Rename custom_nodes/ComfyUI_UltimateSDUpscale/README.md to custom_nodes/ComfyUI_UltimateSDUpscale/

custom_nodes/ComfyUI_UltimateSDUpscale/LICENSE

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-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

custom_nodes/ComfyUI_UltimateSDUpscale/__init__.py

@@ -1,39 +0,0 @@
-import sys
-import os
-repo_dir = os.path.dirname(os.path.realpath(__file__))
-sys.path.insert(0, repo_dir)
-original_modules = sys.modules.copy()
-
-# Place aside potentially conflicting modules
-modules_used = [
-    "modules",
-    "modules.devices",
-    "modules.images",
-    "modules.processing",
-    "modules.scripts",
-    "modules.shared",
-    "modules.upscaler",
-    "utils",
-]
-original_imported_modules = {}
-for module in modules_used:
-    if module in sys.modules:
-        original_imported_modules[module] = sys.modules.pop(module)
-
-# Proceed with node setup
-from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
-__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
-
-# Clean up imports
-# Remove repo directory from path
-sys.path.remove(repo_dir)
-# Remove any new modules
-modules_to_remove = []
-for module in sys.modules:
-    if module not in original_modules:
-        modules_to_remove.append(module)
-for module in modules_to_remove:
-    del sys.modules[module]
-
-# Restore original modules
-sys.modules.update(original_imported_modules)

custom_nodes/ComfyUI_UltimateSDUpscale/gradio.py

@@ -1 +0,0 @@
-# Empty gradio module for the ultimate-upscale.py import because gradio is not needed

custom_nodes/ComfyUI_UltimateSDUpscale/modules/devices.py

@@ -1,2 +0,0 @@
-def torch_gc():
-    pass

custom_nodes/ComfyUI_UltimateSDUpscale/modules/images.py

@@ -1,8 +0,0 @@
-from PIL import Image
-
-
-def flatten(img, bgcolor):
-    # Replace transparency with bgcolor
-    if img.mode in ("RGB"):
-        return img
-    return Image.alpha_composite(Image.new("RGBA", img.size, bgcolor), img).convert("RGB")

custom_nodes/ComfyUI_UltimateSDUpscale/modules/processing.py

@@ -1,163 +0,0 @@
-from PIL import Image, ImageFilter
-import torch
-import math
-from nodes import common_ksampler, VAEEncode, VAEDecode, VAEDecodeTiled
-from utils import pil_to_tensor, tensor_to_pil, get_crop_region, expand_crop, crop_cond
-from modules import shared
-
-if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
-    Image.Resampling = Image
-
-
-class StableDiffusionProcessing:
-
-    def __init__(self, init_img, model, positive, negative, vae, seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, uniform_tile_mode, tiled_decode):
-        # Variables used by the USDU script
-        self.init_images = [init_img]
-        self.image_mask = None
-        self.mask_blur = 0
-        self.inpaint_full_res_padding = 0
-        self.width = init_img.width
-        self.height = init_img.height
-
-        # ComfyUI Sampler inputs
-        self.model = model
-        self.positive = positive
-        self.negative = negative
-        self.vae = vae
-        self.seed = seed
-        self.steps = steps
-        self.cfg = cfg
-        self.sampler_name = sampler_name
-        self.scheduler = scheduler
-        self.denoise = denoise
-
-        # Variables used only by this script
-        self.init_size = init_img.width, init_img.height
-        self.upscale_by = upscale_by
-        self.uniform_tile_mode = uniform_tile_mode
-        self.tiled_decode = tiled_decode
-        self.vae_decoder = VAEDecode()
-        self.vae_encoder = VAEEncode()
-        self.vae_decoder_tiled = VAEDecodeTiled()
-
-        # Other required A1111 variables for the USDU script that is currently unused in this script
-        self.extra_generation_params = {}
-
-
-class Processed:
-
-    def __init__(self, p: StableDiffusionProcessing, images: list, seed: int, info: str):
-        self.images = images
-        self.seed = seed
-        self.info = info
-
-    def infotext(self, p: StableDiffusionProcessing, index):
-        return None
-
-
-def fix_seed(p: StableDiffusionProcessing):
-    pass
-
-
-def process_images(p: StableDiffusionProcessing) -> Processed:
-    # Where the main image generation happens in A1111
-
-    # Setup
-    image_mask = p.image_mask.convert('L')
-    init_image = p.init_images[0]
-
-    # Locate the white region of the mask outlining the tile and add padding
-    crop_region = get_crop_region(image_mask, p.inpaint_full_res_padding)
-
-    if p.uniform_tile_mode:
-        # Expand the crop region to match the processing size ratio and then resize it to the processing size
-        x1, y1, x2, y2 = crop_region
-        crop_width = x2 - x1
-        crop_height = y2 - y1
-        crop_ratio = crop_width / crop_height
-        p_ratio = p.width / p.height
-        if crop_ratio > p_ratio:
-            target_width = crop_width
-            target_height = round(crop_width / p_ratio)
-        else:
-            target_width = round(crop_height * p_ratio)
-            target_height = crop_height
-        crop_region, _ = expand_crop(crop_region, image_mask.width, image_mask.height, target_width, target_height)
-        tile_size = p.width, p.height
-    else:
-        # Uses the minimal size that can fit the mask, minimizes tile size but may lead to image sizes that the model is not trained on
-        x1, y1, x2, y2 = crop_region
-        crop_width = x2 - x1
-        crop_height = y2 - y1
-        target_width = math.ceil(crop_width / 8) * 8
-        target_height = math.ceil(crop_height / 8) * 8
-        crop_region, tile_size = expand_crop(crop_region, image_mask.width,
-                                             image_mask.height, target_width, target_height)
-
-    # Blur the mask
-    if p.mask_blur > 0:
-        image_mask = image_mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
-
-    # Crop the images to get the tiles that will be used for generation
-    tiles = [img.crop(crop_region) for img in shared.batch]
-
-    # Assume the same size for all images in the batch
-    initial_tile_size = tiles[0].size
-
-    # Resize if necessary
-    for i, tile in enumerate(tiles):
-        if tile.size != tile_size:
-            tiles[i] = tile.resize(tile_size, Image.Resampling.LANCZOS)
-
-    # Crop conditioning
-    positive_cropped = crop_cond(p.positive, crop_region, p.init_size, init_image.size, tile_size)
-    negative_cropped = crop_cond(p.negative, crop_region, p.init_size, init_image.size, tile_size)
-
-    # Encode the image
-    batched_tiles = torch.cat([pil_to_tensor(tile) for tile in tiles], dim=0)
-    (latent,) = p.vae_encoder.encode(p.vae, batched_tiles)
-
-    # Generate samples
-    (samples,) = common_ksampler(p.model, p.seed, p.steps, p.cfg, p.sampler_name,
-                                 p.scheduler, positive_cropped, negative_cropped, latent, denoise=p.denoise)
-
-    # Decode the sample
-    if not p.tiled_decode:
-        (decoded,) = p.vae_decoder.decode(p.vae, samples)
-    else:
-        print("[USDU] Using tiled decode")
-        (decoded,) = p.vae_decoder_tiled.decode(p.vae, samples, 512)  # Default tile size is 512
-
-    # Convert the sample to a PIL image
-    tiles_sampled = [tensor_to_pil(decoded, i) for i in range(len(decoded))]
-
-    for i, tile_sampled in enumerate(tiles_sampled):
-        init_image = shared.batch[i]
-
-        # Resize back to the original size
-        if tile_sampled.size != initial_tile_size:
-            tile_sampled = tile_sampled.resize(initial_tile_size, Image.Resampling.LANCZOS)
-            
-
-        # Put the tile into position
-        image_tile_only = Image.new('RGBA', init_image.size)
-        image_tile_only.paste(tile_sampled, crop_region[:2])
-
-        # Add the mask as an alpha channel
-        # Must make a copy due to the possibility of an edge becoming black
-        temp = image_tile_only.copy()
-        temp.putalpha(image_mask)
-        image_tile_only.paste(temp, image_tile_only)
-
-        # Add back the tile to the initial image according to the mask in the alpha channel
-        result = init_image.convert('RGBA')
-        result.alpha_composite(image_tile_only)
-
-        # Convert back to RGB
-        result = result.convert('RGB')
-
-        shared.batch[i] = result
-
-    processed = Processed(p, [shared.batch[0]], p.seed, None)
-    return processed

