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Merge branch 'comfyanonymous:master' into master

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patientx 2025-12-12 02:31:24 +03:00 committed by GitHub
commit 5ab786e521
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2 changed files with 10 additions and 8 deletions
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2
comfy/model_detection.py
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@ -261,6 +261,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
dit_config["nerf_embedder_dtype"] = torch.float32
if "__x0__" in state_dict_keys: # x0 pred
dit_config["use_x0"] = True
else:
dit_config["use_x0"] = False
else:
dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
dit_config["yak_mlp"] = '{}double_blocks.0.img_mlp.gate_proj.weight'.format(key_prefix) in state_dict_keys

12
comfy/supported_models.py
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@ -965,7 +965,7 @@ class CosmosT2IPredict2(supported_models_base.BASE):
def __init__(self, unet_config):
super().__init__(unet_config)
self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.95
def get_model(self, state_dict, prefix="", device=None):
out = model_base.CosmosPredict2(self, device=device)
@ -1289,7 +1289,7 @@ class ChromaRadiance(Chroma):
latent_format = comfy.latent_formats.ChromaRadiance
# Pixel-space model, no spatial compression for model input.
memory_usage_factor = 0.038
memory_usage_factor = 0.044
def get_model(self, state_dict, prefix="", device=None):
return model_base.ChromaRadiance(self, device=device)
@ -1332,7 +1332,7 @@ class Omnigen2(supported_models_base.BASE):
"shift": 2.6,
}
memory_usage_factor = 1.65 #TODO
memory_usage_factor = 1.95 #TODO
unet_extra_config = {}
latent_format = latent_formats.Flux
@ -1397,7 +1397,7 @@ class HunyuanImage21(HunyuanVideo):
latent_format = latent_formats.HunyuanImage21
memory_usage_factor = 7.7
memory_usage_factor = 8.7
supported_inference_dtypes = [torch.bfloat16, torch.float32]
@ -1488,7 +1488,7 @@ class Kandinsky5(supported_models_base.BASE):
unet_extra_config = {}
latent_format = latent_formats.HunyuanVideo
memory_usage_factor = 1.1 #TODO
memory_usage_factor = 1.25 #TODO
supported_inference_dtypes = [torch.bfloat16, torch.float32]
@ -1517,7 +1517,7 @@ class Kandinsky5Image(Kandinsky5):
}
latent_format = latent_formats.Flux
memory_usage_factor = 1.1 #TODO
memory_usage_factor = 1.25 #TODO
def get_model(self, state_dict, prefix="", device=None):
out = model_base.Kandinsky5Image(self, device=device)