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Inferer modification - save_intermediates clashes with latent shape adjustment in latent diffusion inferers #8343

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Merged
KumoLiu merged 11 commits into from
Feb 18, 2025
Merged

Inferer modification - save_intermediates clashes with latent shape adjustment in latent diffusion inferers #8343

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08aef50
There was a bug in the LatentDiffusionInferer and ControlnetLatentDif…
Feb 14, 2025
e5aa008
There was a bug in the LatentDiffusionInferer and ControlnetLatentDif…
Feb 14, 2025
d664f3d
Merge branch 'Project-MONAI:dev' into inferer_modification
virginiafdez Feb 17, 2025
ae32d2e
Added tests for new functionality.
Feb 18, 2025
52de9a7
Added tests for new functionality.
Feb 18, 2025
0f5609e
Added tests for new functionality.
Feb 18, 2025
2e4424e
Merge remote-tracking branch 'origin/inferer_modification' into infer…
Feb 18, 2025
22e21fe
Update monai/inferers/inferer.py
KumoLiu Feb 18, 2025
83e3dd6
Update monai/inferers/inferer.py
KumoLiu Feb 18, 2025
4a985da
Remove debugging related print statements.
Feb 18, 2025
f4dc28e
Merge remote-tracking branch 'origin/inferer_modification' into infer…
Feb 18, 2025
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20 changes: 13 additions & 7 deletions monai/inferers/inferer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1202,15 +1202,19 @@ def sample( # type: ignore[override]

if self.autoencoder_latent_shape is not None:
latent = torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(latent)], 0)
latent_intermediates = [
torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0) for l in latent_intermediates
]
if save_intermediates:
latent_intermediates = [
torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0)
for l in latent_intermediates
]

decode = autoencoder_model.decode_stage_2_outputs
if isinstance(autoencoder_model, SPADEAutoencoderKL):
decode = partial(autoencoder_model.decode_stage_2_outputs, seg=seg)
print("Decoding latents...")
print(latent.shape)
image = decode(latent / self.scale_factor)

print(image.shape)
if save_intermediates:
intermediates = []
for latent_intermediate in latent_intermediates:
Expand Down Expand Up @@ -1727,9 +1731,11 @@ def sample( # type: ignore[override]

if self.autoencoder_latent_shape is not None:
latent = torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(latent)], 0)
latent_intermediates = [
torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0) for l in latent_intermediates
]
if save_intermediates:
latent_intermediates = [
torch.stack([self.autoencoder_resizer(i) for i in decollate_batch(l)], 0)
for l in latent_intermediates
]

decode = autoencoder_model.decode_stage_2_outputs
if isinstance(autoencoder_model, SPADEAutoencoderKL):
Expand Down
82 changes: 80 additions & 2 deletions tests/inferers/test_controlnet_inferers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -722,7 +722,7 @@ def test_prediction_shape(

@parameterized.expand(LATENT_CNDM_TEST_CASES)
@skipUnless(has_einops, "Requires einops")
def test_sample_shape(
def test_pred_shape(
self,
ae_model_type,
autoencoder_params,
Expand Down Expand Up @@ -1165,7 +1165,7 @@ def test_sample_shape_conditioned_concat(

@parameterized.expand(LATENT_CNDM_TEST_CASES_DIFF_SHAPES)
@skipUnless(has_einops, "Requires einops")
def test_sample_shape_different_latents(
def test_shape_different_latents(
self,
ae_model_type,
autoencoder_params,
Expand Down Expand Up @@ -1242,6 +1242,84 @@ def test_sample_shape_different_latents(
)
self.assertEqual(prediction.shape, latent_shape)

@parameterized.expand(LATENT_CNDM_TEST_CASES_DIFF_SHAPES)
@skipUnless(has_einops, "Requires einops")
def test_sample_shape_different_latents(
self,
ae_model_type,
autoencoder_params,
dm_model_type,
stage_2_params,
controlnet_params,
input_shape,
latent_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 ae_model_type == "SPADEAutoencoderKL":
stage_1 = SPADEAutoencoderKL(**autoencoder_params)
if dm_model_type == "SPADEDiffusionModelUNet":
stage_2 = SPADEDiffusionModelUNet(**stage_2_params)
else:
stage_2 = DiffusionModelUNet(**stage_2_params)
controlnet = ControlNet(**controlnet_params)

device = "cuda:0" if torch.cuda.is_available() else "cpu"
stage_1.to(device)
stage_2.to(device)
controlnet.to(device)
stage_1.eval()
stage_2.eval()
controlnet.eval()

