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Merge branch 'comfyanonymous:master' into feature/preview-latent

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Dr.Lt.Data 2023-06-23 14:33:57 +09:00 committed by GitHub
commit d8e5f13103
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2 changed files with 5 additions and 4 deletions
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5
comfy/clip_vision.py
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@ -52,8 +52,9 @@ def convert_to_transformers(sd, prefix):
sd = transformers_convert(sd, prefix, "vision_model.", 32) sd = transformers_convert(sd, prefix, "vision_model.", 32)
return sd return sd
def load_clipvision_from_sd(sd, prefix): def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
sd = convert_to_transformers(sd, prefix) if convert_keys:
sd = convert_to_transformers(sd, prefix)
if "vision_model.encoder.layers.30.layer_norm1.weight" in sd: if "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
else: else:

4
comfy/sd.py
View File

@ -1015,7 +1015,7 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
class EmptyClass: class EmptyClass:
pass pass
clip_target = EmptyClass() clip_target = EmptyClass()
clip_target.params = clip_config["params"] clip_target.params = clip_config.get("params", {})
if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
clip_target.clip = sd2_clip.SD2ClipModel clip_target.clip = sd2_clip.SD2ClipModel
clip_target.tokenizer = sd2_clip.SD2Tokenizer clip_target.tokenizer = sd2_clip.SD2Tokenizer
@ -1049,7 +1049,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
if model_config.clip_vision_prefix is not None: if model_config.clip_vision_prefix is not None:
if output_clipvision: if output_clipvision:
clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix) clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)
model = model_config.get_model(sd) model = model_config.get_model(sd)
model.load_model_weights(sd, "model.diffusion_model.") model.load_model_weights(sd, "model.diffusion_model.")