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Create a UNetAttentionPatcher for patching UNet models with CustomAttnProcessor2_0 modules.

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This commit is contained in:
Ryan Dick
2024-03-08 14:15:16 -05:00
committed by Kent Keirsey
parent 31c456c1e6
commit 7ca677578e
4 changed files with 25 additions and 205 deletions
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6
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -22,12 +22,12 @@ from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
IPAdapterConditioningInfo,
TextConditioningData,
)
from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
from invokeai.backend.util.attention import auto_detect_slice_size
from invokeai.backend.util.devices import normalize_device
@ -412,7 +412,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
elif ip_adapter_data is not None:
# TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active?
# As it is now, the IP-Adapter will silently be skipped.
ip_adapter_unet_patcher = UNetPatcher([ipa.ip_adapter_model for ipa in ip_adapter_data])
ip_adapter_unet_patcher = UNetAttentionPatcher([ipa.ip_adapter_model for ipa in ip_adapter_data])
attn_ctx = ip_adapter_unet_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
self.use_ip_adapter = True
else:
@ -476,7 +476,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
control_data: List[ControlNetData] = None,
ip_adapter_data: Optional[list[IPAdapterData]] = None,
t2i_adapter_data: Optional[list[T2IAdapterData]] = None,
ip_adapter_unet_patcher: Optional[UNetPatcher] = None,
ip_adapter_unet_patcher: Optional[UNetAttentionPatcher] = None,
):
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
timestep = t[0]