AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-29 07:22:12 +03:00
Code Activity

Fix more

Browse Source
This commit is contained in:
Patrick von Platen
2022-11-09 10:13:00 +00:00
parent eab7454f10
commit 10d433f91d
6 changed files with 34 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py
View File

@@ -43,7 +43,7 @@ def preprocess(image):
return 2.0 * image - 1.0
def posterior_sample(scheduler, latents, timestep, clean_latents, eta):
def posterior_sample(scheduler, latents, timestep, clean_latents, generator, eta):
# 1. get previous step value (=t-1)
prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps
@@ -62,7 +62,9 @@ def posterior_sample(scheduler, latents, timestep, clean_latents, eta):
# direction pointing to x_t
e_t = (latents - alpha_prod_t ** (0.5) * clean_latents) / (1 - alpha_prod_t) ** (0.5)
dir_xt = (1.0 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * e_t
noise = std_dev_t * torch.randn(clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device)
noise = std_dev_t * torch.randn(
clean_latents.shape, dtype=clean_latents.dtype, device=clean_latents.device, generator=generator
)
prev_latents = alpha_prod_t_prev ** (0.5) * clean_latents + dir_xt + noise
return prev_latents
@@ -499,7 +501,7 @@ class CycleDiffusionPipeline(DiffusionPipeline):
# Sample source_latents from the posterior distribution.
prev_source_latents = posterior_sample(
self.scheduler, source_latents, t, clean_latents, **extra_step_kwargs
self.scheduler, source_latents, t, clean_latents, generator=generator, **extra_step_kwargs
)
# Compute noise.
noise = compute_noise(

2
src/diffusers/schedulers/scheduling_ddim.py
View File

@@ -288,7 +288,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
if eta > 0:
# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
device = model_output.device
if variance_noise is not None and generator is not None:
raise ValueError(
"Cannot pass both generator and variance_noise. Please make sure that either `generator` or"

2
src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py
View File

@@ -221,7 +221,7 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
prev_sample = sample + derivative * dt
device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
device = model_output.device
if device.type == "mps":
# randn does not work reproducibly on mps
noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(

2
src/diffusers/schedulers/scheduling_euler_discrete.py
View File

@@ -218,7 +218,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
device = model_output.device if torch.is_tensor(model_output) else torch.device("cpu")
device = model_output.device
if device.type == "mps":
# randn does not work reproducibly on mps
noise = torch.randn(model_output.shape, dtype=model_output.dtype, device="cpu", generator=generator).to(

8
tests/pipelines/stable_diffusion/test_cycle_diffusion.py
View File

@@ -293,7 +293,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
source_prompt = "A black colored car"
prompt = "A blue colored car"
torch.manual_seed(0)
generator = torch.Generator(device=torch_device).manual_seed(0)
output = pipe(
prompt=prompt,
source_prompt=source_prompt,
@@ -303,12 +303,13 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
generator=generator,
output_type="np",
)
image = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image).max() < 1e-2
assert np.abs(image - expected_image).max() < 5e-1
def test_cycle_diffusion_pipeline(self):
init_image = load_image(
@@ -331,7 +332,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
source_prompt = "A black colored car"
prompt = "A blue colored car"
torch.manual_seed(0)
generator = torch.Generator(device=torch_device).manual_seed(0)
output = pipe(
prompt=prompt,
source_prompt=source_prompt,
@@ -341,6 +342,7 @@ class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
strength=0.85,
guidance_scale=3,
source_guidance_scale=1,
generator=generator,
output_type="np",
)
image = output.images

33
tests/test_scheduler.py
View File

@@ -1281,10 +1281,11 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
scheduler.set_timesteps(self.num_inference_steps)
generator = torch.Generator().manual_seed(0)
generator = torch.Generator(torch_device).manual_seed(0)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
@@ -1296,7 +1297,6 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
print(result_sum, result_mean)
assert abs(result_sum.item() - 10.0807) < 1e-2
assert abs(result_mean.item() - 0.0131) < 1e-3
@@ -1308,7 +1308,7 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
generator = torch.Generator().manual_seed(0)
generator = torch.Generator(torch_device).manual_seed(0)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
@@ -1324,7 +1324,6 @@ class EulerDiscreteSchedulerTest(SchedulerCommonTest):
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
print(result_sum, result_mean)
assert abs(result_sum.item() - 10.0807) < 1e-2
assert abs(result_mean.item() - 0.0131) < 1e-3
@@ -1365,10 +1364,11 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
scheduler.set_timesteps(self.num_inference_steps)
generator = torch.Generator().manual_seed(0)
generator = torch.Generator(device=torch_device).manual_seed(0)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
sample = sample.to(torch_device)
for i, t in enumerate(scheduler.timesteps):
sample = scheduler.scale_model_input(sample, t)
@@ -1380,9 +1380,14 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
print(result_sum, result_mean)
assert abs(result_sum.item() - 152.3192) < 1e-2
assert abs(result_mean.item() - 0.1983) < 1e-3
if str(torch_device).startswith("cpu"):
assert abs(result_sum.item() - 152.3192) < 1e-2
assert abs(result_mean.item() - 0.1983) < 1e-3
else:
# CUDA
assert abs(result_sum.item() - 144.8084) < 1e-2
assert abs(result_mean.item() - 0.18855) < 1e-3
def test_full_loop_device(self):
scheduler_class = self.scheduler_classes[0]
@@ -1391,7 +1396,7 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
scheduler.set_timesteps(self.num_inference_steps, device=torch_device)
generator = torch.Generator().manual_seed(0)
generator = torch.Generator(device=torch_device).manual_seed(0)
model = self.dummy_model()
sample = self.dummy_sample_deter * scheduler.init_noise_sigma
@@ -1407,14 +1412,18 @@ class EulerAncestralDiscreteSchedulerTest(SchedulerCommonTest):
result_sum = torch.sum(torch.abs(sample))
result_mean = torch.mean(torch.abs(sample))
print(result_sum, result_mean)
if not str(torch_device).startswith("mps"):
if str(torch_device).startswith("cpu"):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 152.3192) < 1e-2
assert abs(result_mean.item() - 0.1983) < 1e-3
else:
elif str(torch_device).startswith("mps"):
# Larger tolerance on mps
assert abs(result_mean.item() - 0.1983) < 1e-2
else:
# CUDA
assert abs(result_sum.item() - 144.8084) < 1e-2
assert abs(result_mean.item() - 0.18855) < 1e-3
class IPNDMSchedulerTest(SchedulerCommonTest):