mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into lstein/feat/simple-mm2-api
This commit is contained in:
Lincoln Stein
GitHub
commit
34cdfc61ab
@ -4,20 +4,8 @@ from typing import List, Literal, Optional, Union
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from pydantic import BaseModel, Field, field_validator, model_validator
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from typing_extensions import Self
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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Input,
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InputField,
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OutputField,
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TensorField,
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UIType,
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)
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from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
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from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, OutputField, TensorField, UIType
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from invokeai.app.invocations.model import ModelIdentifierField
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
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@ -36,6 +24,7 @@ class IPAdapterField(BaseModel):
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ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model to use.")
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image_encoder_model: ModelIdentifierField = Field(description="The name of the CLIP image encoder model.")
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weight: Union[float, List[float]] = Field(default=1, description="The weight given to the IP-Adapter.")
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target_blocks: List[str] = Field(default=[], description="The IP Adapter blocks to apply")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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@ -69,7 +58,7 @@ class IPAdapterOutput(BaseInvocationOutput):
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CLIP_VISION_MODEL_MAP = {"ViT-H": "ip_adapter_sd_image_encoder", "ViT-G": "ip_adapter_sdxl_image_encoder"}
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@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.3.0")
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@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.4.0")
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class IPAdapterInvocation(BaseInvocation):
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"""Collects IP-Adapter info to pass to other nodes."""
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@ -90,6 +79,9 @@ class IPAdapterInvocation(BaseInvocation):
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weight: Union[float, List[float]] = InputField(
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default=1, description="The weight given to the IP-Adapter", title="Weight"
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)
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method: Literal["full", "style", "composition"] = InputField(
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default="full", description="The method to apply the IP-Adapter"
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)
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begin_step_percent: float = InputField(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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@ -124,12 +116,32 @@ class IPAdapterInvocation(BaseInvocation):
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image_encoder_model = self._get_image_encoder(context, image_encoder_model_name)
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if self.method == "style":
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if ip_adapter_info.base == "sd-1":
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target_blocks = ["up_blocks.1"]
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elif ip_adapter_info.base == "sdxl":
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target_blocks = ["up_blocks.0.attentions.1"]
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else:
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raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
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elif self.method == "composition":
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if ip_adapter_info.base == "sd-1":
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target_blocks = ["down_blocks.2", "mid_block"]
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elif ip_adapter_info.base == "sdxl":
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target_blocks = ["down_blocks.2.attentions.1"]
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else:
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raise ValueError(f"Unsupported IP-Adapter base type: '{ip_adapter_info.base}'.")
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elif self.method == "full":
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target_blocks = ["block"]
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else:
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raise ValueError(f"Unexpected IP-Adapter method: '{self.method}'.")
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return IPAdapterOutput(
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ip_adapter=IPAdapterField(
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image=self.image,
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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=ModelIdentifierField.from_config(image_encoder_model),
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weight=self.weight,
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target_blocks=target_blocks,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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mask=self.mask,
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@ -679,6 +679,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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IPAdapterData(
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ip_adapter_model=ip_adapter_model,
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weight=single_ip_adapter.weight,
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target_blocks=single_ip_adapter.target_blocks,
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begin_step_percent=single_ip_adapter.begin_step_percent,
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end_step_percent=single_ip_adapter.end_step_percent,
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ip_adapter_conditioning=IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds),
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@ -36,6 +36,7 @@ class IPAdapterMetadataField(BaseModel):
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image: ImageField = Field(description="The IP-Adapter image prompt.")
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ip_adapter_model: ModelIdentifierField = Field(description="The IP-Adapter model.")
