Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

chore: black & lint fixes

Browse Source
This commit is contained in:
blessedcoolant 2023-09-17 12:00:21 +12:00
parent 11c501fc80
commit b7773c9962
2 changed files with 11 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
invokeai/app/invocations/ip_adapter.py
View File

@ -64,8 +64,9 @@ class IPAdapterInvocation(BaseInvocation):
) )
# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float) # weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
weight: Union[float, List[float]] = InputField(default=1, ge=0, description="The weight given to the IP-Adapter", weight: Union[float, List[float]] = InputField(
ui_type=UIType.Float, title="Weight") default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
)
begin_step_percent: float = InputField( begin_step_percent: float = InputField(
default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)" default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"

11
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -423,7 +423,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# As it is now, the IP-Adapter will silently be skipped. # As it is now, the IP-Adapter will silently be skipped.
weight = ip_adapter_data.weight[0] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight weight = ip_adapter_data.weight[0] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight
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=weight, unet=self.invokeai_diffuser.model,
scale=weight,
) )
self.use_ip_adapter = True self.use_ip_adapter = True
else: else:
@ -510,10 +511,14 @@ 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 self.use_ip_adapter and ip_adapter_data is not None: # somewhat redundant but logic is clearer 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)
weight = ip_adapter_data.weight[step_index] if isinstance(ip_adapter_data.weight, List) else ip_adapter_data.weight weight = (
ip_adapter_data.weight[step_index]
if isinstance(ip_adapter_data.weight, List)
else ip_adapter_data.weight
)
if step_index >= first_adapter_step and step_index <= last_adapter_step: if step_index >= first_adapter_step and step_index <= last_adapter_step:
# only apply IP-Adapter if current step is within the IP-Adapter's begin/end step range # only apply IP-Adapter if current step is within the IP-Adapter's begin/end step range
# ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight) # ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight)