Update version to v3.4.0rc3

docs/index.md

@@ -198,7 +198,6 @@ The list of schedulers has been completely revamped and brought up to date:
 | **dpmpp_2m**   | DPMSolverMultistepScheduler     | original noise scnedule     |
 | **dpmpp_2m_k** | DPMSolverMultistepScheduler     | using karras noise schedule |
 | **unipc**      | UniPCMultistepScheduler         | CPU only                    |
-| **lcm**        | LCMScheduler                    |                             |
 
 Please see [3.0.0 Release Notes](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.0.0) for further details.
 

installer/lib/installer.py

@@ -460,10 +460,10 @@ def get_torch_source() -> (Union[str, None], str):
             url = "https://download.pytorch.org/whl/cpu"
 
     if device == "cuda":
-        url = "https://download.pytorch.org/whl/cu118"
+        url = "https://download.pytorch.org/whl/cu121"
         optional_modules = "[xformers,onnx-cuda]"
     if device == "cuda_and_dml":
-        url = "https://download.pytorch.org/whl/cu118"
+        url = "https://download.pytorch.org/whl/cu121"
         optional_modules = "[xformers,onnx-directml]"
 
     # in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

invokeai/app/api/sockets.py

@@ -34,4 +34,4 @@ class SocketIO:
 
     async def _handle_unsub_queue(self, sid, data, *args, **kwargs):
         if "queue_id" in data:
-            await self.__sio.leave_room(sid, data["queue_id"])
+            await self.__sio.enter_room(sid, data["queue_id"])

invokeai/app/invocations/baseinvocation.py

@@ -16,7 +16,6 @@ from pydantic.fields import FieldInfo, _Unset
 from pydantic_core import PydanticUndefined
 
 from invokeai.app.services.config.config_default import InvokeAIAppConfig
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.misc import uuid_string
 
 if TYPE_CHECKING:
@@ -31,6 +30,74 @@ class InvalidFieldError(TypeError):
     pass
 
 
+class FieldDescriptions:
+    denoising_start = "When to start denoising, expressed a percentage of total steps"
+    denoising_end = "When to stop denoising, expressed a percentage of total steps"
+    cfg_scale = "Classifier-Free Guidance scale"
+    scheduler = "Scheduler to use during inference"
+    positive_cond = "Positive conditioning tensor"
+    negative_cond = "Negative conditioning tensor"
+    noise = "Noise tensor"
+    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
+    unet = "UNet (scheduler, LoRAs)"
+    vae = "VAE"
+    cond = "Conditioning tensor"
+    controlnet_model = "ControlNet model to load"
+    vae_model = "VAE model to load"
+    lora_model = "LoRA model to load"
+    main_model = "Main model (UNet, VAE, CLIP) to load"
+    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
+    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
+    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
+    lora_weight = "The weight at which the LoRA is applied to each model"
+    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
+    raw_prompt = "Raw prompt text (no parsing)"
+    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
+    skipped_layers = "Number of layers to skip in text encoder"
+    seed = "Seed for random number generation"
+    steps = "Number of steps to run"
+    width = "Width of output (px)"
+    height = "Height of output (px)"
+    control = "ControlNet(s) to apply"
+    ip_adapter = "IP-Adapter to apply"
+    t2i_adapter = "T2I-Adapter(s) to apply"
+    denoised_latents = "Denoised latents tensor"
+    latents = "Latents tensor"
+    strength = "Strength of denoising (proportional to steps)"
+    metadata = "Optional metadata to be saved with the image"
+    metadata_collection = "Collection of Metadata"
+    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
+    metadata_item_label = "Label for this metadata item"
+    metadata_item_value = "The value for this metadata item (may be any type)"
+    workflow = "Optional workflow to be saved with the image"
+    interp_mode = "Interpolation mode"
+    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
+    fp32 = "Whether or not to use full float32 precision"
+    precision = "Precision to use"
+    tiled = "Processing using overlapping tiles (reduce memory consumption)"
+    detect_res = "Pixel resolution for detection"
+    image_res = "Pixel resolution for output image"
+    safe_mode = "Whether or not to use safe mode"
+    scribble_mode = "Whether or not to use scribble mode"
+    scale_factor = "The factor by which to scale"
+    blend_alpha = (
+        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
+    )
+    num_1 = "The first number"
+    num_2 = "The second number"
+    mask = "The mask to use for the operation"
+    board = "The board to save the image to"
+    image = "The image to process"
+    tile_size = "Tile size"
+    inclusive_low = "The inclusive low value"
+    exclusive_high = "The exclusive high value"
+    decimal_places = "The number of decimal places to round to"
+    freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
+    freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
+    freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
+    freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."
+
+
 class Input(str, Enum):
     """
     The type of input a field accepts.

invokeai/app/invocations/compel.py

@@ -7,7 +7,6 @@ from compel import Compel, ReturnedEmbeddingsType
 from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     BasicConditioningInfo,
     ExtraConditioningInfo,
@@ -20,6 +19,7 @@ from ...backend.util.devices import torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/controlnet_image_processors.py

@@ -28,12 +28,12 @@ from pydantic import BaseModel, ConfigDict, Field, field_validator
 
 from invokeai.app.invocations.primitives import ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...backend.model_management import BaseModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/image.py

@@ -9,11 +9,19 @@ from PIL import Image, ImageChops, ImageFilter, ImageOps
 
 from invokeai.app.invocations.primitives import BoardField, ColorField, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 
-from .baseinvocation import BaseInvocation, Input, InputField, InvocationContext, WithMetadata, WithWorkflow, invocation
+from .baseinvocation import (
+    BaseInvocation,
+    FieldDescriptions,
+    Input,
+    InputField,
+    InvocationContext,
+    WithMetadata,
+    WithWorkflow,
+    invocation,
+)
 
 
 @invocation("show_image", title="Show Image", tags=["image"], category="image", version="1.0.0")

invokeai/app/invocations/ip_adapter.py

@@ -7,6 +7,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -16,7 +17,6 @@ from invokeai.app.invocations.baseinvocation import (
     invocation_output,
 )
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType, ModelType
 from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
 

invokeai/app/invocations/latent.py

@@ -10,7 +10,7 @@ import torch
 import torchvision.transforms as T
 from diffusers import AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
-from diffusers.models.adapter import T2IAdapter
+from diffusers.models.adapter import FullAdapterXL, T2IAdapter
 from diffusers.models.attention_processor import (
     AttnProcessor2_0,
     LoRAAttnProcessor2_0,
@@ -34,7 +34,6 @@ from invokeai.app.invocations.primitives import (
 )
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.controlnet_utils import prepare_control_image
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend.ip_adapter.ip_adapter import IPAdapter, IPAdapterPlus
@@ -58,6 +57,7 @@ from ...backend.util.devices import choose_precision, choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -562,6 +562,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
             t2i_adapter_model: T2IAdapter
             with t2i_adapter_model_info as t2i_adapter_model:
                 total_downscale_factor = t2i_adapter_model.total_downscale_factor
+                if isinstance(t2i_adapter_model.adapter, FullAdapterXL):
+                    # HACK(ryand): Work around a bug in FullAdapterXL. This is being addressed upstream in diffusers by
+                    # this PR: https://github.com/huggingface/diffusers/pull/5134.
+                    total_downscale_factor = total_downscale_factor // 2
 
                 # Resize the T2I-Adapter input image.
                 # We select the resize dimensions so that after the T2I-Adapter's total_downscale_factor is applied, the

invokeai/app/invocations/math.py

@@ -6,9 +6,8 @@ import numpy as np
 from pydantic import ValidationInfo, field_validator
 
 from invokeai.app.invocations.primitives import FloatOutput, IntegerOutput
-from invokeai.app.shared.fields import FieldDescriptions
 
-from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
+from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
 
 
 @invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")

invokeai/app/invocations/metadata.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     MetadataField,
@@ -18,7 +19,6 @@ from invokeai.app.invocations.ip_adapter import IPAdapterModelField
 from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
 from invokeai.app.invocations.primitives import ImageField
 from invokeai.app.invocations.t2i_adapter import T2IAdapterField
-from invokeai.app.shared.fields import FieldDescriptions
 
 from ...version import __version__
 
@@ -160,14 +160,13 @@ class CoreMetadataInvocation(BaseInvocation):
     )
 
     # High resolution fix metadata.
-    hrf_enabled: Optional[float] = InputField(
+    hrf_width: Optional[int] = InputField(
         default=None,
-        description="Whether or not high resolution fix was enabled.",
+        description="The high resolution fix height and width multipler.",
     )
-    # TODO: should this be stricter or do we just let the UI handle it?
-    hrf_method: Optional[str] = InputField(
+    hrf_height: Optional[int] = InputField(
         default=None,
-        description="The high resolution fix upscale method.",
+        description="The high resolution fix height and width multipler.",
     )
     hrf_strength: Optional[float] = InputField(
         default=None,

invokeai/app/invocations/model.py

@@ -3,13 +3,11 @@ from typing import List, Optional
 
 from pydantic import BaseModel, ConfigDict, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-from invokeai.app.shared.models import FreeUConfig
-
 from ...backend.model_management import BaseModelType, ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -19,6 +17,22 @@ from .baseinvocation import (
     invocation_output,
 )
 
+# TODO: Permanent fix for this
+# from invokeai.app.invocations.shared import FreeUConfig
+
+
+class FreeUConfig(BaseModel):
+    """
+    Configuration for the FreeU hyperparameters.
+    - https://huggingface.co/docs/diffusers/main/en/using-diffusers/freeu
+    - https://github.com/ChenyangSi/FreeU
+    """
+
+    s1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s1)
+    s2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_s2)
+    b1: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b1)
+    b2: float = Field(ge=-1, le=3, description=FieldDescriptions.freeu_b2)
+
 
 class ModelInfo(BaseModel):
     model_name: str = Field(description="Info to load submodel")

invokeai/app/invocations/noise.py

@@ -5,13 +5,13 @@ import torch
 from pydantic import field_validator
 
 from invokeai.app.invocations.latent import LatentsField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.misc import SEED_MAX, get_random_seed
 
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     InputField,
     InvocationContext,
     OutputField,

invokeai/app/invocations/onnx.py

@@ -14,7 +14,6 @@ from tqdm import tqdm
 
 from invokeai.app.invocations.primitives import ConditioningField, ConditioningOutput, ImageField, ImageOutput
 from invokeai.app.services.image_records.image_records_common import ImageCategory, ResourceOrigin
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.app.util.step_callback import stable_diffusion_step_callback
 from invokeai.backend import BaseModelType, ModelType, SubModelType
 
@@ -24,6 +23,7 @@ from ...backend.util import choose_torch_device
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/primitives.py

