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Add tqdm logging to the tile loop of TiledStableDiffusionRefineInvocation.
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@ -6,6 +6,7 @@ import torch
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from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
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from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
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from PIL import Image
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from PIL import Image
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from pydantic import field_validator
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from pydantic import field_validator
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from tqdm import tqdm
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from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
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from invokeai.app.invocations.baseinvocation import BaseInvocation, Classification, invocation
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from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
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from invokeai.app.invocations.constants import DEFAULT_PRECISION, LATENT_SCALE_FACTOR
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@ -284,8 +285,10 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
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controlnet_data_tiles.append(tile_controlnet_data)
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controlnet_data_tiles.append(tile_controlnet_data)
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# Denoise (i.e. "refine") each tile independently.
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# Denoise (i.e. "refine") each tile independently.
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for latent_tile, noise_tile, controlnet_data_tile in zip(
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for latent_tile, noise_tile, controlnet_data_tile in tqdm(
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latent_tiles, noise_tiles, controlnet_data_tiles, strict=True
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zip(latent_tiles, noise_tiles, controlnet_data_tiles, strict=True),
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desc="Refining tiles",
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total=len(latent_tiles),
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):
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):
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assert latent_tile.shape == noise_tile.shape
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assert latent_tile.shape == noise_tile.shape
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