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clip_vision_model: Literal["ViT-H", "ViT-G"] = Field(description="The CLIP Vision model")
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method: Literal["full", "style", "composition"] = Field(description="Method to apply IP Weights with")
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weight: Union[float, list[float]] = Field(description="The weight given to the IP-Adapter")
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begin_step_percent: float = Field(description="When the IP-Adapter is first applied (% of total steps)")
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end_step_percent: float = Field(description="When the IP-Adapter is last applied (% of total steps)")
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@ -763,6 +763,8 @@ class ModelInstallService(ModelInstallServiceBase):
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self._download_cache[download_job.source] = install_job # matches a download job to an install job
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install_job.download_parts.add(download_job)
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# only start the jobs once install_job.download_parts is fully populated
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for download_job in install_job.download_parts:
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self._download_queue.submit_download_job(
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download_job,
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on_start=self._download_started_callback,
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@ -771,6 +773,7 @@ class ModelInstallService(ModelInstallServiceBase):
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on_error=self._download_error_callback,
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on_cancelled=self._download_cancelled_callback,
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)
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return install_job
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def _stat_size(self, path: Path) -> int:
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@ -21,12 +21,9 @@ from pydantic import Field
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from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
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from invokeai.app.services.config.config_default import get_config
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
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IPAdapterData,
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TextConditioningData,
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)
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from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData
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from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
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from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
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from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
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from invokeai.backend.util.attention import auto_detect_slice_size
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from invokeai.backend.util.devices import TorchDevice
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@ -394,8 +391,13 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
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unet_attention_patcher = None
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self.use_ip_adapter = use_ip_adapter
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attn_ctx = nullcontext()
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if use_ip_adapter or use_regional_prompting:
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ip_adapters = [ipa.ip_adapter_model for ipa in ip_adapter_data] if use_ip_adapter else None
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ip_adapters: Optional[List[UNetIPAdapterData]] = (
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[{"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks} for ipa in ip_adapter_data]
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if use_ip_adapter
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else None
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)
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unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
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attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
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@ -53,6 +53,7 @@ class IPAdapterData:
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ip_adapter_model: IPAdapter
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ip_adapter_conditioning: IPAdapterConditioningInfo
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mask: torch.Tensor
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target_blocks: List[str]
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# Either a single weight applied to all steps, or a list of weights for each step.
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weight: Union[float, List[float]] = 1.0
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@ -1,4 +1,5 @@
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from typing import Optional
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from dataclasses import dataclass
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from typing import List, Optional, cast
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import torch
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import torch.nn.functional as F
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@ -9,6 +10,12 @@ from invokeai.backend.stable_diffusion.diffusion.regional_ip_data import Regiona
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from invokeai.backend.stable_diffusion.diffusion.regional_prompt_data import RegionalPromptData
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@dataclass
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class IPAdapterAttentionWeights:
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ip_adapter_weights: IPAttentionProcessorWeights
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skip: bool
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class CustomAttnProcessor2_0(AttnProcessor2_0):
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"""A custom implementation of AttnProcessor2_0 that supports additional Invoke features.
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This implementation is based on
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@ -20,7 +27,7 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
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def __init__(
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self,
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ip_adapter_weights: Optional[list[IPAttentionProcessorWeights]] = None,
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ip_adapter_attention_weights: Optional[List[IPAdapterAttentionWeights]] = None,
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):
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"""Initialize a CustomAttnProcessor2_0.
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Note: Arguments that are the same for all attention layers are passed to __call__(). Arguments that are
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@ -30,23 +37,22 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
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for the i'th IP-Adapter.
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"""
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super().__init__()
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self._ip_adapter_weights = ip_adapter_weights
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def _is_ip_adapter_enabled(self) -> bool:
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return self._ip_adapter_weights is not None
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self._ip_adapter_attention_weights = ip_adapter_attention_weights
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def __call__(
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self,
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attn: Attention,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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temb: Optional[torch.FloatTensor] = None,
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# For regional prompting:
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hidden_states: torch.Tensor,
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encoder_hidden_states: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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temb: Optional[torch.Tensor] = None,
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# For Regional Prompting:
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regional_prompt_data: Optional[RegionalPromptData] = None,
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percent_through: Optional[torch.FloatTensor] = None,
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percent_through: Optional[torch.Tensor] = None,
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# For IP-Adapter:
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regional_ip_data: Optional[RegionalIPData] = None,
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*args,
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**kwargs,
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) -> torch.FloatTensor:
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"""Apply attention.
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Args:
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@ -130,17 +136,19 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
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# Apply IP-Adapter conditioning.
