.node("TestSleep", value=image2, seconds=sleep2) sleep_node2 = g.node("TestSleep", value=image2, seconds=sleep2)
sleep_node3 = g.node("TestSleep", value=image3, seconds=sleep3) sleep_node3 = g.node("TestSleep", value=image3, seconds=sleep3)
# Blend the results using TestVariadicAverage # Blend the results using TestVariadicAverage
blend = g.node("TestVariadicAverage", blend = g.node("TestVariadicAverage",
input1=sleep_node1.out(0), input1=sleep_node1.out(0),
input2=sleep_node2.out(0), input2=sleep_node2.out(0),
input3=sleep_node3.out(0)) input3=sleep_node3.out(0))
return { return {
"result": (blend.out(0),), "result": (blend.out(0),),
"expand": g.finalize(), "expand": g.finalize(),