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

Merge branch 'main' into feat/nodes/freeu

Browse Source
This commit is contained in:
Kent Keirsey
2023-11-06 05:39:58 -08:00
committed by GitHub
parent 14e25bf277 546aaedbe4
commit e66d0f7372
206 changed files with 11705 additions and 8252 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
invokeai/app/invocations/latent.py
View File

@ -23,7 +23,6 @@ from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.metadata import CoreMetadata
from invokeai.app.invocations.primitives import (
DenoiseMaskField,
DenoiseMaskOutput,
@ -64,6 +63,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
WithMetadata,
WithWorkflow,
invocation,
invocation_output,
)
@ -214,7 +215,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.3.0",
version="1.4.0",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -491,16 +492,21 @@ class DenoiseLatentsInvocation(BaseInvocation):
context=context,
)
input_image = context.services.images.get_pil_image(single_ip_adapter.image.image_name)
# `single_ip_adapter.image` could be a list or a single ImageField. Normalize to a list here.
single_ipa_images = single_ip_adapter.image
if not isinstance(single_ipa_images, list):
single_ipa_images = [single_ipa_images]
single_ipa_images = [context.services.images.get_pil_image(image.image_name) for image in single_ipa_images]
# TODO(ryand): With some effort, the step of running the CLIP Vision encoder could be done before any other
# models are needed in memory. This would help to reduce peak memory utilization in low-memory environments.
with image_encoder_model_info as image_encoder_model:
# Get image embeddings from CLIP and ImageProjModel.
(
image_prompt_embeds,
uncond_image_prompt_embeds,
) = ip_adapter_model.get_image_embeds(input_image, image_encoder_model)
image_prompt_embeds, uncond_image_prompt_embeds = ip_adapter_model.get_image_embeds(
single_ipa_images, image_encoder_model
)
conditioning_data.ip_adapter_conditioning.append(
IPAdapterConditioningInfo(image_prompt_embeds, uncond_image_prompt_embeds)
)
@ -708,6 +714,8 @@ class DenoiseLatentsInvocation(BaseInvocation):
ModelPatcher.apply_freeu(unet_info.context.model, self.unet.freeu_config),
set_seamless(unet_info.context.model, self.unet.seamless_axes),
unet_info as unet,
# Apply the LoRA after unet has been moved to its target device for faster patching.
ModelPatcher.apply_lora_unet(unet, _lora_loader()),
):
latents = latents.to(device=unet.device, dtype=unet.dtype)
if noise is not None:
@ -788,7 +796,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
category="latents",
version="1.0.0",
)
class LatentsToImageInvocation(BaseInvocation):
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithWorkflow):
"""Generates an image from latents."""
latents: LatentsField = InputField(
@ -801,11 +809,6 @@ class LatentsToImageInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
metadata: Optional[CoreMetadata] = InputField(
default=None,
description=FieldDescriptions.core_metadata,
ui_hidden=True,
)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -874,7 +877,7 @@ class LatentsToImageInvocation(BaseInvocation):
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
metadata=self.metadata.model_dump() if self.metadata else None,
metadata=self.metadata,
workflow=self.workflow,
)