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if is_cross_attention:
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if self._is_ip_adapter_enabled():
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if self._ip_adapter_attention_weights:
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assert regional_ip_data is not None
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ip_masks = regional_ip_data.get_masks(query_seq_len=query_seq_len)
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assert (
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len(regional_ip_data.image_prompt_embeds)
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== len(self._ip_adapter_weights)
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== len(self._ip_adapter_attention_weights)
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== len(regional_ip_data.scales)
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== ip_masks.shape[1]
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)
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for ipa_index, ipa_embed in enumerate(regional_ip_data.image_prompt_embeds):
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ipa_weights = self._ip_adapter_weights[ipa_index]
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ipa_weights = self._ip_adapter_attention_weights[ipa_index].ip_adapter_weights
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ipa_scale = regional_ip_data.scales[ipa_index]
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ip_mask = ip_masks[0, ipa_index, ...]
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@ -153,29 +161,33 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
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# Expected ip_hidden_state shape: (batch_size, num_ip_images, ip_seq_len, ip_image_embedding)
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ip_key = ipa_weights.to_k_ip(ip_hidden_states)
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ip_value = ipa_weights.to_v_ip(ip_hidden_states)
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if not self._ip_adapter_attention_weights[ipa_index].skip:
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ip_key = ipa_weights.to_k_ip(ip_hidden_states)
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ip_value = ipa_weights.to_v_ip(ip_hidden_states)
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# Expected ip_key and ip_value shape: (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
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# Expected ip_key and ip_value shape:
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# (batch_size, num_ip_images, ip_seq_len, head_dim * num_heads)
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ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
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# Expected ip_key and ip_value shape: (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
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# Expected ip_key and ip_value shape:
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# (batch_size, num_heads, num_ip_images * ip_seq_len, head_dim)
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# TODO: add support for attn.scale when we move to Torch 2.1
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ip_hidden_states = F.scaled_dot_product_attention(
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query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
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)
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# TODO: add support for attn.scale when we move to Torch 2.1
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ip_hidden_states = F.scaled_dot_product_attention(
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query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
|
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)
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|
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# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
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# Expected ip_hidden_states shape: (batch_size, num_heads, query_seq_len, head_dim)
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ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(
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batch_size, -1, attn.heads * head_dim
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)
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|
||||
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
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ip_hidden_states = ip_hidden_states.to(query.dtype)
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ip_hidden_states = ip_hidden_states.to(query.dtype)
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|
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# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
|
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|
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hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
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# Expected ip_hidden_states shape: (batch_size, query_seq_len, num_heads * head_dim)
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hidden_states = hidden_states + ipa_scale * ip_hidden_states * ip_mask
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else:
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# If IP-Adapter is not enabled, then regional_ip_data should not be passed in.
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assert regional_ip_data is None
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@ -188,11 +200,15 @@ class CustomAttnProcessor2_0(AttnProcessor2_0):
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hidden_states = attn.to_out[1](hidden_states)
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if input_ndim == 4:
|
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batch_size, channel, height, width = hidden_states.shape
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hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
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|
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if attn.residual_connection:
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hidden_states = hidden_states + residual
|
||||
|
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hidden_states = hidden_states / attn.rescale_output_factor
|
||||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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||||
# End of unmodified block from AttnProcessor2_0
|
||||
|
||||
return hidden_states
|
||||
# casting torch.Tensor to torch.FloatTensor to avoid type issues
|
||||
return cast(torch.FloatTensor, hidden_states)
|
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|
||||
@ -1,17 +1,25 @@
|
||||
from contextlib import contextmanager
|
||||
from typing import Optional
|
||||
from typing import List, Optional, TypedDict
|
||||
|
||||
from diffusers.models import UNet2DConditionModel
|
||||
|
||||
from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
|
||||
from invokeai.backend.stable_diffusion.diffusion.custom_atttention import CustomAttnProcessor2_0
|
||||
from invokeai.backend.stable_diffusion.diffusion.custom_atttention import (
|
||||
CustomAttnProcessor2_0,
|
||||
IPAdapterAttentionWeights,
|
||||
)
|
||||
|
||||
|
||||
class UNetIPAdapterData(TypedDict):
|
||||
ip_adapter: IPAdapter
|
||||
target_blocks: List[str]
|
||||
|
||||
|
||||
class UNetAttentionPatcher:
|
||||
"""A class for patching a UNet with CustomAttnProcessor2_0 attention layers."""
