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Fix dead code and logic error #8714

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Fix dead code and logic error #8714

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2ec6eb2
Fix dead code and logic error
ytl0623 Jan 21, 2026
778c953
fix missing logic in inferer tests
ytl0623 Jan 21, 2026
e52329c
fix stage_2 selection
ytl0623 Jan 21, 2026
d40604b
missing autoencoder instantiation for non-SPADE cases
ytl0623 Jan 21, 2026
a66124d
inconsistent SPADE handling
ytl0623 Jan 21, 2026
69c9dfb
replace assert_allclose with assert_close
ytl0623 Jan 21, 2026
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73 changes: 57 additions & 16 deletions tests/inferers/test_controlnet_inferers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -201,6 +201,45 @@
(1, 1, 16, 16, 16),
(1, 3, 4, 4, 4),
],
[
"SPADEAutoencoderKL",
{
"spatial_dims": 2,
"in_channels": 1,
"out_channels": 1,
"channels": (4, 4),
"latent_channels": 3,
"attention_levels": [False, False],
"num_res_blocks": 1,
"norm_num_groups": 4,
"label_nc": 5,
},
"SPADEDiffusionModelUNet",
{
"spatial_dims": 2,
"in_channels": 3,
"out_channels": 3,
"channels": [4, 4],
"norm_num_groups": 4,
"attention_levels": [False, False],
"num_res_blocks": 1,
"num_head_channels": 4,
"label_nc": 5,
},
{
"spatial_dims": 2,
"in_channels": 3,
"channels": [4, 4],
"attention_levels": [False, False],
"num_res_blocks": 1,
"norm_num_groups": 4,
"num_head_channels": 4,
"conditioning_embedding_num_channels": [16],
"conditioning_embedding_in_channels": 1,
},
(1, 1, 8, 8),
(1, 3, 4, 4),
],
]
LATENT_CNDM_TEST_CASES_DIFF_SHAPES = [
[
Expand Down Expand Up @@ -661,7 +700,7 @@ def test_normal_cdf(self):
x = torch.linspace(-10, 10, 20)
cdf_approx = inferer._approx_standard_normal_cdf(x)
cdf_true = norm.cdf(x)
torch.testing.assert_allclose(cdf_approx, cdf_true, atol=1e-3, rtol=1e-5)
torch.testing.assert_close(cdf_approx, torch.as_tensor(cdf_true, dtype=cdf_approx.dtype), atol=1e-3, rtol=1e-5)

@parameterized.expand(CNDM_TEST_CASES)
@skipUnless(has_einops, "Requires einops")
Expand Down Expand Up @@ -742,6 +781,8 @@ def test_prediction_shape(
stage_1 = AutoencoderKL(**autoencoder_params)
if ae_model_type == "VQVAE":
stage_1 = VQVAE(**autoencoder_params)
if ae_model_type == "SPADEAutoencoderKL":
stage_1 = SPADEAutoencoderKL(**autoencoder_params)
if dm_model_type == "SPADEDiffusionModelUNet":
stage_2 = SPADEDiffusionModelUNet(**stage_2_params)
else:
Expand All @@ -764,7 +805,7 @@ def test_prediction_shape(
inferer = ControlNetLatentDiffusionInferer(scheduler=scheduler, scale_factor=1.0)
scheduler.set_timesteps(num_inference_steps=10)
timesteps = torch.randint(0, scheduler.num_train_timesteps, (input_shape[0],), device=input.device).long()
if dm_model_type == "SPADEDiffusionModelUNet":
if ae_model_type == "SPADEAutoencoderKL" or dm_model_type == "SPADEDiffusionModelUNet":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down Expand Up @@ -807,14 +848,16 @@ def test_pred_shape(
):
stage_1 = None

if ae_model_type == "AutoencoderKL":
stage_1 = AutoencoderKL(**autoencoder_params)
if ae_model_type == "VQVAE":
stage_1 = VQVAE(**autoencoder_params)
if dm_model_type == "SPADEDiffusionModelUNet":
stage_2 = SPADEDiffusionModelUNet(**stage_2_params)
else:
stage_2 = DiffusionModelUNet(**stage_2_params)
if ae_model_type == "AutoencoderKL":
stage_1 = AutoencoderKL(**autoencoder_params)
if ae_model_type == "VQVAE":
stage_1 = VQVAE(**autoencoder_params)
if ae_model_type == "SPADEAutoencoderKL":
stage_1 = SPADEAutoencoderKL(**autoencoder_params)
controlnet = ControlNet(**controlnet_params)

device = "cuda:0" if torch.cuda.is_available() else "cpu"
Expand Down Expand Up @@ -905,19 +948,17 @@ def test_sample_intermediates(
else:
input_shape_seg[1] = autoencoder_params["label_nc"]
input_seg = torch.randn(input_shape_seg).to(device)
sample = inferer.sample(
sample, intermediates = inferer.sample(
input_noise=noise,
autoencoder_model=stage_1,
diffusion_model=stage_2,
scheduler=scheduler,
seg=input_seg,
controlnet=controlnet,
cn_cond=mask,
save_intermediates=True,
intermediate_steps=1,
)

# TODO: this isn't correct, should the above produce intermediates as well?
# This test has always passed so is this branch not being used?
intermediates = None
else:
sample, intermediates = inferer.sample(
input_noise=noise,
Expand Down Expand Up @@ -973,7 +1014,7 @@ def test_get_likelihoods(
inferer = ControlNetLatentDiffusionInferer(scheduler=scheduler, scale_factor=1.0)
scheduler.set_timesteps(num_inference_steps=10)

if dm_model_type == "SPADEDiffusionModelUNet":
if ae_model_type == "SPADEAutoencoderKL" or dm_model_type == "SPADEDiffusionModelUNet":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down Expand Up @@ -1043,7 +1084,7 @@ def test_resample_likelihoods(
inferer = ControlNetLatentDiffusionInferer(scheduler=scheduler, scale_factor=1.0)
scheduler.set_timesteps(num_inference_steps=10)

if dm_model_type == "SPADEDiffusionModelUNet":
if ae_model_type == "SPADEAutoencoderKL" or dm_model_type == "SPADEDiffusionModelUNet":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down Expand Up @@ -1127,7 +1168,7 @@ def test_prediction_shape_conditioned_concat(

timesteps = torch.randint(0, scheduler.num_train_timesteps, (input_shape[0],), device=input.device).long()

if dm_model_type == "SPADEDiffusionModelUNet":
if ae_model_type == "SPADEAutoencoderKL" or dm_model_type == "SPADEDiffusionModelUNet":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down Expand Up @@ -1209,7 +1250,7 @@ def test_sample_shape_conditioned_concat(
inferer = ControlNetLatentDiffusionInferer(scheduler=scheduler, scale_factor=1.0)
scheduler.set_timesteps(num_inference_steps=10)

if dm_model_type == "SPADEDiffusionModelUNet":
if ae_model_type == "SPADEAutoencoderKL" or dm_model_type == "SPADEDiffusionModelUNet":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down Expand Up @@ -1290,7 +1331,7 @@ def test_shape_different_latents(

timesteps = torch.randint(0, scheduler.num_train_timesteps, (input_shape[0],), device=input.device).long()

if dm_model_type == "SPADEDiffusionModelUNet":
if dm_model_type == "SPADEDiffusionModelUNet" or ae_model_type == "SPADEAutoencoderKL":
input_shape_seg = list(input_shape)
if "label_nc" in stage_2_params.keys():
input_shape_seg[1] = stage_2_params["label_nc"]
Expand Down
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