custom_nodes/ComfyUI_UltimateSDUpscale/modules/scripts.py

@@ -1,2 +0,0 @@
-class Script:
-    pass

custom_nodes/ComfyUI_UltimateSDUpscale/modules/shared.py

@@ -1,24 +0,0 @@
-class Options:
-    img2img_background_color = "#ffffff"  # Set to white for now
-
-
-class State:
-    interrupted = False
-
-    def begin(self):
-        pass
-
-    def end(self):
-        pass
-
-
-opts = Options()
-state = State()
-
-# Will only ever hold 1 upscaler
-sd_upscalers = [None]
-# The upscaler usable by ComfyUI nodes
-actual_upscaler = None
-
-# Batch of images to upscale
-batch = None

custom_nodes/ComfyUI_UltimateSDUpscale/modules/upscaler.py

@@ -1,30 +0,0 @@
-from PIL import Image
-from utils import tensor_to_pil, pil_to_tensor
-from comfy_extras.nodes_upscale_model import ImageUpscaleWithModel
-from modules import shared
-
-if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
-    Image.Resampling = Image
-
-
-class Upscaler:
-
-    def _upscale(self, img: Image, scale):
-        if (shared.actual_upscaler is None):
-            return img.resize((img.width * scale, img.height * scale), Image.Resampling.NEAREST)
-        tensor = pil_to_tensor(img)
-        image_upscale_node = ImageUpscaleWithModel()
-        (upscaled,) = image_upscale_node.upscale(shared.actual_upscaler, tensor)
-        return tensor_to_pil(upscaled)
-
-    def upscale(self, img: Image, scale, selected_model: str = None):
-        shared.batch = [self._upscale(img, scale) for img in shared.batch]
-        return shared.batch[0]
-
-
-class UpscalerData:
-    name = ""
-    data_path = ""
-
-    def __init__(self):
-        self.scaler = Upscaler()