|
||||
|
||||
def __init__(self, ip_adapters: Optional[list[IPAdapter]]):
|
||||
self._ip_adapters = ip_adapters
|
||||
def __init__(self, ip_adapter_data: Optional[List[UNetIPAdapterData]]):
|
||||
self._ip_adapters = ip_adapter_data
|
||||
|
||||
def _prepare_attention_processors(self, unet: UNet2DConditionModel):
|
||||
"""Prepare a dict of attention processors that can be injected into a unet, and load the IP-Adapter attention
|
||||
@ -26,9 +34,22 @@ class UNetAttentionPatcher:
|
||||
attn_procs[name] = CustomAttnProcessor2_0()
|
||||
else:
|
||||
# Collect the weights from each IP Adapter for the idx'th attention processor.
|
||||
attn_procs[name] = CustomAttnProcessor2_0(
|
||||
[ip_adapter.attn_weights.get_attention_processor_weights(idx) for ip_adapter in self._ip_adapters],
|
||||
)
|
||||
ip_adapter_attention_weights_collection: list[IPAdapterAttentionWeights] = []
|
||||
|
||||
for ip_adapter in self._ip_adapters:
|
||||
ip_adapter_weights = ip_adapter["ip_adapter"].attn_weights.get_attention_processor_weights(idx)
|
||||
skip = True
|
||||
for block in ip_adapter["target_blocks"]:
|
||||
if block in name:
|
||||
skip = False
|
||||
break
|
||||
ip_adapter_attention_weights: IPAdapterAttentionWeights = IPAdapterAttentionWeights(
|
||||
ip_adapter_weights=ip_adapter_weights, skip=skip
|
||||
)
|
||||
ip_adapter_attention_weights_collection.append(ip_adapter_attention_weights)
|
||||
|
||||
attn_procs[name] = CustomAttnProcessor2_0(ip_adapter_attention_weights_collection)
|
||||
|
||||
return attn_procs
|
||||
|
||||
@contextmanager
|
||||
|
||||
@ -85,7 +85,8 @@
|
||||
"loadMore": "Mehr laden",
|
||||
"noImagesInGallery": "Keine Bilder in der Galerie",
|
||||
"loading": "Lade",
|
||||
"deleteImage": "Lösche Bild",
|
||||
"deleteImage_one": "Lösche Bild",
|
||||
"deleteImage_other": "",
|
||||
"copy": "Kopieren",
|
||||
"download": "Runterladen",
|
||||
"setCurrentImage": "Setze aktuelle Bild",
|
||||
|
||||
@ -69,6 +69,7 @@
|
||||
"auto": "Auto",
|
||||
"back": "Back",
|
||||
"batch": "Batch Manager",
|
||||
"beta": "Beta",
|
||||
"cancel": "Cancel",
|
||||
"copy": "Copy",
|
||||
"copyError": "$t(gallery.copy) Error",
|
||||
@ -213,6 +214,10 @@
|
||||
"resize": "Resize",
|
||||
"resizeSimple": "Resize (Simple)",
|
||||
"resizeMode": "Resize Mode",
|
||||
"ipAdapterMethod": "Method",
|
||||
"full": "Full",
|
||||
"style": "Style Only",
|
||||
"composition": "Composition Only",
|
||||
"safe": "Safe",
|
||||
"saveControlImage": "Save Control Image",
|
||||
"scribble": "scribble",
|
||||
|
||||
@ -33,7 +33,9 @@
|
||||
"autoSwitchNewImages": "Auto seleccionar Imágenes nuevas",
|
||||
"loadMore": "Cargar más",
|
||||
"noImagesInGallery": "No hay imágenes para mostrar",
|
||||
"deleteImage": "Eliminar Imagen",
|
||||
"deleteImage_one": "Eliminar Imagen",
|
||||
"deleteImage_many": "",
|
||||
"deleteImage_other": "",
|
||||
"deleteImageBin": "Las imágenes eliminadas se enviarán a la papelera de tu sistema operativo.",
|
||||
"deleteImagePermanent": "Las imágenes eliminadas no se pueden restaurar.",
|
||||
"assets": "Activos",
|
||||
|
||||
@ -82,7 +82,9 @@
|
||||
"autoSwitchNewImages": "Passaggio automatico a nuove immagini",
