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Use fp16 if checkpoint weights are fp16 and the model supports it.

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comfyanonymous
2025-02-27 16:39:57 -05:00
parent f4dac8ab6f
commit 1804397952
3 changed files with 12 additions and 20 deletions
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12
comfy/sd.py
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@@ -896,14 +896,14 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
return None
unet_weight_dtype = list(model_config.supported_inference_dtypes)
if weight_dtype is not None and model_config.scaled_fp8 is None:
unet_weight_dtype.append(weight_dtype)
if model_config.scaled_fp8 is not None:
weight_dtype = None
model_config.custom_operations = model_options.get("custom_operations", None)
unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
if unet_dtype is None:
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
@@ -994,11 +994,11 @@ def load_diffusion_model_state_dict(sd, model_options={}): #load unet in diffuse
offload_device = model_management.unet_offload_device()
unet_weight_dtype = list(model_config.supported_inference_dtypes)
if weight_dtype is not None and model_config.scaled_fp8 is None:
unet_weight_dtype.append(weight_dtype)
if model_config.scaled_fp8 is not None:
weight_dtype = None
if dtype is None:
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype, weight_dtype=weight_dtype)
else:
unet_dtype = dtype