custom_nodes/ComfyUI_UltimateSDUpscale/nodes.py

@@ -1,191 +0,0 @@
-# ComfyUI Node for Ultimate SD Upscale by Coyote-A: https://github.com/Coyote-A/ultimate-upscale-for-automatic1111
-
-import torch
-import comfy
-from usdu_patch import usdu
-from utils import tensor_to_pil, pil_to_tensor
-from modules.processing import StableDiffusionProcessing
-import modules.shared as shared
-from modules.upscaler import UpscalerData
-
-MAX_RESOLUTION = 8192
-# The modes available for Ultimate SD Upscale
-MODES = {
-    "Linear": usdu.USDUMode.LINEAR,
-    "Chess": usdu.USDUMode.CHESS,
-    "None": usdu.USDUMode.NONE,
-}
-# The seam fix modes
-SEAM_FIX_MODES = {
-    "None": usdu.USDUSFMode.NONE,
-    "Band Pass": usdu.USDUSFMode.BAND_PASS,
-    "Half Tile": usdu.USDUSFMode.HALF_TILE,
-    "Half Tile + Intersections": usdu.USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS,
-}
-
-
-def USDU_base_inputs():
-    return [
-        ("image", ("IMAGE",)),
-        # Sampling Params
-        ("model", ("MODEL",)),
-        ("positive", ("CONDITIONING",)),
-        ("negative", ("CONDITIONING",)),
-        ("vae", ("VAE",)),
-        ("upscale_by", ("FLOAT", {"default": 2, "min": 0.05, "max": 4, "step": 0.05})),
-        ("seed", ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})),
-        ("steps", ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1})),
-        ("cfg", ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0})),
-        ("sampler_name", (comfy.samplers.KSampler.SAMPLERS,)),
-        ("scheduler", (comfy.samplers.KSampler.SCHEDULERS,)),
-        ("denoise", ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.01})),
-        # Upscale Params
-        ("upscale_model", ("UPSCALE_MODEL",)),
-        ("mode_type", (list(MODES.keys()),)),
-        ("tile_width", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8})),
-        ("tile_height", ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8})),
-        ("mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1})),
-        ("tile_padding", ("INT", {"default": 32, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
-        # Seam fix params
-        ("seam_fix_mode", (list(SEAM_FIX_MODES.keys()),)),
-        ("seam_fix_denoise", ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})),
-        ("seam_fix_width", ("INT", {"default": 64, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
-        ("seam_fix_mask_blur", ("INT", {"default": 8, "min": 0, "max": 64, "step": 1})),
-        ("seam_fix_padding", ("INT", {"default": 16, "min": 0, "max": MAX_RESOLUTION, "step": 8})),
-        # Misc
-        ("force_uniform_tiles", ("BOOLEAN", {"default": True})),
-        ("tiled_decode", ("BOOLEAN", {"default": False})),
-    ]
-
-
-def prepare_inputs(required: list, optional: list = None):
-    inputs = {}
-    if required:
-        inputs["required"] = {}
-        for name, type in required:
-            inputs["required"][name] = type
-    if optional:
-        inputs["optional"] = {}
-        for name, type in optional:
-            inputs["optional"][name] = type
-    return inputs
-
-
-def remove_input(inputs: list, input_name: str):
-    for i, (n, _) in enumerate(inputs):
-        if n == input_name:
-            del inputs[i]
-            break
-
-
-def rename_input(inputs: list, old_name: str, new_name: str):
-    for i, (n, t) in enumerate(inputs):
-        if n == old_name:
-            inputs[i] = (new_name, t)
-            break
-
-
-class UltimateSDUpscale:
-    @classmethod
-    def INPUT_TYPES(s):
-        return prepare_inputs(USDU_base_inputs())
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "upscale"
-    CATEGORY = "image/upscaling"
-
-    def upscale(self, image, model, positive, negative, vae, upscale_by, seed,
-                steps, cfg, sampler_name, scheduler, denoise, upscale_model,
-                mode_type, tile_width, tile_height, mask_blur, tile_padding,
-                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
-                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode):
-        #
-        # Set up A1111 patches
-        #
-
-        # Upscaler
-        # An object that the script works with
-        shared.sd_upscalers[0] = UpscalerData()
-        # Where the actual upscaler is stored, will be used when the script upscales using the Upscaler in UpscalerData
-        shared.actual_upscaler = upscale_model
-
-        # Set the batch of images
-        shared.batch = [tensor_to_pil(image, i) for i in range(len(image))]
-
-        # Processing
-        sdprocessing = StableDiffusionProcessing(
-            tensor_to_pil(image), model, positive, negative, vae,
-            seed, steps, cfg, sampler_name, scheduler, denoise, upscale_by, force_uniform_tiles, tiled_decode
-        )
-
-        #
-        # Running the script
-        #
-        script = usdu.Script()
-        processed = script.run(p=sdprocessing, _=None, tile_width=tile_width, tile_height=tile_height,
-                               mask_blur=mask_blur, padding=tile_padding, seams_fix_width=seam_fix_width,
-                               seams_fix_denoise=seam_fix_denoise, seams_fix_padding=seam_fix_padding,
-                               upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[mode_type],
-                               save_seams_fix_image=False, seams_fix_mask_blur=seam_fix_mask_blur,
-                               seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], target_size_type=2,
-                               custom_width=None, custom_height=None, custom_scale=upscale_by)
-
-        # Return the resulting images
-        images = [pil_to_tensor(img) for img in shared.batch]
-        tensor = torch.cat(images, dim=0)
-        return (tensor,)
-
-
-class UltimateSDUpscaleNoUpscale:
-    @classmethod
-    def INPUT_TYPES(s):
-        required = USDU_base_inputs()
-        remove_input(required, "upscale_model")
-        remove_input(required, "upscale_by")
-        rename_input(required, "image", "upscaled_image")
-        return prepare_inputs(required)
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "upscale"
-    CATEGORY = "image/upscaling"
-
-    def upscale(self, upscaled_image, model, positive, negative, vae, seed,
-                steps, cfg, sampler_name, scheduler, denoise,
-                mode_type, tile_width, tile_height, mask_blur, tile_padding,
-                seam_fix_mode, seam_fix_denoise, seam_fix_mask_blur,
-                seam_fix_width, seam_fix_padding, force_uniform_tiles, tiled_decode):
-
-        shared.sd_upscalers[0] = UpscalerData()
-        shared.actual_upscaler = None
-        shared.batch = [tensor_to_pil(upscaled_image, i) for i in range(len(upscaled_image))]
-        sdprocessing = StableDiffusionProcessing(
-            tensor_to_pil(upscaled_image), model, positive, negative, vae,
-            seed, steps, cfg, sampler_name, scheduler, denoise, 1, force_uniform_tiles, tiled_decode
-        )
-
-        script = usdu.Script()
-        processed = script.run(p=sdprocessing, _=None, tile_width=tile_width, tile_height=tile_height,
-                               mask_blur=mask_blur, padding=tile_padding, seams_fix_width=seam_fix_width,
-                               seams_fix_denoise=seam_fix_denoise, seams_fix_padding=seam_fix_padding,
-                               upscaler_index=0, save_upscaled_image=False, redraw_mode=MODES[mode_type],
-                               save_seams_fix_image=False, seams_fix_mask_blur=seam_fix_mask_blur,
-                               seams_fix_type=SEAM_FIX_MODES[seam_fix_mode], target_size_type=2,
-                               custom_width=None, custom_height=None, custom_scale=1)
-
-        images = [pil_to_tensor(img) for img in shared.batch]
-        tensor = torch.cat(images, dim=0)
-        return (tensor,)
-
-
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "UltimateSDUpscale": UltimateSDUpscale,
-    "UltimateSDUpscaleNoUpscale": UltimateSDUpscaleNoUpscale
-}
-
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "UltimateSDUpscale": "Ultimate SD Upscale",
-    "UltimateSDUpscaleNoUpscale": "Ultimate SD Upscale (No Upscale)"
-}