|
||||
"loadMore": "Carica altro",
|
||||
"noImagesInGallery": "Nessuna immagine da visualizzare",
|
||||
"deleteImage": "Elimina l'immagine",
|
||||
"deleteImage_one": "Elimina l'immagine",
|
||||
"deleteImage_many": "Elimina {{count}} immagini",
|
||||
"deleteImage_other": "Elimina {{count}} immagini",
|
||||
"deleteImagePermanent": "Le immagini eliminate non possono essere ripristinate.",
|
||||
"deleteImageBin": "Le immagini eliminate verranno spostate nel cestino del tuo sistema operativo.",
|
||||
"assets": "Risorse",
|
||||
|
||||
@ -90,7 +90,7 @@
|
||||
"problemDeletingImages": "画像の削除中に問題が発生",
|
||||
"drop": "ドロップ",
|
||||
"dropOrUpload": "$t(gallery.drop) またはアップロード",
|
||||
"deleteImage": "画像を削除",
|
||||
"deleteImage_other": "画像を削除",
|
||||
"deleteImageBin": "削除された画像はOSのゴミ箱に送られます。",
|
||||
"deleteImagePermanent": "削除された画像は復元できません。",
|
||||
"download": "ダウンロード",
|
||||
|
||||
@ -82,7 +82,7 @@
|
||||
"drop": "드랍",
|
||||
"problemDeletingImages": "이미지 삭제 중 발생한 문제",
|
||||
"downloadSelection": "선택 항목 다운로드",
|
||||
"deleteImage": "이미지 삭제",
|
||||
"deleteImage_other": "이미지 삭제",
|
||||
"currentlyInUse": "이 이미지는 현재 다음 기능에서 사용되고 있습니다:",
|
||||
"dropOrUpload": "$t(gallery.drop) 또는 업로드",
|
||||
"copy": "복사",
|
||||
|
||||
@ -42,7 +42,8 @@
|
||||
"autoSwitchNewImages": "Wissel autom. naar nieuwe afbeeldingen",
|
||||
"loadMore": "Laad meer",
|
||||
"noImagesInGallery": "Geen afbeeldingen om te tonen",
|
||||
"deleteImage": "Verwijder afbeelding",
|
||||
"deleteImage_one": "Verwijder afbeelding",
|
||||
"deleteImage_other": "",
|
||||
"deleteImageBin": "Verwijderde afbeeldingen worden naar de prullenbak van je besturingssysteem gestuurd.",
|
||||
"deleteImagePermanent": "Verwijderde afbeeldingen kunnen niet worden hersteld.",
|
||||
"assets": "Eigen onderdelen",
|
||||
|
||||
@ -86,7 +86,9 @@
|
||||
"noImagesInGallery": "Изображений нет",
|
||||
"deleteImagePermanent": "Удаленные изображения невозможно восстановить.",
|
||||
"deleteImageBin": "Удаленные изображения будут отправлены в корзину вашей операционной системы.",
|
||||
"deleteImage": "Удалить изображение",
|
||||
"deleteImage_one": "Удалить изображение",
|
||||
"deleteImage_few": "",
|
||||
"deleteImage_many": "",
|
||||
"assets": "Ресурсы",
|
||||
"autoAssignBoardOnClick": "Авто-назначение доски по клику",
|
||||
"deleteSelection": "Удалить выделенное",
|
||||
|
||||
@ -298,7 +298,8 @@
|
||||
"noImagesInGallery": "Gösterilecek Görsel Yok",
|
||||
"autoSwitchNewImages": "Yeni Görseli Biter Bitmez Gör",
|
||||
"currentlyInUse": "Bu görsel şurada kullanımda:",
|
||||
"deleteImage": "Görseli Sil",
|
||||
"deleteImage_one": "Görseli Sil",
|
||||
"deleteImage_other": "",
|
||||
"loadMore": "Daha Getir",
|
||||
"setCurrentImage": "Çalışma Görseli Yap",
|
||||
"unableToLoad": "Galeri Yüklenemedi",
|
||||
|
||||
@ -78,7 +78,7 @@
|
||||
"autoSwitchNewImages": "自动切换到新图像",
|
||||
"loadMore": "加载更多",
|
||||
"noImagesInGallery": "无图像可用于显示",
|
||||
"deleteImage": "删除图片",
|
||||
"deleteImage_other": "删除图片",
|
||||
"deleteImageBin": "被删除的图片会发送到你操作系统的回收站。",
|
||||
"deleteImagePermanent": "删除的图片无法被恢复。",
|
||||
"assets": "素材",
|
||||
|
||||