noise = torch.randn(latent_shape).to(device)
mask = torch.randn(input_shape).to(device)
scheduler = DDPMScheduler(num_train_timesteps=10)
# We infer the VAE shape
if ae_model_type == "VQVAE":
autoencoder_latent_shape = [i // (2 ** (len(autoencoder_params["channels"]))) for i in input_shape[2:]]
else:
autoencoder_latent_shape = [i // (2 ** (len(autoencoder_params["channels"]) - 1)) for i in input_shape[2:]]

inferer = ControlNetLatentDiffusionInferer(
scheduler=scheduler,
scale_factor=1.0,
ldm_latent_shape=list(latent_shape[2:]),
autoencoder_latent_shape=autoencoder_latent_shape,
)
scheduler.set_timesteps(num_inference_steps=10)

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"]
else:
input_shape_seg[1] = autoencoder_params["label_nc"]
input_seg = torch.randn(input_shape_seg).to(device)
prediction, _ = inferer.sample(
autoencoder_model=stage_1,
diffusion_model=stage_2,
controlnet=controlnet,
cn_cond=mask,
input_noise=noise,
seg=input_seg,
save_intermediates=True,
)
else:
prediction = inferer.sample(
autoencoder_model=stage_1,
diffusion_model=stage_2,
input_noise=noise,
controlnet=controlnet,
cn_cond=mask,
save_intermediates=False,
)
self.assertEqual(prediction.shape, input_shape)

@skipUnless(has_einops, "Requires einops")
def test_incompatible_spade_setup(self):
stage_1 = SPADEAutoencoderKL(
Expand Down
62 changes: 61 additions & 1 deletion tests/inferers/test_latent_diffusion_inferer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -714,7 +714,7 @@ def test_sample_shape_conditioned_concat(

@parameterized.expand(TEST_CASES_DIFF_SHAPES)
@skipUnless(has_einops, "Requires einops")
def test_sample_shape_different_latents(
def test_shape_different_latents(
self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_shape
):
stage_1 = None
Expand Down Expand Up @@ -772,6 +772,66 @@ def test_sample_shape_different_latents(
)
self.assertEqual(prediction.shape, latent_shape)

@parameterized.expand(TEST_CASES_DIFF_SHAPES)
@skipUnless(has_einops, "Requires einops")
def test_sample_shape_different_latents(
self, ae_model_type, autoencoder_params, dm_model_type, stage_2_params, input_shape, latent_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 ae_model_type == "SPADEAutoencoderKL":
stage_1 = SPADEAutoencoderKL(**autoencoder_params)
if dm_model_type == "SPADEDiffusionModelUNet":
stage_2 = SPADEDiffusionModelUNet(**stage_2_params)
else:
stage_2 = DiffusionModelUNet(**stage_2_params)

device = "cuda:0" if torch.cuda.is_available() else "cpu"
stage_1.to(device)
stage_2.to(device)
stage_1.eval()
stage_2.eval()

noise = torch.randn(latent_shape).to(device)
scheduler = DDPMScheduler(num_train_timesteps=10)
# We infer the VAE shape
if ae_model_type == "VQVAE":
autoencoder_latent_shape = [i // (2 ** (len(autoencoder_params["channels"]))) for i in input_shape[2:]]
else:
autoencoder_latent_shape = [i // (2 ** (len(autoencoder_params["channels"]) - 1)) for i in input_shape[2:]]

inferer = LatentDiffusionInferer(
scheduler=scheduler,
scale_factor=1.0,
ldm_latent_shape=list(latent_shape[2:]),
autoencoder_latent_shape=autoencoder_latent_shape,
)
scheduler.set_timesteps(num_inference_steps=10)

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"]
else:
input_shape_seg[1] = autoencoder_params["label_nc"]
input_seg = torch.randn(input_shape_seg).to(device)
prediction, _ = inferer.sample(
autoencoder_model=stage_1,
diffusion_model=stage_2,
input_noise=noise,
save_intermediates=True,
seg=input_seg,
)
else:
prediction = inferer.sample(
autoencoder_model=stage_1, diffusion_model=stage_2, input_noise=noise, save_intermediates=False
)
self.assertEqual(prediction.shape, input_shape)

@skipUnless(has_einops, "Requires einops")
def test_incompatible_spade_setup(self):
stage_1 = SPADEAutoencoderKL(
Expand Down
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