custom_nodes/ComfyUI_UltimateSDUpscale/repositories/__init__.py

@@ -1,14 +0,0 @@
-import os
-import sys
-import importlib.util
-
-repositories_path = os.path.dirname(os.path.realpath(__file__))
-
-# Import the script
-script_name = os.path.join("scripts", "ultimate-upscale")
-repo_name = "ultimate_sd_upscale"
-script_path = os.path.join(repositories_path, repo_name, f"{script_name}.py")
-spec = importlib.util.spec_from_file_location(script_name, script_path)
-ultimate_upscale = importlib.util.module_from_spec(spec)
-sys.modules[script_name] = ultimate_upscale
-spec.loader.exec_module(ultimate_upscale)

custom_nodes/ComfyUI_UltimateSDUpscale/repositories/ultimate_sd_upscale/.gitignore

@@ -1 +0,0 @@
-.vscode

custom_nodes/ComfyUI_UltimateSDUpscale/repositories/ultimate_sd_upscale/LICENSE

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
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-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    ultimate-upscale-for-automatic1111
-    Copyright (C) 2023  Mirzam
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) 2023  Mirzam
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

custom_nodes/ComfyUI_UltimateSDUpscale/repositories/ultimate_sd_upscale/README.md

@@ -1,43 +0,0 @@
-# Ultimate SD Upscale extension for [AUTOMATIC1111 Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
-Now you have the opportunity to use a large denoise (0.3-0.5) and not spawn many artifacts. Works on any video card, since you can use a 512x512 tile size and the image will converge.
-
-News channel: https://t.me/usdunews
-
-# Instructions
-All instructions can be found on the project's [wiki](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki).
-
-# Examples
-More on [wiki page](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/Examples)
-
-<details> 
-  <summary>E1</summary>
-  Original image
-
-  ![Original](https://i.imgur.com/J8mRYOD.png)
-
-  2k upscaled. **Tile size**: 512, **Padding**: 32, **Mask blur**: 16, **Denoise**: 0.4
-  ![2k upscale](https://i.imgur.com/0aKua4r.png)
-</details>
-
-<details> 
-  <summary>E2</summary>
-  Original image
-
-  ![Original](https://i.imgur.com/aALNI2w.png)
-
-  2k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
-  ![2k upscale](https://i.imgur.com/B5PHz0J.png)
-
-  4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.35
-  ![4k upscale](https://i.imgur.com/tIUQ7TJ.jpg)
-</details>
-
-<details> 
-  <summary>E3</summary>
-  Original image
-
-  ![Original](https://i.imgur.com/AGtszA8.png)
-
-  4k upscaled. **Tile size**: 768, **Padding**: 55, **Mask blur**: 20, **Denoise**: 0.4
-  ![4k upscale](https://i.imgur.com/LCYLfCs.jpg)
-</details>

custom_nodes/ComfyUI_UltimateSDUpscale/repositories/ultimate_sd_upscale/scripts/ultimate-upscale.py