@ -21,6 +21,7 @@ import ControlAdapterShouldAutoConfig from './ControlAdapterShouldAutoConfig';
|
||||
import ControlNetCanvasImageImports from './imports/ControlNetCanvasImageImports';
|
||||
import { ParamControlAdapterBeginEnd } from './parameters/ParamControlAdapterBeginEnd';
|
||||
import ParamControlAdapterControlMode from './parameters/ParamControlAdapterControlMode';
|
||||
import ParamControlAdapterIPMethod from './parameters/ParamControlAdapterIPMethod';
|
||||
import ParamControlAdapterProcessorSelect from './parameters/ParamControlAdapterProcessorSelect';
|
||||
import ParamControlAdapterResizeMode from './parameters/ParamControlAdapterResizeMode';
|
||||
import ParamControlAdapterWeight from './parameters/ParamControlAdapterWeight';
|
||||
@ -111,7 +112,8 @@ const ControlAdapterConfig = (props: { id: string; number: number }) => {
|
||||
|
||||
<Flex w="full" flexDir="column" gap={4}>
|
||||
<Flex gap={8} w="full" alignItems="center">
|
||||
<Flex flexDir="column" gap={2} h={32} w="full">
|
||||
<Flex flexDir="column" gap={4} h={controlAdapterType === 'ip_adapter' ? 40 : 32} w="full">
|
||||
<ParamControlAdapterIPMethod id={id} />
|
||||
<ParamControlAdapterWeight id={id} />
|
||||
<ParamControlAdapterBeginEnd id={id} />
|
||||
</Flex>
|
||||
|
||||
@ -0,0 +1,63 @@
|
||||
import type { ComboboxOnChange } from '@invoke-ai/ui-library';
|
||||
import { Combobox, FormControl, FormLabel } from '@invoke-ai/ui-library';
|
||||
import { useAppDispatch } from 'app/store/storeHooks';
|
||||
import { InformationalPopover } from 'common/components/InformationalPopover/InformationalPopover';
|
||||
import { useControlAdapterIPMethod } from 'features/controlAdapters/hooks/useControlAdapterIPMethod';
|
||||
import { useControlAdapterIsEnabled } from 'features/controlAdapters/hooks/useControlAdapterIsEnabled';
|
||||
import { controlAdapterIPMethodChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
|
||||
import type { IPMethod } from 'features/controlAdapters/store/types';
|
||||
import { isIPMethod } from 'features/controlAdapters/store/types';
|
||||
import { memo, useCallback, useMemo } from 'react';
|
||||
import { useTranslation } from 'react-i18next';
|
||||
|
||||
type Props = {
|
||||
id: string;
|
||||
};
|
||||
|
||||
const ParamControlAdapterIPMethod = ({ id }: Props) => {
|
||||
const isEnabled = useControlAdapterIsEnabled(id);
|
||||
const method = useControlAdapterIPMethod(id);
|
||||
const dispatch = useAppDispatch();
|
||||
const { t } = useTranslation();
|
||||
|
||||
const options: { label: string; value: IPMethod }[] = useMemo(
|
||||
() => [
|
||||
{ label: t('controlnet.full'), value: 'full' },
|
||||
{ label: `${t('controlnet.style')} (${t('common.beta')})`, value: 'style' },
|
||||
{ label: `${t('controlnet.composition')} (${t('common.beta')})`, value: 'composition' },
|
||||
],
|
||||
[t]
|
||||
);
|
||||
|
||||
const handleIPMethodChanged = useCallback<ComboboxOnChange>(
|
||||
(v) => {
|
||||
if (!isIPMethod(v?.value)) {
|
||||
return;
|
||||
}
|
||||
dispatch(
|
||||
controlAdapterIPMethodChanged({
|
||||
id,
|
||||
method: v.value,
|
||||
})
|
||||
);
|
||||
},
|
||||
[id, dispatch]
|
||||
);
|
||||
|
||||
