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Fix [core/GLIGEN]: TypeError when iterating over 0-d tensor with In-painting mode when EulerAncestralDiscreteScheduler is used #5305

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Merged
merged 4 commits into from
Oct 9, 2023
Merged

Fix [core/GLIGEN]: TypeError when iterating over 0-d tensor with In-painting mode when EulerAncestralDiscreteScheduler is used #5305

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1735422
fix(gligen_inpaint_pipeline): :bug: Wrap the timestep() 0-d tensor in…
Oct 5, 2023
d797d51
test(gligen/gligen_text_image): unit test using the EulerAncestralDis…
Oct 6, 2023
9f41380
Merge branch 'huggingface:main' into rchuzh99/fix-gligen-add-noise-ti…
chuzhdontcode Oct 6, 2023
6fdb932
Merge branch 'main' into rchuzh99/fix-gligen-add-noise-timesteps
sayakpaul Oct 9, 2023
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2 changes: 1 addition & 1 deletion examples/custom_diffusion/train_custom_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -207,7 +207,7 @@ def __init__(
with open(concept["class_prompt"], "r") as f:
class_prompt = f.read().splitlines()

class_img_path = [(x, y) for (x, y) in zip(class_images_path, class_prompt)]
class_img_path = list(zip(class_images_path, class_prompt))
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@sayakpaul sayakpaul Oct 9, 2023

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Clean.

self.class_images_path.extend(class_img_path[:num_class_images])

random.shuffle(self.instance_images_path)
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4 changes: 3 additions & 1 deletion src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_gligen.py
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Expand Up @@ -803,7 +803,9 @@ def __call__(

if gligen_inpaint_image is not None:
gligen_inpaint_latent_with_noise = (
self.scheduler.add_noise(gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), t)
self.scheduler.add_noise(
gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), torch.tensor([t])
)
.expand(latents.shape[0], -1, -1, -1)
.clone()
)
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4 changes: 3 additions & 1 deletion src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_gligen_text_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -965,7 +965,9 @@ def __call__(

if gligen_inpaint_image is not None:
gligen_inpaint_latent_with_noise = (
self.scheduler.add_noise(gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), t)
self.scheduler.add_noise(
gligen_inpaint_latent, torch.randn_like(gligen_inpaint_latent), torch.tensor([t])
)
.expand(latents.shape[0], -1, -1, -1)
.clone()
)
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21 changes: 20 additions & 1 deletion tests/pipelines/stable_diffusion/test_stable_diffusion_gligen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
StableDiffusionGLIGENPipeline,
UNet2DConditionModel,
)
Expand Down Expand Up @@ -120,7 +121,7 @@ def get_dummy_inputs(self, device, seed=0):
}
return inputs

def test_gligen(self):
def test_stable_diffusion_gligen_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENPipeline(**components)
Expand All @@ -136,6 +137,24 @@ def test_gligen(self):

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_gligen_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENPipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]

assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

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21 changes: 20 additions & 1 deletion tests/pipelines/stable_diffusion/test_stable_diffusion_gligen_text_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
StableDiffusionGLIGENTextImagePipeline,
UNet2DConditionModel,
)
Expand Down Expand Up @@ -150,7 +151,7 @@ def get_dummy_inputs(self, device, seed=0):
}
return inputs

def test_gligen(self):
def test_stable_diffusion_gligen_text_image_default_case(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)
Expand All @@ -166,6 +167,24 @@ def test_gligen(self):

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_gligen_k_euler_ancestral(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionGLIGENTextImagePipeline(**components)
sd_pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)

inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]

assert image.shape == (1, 64, 64, 3)

expected_slice = np.array([0.425, 0.494, 0.429, 0.469, 0.525, 0.417, 0.533, 0.5, 0.47])

assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

Expand Down