@@ -1,557 +0,0 @@
-import math
-import gradio as gr
-from PIL import Image, ImageDraw, ImageOps
-from modules import processing, shared, images, devices, scripts
-from modules.processing import StableDiffusionProcessing
-from modules.processing import Processed
-from modules.shared import opts, state
-from enum import Enum
-
-class USDUMode(Enum):
-    LINEAR = 0
-    CHESS = 1
-    NONE = 2
-
-class USDUSFMode(Enum):
-    NONE = 0
-    BAND_PASS = 1
-    HALF_TILE = 2
-    HALF_TILE_PLUS_INTERSECTIONS = 3
-
-class USDUpscaler():
-
-    def __init__(self, p, image, upscaler_index:int, save_redraw, save_seams_fix, tile_width, tile_height) -> None:
-        self.p:StableDiffusionProcessing = p
-        self.image:Image = image
-        self.scale_factor = math.ceil(max(p.width, p.height) / max(image.width, image.height))
-        self.upscaler = shared.sd_upscalers[upscaler_index]
-        self.redraw = USDURedraw()
-        self.redraw.save = save_redraw
-        self.redraw.tile_width = tile_width if tile_width > 0 else tile_height
-        self.redraw.tile_height = tile_height if tile_height > 0 else tile_width
-        self.seams_fix = USDUSeamsFix()
-        self.seams_fix.save = save_seams_fix
-        self.seams_fix.tile_width = tile_width if tile_width > 0 else tile_height
-        self.seams_fix.tile_height = tile_height if tile_height > 0 else tile_width
-        self.initial_info = None
-        self.rows = math.ceil(self.p.height / self.redraw.tile_height)
-        self.cols = math.ceil(self.p.width / self.redraw.tile_width)
-
-    def get_factor(self, num):
-        # Its just return, don't need elif
-        if num == 1:
-            return 2
-        if num % 4 == 0:
-            return 4
-        if num % 3 == 0:
-            return 3
-        if num % 2 == 0:
-            return 2
-        return 0
-
-    def get_factors(self):
-        scales = []
-        current_scale = 1
-        current_scale_factor = self.get_factor(self.scale_factor)
-        while current_scale_factor == 0:
-            self.scale_factor += 1
-            current_scale_factor = self.get_factor(self.scale_factor)
-        while current_scale < self.scale_factor:
-            current_scale_factor = self.get_factor(self.scale_factor // current_scale)
-            scales.append(current_scale_factor)
-            current_scale = current_scale * current_scale_factor
-            if current_scale_factor == 0:
-                break
-        self.scales = enumerate(scales)
-
-    def upscale(self):
-        # Log info
-        print(f"Canva size: {self.p.width}x{self.p.height}")
-        print(f"Image size: {self.image.width}x{self.image.height}")
-        print(f"Scale factor: {self.scale_factor}")
-        # Check upscaler is not empty
-        if self.upscaler.name == "None":
-            self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
-            return
-        # Get list with scale factors
-        self.get_factors()
-        # Upscaling image over all factors
-        for index, value in self.scales:
-            print(f"Upscaling iteration {index+1} with scale factor {value}")
-            self.image = self.upscaler.scaler.upscale(self.image, value, self.upscaler.data_path)
-        # Resize image to set values
-        self.image = self.image.resize((self.p.width, self.p.height), resample=Image.LANCZOS)
-
-    def setup_redraw(self, redraw_mode, padding, mask_blur):
-        self.redraw.mode = USDUMode(redraw_mode)
-        self.redraw.enabled = self.redraw.mode != USDUMode.NONE
-        self.redraw.padding = padding
-        self.p.mask_blur = mask_blur
-
-    def setup_seams_fix(self, padding, denoise, mask_blur, width, mode):
-        self.seams_fix.padding = padding
-        self.seams_fix.denoise = denoise
-        self.seams_fix.mask_blur = mask_blur
-        self.seams_fix.width = width
-        self.seams_fix.mode = USDUSFMode(mode)
-        self.seams_fix.enabled = self.seams_fix.mode != USDUSFMode.NONE
-
-    def save_image(self):
-        if type(self.p.prompt) != list:
-            images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt, opts.samples_format, info=self.initial_info, p=self.p)
-        else:
-            images.save_image(self.image, self.p.outpath_samples, "", self.p.seed, self.p.prompt[0], opts.samples_format, info=self.initial_info, p=self.p)
-
-    def calc_jobs_count(self):
-        redraw_job_count = (self.rows * self.cols) if self.redraw.enabled else 0
-        seams_job_count = 0
-        if self.seams_fix.mode == USDUSFMode.BAND_PASS:
-            seams_job_count = self.rows + self.cols - 2
-        elif self.seams_fix.mode == USDUSFMode.HALF_TILE:
-            seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols
-        elif self.seams_fix.mode == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
-            seams_job_count = self.rows * (self.cols - 1) + (self.rows - 1) * self.cols + (self.rows - 1) * (self.cols - 1)
-
-        state.job_count = redraw_job_count + seams_job_count
-
-    def print_info(self):
-        print(f"Tile size: {self.redraw.tile_width}x{self.redraw.tile_height}")
-        print(f"Tiles amount: {self.rows * self.cols}")
-        print(f"Grid: {self.rows}x{self.cols}")
-        print(f"Redraw enabled: {self.redraw.enabled}")
-        print(f"Seams fix mode: {self.seams_fix.mode.name}")
-
-    def add_extra_info(self):
-        self.p.extra_generation_params["Ultimate SD upscale upscaler"] = self.upscaler.name
-        self.p.extra_generation_params["Ultimate SD upscale tile_width"] = self.redraw.tile_width
-        self.p.extra_generation_params["Ultimate SD upscale tile_height"] = self.redraw.tile_height
-        self.p.extra_generation_params["Ultimate SD upscale mask_blur"] = self.p.mask_blur
-        self.p.extra_generation_params["Ultimate SD upscale padding"] = self.redraw.padding
-
-    def process(self):
-        state.begin()
-        self.calc_jobs_count()
-        self.result_images = []
-        if self.redraw.enabled:
-            self.image = self.redraw.start(self.p, self.image, self.rows, self.cols)
-            self.initial_info = self.redraw.initial_info
-        self.result_images.append(self.image)
-        if self.redraw.save:
-            self.save_image()
-
-        if self.seams_fix.enabled:
-            self.image = self.seams_fix.start(self.p, self.image, self.rows, self.cols)
-            self.initial_info = self.seams_fix.initial_info
-            self.result_images.append(self.image)
-            if self.seams_fix.save:
-                self.save_image()
-        state.end()
-
-class USDURedraw():
-
-    def init_draw(self, p, width, height):
-        p.inpaint_full_res = True
-        p.inpaint_full_res_padding = self.padding
-        p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
-        p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
-        mask = Image.new("L", (width, height), "black")
-        draw = ImageDraw.Draw(mask)
-        return mask, draw
-
-    def calc_rectangle(self, xi, yi):
-        x1 = xi * self.tile_width
-        y1 = yi * self.tile_height
-        x2 = xi * self.tile_width + self.tile_width
-        y2 = yi * self.tile_height + self.tile_height
-
-        return x1, y1, x2, y2
-
-    def linear_process(self, p, image, rows, cols):
-        mask, draw = self.init_draw(p, image.width, image.height)
-        for yi in range(rows):
-            for xi in range(cols):
-                if state.interrupted:
-                    break
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
-                p.init_images = [image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
-                if (len(processed.images) > 0):
-                    image = processed.images[0]
-
-        p.width = image.width
-        p.height = image.height
-        self.initial_info = processed.infotext(p, 0)
-
-        return image
-
-    def chess_process(self, p, image, rows, cols):
-        mask, draw = self.init_draw(p, image.width, image.height)
-        tiles = []
-        # calc tiles colors
-        for yi in range(rows):
-            for xi in range(cols):
-                if state.interrupted:
-                    break
-                if xi == 0:
-                    tiles.append([])
-                color = xi % 2 == 0
-                if yi > 0 and yi % 2 != 0:
-                    color = not color
-                tiles[yi].append(color)
-
-        for yi in range(len(tiles)):
-            for xi in range(len(tiles[yi])):
-                if state.interrupted:
-                    break
-                if not tiles[yi][xi]:
-                    tiles[yi][xi] = not tiles[yi][xi]
-                    continue
-                tiles[yi][xi] = not tiles[yi][xi]
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
-                p.init_images = [image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
-                if (len(processed.images) > 0):
-                    image = processed.images[0]
-
-        for yi in range(len(tiles)):
-            for xi in range(len(tiles[yi])):
-                if state.interrupted:
-                    break
-                if not tiles[yi][xi]:
-                    continue
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="white")
-                p.init_images = [image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                draw.rectangle(self.calc_rectangle(xi, yi), fill="black")
-                if (len(processed.images) > 0):
-                    image = processed.images[0]
-
-        p.width = image.width
-        p.height = image.height
-        self.initial_info = processed.infotext(p, 0)
-
-        return image
-
-    def start(self, p, image, rows, cols):
-        self.initial_info = None
-        if self.mode == USDUMode.LINEAR:
-            return self.linear_process(p, image, rows, cols)
-        if self.mode == USDUMode.CHESS:
-            return self.chess_process(p, image, rows, cols)
-
-class USDUSeamsFix():
-
-    def init_draw(self, p):
-        self.initial_info = None
-        p.width = math.ceil((self.tile_width+self.padding) / 64) * 64
-        p.height = math.ceil((self.tile_height+self.padding) / 64) * 64
-
-    def half_tile_process(self, p, image, rows, cols):
-
-        self.init_draw(p)
-        processed = None
-
-        gradient = Image.linear_gradient("L")
-        row_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
-        row_gradient.paste(gradient.resize(
-            (self.tile_width, self.tile_height//2), resample=Image.BICUBIC), (0, 0))
-        row_gradient.paste(gradient.rotate(180).resize(
-                (self.tile_width, self.tile_height//2), resample=Image.BICUBIC),
-                (0, self.tile_height//2))
-        col_gradient = Image.new("L", (self.tile_width, self.tile_height), "black")
-        col_gradient.paste(gradient.rotate(90).resize(
-            (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (0, 0))
-        col_gradient.paste(gradient.rotate(270).resize(
-            (self.tile_width//2, self.tile_height), resample=Image.BICUBIC), (self.tile_width//2, 0))
-
-        p.denoising_strength = self.denoise
-        p.mask_blur = self.mask_blur
-
-        for yi in range(rows-1):
-            for xi in range(cols):
-                if state.interrupted:
-                    break
-                p.width = self.tile_width
-                p.height = self.tile_height
-                p.inpaint_full_res = True
-                p.inpaint_full_res_padding = self.padding
-                mask = Image.new("L", (image.width, image.height), "black")
-                mask.paste(row_gradient, (xi*self.tile_width, yi*self.tile_height + self.tile_height//2))
-
-                p.init_images = [image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                if (len(processed.images) > 0):
-                    image = processed.images[0]
-
-        for yi in range(rows):
-            for xi in range(cols-1):
-                if state.interrupted:
-                    break
-                p.width = self.tile_width
-                p.height = self.tile_height
-                p.inpaint_full_res = True
-                p.inpaint_full_res_padding = self.padding
-                mask = Image.new("L", (image.width, image.height), "black")
-                mask.paste(col_gradient, (xi*self.tile_width+self.tile_width//2, yi*self.tile_height))
-
-                p.init_images = [image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                if (len(processed.images) > 0):
-                    image = processed.images[0]
-
-        p.width = image.width
-        p.height = image.height
-        if processed is not None:
-            self.initial_info = processed.infotext(p, 0)
-
-        return image
-
-    def half_tile_process_corners(self, p, image, rows, cols):
-        fixed_image = self.half_tile_process(p, image, rows, cols)
-        processed = None
-        self.init_draw(p)
-        gradient = Image.radial_gradient("L").resize(
-            (self.tile_width, self.tile_height), resample=Image.BICUBIC)
-        gradient = ImageOps.invert(gradient)
-        p.denoising_strength = self.denoise
-        #p.mask_blur = 0
-        p.mask_blur = self.mask_blur
-
-        for yi in range(rows-1):
-            for xi in range(cols-1):
-                if state.interrupted:
-                    break
-                p.width = self.tile_width
-                p.height = self.tile_height
-                p.inpaint_full_res = True
-                p.inpaint_full_res_padding = 0
-                mask = Image.new("L", (fixed_image.width, fixed_image.height), "black")
-                mask.paste(gradient, (xi*self.tile_width + self.tile_width//2,
-                                      yi*self.tile_height + self.tile_height//2))
-
-                p.init_images = [fixed_image]
-                p.image_mask = mask
-                processed = processing.process_images(p)
-                if (len(processed.images) > 0):
-                    fixed_image = processed.images[0]
-
-        p.width = fixed_image.width
-        p.height = fixed_image.height
-        if processed is not None:
-            self.initial_info = processed.infotext(p, 0)
-
-        return fixed_image
-
-    def band_pass_process(self, p, image, cols, rows):
-
-        self.init_draw(p)
-        processed = None
-
-        p.denoising_strength = self.denoise
-        p.mask_blur = 0
-
-        gradient = Image.linear_gradient("L")
-        mirror_gradient = Image.new("L", (256, 256), "black")
-        mirror_gradient.paste(gradient.resize((256, 128), resample=Image.BICUBIC), (0, 0))
-        mirror_gradient.paste(gradient.rotate(180).resize((256, 128), resample=Image.BICUBIC), (0, 128))
-
-        row_gradient = mirror_gradient.resize((image.width, self.width), resample=Image.BICUBIC)
-        col_gradient = mirror_gradient.rotate(90).resize((self.width, image.height), resample=Image.BICUBIC)
-
-        for xi in range(1, rows):
-            if state.interrupted:
-                    break
-            p.width = self.width + self.padding * 2
-            p.height = image.height
-            p.inpaint_full_res = True
-            p.inpaint_full_res_padding = self.padding
-            mask = Image.new("L", (image.width, image.height), "black")
-            mask.paste(col_gradient, (xi * self.tile_width - self.width // 2, 0))
-
-            p.init_images = [image]
-            p.image_mask = mask
-            processed = processing.process_images(p)
-            if (len(processed.images) > 0):
-                image = processed.images[0]
-        for yi in range(1, cols):
-            if state.interrupted:
-                    break
-            p.width = image.width
-            p.height = self.width + self.padding * 2
-            p.inpaint_full_res = True
-            p.inpaint_full_res_padding = self.padding
-            mask = Image.new("L", (image.width, image.height), "black")
-            mask.paste(row_gradient, (0, yi * self.tile_height - self.width // 2))
-
-            p.init_images = [image]
-            p.image_mask = mask
-            processed = processing.process_images(p)
-            if (len(processed.images) > 0):
-                image = processed.images[0]
-
-        p.width = image.width
-        p.height = image.height
-        if processed is not None:
-            self.initial_info = processed.infotext(p, 0)
-
-        return image
-
-    def start(self, p, image, rows, cols):
-        if USDUSFMode(self.mode) == USDUSFMode.BAND_PASS:
-            return self.band_pass_process(p, image, rows, cols)
-        elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE:
-            return self.half_tile_process(p, image, rows, cols)
-        elif USDUSFMode(self.mode) == USDUSFMode.HALF_TILE_PLUS_INTERSECTIONS:
-            return self.half_tile_process_corners(p, image, rows, cols)
-        else:
-            return image
-
-class Script(scripts.Script):
-    def title(self):
-        return "Ultimate SD upscale"
-
-    def show(self, is_img2img):
-        return is_img2img
-
-    def ui(self, is_img2img):
-
-        target_size_types = [
-            "From img2img2 settings",
-            "Custom size",
-            "Scale from image size"
-        ]
-
-        seams_fix_types = [
-            "None",
-            "Band pass",
-            "Half tile offset pass",
-            "Half tile offset pass + intersections"
-        ]
-
-        redrow_modes = [
-            "Linear",
-            "Chess",
-            "None"
-        ]
-
-        info = gr.HTML(
-            "<p style=\"margin-bottom:0.75em\">Will upscale the image depending on the selected target size type</p>")
-
-        with gr.Row():
-            target_size_type = gr.Dropdown(label="Target size type", choices=[k for k in target_size_types], type="index",
-                                  value=next(iter(target_size_types)))
-
-            custom_width = gr.Slider(label='Custom width', minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
-            custom_height = gr.Slider(label='Custom height', minimum=64, maximum=8192, step=64, value=2048, visible=False, interactive=True)
-            custom_scale = gr.Slider(label='Scale', minimum=1, maximum=16, step=0.01, value=2, visible=False, interactive=True)
-
-        gr.HTML("<p style=\"margin-bottom:0.75em\">Redraw options:</p>")
-        with gr.Row():
-            upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers],
-                                value=shared.sd_upscalers[0].name, type="index")
-        with gr.Row():
-            redraw_mode = gr.Dropdown(label="Type", choices=[k for k in redrow_modes], type="index", value=next(iter(redrow_modes)))
-            tile_width = gr.Slider(minimum=0, maximum=2048, step=64, label='Tile width', value=512)
-            tile_height = gr.Slider(minimum=0, maximum=2048, step=64, label='Tile height', value=0)
-            mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8)
-            padding = gr.Slider(label='Padding', minimum=0, maximum=128, step=1, value=32)
-        gr.HTML("<p style=\"margin-bottom:0.75em\">Seams fix:</p>")
-        with gr.Row():
-            seams_fix_type = gr.Dropdown(label="Type", choices=[k for k in seams_fix_types], type="index", value=next(iter(seams_fix_types)))
-            seams_fix_denoise = gr.Slider(label='Denoise', minimum=0, maximum=1, step=0.01, value=0.35, visible=False, interactive=True)
-            seams_fix_width = gr.Slider(label='Width', minimum=0, maximum=128, step=1, value=64, visible=False, interactive=True)
-            seams_fix_mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, visible=False, interactive=True)
-            seams_fix_padding = gr.Slider(label='Padding', minimum=0, maximum=128, step=1, value=16, visible=False, interactive=True)
-        gr.HTML("<p style=\"margin-bottom:0.75em\">Save options:</p>")
-        with gr.Row():
-            save_upscaled_image = gr.Checkbox(label="Upscaled", value=True)
-            save_seams_fix_image = gr.Checkbox(label="Seams fix", value=False)
-
-        def select_fix_type(fix_index):
-            all_visible = fix_index != 0
-            mask_blur_visible = fix_index == 2 or fix_index == 3
-            width_visible = fix_index == 1
-
-            return [gr.update(visible=all_visible),
-                    gr.update(visible=width_visible),
-                    gr.update(visible=mask_blur_visible),
-                    gr.update(visible=all_visible)]
-
-        seams_fix_type.change(
-            fn=select_fix_type,
-            inputs=seams_fix_type,
-            outputs=[seams_fix_denoise, seams_fix_width, seams_fix_mask_blur, seams_fix_padding]
-        )
-
-        def select_scale_type(scale_index):
-            is_custom_size = scale_index == 1
-            is_custom_scale = scale_index == 2
-
-            return [gr.update(visible=is_custom_size),
-                    gr.update(visible=is_custom_size),
-                    gr.update(visible=is_custom_scale),
-                    ]
-
-        target_size_type.change(
-            fn=select_scale_type,
-            inputs=target_size_type,
-            outputs=[custom_width, custom_height, custom_scale]
-        )
-
-        return [info, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
-                upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
-                seams_fix_type, target_size_type, custom_width, custom_height, custom_scale]
-
-    def run(self, p, _, tile_width, tile_height, mask_blur, padding, seams_fix_width, seams_fix_denoise, seams_fix_padding,
-            upscaler_index, save_upscaled_image, redraw_mode, save_seams_fix_image, seams_fix_mask_blur,
-            seams_fix_type, target_size_type, custom_width, custom_height, custom_scale):
-
-        # Init
-        processing.fix_seed(p)
-        devices.torch_gc()
-
-        p.do_not_save_grid = True
-        p.do_not_save_samples = True
-        p.inpaint_full_res = False
-
-        p.inpainting_fill = 1
-        p.n_iter = 1
-        p.batch_size = 1
-
-        seed = p.seed
-
-        # Init image
-        init_img = p.init_images[0]
-        if init_img == None:
-            return Processed(p, [], seed, "Empty image")
-        init_img = images.flatten(init_img, opts.img2img_background_color)
-
-        #override size
-        if target_size_type == 1:
-            p.width = custom_width
-            p.height = custom_height
-        if target_size_type == 2:
-            p.width = math.ceil((init_img.width * custom_scale) / 64) * 64
-            p.height = math.ceil((init_img.height * custom_scale) / 64) * 64
-
-        # Upscaling
-        upscaler = USDUpscaler(p, init_img, upscaler_index, save_upscaled_image, save_seams_fix_image, tile_width, tile_height)
-        upscaler.upscale()
-        
-        # Drawing
-        upscaler.setup_redraw(redraw_mode, padding, mask_blur)
-        upscaler.setup_seams_fix(seams_fix_padding, seams_fix_denoise, seams_fix_mask_blur, seams_fix_width, seams_fix_type)
-        upscaler.print_info()
-        upscaler.add_extra_info()
-        upscaler.process()
-        result_images = upscaler.result_images
-
-        return Processed(p, result_images, seed, upscaler.initial_info if upscaler.initial_info is not None else "")
-

