mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Merge branch 'main' into feat/nodes/freeu
This commit is contained in:
Millun Atluri
GitHub
@ -19,7 +19,7 @@ from diffusers.models.attention_processor import (
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)
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from diffusers.schedulers import DPMSolverSDEScheduler
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from diffusers.schedulers import SchedulerMixin as Scheduler
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from pydantic import validator
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from pydantic import field_validator
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from torchvision.transforms.functional import resize as tv_resize
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from invokeai.app.invocations.ip_adapter import IPAdapterField
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@ -34,6 +34,7 @@ from invokeai.app.invocations.primitives import (
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build_latents_output,
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)
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from invokeai.app.invocations.t2i_adapter import T2IAdapterField
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from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
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from invokeai.app.util.controlnet_utils import prepare_control_image
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from invokeai.app.util.step_callback import stable_diffusion_step_callback
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from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
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@ -54,7 +55,6 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
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from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
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from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
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from ...backend.util.devices import choose_precision, choose_torch_device
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from ..models.image import ImageCategory, ResourceOrigin
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from .baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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@ -84,12 +84,20 @@ class SchedulerOutput(BaseInvocationOutput):
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scheduler: SAMPLER_NAME_VALUES = OutputField(description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler)
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@invocation("scheduler", title="Scheduler", tags=["scheduler"], category="latents", version="1.0.0")
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@invocation(
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"scheduler",
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title="Scheduler",
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tags=["scheduler"],
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category="latents",
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version="1.0.0",
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)
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class SchedulerInvocation(BaseInvocation):
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"""Selects a scheduler."""
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scheduler: SAMPLER_NAME_VALUES = InputField(
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default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
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default="euler",
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description=FieldDescriptions.scheduler,
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ui_type=UIType.Scheduler,
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)
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def invoke(self, context: InvocationContext) -> SchedulerOutput:
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@ -97,7 +105,11 @@ class SchedulerInvocation(BaseInvocation):
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@invocation(
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"create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents", version="1.0.0"
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"create_denoise_mask",
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title="Create Denoise Mask",
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tags=["mask", "denoise"],
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category="latents",
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version="1.0.0",
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)
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class CreateDenoiseMaskInvocation(BaseInvocation):
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"""Creates mask for denoising model run."""
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@ -106,7 +118,11 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
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image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
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mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
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tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
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fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32, ui_order=4)
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fp32: bool = InputField(
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default=DEFAULT_PRECISION == "float32",
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description=FieldDescriptions.fp32,
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ui_order=4,
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)
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def prep_mask_tensor(self, mask_image):
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if mask_image.mode != "L":
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@ -134,7 +150,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
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if image is not None:
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vae_info = context.services.model_manager.get_model(
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**self.vae.vae.dict(),
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**self.vae.vae.model_dump(),
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context=context,
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)
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@ -167,7 +183,7 @@ def get_scheduler(
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) -> Scheduler:
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scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
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orig_scheduler_info = context.services.model_manager.get_model(
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**scheduler_info.dict(),
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**scheduler_info.model_dump(),
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context=context,
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)
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with orig_scheduler_info as orig_scheduler:
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@ -209,34 +225,64 @@ class DenoiseLatentsInvocation(BaseInvocation):
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negative_conditioning: ConditioningField = InputField(
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description=FieldDescriptions.negative_cond, input=Input.Connection, ui_order=1
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)
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noise: Optional[LatentsField] = InputField(description=FieldDescriptions.noise, input=Input.Connection, ui_order=3)
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noise: Optional[LatentsField] = InputField(
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default=None,