@@ -5,11 +5,10 @@ from typing import Optional, Tuple
 import torch
 from pydantic import BaseModel, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
-
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/sdxl.py

@@ -1,9 +1,8 @@
-from invokeai.app.shared.fields import FieldDescriptions
-
 from ...backend.model_management import ModelType, SubModelType
 from .baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,

invokeai/app/invocations/shared.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel, Field
 
-from invokeai.app.shared.fields import FieldDescriptions
+from invokeai.app.invocations.baseinvocation import FieldDescriptions
 
 
 class FreeUConfig(BaseModel):

invokeai/app/invocations/t2i_adapter.py

@@ -5,6 +5,7 @@ from pydantic import BaseModel, ConfigDict, Field
 from invokeai.app.invocations.baseinvocation import (
     BaseInvocation,
     BaseInvocationOutput,
+    FieldDescriptions,
     Input,
     InputField,
     InvocationContext,
@@ -15,7 +16,6 @@ from invokeai.app.invocations.baseinvocation import (
 )
 from invokeai.app.invocations.controlnet_image_processors import CONTROLNET_RESIZE_VALUES
 from invokeai.app.invocations.primitives import ImageField
-from invokeai.app.shared.fields import FieldDescriptions
 from invokeai.backend.model_management.models.base import BaseModelType
 
 

invokeai/app/shared/__init__.py

@@ -1,5 +0,0 @@
-"""
-This module contains various classes, functions and models which are shared across the app, particularly by invocations.
-
-Lifting these classes, functions and models into this shared module helps to reduce circular imports.
-"""

invokeai/app/shared/fields.py

@@ -1,66 +0,0 @@
-class FieldDescriptions:
-    denoising_start = "When to start denoising, expressed a percentage of total steps"
-    denoising_end = "When to stop denoising, expressed a percentage of total steps"
-    cfg_scale = "Classifier-Free Guidance scale"
-    scheduler = "Scheduler to use during inference"
-    positive_cond = "Positive conditioning tensor"
-    negative_cond = "Negative conditioning tensor"
-    noise = "Noise tensor"
-    clip = "CLIP (tokenizer, text encoder, LoRAs) and skipped layer count"
-    unet = "UNet (scheduler, LoRAs)"
-    vae = "VAE"
-    cond = "Conditioning tensor"
-    controlnet_model = "ControlNet model to load"
-    vae_model = "VAE model to load"
-    lora_model = "LoRA model to load"
-    main_model = "Main model (UNet, VAE, CLIP) to load"
-    sdxl_main_model = "SDXL Main model (UNet, VAE, CLIP1, CLIP2) to load"
-    sdxl_refiner_model = "SDXL Refiner Main Modde (UNet, VAE, CLIP2) to load"
-    onnx_main_model = "ONNX Main model (UNet, VAE, CLIP) to load"
-    lora_weight = "The weight at which the LoRA is applied to each model"
-    compel_prompt = "Prompt to be parsed by Compel to create a conditioning tensor"
-    raw_prompt = "Raw prompt text (no parsing)"
-    sdxl_aesthetic = "The aesthetic score to apply to the conditioning tensor"
-    skipped_layers = "Number of layers to skip in text encoder"
-    seed = "Seed for random number generation"
-    steps = "Number of steps to run"
-    width = "Width of output (px)"
-    height = "Height of output (px)"
-    control = "ControlNet(s) to apply"
-    ip_adapter = "IP-Adapter to apply"
-    t2i_adapter = "T2I-Adapter(s) to apply"
-    denoised_latents = "Denoised latents tensor"
-    latents = "Latents tensor"
-    strength = "Strength of denoising (proportional to steps)"
-    metadata = "Optional metadata to be saved with the image"
-    metadata_collection = "Collection of Metadata"
-    metadata_item_polymorphic = "A single metadata item or collection of metadata items"
-    metadata_item_label = "Label for this metadata item"
-    metadata_item_value = "The value for this metadata item (may be any type)"
-    workflow = "Optional workflow to be saved with the image"
-    interp_mode = "Interpolation mode"
-    torch_antialias = "Whether or not to apply antialiasing (bilinear or bicubic only)"
-    fp32 = "Whether or not to use full float32 precision"
-    precision = "Precision to use"
-    tiled = "Processing using overlapping tiles (reduce memory consumption)"
-    detect_res = "Pixel resolution for detection"
-    image_res = "Pixel resolution for output image"
-    safe_mode = "Whether or not to use safe mode"
-    scribble_mode = "Whether or not to use scribble mode"
-    scale_factor = "The factor by which to scale"
-    blend_alpha = (
-        "Blending factor. 0.0 = use input A only, 1.0 = use input B only, 0.5 = 50% mix of input A and input B."
-    )
-    num_1 = "The first number"
-    num_2 = "The second number"
-    mask = "The mask to use for the operation"
-    board = "The board to save the image to"
-    image = "The image to process"
-    tile_size = "Tile size"
-    inclusive_low = "The inclusive low value"
-    exclusive_high = "The exclusive high value"
-    decimal_places = "The number of decimal places to round to"
-    freeu_s1 = 'Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_s2 = 'Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process.'
-    freeu_b1 = "Scaling factor for stage 1 to amplify the contributions of backbone features."
-    freeu_b2 = "Scaling factor for stage 2 to amplify the contributions of backbone features."

invokeai/backend/install/invokeai_configure.py

@@ -32,7 +32,7 @@ from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionS
 from huggingface_hub import HfFolder
 from huggingface_hub import login as hf_hub_login
 from omegaconf import OmegaConf
-from pydantic import ValidationError
+from pydantic.error_wrappers import ValidationError
 from tqdm import tqdm
 from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 

invokeai/backend/install/legacy_arg_parsing.py

@@ -38,7 +38,6 @@ SAMPLER_CHOICES = [
     "k_heun",
     "k_lms",
     "plms",
-    "lcm",
 ]
 
 PRECISION_CHOICES = [

invokeai/backend/install/model_install_backend.py

@@ -254,13 +254,7 @@ class ModelInstall(object):
         elif path.is_dir() and any(
             [
                 (path / x).exists()
-                for x in {
-                    "config.json",
-                    "model_index.json",
-                    "learned_embeds.bin",
-                    "pytorch_lora_weights.bin",
-                    "pytorch_lora_weights.safetensors",
-                }
+                for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
             ]
         ):
             models_installed.update({str(model_path_id_or_url): self._install_path(path)})
@@ -363,7 +357,7 @@ class ModelInstall(object):
                 for suffix in ["safetensors", "bin"]:
                     if f"{prefix}pytorch_lora_weights.{suffix}" in files:
                         location = self._download_hf_model(
-                            repo_id, [f"pytorch_lora_weights.{suffix}"], staging, subfolder=subfolder
+                            repo_id, ["pytorch_lora_weights.bin"], staging, subfolder=subfolder
                         )  # LoRA
                         break
                     elif (

invokeai/backend/model_management/lora.py

@@ -12,7 +12,7 @@ from diffusers.models import UNet2DConditionModel
 from safetensors.torch import load_file
 from transformers import CLIPTextModel, CLIPTokenizer
 
-from invokeai.app.shared.models import FreeUConfig
+from invokeai.app.invocations.shared import FreeUConfig
 
 from .models.lora import LoRAModel
 
@@ -166,15 +166,6 @@ class ModelPatcher:
         init_tokens_count = None
         new_tokens_added = None
 
-        # TODO: This is required since Transformers 4.32 see
-        # https://github.com/huggingface/transformers/pull/25088
-        # More information by NVIDIA:
-        # https://docs.nvidia.com/deeplearning/performance/dl-performance-matrix-multiplication/index.html#requirements-tc
-        # This value might need to be changed in the future and take the GPUs model into account as there seem
-        # to be ideal values for different GPUS. This value is temporary!
-        # For references to the current discussion please see https://github.com/invoke-ai/InvokeAI/pull/4817
-        pad_to_multiple_of = 8
-
         try:
             # HACK: The CLIPTokenizer API does not include a way to remove tokens after calling add_tokens(...). As a
             # workaround, we create a full copy of `tokenizer` so that its original behavior can be restored after
@@ -184,7 +175,7 @@ class ModelPatcher:
             # but a pickle roundtrip was found to be much faster (1 sec vs. 0.05 secs).
             ti_tokenizer = pickle.loads(pickle.dumps(tokenizer))
             ti_manager = TextualInversionManager(ti_tokenizer)
-            init_tokens_count = text_encoder.resize_token_embeddings(None, pad_to_multiple_of).num_embeddings
+            init_tokens_count = text_encoder.resize_token_embeddings(None).num_embeddings
 
             def _get_trigger(ti_name, index):
                 trigger = ti_name
@@ -199,7 +190,7 @@ class ModelPatcher:
                     new_tokens_added += ti_tokenizer.add_tokens(_get_trigger(ti_name, i))
 
             # modify text_encoder
-            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added, pad_to_multiple_of)
+            text_encoder.resize_token_embeddings(init_tokens_count + new_tokens_added)
             model_embeddings = text_encoder.get_input_embeddings()
 
             for ti_name, ti in ti_list:
@@ -231,7 +222,7 @@ class ModelPatcher:
 
         finally:
             if init_tokens_count and new_tokens_added:
-                text_encoder.resize_token_embeddings(init_tokens_count, pad_to_multiple_of)
+                text_encoder.resize_token_embeddings(init_tokens_count)
 
