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@ -2,7 +2,9 @@ import comfy.sd
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import comfy.utils
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import comfy.model_base
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import comfy.model_management
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import comfy.model_sampling
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import torch
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import folder_paths
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import json
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import os
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@ -189,6 +191,13 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
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# "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
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# "v2-inpainting"
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extra_keys = {}
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model_sampling = model.get_model_object("model_sampling")
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if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
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if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
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extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
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extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()
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if model.model.model_type == comfy.model_base.ModelType.EPS:
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metadata["modelspec.predict_key"] = "epsilon"
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elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
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@ -203,7 +212,7 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
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output_checkpoint = f"{filename}_{counter:05}_.safetensors"
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output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
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comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata)
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comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
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class CheckpointSave:
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def __init__(self):
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