const value = useMemo(() => options.find((o) => o.value === method), [options, method]);
|
||||
|
||||
if (!method) {
|
||||
return null;
|
||||
}
|
||||
|
||||
return (
|
||||
<FormControl>
|
||||
<InformationalPopover feature="controlNetResizeMode">
|
||||
<FormLabel>{t('controlnet.ipAdapterMethod')}</FormLabel>
|
||||
</InformationalPopover>
|
||||
<Combobox value={value} options={options} isDisabled={!isEnabled} onChange={handleIPMethodChanged} />
|
||||
</FormControl>
|
||||
);
|
||||
};
|
||||
|
||||
export default memo(ParamControlAdapterIPMethod);
|
||||
@ -0,0 +1,24 @@
|
||||
import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
|
||||
import { useAppSelector } from 'app/store/storeHooks';
|
||||
import {
|
||||
selectControlAdapterById,
|
||||
selectControlAdaptersSlice,
|
||||
} from 'features/controlAdapters/store/controlAdaptersSlice';
|
||||
import { useMemo } from 'react';
|
||||
|
||||
export const useControlAdapterIPMethod = (id: string) => {
|
||||
const selector = useMemo(
|
||||
() =>
|
||||
createMemoizedSelector(selectControlAdaptersSlice, (controlAdapters) => {
|
||||
const cn = selectControlAdapterById(controlAdapters, id);
|
||||
if (cn && cn?.type === 'ip_adapter') {
|
||||
return cn.method;
|
||||
}
|
||||
}),
|
||||
[id]
|
||||
);
|
||||
|
||||
const method = useAppSelector(selector);
|
||||
|
||||
return method;
|
||||
};
|
||||
@ -21,6 +21,7 @@ import type {
|
||||
ControlAdapterType,
|
||||
ControlMode,
|
||||
ControlNetConfig,
|
||||
IPMethod,
|
||||
RequiredControlAdapterProcessorNode,
|
||||
ResizeMode,
|
||||
T2IAdapterConfig,
|
||||
@ -245,6 +246,10 @@ export const controlAdaptersSlice = createSlice({
|
||||
}
|
||||
caAdapter.updateOne(state, { id, changes: { controlMode } });
|
||||
},
|
||||
controlAdapterIPMethodChanged: (state, action: PayloadAction<{ id: string; method: IPMethod }>) => {
|
||||
const { id, method } = action.payload;
|
||||
caAdapter.updateOne(state, { id, changes: { method } });
|
||||
},
|
||||
controlAdapterCLIPVisionModelChanged: (
|
||||
state,
|
||||
action: PayloadAction<{ id: string; clipVisionModel: CLIPVisionModel }>
|
||||
@ -390,6 +395,7 @@ export const {
|
||||
controlAdapterIsEnabledChanged,
|
||||
controlAdapterModelChanged,
|
||||
controlAdapterCLIPVisionModelChanged,
|
||||
controlAdapterIPMethodChanged,
|
||||
controlAdapterWeightChanged,
|
||||
controlAdapterBeginStepPctChanged,
|
||||
controlAdapterEndStepPctChanged,
|
||||
|
||||
@ -210,6 +210,10 @@ const zResizeMode = z.enum(['just_resize', 'crop_resize', 'fill_resize', 'just_r
|
||||
export type ResizeMode = z.infer<typeof zResizeMode>;
|
||||
export const isResizeMode = (v: unknown): v is ResizeMode => zResizeMode.safeParse(v).success;
|
||||
|
||||
const zIPMethod = z.enum(['full', 'style', 'composition']);
|
||||
export type IPMethod = z.infer<typeof zIPMethod>;
|
||||
export const isIPMethod = (v: unknown): v is IPMethod => zIPMethod.safeParse(v).success;
|
||||
|
||||
export type ControlNetConfig = {
|
||||
type: 'controlnet';
|
||||
id: string;
|
||||
@ -253,6 +257,7 @@ export type IPAdapterConfig = {
|
||||
model: ParameterIPAdapterModel | null;
|
||||
clipVisionModel: CLIPVisionModel;
|
||||