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description=FieldDescriptions.noise,
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input=Input.Connection,
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ui_order=3,
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)
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steps: int = InputField(default=10, gt=0, description=FieldDescriptions.steps)
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cfg_scale: Union[float, List[float]] = InputField(
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default=7.5, ge=1, description=FieldDescriptions.cfg_scale, title="CFG Scale"
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)
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denoising_start: float = InputField(default=0.0, ge=0, le=1, description=FieldDescriptions.denoising_start)
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denoising_start: float = InputField(
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default=0.0,
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ge=0,
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le=1,
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description=FieldDescriptions.denoising_start,
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)
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denoising_end: float = InputField(default=1.0, ge=0, le=1, description=FieldDescriptions.denoising_end)
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scheduler: SAMPLER_NAME_VALUES = InputField(
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default="euler", description=FieldDescriptions.scheduler, ui_type=UIType.Scheduler
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default="euler",
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description=FieldDescriptions.scheduler,
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ui_type=UIType.Scheduler,
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)
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unet: UNetField = InputField(description=FieldDescriptions.unet, input=Input.Connection, title="UNet", ui_order=2)
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control: Union[ControlField, list[ControlField]] = InputField(
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unet: UNetField = InputField(
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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ui_order=2,
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)
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control: Optional[Union[ControlField, list[ControlField]]] = InputField(
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default=None,
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input=Input.Connection,
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ui_order=5,
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)
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ip_adapter: Optional[Union[IPAdapterField, list[IPAdapterField]]] = InputField(
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description=FieldDescriptions.ip_adapter, title="IP-Adapter", default=None, input=Input.Connection, ui_order=6
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description=FieldDescriptions.ip_adapter,
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title="IP-Adapter",
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default=None,
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input=Input.Connection,
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ui_order=6,
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)
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t2i_adapter: Union[T2IAdapterField, list[T2IAdapterField]] = InputField(
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description=FieldDescriptions.t2i_adapter, title="T2I-Adapter", default=None, input=Input.Connection, ui_order=7
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t2i_adapter: Optional[Union[T2IAdapterField, list[T2IAdapterField]]] = InputField(
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description=FieldDescriptions.t2i_adapter,
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title="T2I-Adapter",
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default=None,
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input=Input.Connection,
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ui_order=7,
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)
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latents: Optional[LatentsField] = InputField(
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default=None, description=FieldDescriptions.latents, input=Input.Connection
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)
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latents: Optional[LatentsField] = InputField(description=FieldDescriptions.latents, input=Input.Connection)
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denoise_mask: Optional[DenoiseMaskField] = InputField(
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default=None, description=FieldDescriptions.mask, input=Input.Connection, ui_order=8
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default=None,
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description=FieldDescriptions.mask,
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input=Input.Connection,
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ui_order=8,
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)
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@validator("cfg_scale")
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@field_validator("cfg_scale")
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def ge_one(cls, v):
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"""validate that all cfg_scale values are >= 1"""
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if isinstance(v, list):
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@ -259,7 +305,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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stable_diffusion_step_callback(
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context=context,
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intermediate_state=intermediate_state,
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node=self.dict(),
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node=self.model_dump(),
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source_node_id=source_node_id,
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base_model=base_model,
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)
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@ -451,9 +497,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
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# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
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with image_encoder_model_info as image_encoder_model:
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# Get image embeddings from CLIP and ImageProjModel.
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image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
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input_image, image_encoder_model
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)
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(
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image_prompt_embeds,
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uncond_image_prompt_embeds,
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) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
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conditioning_data.ip_adapter_conditioning.append(
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IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
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)
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@ -628,7 +675,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
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# TODO(ryand): I have hard-coded `do_classifier_free_guidance=True` to mirror the behaviour of ControlNets,
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# below. Investigate whether this is appropriate.