     @classmethod
     @contextmanager

invokeai/backend/model_management/model_probe.py

@@ -183,13 +183,12 @@ class ModelProbe(object):
         if model:
             class_name = model.__class__.__name__
         else:
-            for suffix in ["bin", "safetensors"]:
-                if (folder_path / f"learned_embeds.{suffix}").exists():
-                    return ModelType.TextualInversion
-                if (folder_path / f"pytorch_lora_weights.{suffix}").exists():
-                    return ModelType.Lora
             if (folder_path / "unet/model.onnx").exists():
                 return ModelType.ONNX
+            if (folder_path / "learned_embeds.bin").exists():
+                return ModelType.TextualInversion
+            if (folder_path / "pytorch_lora_weights.bin").exists():
+                return ModelType.Lora
             if (folder_path / "image_encoder.txt").exists():
                 return ModelType.IPAdapter
 

invokeai/backend/model_management/models/lora.py

@@ -68,9 +68,8 @@ class LoRAModel(ModelBase):
             raise ModelNotFoundException()
 
         if os.path.isdir(path):
-            for ext in ["safetensors", "bin"]:
-                if os.path.exists(os.path.join(path, f"pytorch_lora_weights.{ext}")):
-                    return LoRAModelFormat.Diffusers
+            if os.path.exists(os.path.join(path, "pytorch_lora_weights.bin")):
+                return LoRAModelFormat.Diffusers
 
         if os.path.isfile(path):
             if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt"]]):
@@ -87,10 +86,8 @@ class LoRAModel(ModelBase):
         base_model: BaseModelType,
     ) -> str:
         if cls.detect_format(model_path) == LoRAModelFormat.Diffusers:
-            for ext in ["safetensors", "bin"]:  # return path to the safetensors file inside the folder
-                path = Path(model_path, f"pytorch_lora_weights.{ext}")
-                if path.exists():
-                    return path
+            # TODO: add diffusers lora when it stabilizes a bit
+            raise NotImplementedError("Diffusers lora not supported")
         else:
             return model_path
 

invokeai/backend/stable_diffusion/schedulers/schedulers.py

@@ -10,7 +10,6 @@ from diffusers import (
     HeunDiscreteScheduler,
     KDPM2AncestralDiscreteScheduler,
     KDPM2DiscreteScheduler,
-    LCMScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
     UniPCMultistepScheduler,
@@ -39,5 +38,4 @@ SCHEDULER_MAP = dict(
     dpmpp_sde=(DPMSolverSDEScheduler, dict(use_karras_sigmas=False, noise_sampler_seed=0)),
     dpmpp_sde_k=(DPMSolverSDEScheduler, dict(use_karras_sigmas=True, noise_sampler_seed=0)),
     unipc=(UniPCMultistepScheduler, dict(cpu_only=True)),
-    lcm=(LCMScheduler, dict()),
 )

invokeai/frontend/install/invokeai_update.py

@@ -63,8 +63,8 @@ def welcome(latest_release: str, latest_prerelease: str):
         yield "[bold yellow]Options:"
         yield f"""[1] Update to the latest [bold]official release[/bold] ([italic]{latest_release}[/italic])
 [2] Update to the latest [bold]pre-release[/bold] (may be buggy; caveat emptor!) ([italic]{latest_prerelease}[/italic])
-[3] Manually enter the [bold]tag name[/bold] for the version you wish to update to
-[4] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
+[2] Manually enter the [bold]tag name[/bold] for the version you wish to update to
+[3] Manually enter the [bold]branch name[/bold] for the version you wish to update to"""
 
     console.rule()
     print(

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-109c3742.js

@@ -1,4 +1,4 @@
-import{w as s,ib as T,v as l,a1 as I,ic as R,ad as V,id as z,ie as j,ig as D,ih as F,ii as G,ij as W,ik as K,aG as H,il as U,im as Y}from"./index-7ba6700b.js";import{M as Z}from"./MantineProvider-bf828180.js";var P=String.raw,E=P`
+import{w as s,ia as T,v as l,a1 as I,ib as R,ad as V,ic as z,id as j,ie as D,ig as F,ih as G,ii as W,ij as K,aG as H,ik as U,il as Y}from"./index-ec1d7161.js";import{M as Z}from"./MantineProvider-7e0d59f3.js";var P=String.raw,E=P`
   :root,
   :host {
     --chakra-vh: 100vh;
@@ -277,4 +277,4 @@ import{w as s,ib as T,v as l,a1 as I,ic as R,ad as V,id as z,ie as j,ig as D,ih
       }
 
       ${E}
-    `}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(m=>{const f=m==="system"?w():m;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const m=a.get();if(m){c(m);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function h(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>h(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((h(e)||h(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=h(e)?e(...t):e,a=h(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function me({children:e}){const{i18n:o}=H(),n=o.dir(),r=l.useMemo(()=>ie({...U,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(Z,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:Y,children:e})})}const ve=l.memo(me);export{ve as default};
+    `}),g={light:"chakra-ui-light",dark:"chakra-ui-dark"};function Q(e={}){const{preventTransition:o=!0}=e,n={setDataset:r=>{const t=o?n.preventTransition():void 0;document.documentElement.dataset.theme=r,document.documentElement.style.colorScheme=r,t==null||t()},setClassName(r){document.body.classList.add(r?g.dark:g.light),document.body.classList.remove(r?g.light:g.dark)},query(){return window.matchMedia("(prefers-color-scheme: dark)")},getSystemTheme(r){var t;return((t=n.query().matches)!=null?t:r==="dark")?"dark":"light"},addListener(r){const t=n.query(),i=a=>{r(a.matches?"dark":"light")};return typeof t.addListener=="function"?t.addListener(i):t.addEventListener("change",i),()=>{typeof t.removeListener=="function"?t.removeListener(i):t.removeEventListener("change",i)}},preventTransition(){const r=document.createElement("style");return r.appendChild(document.createTextNode("*{-webkit-transition:none!important;-moz-transition:none!important;-o-transition:none!important;-ms-transition:none!important;transition:none!important}")),document.head.appendChild(r),()=>{window.getComputedStyle(document.body),requestAnimationFrame(()=>{requestAnimationFrame(()=>{document.head.removeChild(r)})})}}};return n}var X="chakra-ui-color-mode";function L(e){return{ssr:!1,type:"localStorage",get(o){if(!(globalThis!=null&&globalThis.document))return o;let n;try{n=localStorage.getItem(e)||o}catch{}return n||o},set(o){try{localStorage.setItem(e,o)}catch{}}}}var ee=L(X),M=()=>{};function S(e,o){return e.type==="cookie"&&e.ssr?e.get(o):o}function O(e){const{value:o,children:n,options:{useSystemColorMode:r,initialColorMode:t,disableTransitionOnChange:i}={},colorModeManager:a=ee}=e,d=t==="dark"?"dark":"light",[u,p]=l.useState(()=>S(a,d)),[y,b]=l.useState(()=>S(a)),{getSystemTheme:w,setClassName:k,setDataset:x,addListener:$}=l.useMemo(()=>Q({preventTransition:i}),[i]),v=t==="system"&&!u?y:u,c=l.useCallback(h=>{const f=h==="system"?w():h;p(f),k(f==="dark"),x(f),a.set(f)},[a,w,k,x]);I(()=>{t==="system"&&b(w())},[]),l.useEffect(()=>{const h=a.get();if(h){c(h);return}if(t==="system"){c("system");return}c(d)},[a,d,t,c]);const C=l.useCallback(()=>{c(v==="dark"?"light":"dark")},[v,c]);l.useEffect(()=>{if(r)return $(c)},[r,$,c]);const A=l.useMemo(()=>({colorMode:o??v,toggleColorMode:o?M:C,setColorMode:o?M:c,forced:o!==void 0}),[v,C,c,o]);return s.jsx(R.Provider,{value:A,children:n})}O.displayName="ColorModeProvider";var te=["borders","breakpoints","colors","components","config","direction","fonts","fontSizes","fontWeights","letterSpacings","lineHeights","radii","shadows","sizes","space","styles","transition","zIndices"];function re(e){return V(e)?te.every(o=>Object.prototype.hasOwnProperty.call(e,o)):!1}function m(e){return typeof e=="function"}function oe(...e){return o=>e.reduce((n,r)=>r(n),o)}var ne=e=>function(...n){let r=[...n],t=n[n.length-1];return re(t)&&r.length>1?r=r.slice(0,r.length-1):t=e,oe(...r.map(i=>a=>m(i)?i(a):ae(a,i)))(t)},ie=ne(j);function ae(...e){return z({},...e,_)}function _(e,o,n,r){if((m(e)||m(o))&&Object.prototype.hasOwnProperty.call(r,n))return(...t)=>{const i=m(e)?e(...t):e,a=m(o)?o(...t):o;return z({},i,a,_)}}var q=l.createContext({getDocument(){return document},getWindow(){return window}});q.displayName="EnvironmentContext";function N(e){const{children:o,environment:n,disabled:r}=e,t=l.useRef(null),i=l.useMemo(()=>n||{getDocument:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument)!=null?u:document},getWindow:()=>{var d,u;return(u=(d=t.current)==null?void 0:d.ownerDocument.defaultView)!=null?u:window}},[n]),a=!r||!n;return s.jsxs(q.Provider,{value:i,children:[o,a&&s.jsx("span",{id:"__chakra_env",hidden:!0,ref:t})]})}N.displayName="EnvironmentProvider";var se=e=>{const{children:o,colorModeManager:n,portalZIndex:r,resetScope:t,resetCSS:i=!0,theme:a={},environment:d,cssVarsRoot:u,disableEnvironment:p,disableGlobalStyle:y}=e,b=s.jsx(N,{environment:d,disabled:p,children:o});return s.jsx(D,{theme:a,cssVarsRoot:u,children:s.jsxs(O,{colorModeManager:n,options:a.config,children:[i?s.jsx(J,{scope:t}):s.jsx(B,{}),!y&&s.jsx(F,{}),r?s.jsx(G,{zIndex:r,children:b}):b]})})},le=e=>function({children:n,theme:r=e,toastOptions:t,...i}){return s.jsxs(se,{theme:r,...i,children:[s.jsx(W,{value:t==null?void 0:t.defaultOptions,children:n}),s.jsx(K,{...t})]})},de=le(j);const ue=()=>l.useMemo(()=>({colorScheme:"dark",fontFamily:"'Inter Variable', sans-serif",components:{ScrollArea:{defaultProps:{scrollbarSize:10},styles:{scrollbar:{"&:hover":{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}},thumb:{backgroundColor:"var(--invokeai-colors-baseAlpha-300)"}}}}}),[]),ce=L("@@invokeai-color-mode");function he({children:e}){const{i18n:o}=H(),n=o.dir(),r=l.useMemo(()=>ie({...U,direction:n}),[n]);l.useEffect(()=>{document.body.dir=n},[n]);const t=ue();return s.jsx(Z,{theme:t,children:s.jsx(de,{theme:r,colorModeManager:ce,toastOptions:Y,children:e})})}const ve=l.memo(he);export{ve as default};

invokeai/frontend/web/dist/index.html

@@ -15,7 +15,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-7ba6700b.js"></script>
+    <script type="module" crossorigin src="./assets/index-ec1d7161.js"></script>
   </head>
 
   <body dir="ltr">

invokeai/frontend/web/dist/locales/en.json

@@ -221,19 +221,6 @@
         "resetIPAdapterImage": "Reset IP Adapter Image",
         "ipAdapterImageFallback": "No IP Adapter Image Selected"
     },
-    "hrf": {
-        "hrf": "High Resolution Fix",
-        "enableHrf": "Enable High Resolution Fix",
-        "enableHrfTooltip": "Generate with a lower initial resolution, upscale to the base resolution, then run Image-to-Image.",
-        "upscaleMethod": "Upscale Method",
-        "hrfStrength": "High Resolution Fix Strength",
-        "strengthTooltip": "Lower values result in fewer details, which may reduce potential artifacts.",
-        "metadata": {
-            "enabled": "High Resolution Fix Enabled",
-            "strength": "High Resolution Fix Strength",
-            "method": "High Resolution Fix Method"
-        }
-    },
     "embedding": {
         "addEmbedding": "Add Embedding",
         "incompatibleModel": "Incompatible base model:",
@@ -1271,11 +1258,15 @@
         },
         "compositingBlur": {
             "heading": "Blur",
-            "paragraphs": ["The blur radius of the mask."]
+            "paragraphs": [
+                "The blur radius of the mask."
+            ]
         },
         "compositingBlurMethod": {
             "heading": "Blur Method",
-            "paragraphs": ["The method of blur applied to the masked area."]
+            "paragraphs": [
+                "The method of blur applied to the masked area."
+            ]
         },
         "compositingCoherencePass": {
             "heading": "Coherence Pass",
@@ -1285,7 +1276,9 @@
         },
         "compositingCoherenceMode": {
             "heading": "Mode",
-            "paragraphs": ["The mode of the Coherence Pass."]
+            "paragraphs": [
+                "The mode of the Coherence Pass."
+            ]
         },
         "compositingCoherenceSteps": {
             "heading": "Steps",
@@ -1303,7 +1296,9 @@
         },
         "compositingMaskAdjustments": {
             "heading": "Mask Adjustments",
-            "paragraphs": ["Adjust the mask."]
+            "paragraphs": [
+                "Adjust the mask."
+            ]
         },
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
@@ -1361,7 +1356,9 @@
         },
         "infillMethod": {
             "heading": "Infill Method",
-            "paragraphs": ["Method to infill the selected area."]
+            "paragraphs": [
+                "Method to infill the selected area."
+            ]
         },
         "lora": {
             "heading": "LoRA Weight",

invokeai/frontend/web/dist/locales/zh_CN.json

@@ -1231,9 +1231,7 @@
         "noLoRAsAvailable": "无可用 LoRA",
         "noModelsAvailable": "无可用模型",
         "selectModel": "选择一个模型",
-        "selectLoRA": "选择一个 LoRA",
-        "noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
-        "noLoRAsInstalled": "无已安装的 LoRA"
+        "selectLoRA": "选择一个 LoRA"
     },
     "boards": {
         "autoAddBoard": "自动添加面板",

invokeai/frontend/web/package.json

@@ -54,35 +54,42 @@
     ]
   },
   "dependencies": {
-    "@chakra-ui/anatomy": "^2.2.2",
+    "@chakra-ui/anatomy": "^2.2.1",
     "@chakra-ui/icons": "^2.1.1",
-    "@chakra-ui/react": "^2.8.2",
-    "@chakra-ui/styled-system": "^2.9.2",
-    "@chakra-ui/theme-tools": "^2.1.2",
+    "@chakra-ui/react": "^2.8.1",
+    "@chakra-ui/styled-system": "^2.9.1",
+    "@chakra-ui/theme-tools": "^2.1.1",
     "@dagrejs/graphlib": "^2.1.13",
-    "@dnd-kit/core": "^6.1.0",
-    "@dnd-kit/utilities": "^3.2.2",
+    "@dnd-kit/core": "^6.0.8",
+    "@dnd-kit/modifiers": "^6.0.1",
+    "@dnd-kit/utilities": "^3.2.1",
     "@emotion/react": "^11.11.1",
     "@emotion/styled": "^11.11.0",
-    "@fontsource-variable/inter": "^5.0.15",
+    "@floating-ui/react-dom": "^2.0.2",
+    "@fontsource-variable/inter": "^5.0.13",
+    "@fontsource/inter": "^5.0.13",
     "@mantine/core": "^6.0.19",
     "@mantine/form": "^6.0.19",
     "@mantine/hooks": "^6.0.19",
     "@nanostores/react": "^0.7.1",
     "@reduxjs/toolkit": "^1.9.7",
     "@roarr/browser-log-writer": "^1.3.0",
+    "@stevebel/png": "^1.5.1",
     "compare-versions": "^6.1.0",
     "dateformat": "^5.0.3",
+    "formik": "^2.4.5",
     "framer-motion": "^10.16.4",
-    "i18next": "^23.6.0",
-    "i18next-http-backend": "^2.3.1",
-    "konva": "^9.2.3",
+    "fuse.js": "^6.6.2",
+    "i18next": "^23.5.1",
+    "i18next-browser-languagedetector": "^7.0.2",
+    "i18next-http-backend": "^2.2.2",
+    "konva": "^9.2.2",
     "lodash-es": "^4.17.21",
-    "nanostores": "^0.9.4",
+    "nanostores": "^0.9.2",
     "new-github-issue-url": "^1.0.0",
-    "openapi-fetch": "^0.8.1",
-    "overlayscrollbars": "^2.4.4",
-    "overlayscrollbars-react": "^0.5.3",
+    "openapi-fetch": "^0.7.10",
+    "overlayscrollbars": "^2.3.2",
+    "overlayscrollbars-react": "^0.5.2",
     "patch-package": "^8.0.0",
     "query-string": "^8.1.0",
     "react": "^18.2.0",
@@ -91,25 +98,26 @@
     "react-dropzone": "^14.2.3",
     "react-error-boundary": "^4.0.11",
     "react-hotkeys-hook": "4.4.1",
-    "react-i18next": "^13.3.1",
+    "react-i18next": "^13.3.0",
     "react-icons": "^4.11.0",
     "react-konva": "^18.2.10",
     "react-redux": "^8.1.3",
     "react-resizable-panels": "^0.0.55",
     "react-use": "^17.4.0",
-    "react-virtuoso": "^4.6.2",
-    "reactflow": "^11.9.4",
+    "react-virtuoso": "^4.6.1",
+    "react-zoom-pan-pinch": "^3.2.0",
+    "reactflow": "^11.9.3",
     "redux-dynamic-middlewares": "^2.2.0",
     "redux-remember": "^4.0.4",
-    "roarr": "^7.18.3",
+    "roarr": "^7.15.1",
     "serialize-error": "^11.0.2",
     "socket.io-client": "^4.7.2",
-    "type-fest": "^4.7.1",
-    "use-debounce": "^10.0.0",
+    "type-fest": "^4.4.0",
+    "use-debounce": "^9.0.4",
     "use-image": "^1.1.1",
     "uuid": "^9.0.1",
     "zod": "^3.22.4",
-    "zod-validation-error": "^2.1.0"
+    "zod-validation-error": "^1.5.0"
   },
   "peerDependencies": {
     "@chakra-ui/cli": "^2.4.0",
@@ -120,33 +128,39 @@
   },
   "devDependencies": {
     "@chakra-ui/cli": "^2.4.1",
-    "@types/dateformat": "^5.0.2",
-    "@types/lodash-es": "^4.17.11",
-    "@types/node": "^20.9.0",
-    "@types/react": "^18.2.37",
-    "@types/react-dom": "^18.2.15",
-    "@types/react-redux": "^7.1.30",
-    "@types/uuid": "^9.0.7",
-    "@typescript-eslint/eslint-plugin": "^6.10.0",
-    "@typescript-eslint/parser": "^6.10.0",
-    "@vitejs/plugin-react-swc": "^3.4.1",
-    "concurrently": "^8.2.2",
-    "eslint": "^8.53.0",
+    "@types/dateformat": "^5.0.0",
+    "@types/lodash-es": "^4.17.9",
+    "@types/node": "^20.8.6",
+    "@types/react": "^18.2.28",
+    "@types/react-dom": "^18.2.13",
+    "@types/react-redux": "^7.1.27",
+    "@types/react-transition-group": "^4.4.7",
+    "@types/uuid": "^9.0.5",
+    "@typescript-eslint/eslint-plugin": "^6.7.5",
+    "@typescript-eslint/parser": "^6.7.5",
+    "@vitejs/plugin-react-swc": "^3.4.0",
+    "axios": "^1.5.1",
+    "babel-plugin-transform-imports": "^2.0.0",
+    "concurrently": "^8.2.1",
+    "eslint": "^8.51.0",
     "eslint-config-prettier": "^9.0.0",
+    "eslint-plugin-prettier": "^5.0.1",
     "eslint-plugin-react": "^7.33.2",
     "eslint-plugin-react-hooks": "^4.6.0",
+    "form-data": "^4.0.0",
     "husky": "^8.0.3",
-    "lint-staged": "^15.0.2",
+    "lint-staged": "^15.0.1",
     "madge": "^6.1.0",
     "openapi-types": "^12.1.3",
     "openapi-typescript": "^6.7.0",
+    "postinstall-postinstall": "^2.1.0",
     "prettier": "^3.0.3",
     "rollup-plugin-visualizer": "^5.9.2",
     "ts-toolbelt": "^9.6.0",
     "typescript": "^5.2.2",
-    "vite": "^4.5.0",
+    "vite": "^4.4.11",
     "vite-plugin-css-injected-by-js": "^3.3.0",
-    "vite-plugin-dts": "^3.6.3",
+    "vite-plugin-dts": "^3.6.0",
     "vite-plugin-eslint": "^1.8.1",
     "vite-tsconfig-paths": "^4.2.1",
     "yarn": "^1.22.19"

invokeai/frontend/web/public/locales/en.json

@@ -221,19 +221,6 @@
         "resetIPAdapterImage": "Reset IP Adapter Image",
         "ipAdapterImageFallback": "No IP Adapter Image Selected"
     },
-    "hrf": {
-        "hrf": "High Resolution Fix",
-        "enableHrf": "Enable High Resolution Fix",
-        "enableHrfTooltip": "Generate with a lower initial resolution, upscale to the base resolution, then run Image-to-Image.",
-        "upscaleMethod": "Upscale Method",
-        "hrfStrength": "High Resolution Fix Strength",
-        "strengthTooltip": "Lower values result in fewer details, which may reduce potential artifacts.",
-        "metadata": {
-            "enabled": "High Resolution Fix Enabled",
-            "strength": "High Resolution Fix Strength",
-            "method": "High Resolution Fix Method"
-        }
-    },
     "embedding": {
         "addEmbedding": "Add Embedding",
         "incompatibleModel": "Incompatible base model:",
@@ -1271,11 +1258,15 @@
         },
         "compositingBlur": {
             "heading": "Blur",
-            "paragraphs": ["The blur radius of the mask."]
+            "paragraphs": [
+                "The blur radius of the mask."
+            ]
         },
         "compositingBlurMethod": {
             "heading": "Blur Method",
-            "paragraphs": ["The method of blur applied to the masked area."]
+            "paragraphs": [
+                "The method of blur applied to the masked area."
+            ]
         },
         "compositingCoherencePass": {
             "heading": "Coherence Pass",
@@ -1285,7 +1276,9 @@
         },
         "compositingCoherenceMode": {
             "heading": "Mode",
-            "paragraphs": ["The mode of the Coherence Pass."]
+            "paragraphs": [
+                "The mode of the Coherence Pass."
+            ]
         },
         "compositingCoherenceSteps": {
             "heading": "Steps",
@@ -1303,7 +1296,9 @@
         },
         "compositingMaskAdjustments": {
             "heading": "Mask Adjustments",
-            "paragraphs": ["Adjust the mask."]
+            "paragraphs": [
+                "Adjust the mask."
+            ]
         },
         "controlNetBeginEnd": {
             "heading": "Begin / End Step Percentage",
@@ -1361,7 +1356,9 @@
         },
         "infillMethod": {
             "heading": "Infill Method",
-            "paragraphs": ["Method to infill the selected area."]
+            "paragraphs": [
+                "Method to infill the selected area."
+            ]
         },
         "lora": {
             "heading": "LoRA Weight",

invokeai/frontend/web/public/locales/zh_CN.json

@@ -1231,9 +1231,7 @@
         "noLoRAsAvailable": "无可用 LoRA",
         "noModelsAvailable": "无可用模型",
         "selectModel": "选择一个模型",
-        "selectLoRA": "选择一个 LoRA",
-        "noRefinerModelsInstalled": "无已安装的 SDXL Refiner 模型",
-        "noLoRAsInstalled": "无已安装的 LoRA"
+        "selectLoRA": "选择一个 LoRA"
     },
     "boards": {
         "autoAddBoard": "自动添加面板",

invokeai/frontend/web/src/features/gallery/components/ImageMetadataViewer/ImageMetadataActions.tsx

@@ -35,9 +35,6 @@ const ImageMetadataActions = (props: Props) => {
     recallWidth,
     recallHeight,
     recallStrength,
-    recallHrfEnabled,
-    recallHrfStrength,
-    recallHrfMethod,
     recallLoRA,
     recallControlNet,
     recallIPAdapter,
@@ -84,18 +81,6 @@ const ImageMetadataActions = (props: Props) => {
     recallStrength(metadata?.strength);
   }, [metadata?.strength, recallStrength]);
 
-  const handleRecallHrfEnabled = useCallback(() => {
-    recallHrfEnabled(metadata?.hrf_enabled);
-  }, [metadata?.hrf_enabled, recallHrfEnabled]);
-
-  const handleRecallHrfStrength = useCallback(() => {
-    recallHrfStrength(metadata?.hrf_strength);
-  }, [metadata?.hrf_strength, recallHrfStrength]);
-
-  const handleRecallHrfMethod = useCallback(() => {
-    recallHrfMethod(metadata?.hrf_method);
-  }, [metadata?.hrf_method, recallHrfMethod]);
-
   const handleRecallLoRA = useCallback(
     (lora: LoRAMetadataItem) => {
       recallLoRA(lora);
@@ -240,27 +225,6 @@ const ImageMetadataActions = (props: Props) => {
           onClick={handleRecallStrength}
         />
       )}
-      {metadata.hrf_enabled && (
-        <ImageMetadataItem
-          label={t('hrf.metadata.enabled')}
-          value={metadata.hrf_enabled}
-          onClick={handleRecallHrfEnabled}
-        />
-      )}
-      {metadata.hrf_enabled && metadata.hrf_strength && (
-        <ImageMetadataItem
-          label={t('hrf.metadata.strength')}
-          value={metadata.hrf_strength}
-          onClick={handleRecallHrfStrength}
-        />
-      )}
-      {metadata.hrf_enabled && metadata.hrf_method && (
-        <ImageMetadataItem
-          label={t('hrf.metadata.method')}
-          value={metadata.hrf_method}
-          onClick={handleRecallHrfMethod}
-        />
-      )}
       {metadata.loras &&
         metadata.loras.map((lora, index) => {
           if (isValidLoRAModel(lora.lora)) {

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -1424,9 +1424,6 @@ export const zCoreMetadata = z
     loras: z.array(zLoRAMetadataItem).nullish().catch(null),
     vae: zVaeModelField.nullish().catch(null),
     strength: z.number().nullish().catch(null),
-    hrf_enabled: z.boolean().nullish().catch(null),
-    hrf_strength: z.number().nullish().catch(null),
-    hrf_method: z.string().nullish().catch(null),
     init_image: z.string().nullish().catch(null),
     positive_style_prompt: z.string().nullish().catch(null),
     negative_style_prompt: z.string().nullish().catch(null),

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addHrfToGraph.ts

@@ -1,26 +1,22 @@
 import { logger } from 'app/logging/logger';
 import { RootState } from 'app/store/store';
-import { roundToMultiple } from 'common/util/roundDownToMultiple';
 import { NonNullableGraph } from 'features/nodes/types/types';
 import {
   DenoiseLatentsInvocation,
-  ESRGANInvocation,
   Edge,
   LatentsToImageInvocation,
   NoiseInvocation,
+  ResizeLatentsInvocation,
 } from 'services/api/types';
 import {
   DENOISE_LATENTS,
   DENOISE_LATENTS_HRF,
-  ESRGAN_HRF,
-  IMAGE_TO_LATENTS_HRF,
   LATENTS_TO_IMAGE,
-  LATENTS_TO_IMAGE_HRF_HR,
-  LATENTS_TO_IMAGE_HRF_LR,
+  LATENTS_TO_IMAGE_HRF,
   MAIN_MODEL_LOADER,
   NOISE,
   NOISE_HRF,
-  RESIZE_HRF,
+  RESCALE_LATENTS,
   VAE_LOADER,
 } from './constants';
 import { upsertMetadata } from './metadata';
@@ -60,52 +56,6 @@ function copyConnectionsToDenoiseLatentsHrf(graph: NonNullableGraph): void {
   graph.edges = graph.edges.concat(newEdges);
 }
 
-/**
- * Calculates the new resolution for high-resolution features (HRF) based on base model type.
- * Adjusts the width and height to maintain the aspect ratio and constrains them by the model's dimension limits,
- * rounding down to the nearest multiple of 8.
- *
- * @param {string} baseModel The base model type, which determines the base dimension used in calculations.
- * @param {number} width The current width to be adjusted for HRF.
- * @param {number} height The current height to be adjusted for HRF.
- * @return {{newWidth: number, newHeight: number}} The new width and height, adjusted and rounded as needed.
- */
-function calculateHrfRes(
-  baseModel: string,
-  width: number,
-  height: number
-): { newWidth: number; newHeight: number } {
-  const aspect = width / height;
-  let dimension;
-  if (baseModel == 'sdxl') {
-    dimension = 1024;
-  } else {
-    dimension = 512;
-  }
-
-  const minDimension = Math.floor(dimension * 0.5);
-  const modelArea = dimension * dimension; // Assuming square images for model_area
-
-  let initWidth;
-  let initHeight;
-
-  if (aspect > 1.0) {
-    initHeight = Math.max(minDimension, Math.sqrt(modelArea / aspect));
-    initWidth = initHeight * aspect;
-  } else {
-    initWidth = Math.max(minDimension, Math.sqrt(modelArea * aspect));
-    initHeight = initWidth / aspect;
-  }
-  // Cap initial height and width to final height and width.
-  initWidth = Math.min(width, initWidth);
-  initHeight = Math.min(height, initHeight);
-
-  const newWidth = roundToMultiple(Math.floor(initWidth), 8);
-  const newHeight = roundToMultiple(Math.floor(initHeight), 8);
-
-  return { newWidth, newHeight };
-}
-
 // Adds the high-res fix feature to the given graph.
 export const addHrfToGraph = (
   state: RootState,
@@ -121,140 +71,105 @@ export const addHrfToGraph = (
   }
   const log = logger('txt2img');
 
-  const { vae, hrfStrength, hrfEnabled, hrfMethod } = state.generation;
+  const { vae, hrfWidth, hrfHeight, hrfStrength } = state.generation;
   const isAutoVae = !vae;
-  const width = state.generation.width;
-  const height = state.generation.height;
-  const baseModel = state.generation.model
-    ? state.generation.model.base_model
-    : 'sd1';
-  const { newWidth: hrfWidth, newHeight: hrfHeight } = calculateHrfRes(
-    baseModel,
-    width,
-    height
-  );
 
   // Pre-existing (original) graph nodes.
   const originalDenoiseLatentsNode = graph.nodes[DENOISE_LATENTS] as
     | DenoiseLatentsInvocation
     | undefined;
   const originalNoiseNode = graph.nodes[NOISE] as NoiseInvocation | undefined;
+  // Original latents to image should pick this up.
   const originalLatentsToImageNode = graph.nodes[LATENTS_TO_IMAGE] as
     | LatentsToImageInvocation
     | undefined;
+  // Check if originalDenoiseLatentsNode is undefined and log an error
   if (!originalDenoiseLatentsNode) {
     log.error('originalDenoiseLatentsNode is undefined');
     return;
   }
+  // Check if originalNoiseNode is undefined and log an error
   if (!originalNoiseNode) {
     log.error('originalNoiseNode is undefined');
     return;
   }
+
+  // Check if originalLatentsToImageNode is undefined and log an error
   if (!originalLatentsToImageNode) {
     log.error('originalLatentsToImageNode is undefined');
     return;
   }
-
   // Change height and width of original noise node to initial resolution.
   if (originalNoiseNode) {
     originalNoiseNode.width = hrfWidth;
     originalNoiseNode.height = hrfHeight;
   }
 
-  // Define new nodes and their connections, roughly in order of operations.
-  graph.nodes[LATENTS_TO_IMAGE_HRF_LR] = {
-    type: 'l2i',
-    id: LATENTS_TO_IMAGE_HRF_LR,
-    fp32: originalLatentsToImageNode?.fp32,
-    is_intermediate: true,
+  // Define new nodes.
+  // Denoise latents node to be run on upscaled latents.
+  const denoiseLatentsHrfNode: DenoiseLatentsInvocation = {
+    type: 'denoise_latents',
+    id: DENOISE_LATENTS_HRF,
+    is_intermediate: originalDenoiseLatentsNode?.is_intermediate,
+    cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
+    scheduler: originalDenoiseLatentsNode?.scheduler,
+    steps: originalDenoiseLatentsNode?.steps,
+    denoising_start: 1 - hrfStrength,
+    denoising_end: 1,
   };
+
+  // New base resolution noise node.
+  const hrfNoiseNode: NoiseInvocation = {
+    type: 'noise',
+    id: NOISE_HRF,
+    seed: originalNoiseNode?.seed,
+    use_cpu: originalNoiseNode?.use_cpu,
+    is_intermediate: originalNoiseNode?.is_intermediate,
+  };
+
+  const rescaleLatentsNode: ResizeLatentsInvocation = {
+    id: RESCALE_LATENTS,
+    type: 'lresize',
+    width: state.generation.width,
+    height: state.generation.height,
+  };
+
+  // New node to convert latents to image.
+  const latentsToImageHrfNode: LatentsToImageInvocation | undefined =
+    originalLatentsToImageNode
+      ? {
+          type: 'l2i',
+          id: LATENTS_TO_IMAGE_HRF,
+          fp32: originalLatentsToImageNode?.fp32,
+          is_intermediate: originalLatentsToImageNode?.is_intermediate,
+        }
+      : undefined;
+
+  // Add new nodes to graph.
+  graph.nodes[LATENTS_TO_IMAGE_HRF] =
+    latentsToImageHrfNode as LatentsToImageInvocation;
+  graph.nodes[DENOISE_LATENTS_HRF] =
+    denoiseLatentsHrfNode as DenoiseLatentsInvocation;
+  graph.nodes[NOISE_HRF] = hrfNoiseNode as NoiseInvocation;
+  graph.nodes[RESCALE_LATENTS] = rescaleLatentsNode as ResizeLatentsInvocation;
+
+  // Connect nodes.
   graph.edges.push(
     {
+      // Set up rescale latents.
       source: {
         node_id: DENOISE_LATENTS,
         field: 'latents',
       },
       destination: {
-        node_id: LATENTS_TO_IMAGE_HRF_LR,
+        node_id: RESCALE_LATENTS,
         field: 'latents',
       },
     },
+    // Set up new noise node
     {
       source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
-      },
-      destination: {
-        node_id: LATENTS_TO_IMAGE_HRF_LR,
-        field: 'vae',
-      },
-    }
-  );
-
-  graph.nodes[RESIZE_HRF] = {
-    id: RESIZE_HRF,
-    type: 'img_resize',
-    is_intermediate: true,
-    width: width,
-    height: height,
-  };
-  if (hrfMethod == 'ESRGAN') {
-    let model_name: ESRGANInvocation['model_name'] = 'RealESRGAN_x2plus.pth';
-    if ((width * height) / (hrfWidth * hrfHeight) > 2) {
-      model_name = 'RealESRGAN_x4plus.pth';
-    }
-    graph.nodes[ESRGAN_HRF] = {
-      id: ESRGAN_HRF,
-      type: 'esrgan',
-      model_name,
-      is_intermediate: true,
-    };
-    graph.edges.push(
-      {
-        source: {
-          node_id: LATENTS_TO_IMAGE_HRF_LR,
-          field: 'image',
-        },
-        destination: {
-          node_id: ESRGAN_HRF,
-          field: 'image',
-        },
-      },
-      {
-        source: {
-          node_id: ESRGAN_HRF,
-          field: 'image',
-        },
-        destination: {
-          node_id: RESIZE_HRF,
-          field: 'image',
-        },
-      }
-    );
-  } else {
-    graph.edges.push({
-      source: {
-        node_id: LATENTS_TO_IMAGE_HRF_LR,
-        field: 'image',
-      },
-      destination: {
-        node_id: RESIZE_HRF,
-        field: 'image',
-      },
-    });
-  }
-
-  graph.nodes[NOISE_HRF] = {
-    type: 'noise',
-    id: NOISE_HRF,
-    seed: originalNoiseNode?.seed,
-    use_cpu: originalNoiseNode?.use_cpu,
-    is_intermediate: true,
-  };
-  graph.edges.push(
-    {
-      source: {
-        node_id: RESIZE_HRF,
+        node_id: RESCALE_LATENTS,
         field: 'height',
       },
       destination: {
@@ -264,58 +179,18 @@ export const addHrfToGraph = (
     },
     {
       source: {
-        node_id: RESIZE_HRF,
+        node_id: RESCALE_LATENTS,
         field: 'width',
       },
       destination: {
         node_id: NOISE_HRF,
         field: 'width',
       },
-    }
-  );
-
-  graph.nodes[IMAGE_TO_LATENTS_HRF] = {
-    type: 'i2l',
-    id: IMAGE_TO_LATENTS_HRF,
-    is_intermediate: true,
-  };
-  graph.edges.push(
-    {
-      source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
-      },
-      destination: {
-        node_id: IMAGE_TO_LATENTS_HRF,
-        field: 'vae',
-      },
     },
+    // Set up new denoise node.
     {
       source: {
-        node_id: RESIZE_HRF,
-        field: 'image',
-      },
-      destination: {
-        node_id: IMAGE_TO_LATENTS_HRF,
-        field: 'image',
-      },
-    }
-  );
-
-  graph.nodes[DENOISE_LATENTS_HRF] = {
-    type: 'denoise_latents',
-    id: DENOISE_LATENTS_HRF,
-    is_intermediate: true,
-    cfg_scale: originalDenoiseLatentsNode?.cfg_scale,
-    scheduler: originalDenoiseLatentsNode?.scheduler,
-    steps: originalDenoiseLatentsNode?.steps,
-    denoising_start: 1 - state.generation.hrfStrength,
-    denoising_end: 1,
-  };
-  graph.edges.push(
-    {
-      source: {
-        node_id: IMAGE_TO_LATENTS_HRF,
+        node_id: RESCALE_LATENTS,
         field: 'latents',
       },
       destination: {
@@ -332,41 +207,35 @@ export const addHrfToGraph = (
         node_id: DENOISE_LATENTS_HRF,
         field: 'noise',
       },
-    }
-  );
-  copyConnectionsToDenoiseLatentsHrf(graph);
-
-  graph.nodes[LATENTS_TO_IMAGE_HRF_HR] = {
-    type: 'l2i',
-    id: LATENTS_TO_IMAGE_HRF_HR,
-    fp32: originalLatentsToImageNode?.fp32,
-    is_intermediate: true,
-  };
-  graph.edges.push(
-    {
-      source: {
-        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
-        field: 'vae',
-      },
-      destination: {
-        node_id: LATENTS_TO_IMAGE_HRF_HR,
-        field: 'vae',
-      },
     },
+    // Set up new latents to image node.
     {
       source: {
         node_id: DENOISE_LATENTS_HRF,
         field: 'latents',
       },
       destination: {
-        node_id: LATENTS_TO_IMAGE_HRF_HR,
+        node_id: LATENTS_TO_IMAGE_HRF,
         field: 'latents',
       },
+    },
+    {
+      source: {
+        node_id: isAutoVae ? MAIN_MODEL_LOADER : VAE_LOADER,
+        field: 'vae',
+      },
+      destination: {
+        node_id: LATENTS_TO_IMAGE_HRF,
+        field: 'vae',
+      },
     }
   );
+
   upsertMetadata(graph, {
+    hrf_height: hrfHeight,
+    hrf_width: hrfWidth,
     hrf_strength: hrfStrength,
-    hrf_enabled: hrfEnabled,
-    hrf_method: hrfMethod,
   });
+
+  copyConnectionsToDenoiseLatentsHrf(graph);
 };

invokeai/frontend/web/src/features/nodes/util/graphBuilders/addSaveImageNode.ts

@@ -5,7 +5,7 @@ import { SaveImageInvocation } from 'services/api/types';
 import {
   CANVAS_OUTPUT,
   LATENTS_TO_IMAGE,
-  LATENTS_TO_IMAGE_HRF_HR,
+  LATENTS_TO_IMAGE_HRF,
   NSFW_CHECKER,
   SAVE_IMAGE,
   WATERMARKER,
@@ -62,10 +62,10 @@ export const addSaveImageNode = (
       },
       destination,
     });
-  } else if (LATENTS_TO_IMAGE_HRF_HR in graph.nodes) {
+  } else if (LATENTS_TO_IMAGE_HRF in graph.nodes) {
     graph.edges.push({
       source: {
-        node_id: LATENTS_TO_IMAGE_HRF_HR,
+        node_id: LATENTS_TO_IMAGE_HRF,
         field: 'image',
       },
       destination,

invokeai/frontend/web/src/features/nodes/util/graphBuilders/constants.ts

@@ -4,11 +4,7 @@ export const NEGATIVE_CONDITIONING = 'negative_conditioning';
 export const DENOISE_LATENTS = 'denoise_latents';
 export const DENOISE_LATENTS_HRF = 'denoise_latents_hrf';
 export const LATENTS_TO_IMAGE = 'latents_to_image';
-export const LATENTS_TO_IMAGE_HRF_HR = 'latents_to_image_hrf_hr';
-export const LATENTS_TO_IMAGE_HRF_LR = 'latents_to_image_hrf_lr';
-export const IMAGE_TO_LATENTS_HRF = 'image_to_latents_hrf';
-export const RESIZE_HRF = 'resize_hrf';
-export const ESRGAN_HRF = 'esrgan_hrf';
+export const LATENTS_TO_IMAGE_HRF = 'latents_to_image_hrf';
 export const SAVE_IMAGE = 'save_image';
 export const NSFW_CHECKER = 'nsfw_checker';
 export const WATERMARKER = 'invisible_watermark';
@@ -25,6 +21,7 @@ export const CLIP_SKIP = 'clip_skip';
 export const IMAGE_TO_LATENTS = 'image_to_latents';
 export const LATENTS_TO_LATENTS = 'latents_to_latents';
 export const RESIZE = 'resize_image';
+export const RESCALE_LATENTS = 'rescale_latents';
 export const IMG2IMG_RESIZE = 'img2img_resize';
 export const CANVAS_OUTPUT = 'canvas_output';
 export const INPAINT_IMAGE = 'inpaint_image';

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfCollapse.tsx

@@ -7,9 +7,10 @@ import IAICollapse from 'common/components/IAICollapse';
 import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
 import { useMemo } from 'react';
 import { useTranslation } from 'react-i18next';
+import ParamHrfHeight from './ParamHrfHeight';
 import ParamHrfStrength from './ParamHrfStrength';
 import ParamHrfToggle from './ParamHrfToggle';
-import ParamHrfMethod from './ParamHrfMethod';
+import ParamHrfWidth from './ParamHrfWidth';
 
 const selector = createSelector(
   stateSelector,
@@ -36,11 +37,28 @@ export default function ParamHrfCollapse() {
   }
 
   return (
-    <IAICollapse label={t('hrf.hrf')} activeLabel={activeLabel}>
+    <IAICollapse label="High Resolution Fix" activeLabel={activeLabel}>
       <Flex sx={{ flexDir: 'column', gap: 2 }}>
         <ParamHrfToggle />
-        <ParamHrfStrength />
-        <ParamHrfMethod />
+        {hrfEnabled && (
+          <Flex
+            sx={{
+              gap: 2,
+              p: 4,
+              borderRadius: 4,
+              flexDirection: 'column',
+              w: 'full',
+              bg: 'base.100',
+              _dark: {
+                bg: 'base.750',
+              },
+            }}
+          >
+            <ParamHrfWidth />
+            <ParamHrfHeight />
+          </Flex>
+        )}
+        {hrfEnabled && <ParamHrfStrength />}
       </Flex>
     </IAICollapse>
   );

