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Added check in IP-Adapter to avoid begin/end step percent handling if use of IP-Adapter is already turned off due to potential clash with other cross attention control.

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user1 2023-09-16 09:29:50 -07:00
parent ced297ed21
commit 7ee13879e3
1 changed files with 4 additions and 1 deletions
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5
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -253,6 +253,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
) )
self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward) self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
self.control_model = control_model self.control_model = control_model
self.use_ip_adapter = False
def _adjust_memory_efficient_attention(self, latents: torch.Tensor): def _adjust_memory_efficient_attention(self, latents: torch.Tensor):
""" """
@ -416,12 +417,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
extra_conditioning_info=conditioning_data.extra, extra_conditioning_info=conditioning_data.extra,
step_count=len(self.scheduler.timesteps), step_count=len(self.scheduler.timesteps),
) )
self.use_ip_adapter = False
elif ip_adapter_data is not None: elif ip_adapter_data is not None:
# TODO(ryand): Should we raise an exception if both custom attention and IP-Adapter attention are active? # 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. # As it is now, the IP-Adapter will silently be skipped.
attn_ctx = ip_adapter_data.ip_adapter_model.apply_ip_adapter_attention( attn_ctx = ip_adapter_data.ip_adapter_model.apply_ip_adapter_attention(
unet=self.invokeai_diffuser.model, scale=ip_adapter_data.weight unet=self.invokeai_diffuser.model, scale=ip_adapter_data.weight
) )
self.use_ip_adapter = True
else: else:
attn_ctx = nullcontext() attn_ctx = nullcontext()
@ -506,7 +509,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
latent_model_input = self.scheduler.scale_model_input(latents, timestep) latent_model_input = self.scheduler.scale_model_input(latents, timestep)
# handle IP-Adapter # handle IP-Adapter
if ip_adapter_data is not None: if self.use_ip_adapter and ip_adapter_data is not None: # somewhat redundant but logic is clearer
first_adapter_step = math.floor(ip_adapter_data.begin_step_percent * total_step_count) first_adapter_step = math.floor(ip_adapter_data.begin_step_percent * total_step_count)
last_adapter_step = math.ceil(ip_adapter_data.end_step_percent * total_step_count) last_adapter_step = math.ceil(ip_adapter_data.end_step_percent * total_step_count)
if step_index >= first_adapter_step and step_index <= last_adapter_step: if step_index >= first_adapter_step and step_index <= last_adapter_step: