Merge branch 'main' into feat/batch-graphs

invokeai/backend/model_management/model_merge.py

@@ -109,7 +109,7 @@ class ModelMerger(object):
             # pick up the first model's vae
             if mod == model_names[0]:
                 vae = info.get("vae")
-            model_paths.extend([config.root_path / info["path"]])
+            model_paths.extend([(config.root_path / info["path"]).as_posix()])
 
         merge_method = None if interp == "weighted_sum" else MergeInterpolationMethod(interp)
         logger.debug(f"interp = {interp}, merge_method={merge_method}")
@@ -120,11 +120,11 @@ class ModelMerger(object):
             else config.models_path / base_model.value / ModelType.Main.value
         )
         dump_path.mkdir(parents=True, exist_ok=True)
-        dump_path = dump_path / merged_model_name
+        dump_path = (dump_path / merged_model_name).as_posix()
 
         merged_pipe.save_pretrained(dump_path, safe_serialization=True)
         attributes = dict(
-            path=str(dump_path),
+            path=dump_path,
             description=f"Merge of models {', '.join(model_names)}",
             model_format="diffusers",
             variant=ModelVariantType.Normal.value,

invokeai/backend/model_management/model_probe.py

@@ -481,9 +481,19 @@ class ControlNetFolderProbe(FolderProbeBase):
         with open(config_file, "r") as file:
             config = json.load(file)
         # no obvious way to distinguish between sd2-base and sd2-768
-        return (
-            BaseModelType.StableDiffusion1 if config["cross_attention_dim"] == 768 else BaseModelType.StableDiffusion2
+        dimension = config["cross_attention_dim"]
+        base_model = (
+            BaseModelType.StableDiffusion1
+            if dimension == 768
+            else BaseModelType.StableDiffusion2
+            if dimension == 1024
+            else BaseModelType.StableDiffusionXL
+            if dimension == 2048
+            else None
         )
+        if not base_model:
+            raise InvalidModelException(f"Unable to determine model base for {self.folder_path}")
+        return base_model
 
 
 class LoRAFolderProbe(FolderProbeBase):

invokeai/backend/web/__init__.py

@@ -1,4 +0,0 @@
-"""
-Initialization file for the web backend.
-"""
-from .invoke_ai_web_server import InvokeAIWebServer

invokeai/backend/web/modules/create_cmd_parser.py

@@ -1,56 +0,0 @@
-import argparse
-import os
-
-from ...args import PRECISION_CHOICES
-
-
-def create_cmd_parser():
-    parser = argparse.ArgumentParser(description="InvokeAI web UI")
-    parser.add_argument(
-        "--host",
-        type=str,
-        help="The host to serve on",
-        default="localhost",
-    )
-    parser.add_argument("--port", type=int, help="The port to serve on", default=9090)
-    parser.add_argument(
-        "--cors",
-        nargs="*",
-        type=str,
-        help="Additional allowed origins, comma-separated",
-    )
-    parser.add_argument(
-        "--embedding_path",
-        type=str,
-        help="Path to a pre-trained embedding manager checkpoint - can only be set on command line",
-    )
-    # TODO: Can't get flask to serve images from any dir (saving to the dir does work when specified)
-    # parser.add_argument(
-    #     "--output_dir",
-    #     default="outputs/",
-    #     type=str,
-    #     help="Directory for output images",
-    # )
-    parser.add_argument(
-        "-v",
-        "--verbose",
-        action="store_true",
-        help="Enables verbose logging",
-    )
-    parser.add_argument(
-        "--precision",
-        dest="precision",
-        type=str,
-        choices=PRECISION_CHOICES,
-        metavar="PRECISION",
-        help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
-        default="auto",
-    )
-    parser.add_argument(
-        "--free_gpu_mem",
-        dest="free_gpu_mem",
-        action="store_true",
-        help="Force free gpu memory before final decoding",
-    )
-
-    return parser

invokeai/backend/web/modules/get_canvas_generation_mode.py

@@ -1,113 +0,0 @@
-from typing import Literal, Union
-
-from PIL import Image, ImageChops
-from PIL.Image import Image as ImageType
-
-
-# https://stackoverflow.com/questions/43864101/python-pil-check-if-image-is-transparent
-def check_for_any_transparency(img: Union[ImageType, str]) -> bool:
-    if type(img) is str:
-        img = Image.open(str)
-
-    if img.info.get("transparency", None) is not None:
-        return True
-    if img.mode == "P":
-        transparent = img.info.get("transparency", -1)
-        for _, index in img.getcolors():
-            if index == transparent:
-                return True
-    elif img.mode == "RGBA":
-        extrema = img.getextrema()
-        if extrema[3][0] < 255:
-            return True
-    return False
-
-
-def get_canvas_generation_mode(
-    init_img: Union[ImageType, str], init_mask: Union[ImageType, str]
-) -> Literal["txt2img", "outpainting", "inpainting", "img2img",]:
-    if type(init_img) is str:
-        init_img = Image.open(init_img)
-
-    if type(init_mask) is str:
-        init_mask = Image.open(init_mask)
-
-    init_img = init_img.convert("RGBA")
-
-    # Get alpha from init_img
-    init_img_alpha = init_img.split()[-1]
-    init_img_alpha_mask = init_img_alpha.convert("L")
-    init_img_has_transparency = check_for_any_transparency(init_img)
-
-    if init_img_has_transparency:
-        init_img_is_fully_transparent = True if init_img_alpha_mask.getbbox() is None else False
-
-    """
-    Mask images are white in areas where no change should be made, black where changes
-    should be made.
-    """
-
-    # Fit the mask to init_img's size and convert it to greyscale
-    init_mask = init_mask.resize(init_img.size).convert("L")
-
-    """
-    PIL.Image.getbbox() returns the bounding box of non-zero areas of the image, so we first
-    invert the mask image so that masked areas are white and other areas black == zero.
-    getbbox() now tells us if the are any masked areas.
-    """
-    init_mask_bbox = ImageChops.invert(init_mask).getbbox()
-    init_mask_exists = False if init_mask_bbox is None else True
-
-    if init_img_has_transparency:
-        if init_img_is_fully_transparent:
-            return "txt2img"
-        else:
-            return "outpainting"
-    else:
-        if init_mask_exists:
-            return "inpainting"
-        else:
-            return "img2img"
-
-
-def main():
-    # Testing
-    init_img_opaque = "test_images/init-img_opaque.png"
-    init_img_partial_transparency = "test_images/init-img_partial_transparency.png"
-    init_img_full_transparency = "test_images/init-img_full_transparency.png"
-    init_mask_no_mask = "test_images/init-mask_no_mask.png"
-    init_mask_has_mask = "test_images/init-mask_has_mask.png"
-
-    print(
-        "OPAQUE IMAGE, NO MASK, expect img2img, got ",
-        get_canvas_generation_mode(init_img_opaque, init_mask_no_mask),
-    )
-
-    print(
-        "IMAGE WITH TRANSPARENCY, NO MASK, expect outpainting, got ",
-        get_canvas_generation_mode(init_img_partial_transparency, init_mask_no_mask),
-    )
-
-    print(
-        "FULLY TRANSPARENT IMAGE NO MASK, expect txt2img, got ",
-        get_canvas_generation_mode(init_img_full_transparency, init_mask_no_mask),
-    )
-
-    print(
-        "OPAQUE IMAGE, WITH MASK, expect inpainting, got ",
-        get_canvas_generation_mode(init_img_opaque, init_mask_has_mask),
-    )
-
-    print(
-        "IMAGE WITH TRANSPARENCY, WITH MASK, expect outpainting, got ",
-        get_canvas_generation_mode(init_img_partial_transparency, init_mask_has_mask),
-    )
-
-    print(
-        "FULLY TRANSPARENT IMAGE WITH MASK, expect txt2img, got ",
-        get_canvas_generation_mode(init_img_full_transparency, init_mask_has_mask),
-    )
-
-
-if __name__ == "__main__":
-    main()

