Add support for SD 2.1 Turbo, by converting the state dict from SGM to LDM on load
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@ -230,15 +230,19 @@ def select_checkpoint():
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return checkpoint_info
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checkpoint_dict_replacements = {
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checkpoint_dict_replacements_sd1 = {
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'cond_stage_model.transformer.embeddings.': 'cond_stage_model.transformer.text_model.embeddings.',
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'cond_stage_model.transformer.encoder.': 'cond_stage_model.transformer.text_model.encoder.',
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'cond_stage_model.transformer.final_layer_norm.': 'cond_stage_model.transformer.text_model.final_layer_norm.',
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}
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checkpoint_dict_replacements_sd2_turbo = { # Converts SD 2.1 Turbo from SGM to LDM format.
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'conditioner.embedders.0.': 'cond_stage_model.',
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}
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def transform_checkpoint_dict_key(k):
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for text, replacement in checkpoint_dict_replacements.items():
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def transform_checkpoint_dict_key(k, replacements):
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for text, replacement in replacements.items():
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if k.startswith(text):
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k = replacement + k[len(text):]
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@ -249,9 +253,14 @@ def get_state_dict_from_checkpoint(pl_sd):
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pl_sd = pl_sd.pop("state_dict", pl_sd)
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pl_sd.pop("state_dict", None)
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is_sd2_turbo = 'conditioner.embedders.0.model.ln_final.weight' in pl_sd and pl_sd['conditioner.embedders.0.model.ln_final.weight'].size()[0] == 1024
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sd = {}
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for k, v in pl_sd.items():
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new_key = transform_checkpoint_dict_key(k)
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if is_sd2_turbo:
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new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd2_turbo)
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else:
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new_key = transform_checkpoint_dict_key(k, checkpoint_dict_replacements_sd1)
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if new_key is not None:
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sd[new_key] = v
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