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convert to bgr on sdxl t2i

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dunkeroni
2024-10-25 23:04:17 -04:00
parent 64e0f6d688
commit 5f015e76ba
1 changed files with 13 additions and 2 deletions
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15
invokeai/app/invocations/denoise_latents.py
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@@ -13,6 +13,7 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
from diffusers.schedulers.scheduling_tcd import TCDScheduler
from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
from PIL import Image
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPVisionModelWithProjection
@@ -510,6 +511,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
context: InvocationContext,
t2i_adapters: Optional[Union[T2IAdapterField, list[T2IAdapterField]]],
ext_manager: ExtensionsManager,
bgr_mode: bool = False,
) -> None:
if t2i_adapters is None:
return
@@ -519,6 +521,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
t2i_adapters = [t2i_adapters]
for t2i_adapter_field in t2i_adapters:
image = context.images.get_pil(t2i_adapter_field.image.image_name)
if bgr_mode:#SDXL t2i trained on cv2's BGR outputs, but PIL won't convert straight to BGR
r, g, b = image.split()
image = Image.merge("RGB", (b, g, r))
ext_manager.add_extension(
T2IAdapterExt(
node_context=context,
@@ -617,12 +623,16 @@ class DenoiseLatentsInvocation(BaseInvocation):
t2i_adapter_model_config = context.models.get_config(t2i_adapter_field.t2i_adapter_model.key)
t2i_adapter_loaded_model = context.models.load(t2i_adapter_field.t2i_adapter_model)
image = context.images.get_pil(t2i_adapter_field.image.image_name)
# The max_unet_downscale is the maximum amount that the UNet model downscales the latent image internally.
if t2i_adapter_model_config.base == BaseModelType.StableDiffusion1:
max_unet_downscale = 8
elif t2i_adapter_model_config.base == BaseModelType.StableDiffusionXL:
max_unet_downscale = 4
# SDXL adapters are trained on cv2's BGR outputs
r, g, b = image.split()
image = Image.merge("RGB", (b, g, r))
else:
raise ValueError(f"Unexpected T2I-Adapter base model type: '{t2i_adapter_model_config.base}'.")
@@ -900,7 +910,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
# ext = extension_field.to_extension(exit_stack, context, ext_manager)
# ext_manager.add_extension(ext)
self.parse_controlnet_field(exit_stack, context, self.control, ext_manager)
self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager)
bgr_mode = self.unet.unet.base == BaseModelType.StableDiffusionXL
self.parse_t2i_adapter_field(exit_stack, context, self.t2i_adapter, ext_manager, bgr_mode)
# ext: t2i/ip adapter
ext_manager.run_callback(ExtensionCallbackType.SETUP, denoise_ctx)