invokeai/backend/web/modules/parameters.py

@@ -1,82 +0,0 @@
-import argparse
-
-from .parse_seed_weights import parse_seed_weights
-
-SAMPLER_CHOICES = [
-    "ddim",
-    "ddpm",
-    "deis",
-    "lms",
-    "lms_k",
-    "pndm",
-    "heun",
-    "heun_k",
-    "euler",
-    "euler_k",
-    "euler_a",
-    "kdpm_2",
-    "kdpm_2_a",
-    "dpmpp_2s",
-    "dpmpp_2s_k",
-    "dpmpp_2m",
-    "dpmpp_2m_k",
-    "dpmpp_2m_sde",
-    "dpmpp_2m_sde_k",
-    "dpmpp_sde",
-    "dpmpp_sde_k",
-    "unipc",
-]
-
-
-def parameters_to_command(params):
-    """
-    Converts dict of parameters into a `invoke.py` REPL command.
-    """
-
-    switches = list()
-
-    if "prompt" in params:
-        switches.append(f'"{params["prompt"]}"')
-    if "steps" in params:
-        switches.append(f'-s {params["steps"]}')
-    if "seed" in params:
-        switches.append(f'-S {params["seed"]}')
-    if "width" in params:
-        switches.append(f'-W {params["width"]}')
-    if "height" in params:
-        switches.append(f'-H {params["height"]}')
-    if "cfg_scale" in params:
-        switches.append(f'-C {params["cfg_scale"]}')
-    if "sampler_name" in params:
-        switches.append(f'-A {params["sampler_name"]}')
-    if "seamless" in params and params["seamless"] == True:
-        switches.append(f"--seamless")
-    if "hires_fix" in params and params["hires_fix"] == True:
-        switches.append(f"--hires")
-    if "init_img" in params and len(params["init_img"]) > 0:
-        switches.append(f'-I {params["init_img"]}')
-    if "init_mask" in params and len(params["init_mask"]) > 0:
-        switches.append(f'-M {params["init_mask"]}')
-    if "init_color" in params and len(params["init_color"]) > 0:
-        switches.append(f'--init_color {params["init_color"]}')
-    if "strength" in params and "init_img" in params:
-        switches.append(f'-f {params["strength"]}')
-        if "fit" in params and params["fit"] == True:
-            switches.append(f"--fit")
-    if "facetool" in params:
-        switches.append(f'-ft {params["facetool"]}')
-    if "facetool_strength" in params and params["facetool_strength"]:
-        switches.append(f'-G {params["facetool_strength"]}')
-    elif "gfpgan_strength" in params and params["gfpgan_strength"]:
-        switches.append(f'-G {params["gfpgan_strength"]}')
-    if "codeformer_fidelity" in params:
-        switches.append(f'-cf {params["codeformer_fidelity"]}')
-    if "upscale" in params and params["upscale"]:
-        switches.append(f'-U {params["upscale"][0]} {params["upscale"][1]}')
-    if "variation_amount" in params and params["variation_amount"] > 0:
-        switches.append(f'-v {params["variation_amount"]}')
-        if "with_variations" in params:
-            seed_weight_pairs = ",".join(f"{seed}:{weight}" for seed, weight in params["with_variations"])
-            switches.append(f"-V {seed_weight_pairs}")
-
-    return " ".join(switches)

invokeai/backend/web/modules/parse_seed_weights.py

@@ -1,47 +0,0 @@
-def parse_seed_weights(seed_weights):
-    """
-    Accepts seed weights as string in "12345:0.1,23456:0.2,3456:0.3" format
-    Validates them
-    If valid: returns as [[12345, 0.1], [23456, 0.2], [3456, 0.3]]
-    If invalid: returns False
-    """
-
-    # Must be a string
-    if not isinstance(seed_weights, str):
-        return False
-    # String must not be empty
-    if len(seed_weights) == 0:
-        return False
-
-    pairs = []
-
-    for pair in seed_weights.split(","):
-        split_values = pair.split(":")
-
-        # Seed and weight are required
-        if len(split_values) != 2:
-            return False
-
-        if len(split_values[0]) == 0 or len(split_values[1]) == 1:
-            return False
-
-        # Try casting the seed to int and weight to float
-        try:
-            seed = int(split_values[0])
-            weight = float(split_values[1])
-        except ValueError:
-            return False
-
-        # Seed must be 0 or above
-        if not seed >= 0:
-            return False
-
-        # Weight must be between 0 and 1
-        if not (weight >= 0 and weight <= 1):
-            return False
-
-        # This pair is valid
-        pairs.append([seed, weight])
-
-    # All pairs are valid
-    return pairs