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfHeight.tsx

@@ -0,0 +1,87 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
+import { roundToMultiple } from 'common/util/roundDownToMultiple';
+import {
+  setHrfHeight,
+  setHrfWidth,
+} from 'features/parameters/store/generationSlice';
+import { memo, useCallback } from 'react';
+
+function findPrevMultipleOfEight(n: number): number {
+  return Math.floor((n - 1) / 8) * 8;
+}
+
+const selector = createSelector(
+  [stateSelector],
+  ({ generation, hotkeys, config }) => {
+    const { min, fineStep, coarseStep } = config.sd.height;
+    const { model, height, hrfHeight, aspectRatio, hrfEnabled } = generation;
+
+    const step = hotkeys.shift ? fineStep : coarseStep;
+
+    return {
+      model,
+      height,
+      hrfHeight,
+      min,
+      step,
+      aspectRatio,
+      hrfEnabled,
+    };
+  },
+  defaultSelectorOptions
+);
+
+type ParamHeightProps = Omit<
+  IAIFullSliderProps,
+  'label' | 'value' | 'onChange'
+>;
+
+const ParamHrfHeight = (props: ParamHeightProps) => {
+  const { height, hrfHeight, min, step, aspectRatio, hrfEnabled } =
+    useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const maxHrfHeight = Math.max(findPrevMultipleOfEight(height), min);
+
+  const handleChange = useCallback(
+    (v: number) => {
+      dispatch(setHrfHeight(v));
+      if (aspectRatio) {
+        const newWidth = roundToMultiple(v * aspectRatio, 8);
+        dispatch(setHrfWidth(newWidth));
+      }
+    },
+    [dispatch, aspectRatio]
+  );
+
+  const handleReset = useCallback(() => {
+    dispatch(setHrfHeight(maxHrfHeight));
+    if (aspectRatio) {
+      const newWidth = roundToMultiple(maxHrfHeight * aspectRatio, 8);
+      dispatch(setHrfWidth(newWidth));
+    }
+  }, [dispatch, maxHrfHeight, aspectRatio]);
+
+  return (
+    <IAISlider
+      label="Initial Height"
+      value={hrfHeight}
+      min={min}
+      step={step}
+      max={maxHrfHeight}
+      onChange={handleChange}
+      handleReset={handleReset}
+      withInput
+      withReset
+      withSliderMarks
+      sliderNumberInputProps={{ max: maxHrfHeight }}
+      isDisabled={!hrfEnabled}
+      {...props}
+    />
+  );
+};
+
+export default memo(ParamHrfHeight);

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfMethod.tsx

@@ -1,49 +0,0 @@
-import { createSelector } from '@reduxjs/toolkit';
-import { stateSelector } from 'app/store/store';
-import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
-import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
-import IAIMantineSelect from 'common/components/IAIMantineSelect';
-import { setHrfMethod } from 'features/parameters/store/generationSlice';
-import { HrfMethodParam } from 'features/parameters/types/parameterSchemas';
-import { memo, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
-
-const selector = createSelector(
-  stateSelector,
-  ({ generation }) => {
-    const { hrfMethod, hrfEnabled } = generation;
-    return { hrfMethod, hrfEnabled };
-  },
-  defaultSelectorOptions
-);
-
-const DATA = ['ESRGAN', 'bilinear'];
-
-// Dropdown selection for the type of high resolution fix method to use.
-const ParamHrfMethodSelect = () => {
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
-  const { hrfMethod, hrfEnabled } = useAppSelector(selector);
-
-  const handleChange = useCallback(
-    (v: HrfMethodParam | null) => {
-      if (!v) {
-        return;
-      }
-      dispatch(setHrfMethod(v));
-    },
-    [dispatch]
-  );
-
-  return (
-    <IAIMantineSelect
-      label={t('hrf.upscaleMethod')}
-      value={hrfMethod}
-      data={DATA}
-      onChange={handleChange}
-      disabled={!hrfEnabled}
-    />
-  );
-};
-
-export default memo(ParamHrfMethodSelect);

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfStrength.tsx

@@ -5,8 +5,6 @@ import { memo, useCallback } from 'react';
 import { stateSelector } from 'app/store/store';
 import { setHrfStrength } from 'features/parameters/store/generationSlice';
 import IAISlider from 'common/components/IAISlider';
-import { Tooltip } from '@chakra-ui/react';
-import { useTranslation } from 'react-i18next';
 
 const selector = createSelector(
   [stateSelector],
@@ -33,7 +31,6 @@ const ParamHrfStrength = () => {
   const { hrfStrength, initial, min, sliderMax, step, hrfEnabled } =
     useAppSelector(selector);
   const dispatch = useAppDispatch();
-  const { t } = useTranslation();
 
   const handleHrfStrengthReset = useCallback(() => {
     dispatch(setHrfStrength(initial));
@@ -47,21 +44,20 @@ const ParamHrfStrength = () => {
   );
 
   return (
-    <Tooltip label={t('hrf.strengthTooltip')} placement="right" hasArrow>
-      <IAISlider
-        label={t('parameters.denoisingStrength')}
-        min={min}
-        max={sliderMax}
-        step={step}
-        value={hrfStrength}
-        onChange={handleHrfStrengthChange}
-        withSliderMarks
-        withInput
-        withReset
-        handleReset={handleHrfStrengthReset}
-        isDisabled={!hrfEnabled}
-      />
-    </Tooltip>
+    <IAISlider
+      label="Denoising Strength"
+      aria-label="High Resolution Denoising Strength"
+      min={min}
+      max={sliderMax}
+      step={step}
+      value={hrfStrength}
+      onChange={handleHrfStrengthChange}
+      withSliderMarks
+      withInput
+      withReset
+      handleReset={handleHrfStrengthReset}
+      isDisabled={!hrfEnabled}
+    />
   );
 };
 

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfToggle.tsx

@@ -3,11 +3,9 @@ import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
 import IAISwitch from 'common/components/IAISwitch';
 import { setHrfEnabled } from 'features/parameters/store/generationSlice';
 import { ChangeEvent, useCallback } from 'react';
-import { useTranslation } from 'react-i18next';
 
 export default function ParamHrfToggle() {
   const dispatch = useAppDispatch();
-  const { t } = useTranslation();
 
   const hrfEnabled = useAppSelector(
     (state: RootState) => state.generation.hrfEnabled
@@ -21,10 +19,9 @@ export default function ParamHrfToggle() {
 
   return (
     <IAISwitch
-      label={t('hrf.enableHrf')}
+      label="Enable High Resolution Fix"
       isChecked={hrfEnabled}
       onChange={handleHrfEnabled}
-      tooltip={t('hrf.enableHrfTooltip')}
     />
   );
 }

invokeai/frontend/web/src/features/parameters/components/Parameters/HighResFix/ParamHrfWidth.tsx

@@ -0,0 +1,84 @@
+import { createSelector } from '@reduxjs/toolkit';
+import { stateSelector } from 'app/store/store';
+import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
+import { defaultSelectorOptions } from 'app/store/util/defaultMemoizeOptions';
+import IAISlider, { IAIFullSliderProps } from 'common/components/IAISlider';
+import { roundToMultiple } from 'common/util/roundDownToMultiple';
+import {
+  setHrfHeight,
+  setHrfWidth,
+} from 'features/parameters/store/generationSlice';
+import { memo, useCallback } from 'react';
+
+function findPrevMultipleOfEight(n: number): number {
+  return Math.floor((n - 1) / 8) * 8;
+}
+
+const selector = createSelector(
+  [stateSelector],
+  ({ generation, hotkeys, config }) => {
+    const { min, fineStep, coarseStep } = config.sd.width;
+    const { model, width, hrfWidth, aspectRatio, hrfEnabled } = generation;
+
+    const step = hotkeys.shift ? fineStep : coarseStep;
+
+    return {
+      model,
+      width,
+      hrfWidth,
+      min,
+      step,
+      aspectRatio,
+      hrfEnabled,
+    };
+  },
+  defaultSelectorOptions
+);
+
+type ParamWidthProps = Omit<IAIFullSliderProps, 'label' | 'value' | 'onChange'>;
+
+const ParamHrfWidth = (props: ParamWidthProps) => {
+  const { width, hrfWidth, min, step, aspectRatio, hrfEnabled } =
+    useAppSelector(selector);
+  const dispatch = useAppDispatch();
+  const maxHrfWidth = Math.max(findPrevMultipleOfEight(width), min);
+
+  const handleChange = useCallback(
+    (v: number) => {
+      dispatch(setHrfWidth(v));
+      if (aspectRatio) {
+        const newHeight = roundToMultiple(v / aspectRatio, 8);
+        dispatch(setHrfHeight(newHeight));
+      }
+    },
+    [dispatch, aspectRatio]
+  );
+
+  const handleReset = useCallback(() => {
+    dispatch(setHrfWidth(maxHrfWidth));
+    if (aspectRatio) {
+      const newHeight = roundToMultiple(maxHrfWidth / aspectRatio, 8);
+      dispatch(setHrfHeight(newHeight));
+    }
+  }, [dispatch, maxHrfWidth, aspectRatio]);
+
+  return (
+    <IAISlider
+      label="Initial Width"
+      value={hrfWidth}
+      min={min}
+      step={step}
+      max={maxHrfWidth}
+      onChange={handleChange}
+      handleReset={handleReset}
+      withInput
+      withReset
+      withSliderMarks
+      sliderNumberInputProps={{ max: maxHrfWidth }}
+      isDisabled={!hrfEnabled}
+      {...props}
+    />
+  );
+};
+
+export default memo(ParamHrfWidth);

invokeai/frontend/web/src/features/parameters/hooks/useRecallParameters.ts

@@ -55,9 +55,6 @@ import { initialImageSelected, modelSelected } from '../store/actions';
 import {
   setCfgScale,
   setHeight,
-  setHrfEnabled,
-  setHrfMethod,
-  setHrfStrength,
   setImg2imgStrength,
   setNegativePrompt,
   setPositivePrompt,
@@ -70,7 +67,6 @@ import {
   isValidCfgScale,
   isValidControlNetModel,
   isValidHeight,
-  isValidHrfMethod,
   isValidIPAdapterModel,
   isValidLoRAModel,
   isValidMainModel,
@@ -87,7 +83,6 @@ import {
   isValidSteps,
   isValidStrength,
   isValidWidth,
-  isValidBoolean,
 } from '../types/parameterSchemas';
 
 const selector = createSelector(
@@ -366,51 +361,6 @@ export const useRecallParameters = () => {
     [dispatch, parameterSetToast, parameterNotSetToast]
   );
 