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t2i_adapter_data = self.run_t2i_adapters(
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context, self.t2i_adapter, latents.shape, do_classifier_free_guidance=True
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context,
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self.t2i_adapter,
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latents.shape,
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do_classifier_free_guidance=True,
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)
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# Get the source node id (we are invoking the prepared node)
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@ -641,7 +691,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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def _lora_loader():
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for lora in self.unet.loras:
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lora_info = context.services.model_manager.get_model(
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**lora.dict(exclude={"weight"}),
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**lora.model_dump(exclude={"weight"}),
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context=context,
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)
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yield (lora_info.context.model, lora.weight)
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@ -649,7 +699,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
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return
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unet_info = context.services.model_manager.get_model(
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**self.unet.unet.dict(),
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**self.unet.unet.model_dump(),
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context=context,
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)
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with (
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@ -701,7 +751,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
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denoising_end=self.denoising_end,
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)
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result_latents, result_attention_map_saver = pipeline.latents_from_embeddings(
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(
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result_latents,
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result_attention_map_saver,
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) = pipeline.latents_from_embeddings(
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latents=latents,
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timesteps=timesteps,
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init_timestep=init_timestep,
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@ -729,7 +782,11 @@ class DenoiseLatentsInvocation(BaseInvocation):
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@invocation(
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"l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents", version="1.0.0"
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"l2i",
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title="Latents to Image",
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tags=["latents", "image", "vae", "l2i"],
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category="latents",
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version="1.0.0",
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)
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class LatentsToImageInvocation(BaseInvocation):
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"""Generates an image from latents."""
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@ -744,7 +801,7 @@ class LatentsToImageInvocation(BaseInvocation):
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)
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tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
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fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
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metadata: CoreMetadata = InputField(
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metadata: Optional[CoreMetadata] = InputField(
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default=None,
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description=FieldDescriptions.core_metadata,
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ui_hidden=True,
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@ -755,7 +812,7 @@ class LatentsToImageInvocation(BaseInvocation):
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latents = context.services.latents.get(self.latents.latents_name)
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vae_info = context.services.model_manager.get_model(
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**self.vae.vae.dict(),
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**self.vae.vae.model_dump(),
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context=context,
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)
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@ -817,7 +874,7 @@ class LatentsToImageInvocation(BaseInvocation):
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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is_intermediate=self.is_intermediate,
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metadata=self.metadata.dict() if self.metadata else None,
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metadata=self.metadata.model_dump() if self.metadata else None,
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workflow=self.workflow,
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)
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@ -831,7 +888,13 @@ class LatentsToImageInvocation(BaseInvocation):
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LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
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@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
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@invocation(
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"lresize",
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title="Resize Latents",
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tags=["latents", "resize"],
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category="latents",
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version="1.0.0",
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)
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class ResizeLatentsInvocation(BaseInvocation):
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"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
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@ -877,7 +940,13 @@ class ResizeLatentsInvocation(BaseInvocation):
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return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
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@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents", version="1.0.0")
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@invocation(
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"lscale",
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title="Scale Latents",
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tags=["latents", "resize"],
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category="latents",
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version="1.0.0",
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)
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class ScaleLatentsInvocation(BaseInvocation):
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"""Scales latents by a given factor."""
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@ -916,7 +985,11 @@ class ScaleLatentsInvocation(BaseInvocation):
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@invocation(
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"i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents", version="1.0.0"
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"i2l",
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title="Image to Latents",
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tags=["latents", "image", "vae", "i2l"],
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category="latents",
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version="1.0.0",
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)
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class ImageToLatentsInvocation(BaseInvocation):
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"""Encodes an image into latents."""
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@ -980,7 +1053,7 @@ class ImageToLatentsInvocation(BaseInvocation):
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image = context.services.images.get_pil_image(self.image.image_name)
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vae_info = context.services.model_manager.get_model(
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**self.vae.vae.dict(),
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**self.vae.vae.model_dump(),
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context=context,
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)
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@ -1008,7 +1081,13 @@ class ImageToLatentsInvocation(BaseInvocation):
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return vae.encode(image_tensor).latents
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@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents", version="1.0.0")
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@invocation(
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"lblend",
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title="Blend Latents",
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tags=["latents", "blend"],
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category="latents",
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version="1.0.0",
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)
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class BlendLatentsInvocation(BaseInvocation):
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"""Blend two latents using a given alpha. Latents must have same size."""
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