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Initial implementation of IP-Adapter "begin_step_percent" and "end_step_percent" for controlling on which steps IP-Adapter is applied in the denoising loop.

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user1
2023-09-16 08:24:12 -07:00
parent 834751e877
commit ced297ed21
4 changed files with 45 additions and 5 deletions
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21
invokeai/backend/ip_adapter/ip_adapter.py
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@ -131,8 +131,18 @@ class IPAdapter:
self._attn_processors = attn_procs
self._state_dict = None
# @genomancer: pushed scaling back out into its own method (like original Tencent implementation)
# which makes implementing begin_step_percent and end_step_percent easier
# but based on self._attn_processors (ala @Ryan) instead of original Tencent unet.attn_processors,
# which should make it easier to implement multiple IPAdapters
def set_scale(self, scale):
if self._attn_processors is not None:
for attn_processor in self._attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = scale
@contextmanager
def apply_ip_adapter_attention(self, unet: UNet2DConditionModel, scale: int):
def apply_ip_adapter_attention(self, unet: UNet2DConditionModel, scale: float):
"""A context manager that patches `unet` with this IP-Adapter's attention processors while it is active.
Yields:
@ -143,10 +153,11 @@ class IPAdapter:
# used on any UNet model (with the same dimensions).
self._prepare_attention_processors(unet)
# Set scale.
for attn_processor in self._attn_processors.values():
if isinstance(attn_processor, IPAttnProcessor):
attn_processor.scale = scale
# Set scale
self.set_scale(scale)
# for attn_processor in self._attn_processors.values():
# if isinstance(attn_processor, IPAttnProcessor):
# attn_processor.scale = scale
orig_attn_processors = unet.attn_processors