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fix: fix resolution handling for different model types

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This commit is contained in:
OleehyO 2025-01-20 09:48:17 +00:00
parent 0e26f54cbe
commit d9e2a415e8
1 changed files with 2 additions and 2 deletions
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4
inference/cli_demo.py
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@ -100,7 +100,7 @@ def generate_video(
if width is None or height is None: if width is None or height is None:
height, width = desired_resolution height, width = desired_resolution
logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m") logging.info(f"\033[1mUsing default resolution {desired_resolution} for {model_name}\033[0m")
elif (width, height) != desired_resolution: elif (height, width) != desired_resolution:
if generate_type == "i2v": if generate_type == "i2v":
# For i2v models, use user-defined width and height # For i2v models, use user-defined width and height
logging.warning( logging.warning(
@ -111,7 +111,7 @@ def generate_video(
logging.warning( logging.warning(
f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m" f"\033[1;31m{model_name} is not supported for custom resolution. Setting back to default resolution {desired_resolution}.\033[0m"
) )
width, height = desired_resolution height, width = desired_resolution
if generate_type == "i2v": if generate_type == "i2v":
pipe = CogVideoXImageToVideoPipeline.from_pretrained(model_path, torch_dtype=dtype) pipe = CogVideoXImageToVideoPipeline.from_pretrained(model_path, torch_dtype=dtype)