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hub / github.com/Monalissaa/DisenDiff / load_model_from_config

Function load_model_from_config

sample.py:115–136  ·  view source on GitHub ↗
(config, ckpt, verbose=False)

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113
114
115def load_model_from_config(config, ckpt, verbose=False):
116 print(f"Loading model from {ckpt}")
117 pl_sd = torch.load(ckpt, map_location="cpu")
118 if "global_step" in pl_sd:
119 print(f"Global Step: {pl_sd['global_step']}")
120 sd = pl_sd["state_dict"]
121 model = instantiate_from_config(config.model)
122
123 token_weights = sd["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]
124 del sd["cond_stage_model.transformer.text_model.embeddings.token_embedding.weight"]
125 m, u = model.load_state_dict(sd, strict=False)
126 model.cond_stage_model.transformer.text_model.embeddings.token_embedding.weight.data[:token_weights.shape[0]] = token_weights
127 if len(m) > 0 and verbose:
128 print("missing keys:")
129 print(m)
130 if len(u) > 0 and verbose:
131 print("unexpected keys:")
132 print(u)
133
134 model.cuda()
135 model.eval()
136 return model
137
138
139from PIL import Image

Callers 1

mainFunction · 0.70

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