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Replace print with logger if they are logs (#905)
* Add get_my_logger() * Use logger instead of print * Fix log level * Removed line-breaks for readability * Use setup_logging() * Add rich to requirements.txt * Make simple * Use logger instead of print --------- Co-authored-by: Kohya S <52813779+kohya-ss@users.noreply.github.com>
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@@ -113,6 +113,10 @@ import torch
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from torch import nn
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from torch.nn import functional as F
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from einops import rearrange
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from library.utils import setup_logging
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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BLOCK_OUT_CHANNELS: Tuple[int] = (320, 640, 1280, 1280)
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TIMESTEP_INPUT_DIM = BLOCK_OUT_CHANNELS[0]
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@@ -1380,7 +1384,7 @@ class UNet2DConditionModel(nn.Module):
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):
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super().__init__()
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assert sample_size is not None, "sample_size must be specified"
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print(
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logger.info(
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f"UNet2DConditionModel: {sample_size}, {attention_head_dim}, {cross_attention_dim}, {use_linear_projection}, {upcast_attention}"
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)
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@@ -1514,7 +1518,7 @@ class UNet2DConditionModel(nn.Module):
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def set_gradient_checkpointing(self, value=False):
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modules = self.down_blocks + [self.mid_block] + self.up_blocks
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for module in modules:
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print(module.__class__.__name__, module.gradient_checkpointing, "->", value)
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logger.info(f"{module.__class__.__name__} {module.gradient_checkpointing} -> {value}")
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module.gradient_checkpointing = value
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# endregion
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@@ -1709,14 +1713,14 @@ class InferUNet2DConditionModel:
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def set_deep_shrink(self, ds_depth_1, ds_timesteps_1=650, ds_depth_2=None, ds_timesteps_2=None, ds_ratio=0.5):
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if ds_depth_1 is None:
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print("Deep Shrink is disabled.")
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logger.info("Deep Shrink is disabled.")
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self.ds_depth_1 = None
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self.ds_timesteps_1 = None
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self.ds_depth_2 = None
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self.ds_timesteps_2 = None
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self.ds_ratio = None
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else:
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print(
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logger.info(
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f"Deep Shrink is enabled: [depth={ds_depth_1}/{ds_depth_2}, timesteps={ds_timesteps_1}/{ds_timesteps_2}, ratio={ds_ratio}]"
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)
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self.ds_depth_1 = ds_depth_1
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