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Support AuraFlow Lora and loading model weights in diffusers format.

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You can load model weights in diffusers format using the UNETLoader node.
This commit is contained in:
comfyanonymous
2024-07-13 13:51:40 -04:00
parent ce2473bb01
commit a3dffc447a
4 changed files with 133 additions and 44 deletions
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33
comfy/sd.py
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@@ -562,26 +562,25 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
if model_config is not None:
new_sd = sd
elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
else:
new_sd = model_detection.convert_diffusers_mmdit(sd, "")
if new_sd is None:
return None
model_config = model_detection.model_config_from_unet(new_sd, "")
if model_config is None:
return None
else: #diffusers
model_config = model_detection.model_config_from_diffusers_unet(sd)
if model_config is None:
return None
if new_sd is not None: #diffusers mmdit
model_config = model_detection.model_config_from_unet(new_sd, "")
if model_config is None:
return None
else: #diffusers unet
model_config = model_detection.model_config_from_diffusers_unet(sd)
if model_config is None:
return None
diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
new_sd = {}
for k in diffusers_keys:
if k in sd:
new_sd[diffusers_keys[k]] = sd.pop(k)
else:
logging.warning("{} {}".format(diffusers_keys[k], k))
new_sd = {}
for k in diffusers_keys:
if k in sd:
new_sd[diffusers_keys[k]] = sd.pop(k)
else:
logging.warning("{} {}".format(diffusers_keys[k], k))
offload_device = model_management.unet_offload_device()
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)