-  /**
-   * Recall high resolution enabled with toast
-   */
-  const recallHrfEnabled = useCallback(
-    (hrfEnabled: unknown) => {
-      if (!isValidBoolean(hrfEnabled)) {
-        parameterNotSetToast();
-        return;
-      }
-      dispatch(setHrfEnabled(hrfEnabled));
-      parameterSetToast();
-    },
-    [dispatch, parameterSetToast, parameterNotSetToast]
-  );
-
-  /**
-   * Recall high resolution strength with toast
-   */
-  const recallHrfStrength = useCallback(
-    (hrfStrength: unknown) => {
-      if (!isValidStrength(hrfStrength)) {
-        parameterNotSetToast();
-        return;
-      }
-      dispatch(setHrfStrength(hrfStrength));
-      parameterSetToast();
-    },
-    [dispatch, parameterSetToast, parameterNotSetToast]
-  );
-
-  /**
-   * Recall high resolution method with toast
-   */
-  const recallHrfMethod = useCallback(
-    (hrfMethod: unknown) => {
-      if (!isValidHrfMethod(hrfMethod)) {
-        parameterNotSetToast();
-        return;
-      }
-      dispatch(setHrfMethod(hrfMethod));
-      parameterSetToast();
-    },
-    [dispatch, parameterSetToast, parameterNotSetToast]
-  );
-
   /**
    * Recall LoRA with toast
    */
@@ -761,9 +711,6 @@ export const useRecallParameters = () => {
         steps,
         width,
         strength,
-        hrf_enabled,
-        hrf_strength,
-        hrf_method,
         positive_style_prompt,
         negative_style_prompt,
         refiner_model,
@@ -782,55 +729,34 @@ export const useRecallParameters = () => {
       if (isValidCfgScale(cfg_scale)) {
         dispatch(setCfgScale(cfg_scale));
       }
-
       if (isValidMainModel(model)) {
         dispatch(modelSelected(model));
       }
-
       if (isValidPositivePrompt(positive_prompt)) {
         dispatch(setPositivePrompt(positive_prompt));
       }
-
       if (isValidNegativePrompt(negative_prompt)) {
         dispatch(setNegativePrompt(negative_prompt));
       }
-
       if (isValidScheduler(scheduler)) {
         dispatch(setScheduler(scheduler));
       }
-
       if (isValidSeed(seed)) {
         dispatch(setSeed(seed));
       }
-
       if (isValidSteps(steps)) {
         dispatch(setSteps(steps));
       }
-
       if (isValidWidth(width)) {
         dispatch(setWidth(width));
       }
-
       if (isValidHeight(height)) {
         dispatch(setHeight(height));
       }
-
       if (isValidStrength(strength)) {
         dispatch(setImg2imgStrength(strength));
       }
 
-      if (isValidBoolean(hrf_enabled)) {
-        dispatch(setHrfEnabled(hrf_enabled));
-      }
-
-      if (isValidStrength(hrf_strength)) {
-        dispatch(setHrfStrength(hrf_strength));
-      }
-
-      if (isValidHrfMethod(hrf_method)) {
-        dispatch(setHrfMethod(hrf_method));
-      }
-
       if (isValidSDXLPositiveStylePrompt(positive_style_prompt)) {
         dispatch(setPositiveStylePromptSDXL(positive_style_prompt));
       }
@@ -936,9 +862,6 @@ export const useRecallParameters = () => {
     recallWidth,
     recallHeight,
     recallStrength,
-    recallHrfEnabled,
-    recallHrfStrength,
-    recallHrfMethod,
     recallLoRA,
     recallControlNet,
     recallIPAdapter,

invokeai/frontend/web/src/features/parameters/store/generationSlice.ts

@@ -11,7 +11,6 @@ import {
   CanvasCoherenceModeParam,
   CfgScaleParam,
   HeightParam,
-  HrfMethodParam,
   MainModelParam,
   MaskBlurMethodParam,
   NegativePromptParam,
@@ -28,9 +27,10 @@ import {
 } from '../types/parameterSchemas';
 
 export interface GenerationState {
+  hrfHeight: HeightParam;
+  hrfWidth: WidthParam;
   hrfEnabled: boolean;
   hrfStrength: StrengthParam;
-  hrfMethod: HrfMethodParam;
   cfgScale: CfgScaleParam;
   height: HeightParam;
   img2imgStrength: StrengthParam;
@@ -73,9 +73,10 @@ export interface GenerationState {
 }
 
 export const initialGenerationState: GenerationState = {
-  hrfStrength: 0.45,
+  hrfHeight: 64,
+  hrfWidth: 64,
+  hrfStrength: 0.75,
   hrfEnabled: false,
-  hrfMethod: 'ESRGAN',
   cfgScale: 7.5,
   height: 512,
   img2imgStrength: 0.75,
@@ -278,15 +279,18 @@ export const generationSlice = createSlice({
     setClipSkip: (state, action: PayloadAction<number>) => {
       state.clipSkip = action.payload;
     },
+    setHrfHeight: (state, action: PayloadAction<number>) => {
+      state.hrfHeight = action.payload;
+    },
+    setHrfWidth: (state, action: PayloadAction<number>) => {
+      state.hrfWidth = action.payload;
+    },
     setHrfStrength: (state, action: PayloadAction<number>) => {
       state.hrfStrength = action.payload;
     },
     setHrfEnabled: (state, action: PayloadAction<boolean>) => {
       state.hrfEnabled = action.payload;
     },
-    setHrfMethod: (state, action: PayloadAction<HrfMethodParam>) => {
-      state.hrfMethod = action.payload;
-    },
     shouldUseCpuNoiseChanged: (state, action: PayloadAction<boolean>) => {
       state.shouldUseCpuNoise = action.payload;
     },
@@ -371,9 +375,10 @@ export const {
   setSeamlessXAxis,
   setSeamlessYAxis,
   setClipSkip,
-  setHrfEnabled,
+  setHrfHeight,
+  setHrfWidth,
   setHrfStrength,
-  setHrfMethod,
+  setHrfEnabled,
   shouldUseCpuNoiseChanged,
   setAspectRatio,
   setShouldLockAspectRatio,

invokeai/frontend/web/src/features/parameters/types/parameterSchemas.ts

@@ -132,7 +132,6 @@ export const zScheduler = z.enum([
   'lms_k',
   'euler_a',
   'kdpm_2_a',
-  'lcm',
 ]);
 /**
  * Type alias for scheduler parameter, inferred from its zod schema
@@ -167,7 +166,6 @@ export const SCHEDULER_LABEL_MAP: Record<SchedulerParam, string> = {
   lms_k: 'LMS Karras',
   euler_a: 'Euler Ancestral',
   kdpm_2_a: 'KDPM 2 Ancestral',
-  lcm: 'LCM',
 };
 
 /**
@@ -400,20 +398,6 @@ export type PrecisionParam = z.infer<typeof zPrecision>;
 export const isValidPrecision = (val: unknown): val is PrecisionParam =>
   zPrecision.safeParse(val).success;
 
-/**
- * Zod schema for a high resolution fix method parameter.
- */
-export const zHrfMethod = z.enum(['ESRGAN', 'bilinear']);
-/**
- * Type alias for high resolution fix method parameter, inferred from its zod schema
- */
-export type HrfMethodParam = z.infer<typeof zHrfMethod>;
-/**
- * Validates/type-guards a value as a high resolution fix method parameter
- */
-export const isValidHrfMethod = (val: unknown): val is HrfMethodParam =>
-  zHrfMethod.safeParse(val).success;
-
 /**
  * Zod schema for SDXL refiner positive aesthetic score parameter
  */
@@ -496,17 +480,6 @@ export const isValidCoherenceModeParam = (
 ): val is CanvasCoherenceModeParam =>
   zCanvasCoherenceMode.safeParse(val).success;
 
-/**
- * Zod schema for a boolean.
- */
-export const zBoolean = z.boolean();
-
-/**
- * Validates/type-guards a value as a boolean parameter
- */
-export const isValidBoolean = (val: unknown): val is boolean =>
-  zBoolean.safeParse(val).success && val !== null && val !== undefined;
-
 // /**
 //  * Zod schema for BaseModelType
 //  */

invokeai/frontend/web/src/features/system/store/configSlice.ts

@@ -70,7 +70,7 @@ export const initialConfigState: AppConfig = {
       coarseStep: 0.05,
     },
     hrfStrength: {
-      initial: 0.45,
+      initial: 0.7,
       min: 0,
       sliderMax: 1,
       inputMax: 1,

invokeai/frontend/web/src/services/api/types.ts

@@ -127,6 +127,7 @@ export type CompelInvocation = s['CompelInvocation'];
 export type DynamicPromptInvocation = s['DynamicPromptInvocation'];
 export type NoiseInvocation = s['NoiseInvocation'];
 export type DenoiseLatentsInvocation = s['DenoiseLatentsInvocation'];
+export type ResizeLatentsInvocation = s['ResizeLatentsInvocation'];
 export type ONNXTextToLatentsInvocation = s['ONNXTextToLatentsInvocation'];
 export type SDXLLoraLoaderInvocation = s['SDXLLoraLoaderInvocation'];
 export type ImageToLatentsInvocation = s['ImageToLatentsInvocation'];

invokeai/version/invokeai_version.py

@@ -1 +1 @@
-__version__ = "3.4.0rc4"
+__version__ = "3.4.0rc3"

pyproject.toml

@@ -40,7 +40,9 @@ dependencies = [
   "controlnet-aux>=0.0.6",
   "timm==0.6.13",          # needed to override timm latest in controlnet_aux, see  https://github.com/isl-org/ZoeDepth/issues/26
   "datasets",
-  "diffusers[torch]~=0.23.0",
+  # When bumping diffusers beyond 0.21, make sure to address this:
+  # https://github.com/invoke-ai/InvokeAI/blob/fc09ab7e13cb7ca5389100d149b6422ace7b8ed3/invokeai/app/invocations/latent.py#L513
+  "diffusers[torch]~=0.22.0",
   "dnspython~=2.4.0",
   "dynamicprompts",
   "easing-functions",
@@ -82,7 +84,7 @@ dependencies = [
   "torchvision~=0.16",
   "torchmetrics~=0.11.0",
   "torchsde~=0.2.5",
-  "transformers~=4.35.0",
+  "transformers~=4.31.0",
   "uvicorn[standard]~=0.21.1",
   "windows-curses; sys_platform=='win32'",
 ]