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fix tests

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This commit is contained in:
Patrick von Platen
2022-12-02 17:27:58 +00:00
parent 7222a8eadf
commit cf4664e885
4 changed files with 4 additions and 23 deletions
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2
src/diffusers/models/unet_2d_condition.py
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@@ -301,7 +301,7 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if torch.is_floating_point(timesteps):
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64

2
src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
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@@ -379,7 +379,7 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if torch.is_floating_point(timesteps):
if isinstance(timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64

4
src/diffusers/schedulers/scheduling_lms_discrete_flax.py
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@@ -117,8 +117,8 @@ class FlaxLMSDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):
Returns:
`jnp.ndarray`: scaled input sample
"""
(step_index,) = jnp.where(scheduler_state.timesteps == timestep, size=1)
sigma = scheduler_state.sigmas[step_index]
(step_index,) = jnp.where(state.timesteps == timestep, size=1)
sigma = state.sigmas[step_index]
sample = sample / ((sigma**2 + 1) ** 0.5)
return sample

19
tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
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@@ -15,7 +15,6 @@
import gc
import tempfile
import time
import unittest
import numpy as np
@@ -694,24 +693,6 @@ class StableDiffusion2PipelineIntegrationTests(unittest.TestCase):
assert test_callback_fn.has_been_called
assert number_of_steps == 20
def test_stable_diffusion_low_cpu_mem_usage(self):
pipeline_id = "stabilityai/stable-diffusion-2-base"
start_time = time.time()
pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16
)
pipeline_low_cpu_mem_usage.to(torch_device)
low_cpu_mem_usage_time = time.time() - start_time
start_time = time.time()
_ = StableDiffusionPipeline.from_pretrained(
pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, low_cpu_mem_usage=False
)
normal_load_time = time.time() - start_time
assert 2 * low_cpu_mem_usage_time < normal_load_time
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()