weight: number;
|
||||
method: IPMethod;
|
||||
beginStepPct: number;
|
||||
endStepPct: number;
|
||||
};
|
||||
|
||||
@ -46,6 +46,7 @@ export const initialIPAdapter: Omit<IPAdapterConfig, 'id'> = {
|
||||
isEnabled: true,
|
||||
controlImage: null,
|
||||
model: null,
|
||||
method: 'full',
|
||||
clipVisionModel: 'ViT-H',
|
||||
weight: 1,
|
||||
beginStepPct: 0,
|
||||
|
||||
@ -386,6 +386,10 @@ const parseIPAdapter: MetadataParseFunc<IPAdapterConfigMetadata> = async (metada
|
||||
.nullish()
|
||||
.catch(null)
|
||||
.parse(await getProperty(metadataItem, 'weight'));
|
||||
const method = zIPAdapterField.shape.method
|
||||
.nullish()
|
||||
.catch(null)
|
||||
.parse(await getProperty(metadataItem, 'method'));
|
||||
const begin_step_percent = zIPAdapterField.shape.begin_step_percent
|
||||
.nullish()
|
||||
.catch(null)
|
||||
@ -403,6 +407,7 @@ const parseIPAdapter: MetadataParseFunc<IPAdapterConfigMetadata> = async (metada
|
||||
clipVisionModel: 'ViT-H',
|
||||
controlImage: image?.image_name ?? null,
|
||||
weight: weight ?? initialIPAdapter.weight,
|
||||
method: method ?? initialIPAdapter.method,
|
||||
beginStepPct: begin_step_percent ?? initialIPAdapter.beginStepPct,
|
||||
endStepPct: end_step_percent ?? initialIPAdapter.endStepPct,
|
||||
};
|
||||
|
||||
@ -109,6 +109,7 @@ export const zIPAdapterField = z.object({
|
||||
image: zImageField,
|
||||
ip_adapter_model: zModelIdentifierField,
|
||||
weight: z.number(),
|
||||
method: z.enum(['full', 'style', 'composition']),
|
||||
begin_step_percent: z.number().optional(),
|
||||
end_step_percent: z.number().optional(),
|
||||
});
|
||||
|
||||
@ -48,7 +48,7 @@ export const addIPAdapterToLinearGraph = async (
|
||||
if (!ipAdapter.model) {
|
||||
return;
|
||||
}
|
||||
const { id, weight, model, clipVisionModel, beginStepPct, endStepPct, controlImage } = ipAdapter;
|
||||
const { id, weight, model, clipVisionModel, method, beginStepPct, endStepPct, controlImage } = ipAdapter;
|
||||
|
||||
assert(controlImage, 'IP Adapter image is required');
|
||||
|
||||
@ -57,6 +57,7 @@ export const addIPAdapterToLinearGraph = async (
|
||||
type: 'ip_adapter',
|
||||
is_intermediate: true,
|
||||
weight: weight,
|
||||
method: method,
|
||||
ip_adapter_model: model,
|
||||
clip_vision_model: clipVisionModel,
|
||||
begin_step_percent: beginStepPct,
|
||||
@ -84,7 +85,7 @@ export const addIPAdapterToLinearGraph = async (
|
||||
};
|
||||
|
||||
const buildIPAdapterMetadata = (ipAdapter: IPAdapterConfig): S['IPAdapterMetadataField'] => {
|
||||
const { controlImage, beginStepPct, endStepPct, model, clipVisionModel, weight } = ipAdapter;
|
||||
const { controlImage, beginStepPct, endStepPct, model, clipVisionModel, method, weight } = ipAdapter;
|
||||
|
||||
assert(model, 'IP Adapter model is required');
|
||||
|
||||
@ -102,6 +103,7 @@ const buildIPAdapterMetadata = (ipAdapter: IPAdapterConfig): S['IPAdapterMetadat
|
||||
ip_adapter_model: model,
|
||||
clip_vision_model: clipVisionModel,
|
||||
weight,
|
||||
method,
|
||||
begin_step_percent: beginStepPct,
|
||||
end_step_percent: endStepPct,
|
||||
image,
|
||||
|
||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user