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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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10
comfy/controlnet.py
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@ -418,10 +418,7 @@ def controlnet_config(sd, model_options={}):
weight_dtype = comfy.utils.weight_dtype(sd) weight_dtype = comfy.utils.weight_dtype(sd)
supported_inference_dtypes = list(model_config.supported_inference_dtypes) supported_inference_dtypes = list(model_config.supported_inference_dtypes)
if weight_dtype is not None: unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)
supported_inference_dtypes.append(weight_dtype)
unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
load_device = comfy.model_management.get_torch_device() load_device = comfy.model_management.get_torch_device()
manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device) manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
@ -689,10 +686,7 @@ def load_controlnet_state_dict(state_dict, model=None, model_options={}):
if supported_inference_dtypes is None: if supported_inference_dtypes is None:
supported_inference_dtypes = [comfy.model_management.unet_dtype()] supported_inference_dtypes = [comfy.model_management.unet_dtype()]
if weight_dtype is not None: unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes, weight_dtype=weight_dtype)
supported_inference_dtypes.append(weight_dtype)
unet_dtype = comfy.model_management.unet_dtype(model_params=-1, supported_dtypes=supported_inference_dtypes)
load_device = comfy.model_management.get_torch_device() load_device = comfy.model_management.get_torch_device()

10
comfy/model_management.py
View File

@ -674,7 +674,7 @@ def unet_inital_load_device(parameters, dtype):
def maximum_vram_for_weights(device=None): def maximum_vram_for_weights(device=None):
return (get_total_memory(device) * 0.88 - minimum_inference_memory()) return (get_total_memory(device) * 0.88 - minimum_inference_memory())
def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]): def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32], weight_dtype=None):
if model_params < 0: if model_params < 0:
model_params = 1000000000000000000000 model_params = 1000000000000000000000
if args.fp32_unet: if args.fp32_unet:
@ -692,10 +692,8 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
fp8_dtype = None fp8_dtype = None
try: try:
for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: if weight_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
if dtype in supported_dtypes: fp8_dtype = weight_dtype
fp8_dtype = dtype
break
except: except:
pass pass
@ -707,7 +705,7 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
if model_params * 2 > free_model_memory: if model_params * 2 > free_model_memory:
return fp8_dtype return fp8_dtype
if PRIORITIZE_FP16: if PRIORITIZE_FP16 or weight_dtype == torch.float16:
if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params): if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params):
return torch.float16 return torch.float16

12
comfy/sd.py
View File

@ -896,14 +896,14 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
return None return None
unet_weight_dtype = list(model_config.supported_inference_dtypes) unet_weight_dtype = list(model_config.supported_inference_dtypes)
if weight_dtype is not None and model_config.scaled_fp8 is None: if model_config.scaled_fp8 is not None:
unet_weight_dtype.append(weight_dtype) weight_dtype = None
model_config.custom_operations = model_options.get("custom_operations", None) model_config.custom_operations = model_options.get("custom_operations", None)
unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None)) unet_dtype = model_options.get("dtype", model_options.get("weight_dtype", None))
if unet_dtype is 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) 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) 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() offload_device = model_management.unet_offload_device()
unet_weight_dtype = list(model_config.supported_inference_dtypes) unet_weight_dtype = list(model_config.supported_inference_dtypes)
if weight_dtype is not None and model_config.scaled_fp8 is None: if model_config.scaled_fp8 is not None:
unet_weight_dtype.append(weight_dtype) weight_dtype = None
if dtype is 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: else:
unet_dtype = dtype unet_dtype = dtype