xinyun/ComfyUI-CogVideoXWrapper
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Allow orbit LoRAs with Fun models as well

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kijai 2024-11-19 15:49:43 +02:00
parent f606d745e9
commit 6302e4b668
2 changed files with 40 additions and 45 deletions
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4
examples/cogvideox_Fun_I2V_02.json
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@ -84,7 +84,7 @@
}, },
"widgets_values": [ "widgets_values": [
49, 49,
50, 25,
6, 6,
458091243358272, 458091243358272,
"randomize", "randomize",
@ -268,7 +268,7 @@
}, },
"widgets_values": [ "widgets_values": [
49, 49,
false, true,
0 0
] ]
}, },

29
model_loading.py
View File

@ -240,12 +240,12 @@ class DownloadAndLoadCogVideoModel:
#LoRAs #LoRAs
if lora is not None: if lora is not None:
from .lora_utils import merge_lora#, load_lora_into_transformer # from .lora_utils import merge_lora#, load_lora_into_transformer
if "fun" in model.lower(): # if "fun" in model.lower():
for l in lora: # for l in lora:
log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}") # log.info(f"Merging LoRA weights from {l['path']} with strength {l['strength']}")
transformer = merge_lora(transformer, l["path"], l["strength"]) # transformer = merge_lora(transformer, l["path"], l["strength"])
else: #else:
adapter_list = [] adapter_list = []
adapter_weights = [] adapter_weights = []
for l in lora: for l in lora:
@ -653,27 +653,22 @@ class CogVideoXModelLoader:
with open(transformer_config_path) as f: with open(transformer_config_path) as f:
transformer_config = json.load(f) transformer_config = json.load(f)
with init_empty_weights():
if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5"]: if model_type in ["I2V", "I2V_5b", "fun_5b_pose", "5b_I2V_1_5"]:
transformer_config["in_channels"] = 32 transformer_config["in_channels"] = 32
if "1_5" in model_type: if "1_5" in model_type:
transformer_config["ofs_embed_dim"] = 512 transformer_config["ofs_embed_dim"] = 512
elif "fun" in model_type:
transformer_config["in_channels"] = 33
else:
transformer_config["in_channels"] = 16
if "1_5" in model_type:
transformer_config["use_learned_positional_embeddings"] = False transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2 transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False transformer_config["patch_bias"] = False
transformer_config["sample_height"] = 300 transformer_config["sample_height"] = 300
transformer_config["sample_width"] = 300 transformer_config["sample_width"] = 300
elif "fun" in model_type:
transformer_config["in_channels"] = 33
else:
if "1_5" in model_type:
transformer_config["use_learned_positional_embeddings"] = False
transformer_config["patch_size_t"] = 2
transformer_config["patch_bias"] = False
#transformer_config["sample_height"] = 300 todo: check if this is needed
#transformer_config["sample_width"] = 300
transformer_config["in_channels"] = 16
with init_empty_weights():
transformer = CogVideoXTransformer3DModel.from_config(transformer_config) transformer = CogVideoXTransformer3DModel.from_config(transformer_config)
#load weights #load weights