invokeai/frontend/web/dist/assets/ThemeLocaleProvider-bc3e6f20.js

@@ -1,4 +1,4 @@
-import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-deaa1f26.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-b4489359.js";var za=String.raw,Ca=za`
+import{B as m,g7 as Je,A as y,a5 as Ka,g8 as Xa,af as va,aj as d,g9 as b,ga as t,gb as Ya,gc as h,gd as ua,ge as Ja,gf as Qa,aL as Za,gg as et,ad as rt,gh as at}from"./index-2c171c8f.js";import{s as fa,n as o,t as tt,o as ha,p as ot,q as ma,v as ga,w as ya,x as it,y as Sa,z as pa,A as xr,B as nt,D as lt,E as st,F as xa,G as $a,H as ka,J as dt,K as _a,L as ct,M as bt,N as vt,O as ut,Q as wa,R as ft,S as ht,T as mt,U as gt,V as yt,W as St,e as pt,X as xt}from"./menu-971c0572.js";var za=String.raw,Ca=za`
   :root,
   :host {
     --chakra-vh: 100vh;

invokeai/frontend/web/dist/index.html

@@ -12,7 +12,7 @@
         margin: 0;
       }
     </style>
-    <script type="module" crossorigin src="./assets/index-deaa1f26.js"></script>
+    <script type="module" crossorigin src="./assets/index-2c171c8f.js"></script>
   </head>
 
   <body dir="ltr">

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

@@ -503,6 +503,9 @@
         "hiresStrength": "High Res Strength",
         "imageFit": "Fit Initial Image To Output Size",
         "codeformerFidelity": "Fidelity",
+        "maskAdjustmentsHeader": "Mask Adjustments",
+        "maskBlur": "Mask Blur",
+        "maskBlurMethod": "Mask Blur Method",
         "seamSize": "Seam Size",
         "seamBlur": "Seam Blur",
         "seamStrength": "Seam Strength",

invokeai/frontend/web/src/features/sdxl/components/SDXLRefiner/ParamSDXLRefinerStart.tsx

@@ -28,7 +28,7 @@ const ParamSDXLRefinerStart = () => {
   );
 
   const handleReset = useCallback(
-    () => dispatch(setRefinerStart(0.7)),
+    () => dispatch(setRefinerStart(0.8)),
     [dispatch]
   );
 

invokeai/frontend/web/src/features/sdxl/store/sdxlSlice.ts

@@ -33,7 +33,7 @@ const sdxlInitialState: SDXLInitialState = {
   refinerScheduler: 'euler',
   refinerPositiveAestheticScore: 6,
   refinerNegativeAestheticScore: 2.5,
-  refinerStart: 0.7,
+  refinerStart: 0.8,
 };
 
 const sdxlSlice = createSlice({

pyproject.toml

@@ -45,15 +45,10 @@ dependencies = [
   "dynamicprompts",
   "easing-functions",
   "einops",
-  "eventlet",
   "facexlib",
   "fastapi==0.88.0",
   "fastapi-events==0.8.0",
   "fastapi-socketio==0.0.10",
-  "flask==2.1.3",
-  "flask_cors==3.0.10",
-  "flask_socketio==5.3.0",
-  "flaskwebgui==1.0.3",
   "huggingface-hub>=0.11.1",
   "invisible-watermark~=0.2.0", # needed to install SDXL base and refiner using their repo_ids
   "matplotlib",                 # needed for plotting of Penner easing functions