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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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17
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -2,6 +2,7 @@ from __future__ import annotations
from contextlib import nullcontext
from dataclasses import dataclass
import math
from typing import Any, Callable, List, Optional, Union
import einops
@ -168,6 +169,8 @@ class IPAdapterData:
# TODO: change to polymorphic so can do different weights per step (once implemented...)
# weight: Union[float, List[float]] = Field(default=1.0)
weight: float = Field(default=1.0)
begin_step_percent: float = Field(default=0.0)
end_step_percent: float = Field(default=1.0)
@dataclass
@ -445,6 +448,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
total_step_count=len(timesteps),
additional_guidance=additional_guidance,
control_data=control_data,
ip_adapter_data=ip_adapter_data,
)
latents = step_output.prev_sample
@ -490,6 +494,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
total_step_count: int,
additional_guidance: List[Callable] = None,
control_data: List[ControlNetData] = None,
ip_adapter_data: Optional[IPAdapterData] = None,
):
# invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
timestep = t[0]
@ -500,6 +505,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# i.e. before or after passing it to InvokeAIDiffuserComponent
latent_model_input = self.scheduler.scale_model_input(latents, timestep)
# handle IP-Adapter
if ip_adapter_data is not None:
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)
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
ip_adapter_data.ip_adapter_model.set_scale(ip_adapter_data.weight)
else:
# otherwise, set IP-Adapter scale to 0, so it has no effect
ip_adapter_data.ip_adapter_model.set_scale(0.0)
# handle ControlNet(s)
# default is no controlnet, so set controlnet processing output to None
controlnet_down_block_samples, controlnet_mid_block_sample = None, None
if control_data is not None: