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[Add] torch_xla support to pipeline_sana.py #10364

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merged 5 commits into from
Dec 27, 2024
Merged

[Add] torch_xla support to pipeline_sana.py #10364

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f9ea86b
[Add] torch_xla support in pipeline_sana.py
SahilCarterr Dec 23, 2024
b498876
Merge branch 'main' into xla_sana
SahilCarterr Dec 24, 2024
4eabcf3
Merge branch 'main' into xla_sana
SahilCarterr Dec 24, 2024
7a14bd5
Merge branch 'main' into xla_sana
SahilCarterr Dec 24, 2024
d3daf1e
Merge branch 'main' into xla_sana
SahilCarterr Dec 25, 2024
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11 changes: 11 additions & 0 deletions src/diffusers/pipelines/sana/pipeline_sana.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,6 +31,7 @@
USE_PEFT_BACKEND,
is_bs4_available,
is_ftfy_available,
is_torch_xla_available,
logging,
replace_example_docstring,
scale_lora_layers,
Expand All @@ -46,6 +47,13 @@
from .pipeline_output import SanaPipelineOutput


if is_torch_xla_available():
import torch_xla.core.xla_model as xm

XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False

logger = logging.get_logger(__name__) # pylint: disable=invalid-name

if is_bs4_available():
Expand Down Expand Up @@ -864,6 +872,9 @@ def __call__(
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()

if XLA_AVAILABLE:
xm.mark_step()

if output_type == "latent":
image = latents
else:
Expand Down
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