[Conversion] Make sure ema weights are extracted correctly (#1937)
* [Conversion] Make sure ema weights are extracted correctly * up * finish
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@ -315,9 +315,8 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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unet_key = "model.diffusion_model."
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unet_key = "model.diffusion_model."
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# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
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# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
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if sum(k.startswith("model_ema") for k in keys) > 100:
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if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema:
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print(f"Checkpoint {path} has both EMA and non-EMA weights.")
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print(f"Checkpoint {path} has both EMA and non-EMA weights.")
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if extract_ema:
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print(
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print(
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"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
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"In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
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" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
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" weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
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@ -327,6 +326,7 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
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flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
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flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
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unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
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else:
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else:
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if sum(k.startswith("model_ema") for k in keys) > 100:
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print(
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print(
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"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
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"In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
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" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
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" weights (usually better for inference), please make sure to add the `--extract_ema` flag."
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