lllyasviel/stable-diffusion-webui-forge
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revise inference dtype

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layerdiffusion
2024-08-07 17:08:47 -07:00
parent 89ea50a19d
commit b61bf553ea
5 changed files with 10 additions and 6 deletions
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3
backend/diffusion_engine/flux.py
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@@ -38,7 +38,8 @@ class Flux(ForgeDiffusionEngine):
unet = UnetPatcher.from_model(
model=huggingface_components['transformer'],
diffusers_scheduler=None,
k_predictor=PredictionFlux(sigma_data=1.0, prediction_type='const', shift=1.15, timesteps=10000)
k_predictor=PredictionFlux(sigma_data=1.0, prediction_type='const', shift=1.15, timesteps=10000),
config=estimated_config
)
self.text_processing_engine_l = ClassicTextProcessingEngine(

3
backend/diffusion_engine/sd15.py
View File

@@ -29,7 +29,8 @@ class StableDiffusion(ForgeDiffusionEngine):
unet = UnetPatcher.from_model(
model=huggingface_components['unet'],
diffusers_scheduler=huggingface_components['scheduler']
diffusers_scheduler=huggingface_components['scheduler'],
config=estimated_config
)
self.text_processing_engine = ClassicTextProcessingEngine(

3
backend/diffusion_engine/sd20.py
View File

@@ -29,7 +29,8 @@ class StableDiffusion2(ForgeDiffusionEngine):
unet = UnetPatcher.from_model(
model=huggingface_components['unet'],
diffusers_scheduler=huggingface_components['scheduler']
diffusers_scheduler=huggingface_components['scheduler'],
config=estimated_config
)
self.text_processing_engine = ClassicTextProcessingEngine(

3
backend/diffusion_engine/sdxl.py
View File

@@ -32,7 +32,8 @@ class StableDiffusionXL(ForgeDiffusionEngine):
unet = UnetPatcher.from_model(
model=huggingface_components['unet'],
diffusers_scheduler=huggingface_components['scheduler']
diffusers_scheduler=huggingface_components['scheduler'],
config=estimated_config
)
self.text_processing_engine_l = ClassicTextProcessingEngine(

4
backend/patcher/unet.py
View File

@@ -8,12 +8,12 @@ from backend import memory_management
class UnetPatcher(ModelPatcher):
@classmethod
def from_model(cls, model, diffusers_scheduler, k_predictor=None):
def from_model(cls, model, diffusers_scheduler, config, k_predictor=None):
parameters = memory_management.module_size(model)
unet_dtype = memory_management.unet_dtype(model_params=parameters)
load_device = memory_management.get_torch_device()
initial_load_device = memory_management.unet_inital_load_device(parameters, unet_dtype)
manual_cast_dtype = memory_management.unet_manual_cast(unet_dtype, load_device)
manual_cast_dtype = memory_management.unet_manual_cast(unet_dtype, load_device, supported_dtypes=config.supported_inference_dtypes)
manual_cast_dtype = unet_dtype if manual_cast_dtype is None else manual_cast_dtype
model.to(device=initial_load_device, dtype=unet_dtype)
model = KModel(model=model, diffusers_scheduler=diffusers_scheduler, k_predictor=k_predictor, storage_dtype=unet_dtype, computation_dtype=manual_cast_dtype)