custom_nodes/ComfyUI_UltimateSDUpscale/usdu_patch.py

@@ -1,66 +0,0 @@
-# Make some patches to the script
-from repositories import ultimate_upscale as usdu
-import modules.shared as shared
-import math
-from PIL import Image
-
-
-if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
-    Image.Resampling = Image
-
-#
-# Instead of using multiples of 64, use multiples of 8
-#
-
-# Upscaler
-old_init = usdu.USDUpscaler.__init__
-
-
-def new_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height):
-    p.width = math.ceil((image.width * p.upscale_by) / 8) * 8
-    p.height = math.ceil((image.height * p.upscale_by) / 8) * 8
-    old_init(self, p, image, upscaler_index, save_redraw, save_seams_fix, tile_width, tile_height)
-
-
-usdu.USDUpscaler.__init__ = new_init
-
-# Redraw
-old_setup_redraw = usdu.USDURedraw.init_draw
-
-
-def new_setup_redraw(self, p, width, height):
-    mask, draw = old_setup_redraw(self, p, width, height)
-    p.width = math.ceil((self.tile_width + self.padding) / 8) * 8
-    p.height = math.ceil((self.tile_height + self.padding) / 8) * 8
-    return mask, draw
-
-
-usdu.USDURedraw.init_draw = new_setup_redraw
-
-# Seams fix
-old_setup_seams_fix = usdu.USDUSeamsFix.init_draw
-
-
-def new_setup_seams_fix(self, p):
-    old_setup_seams_fix(self, p)
-    p.width = math.ceil((self.tile_width + self.padding) / 8) * 8
-    p.height = math.ceil((self.tile_height + self.padding) / 8) * 8
-
-
-usdu.USDUSeamsFix.init_draw = new_setup_seams_fix
-
-
-#
-# Make the script upscale on a batch of images instead of one image
-#
-
-old_upscale = usdu.USDUpscaler.upscale
-
-
-def new_upscale(self):
-    old_upscale(self)
-    shared.batch = [self.image] + \
-        [img.resize((self.p.width, self.p.height), resample=Image.LANCZOS) for img in shared.batch[1:]]
-
-
-usdu.USDUpscaler.upscale = new_upscale

custom_nodes/ComfyUI_UltimateSDUpscale/utils.py

@@ -1,460 +0,0 @@
-import numpy as np
-from PIL import Image, ImageFilter
-import torch
-import torch.nn.functional as F
-from torchvision.transforms import GaussianBlur
-import math
-
-if (not hasattr(Image, 'Resampling')):  # For older versions of Pillow
-    Image.Resampling = Image
-
-BLUR_KERNEL_SIZE = 15
-
-
-def tensor_to_pil(img_tensor, batch_index=0):
-    # Takes an image in a batch in the form of a tensor of shape [batch_size, channels, height, width]
-    # and returns an PIL Image with the corresponding mode deduced by the number of channels
-
-    # Take the image in the batch given by batch_index
-    img_tensor = img_tensor[batch_index].unsqueeze(0)
-    i = 255. * img_tensor.cpu().numpy()
-    img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8).squeeze())
-    return img
-
-
-def pil_to_tensor(image):
-    # Takes a PIL image and returns a tensor of shape [1, height, width, channels]
-    image = np.array(image).astype(np.float32) / 255.0
-    image = torch.from_numpy(image).unsqueeze(0)
-    if len(image.shape) == 3:  # If the image is grayscale, add a channel dimension
-        image = image.unsqueeze(-1)
-    return image
-
-
-def controlnet_hint_to_pil(tensor, batch_index=0):
-    return tensor_to_pil(tensor.movedim(1, -1), batch_index)
-
-
-def pil_to_controlnet_hint(img):
-    return pil_to_tensor(img).movedim(-1, 1)
-
-
-def crop_tensor(tensor, region):
-    # Takes a tensor of shape [batch_size, height, width, channels] and crops it to the given region
-    x1, y1, x2, y2 = region
-    return tensor[:, y1:y2, x1:x2, :]
-
-
-def resize_tensor(tensor, size, mode="nearest-exact"):
-    # Takes a tensor of shape [B, C, H, W] and resizes
-    # it to a shape of [B, C, size[0], size[1]] using the given mode
-    return torch.nn.functional.interpolate(tensor, size=size, mode=mode)
-
-
-def get_crop_region(mask, pad=0):
-    # Takes a black and white PIL image in 'L' mode and returns the coordinates of the white rectangular mask region
-    # Should be equivalent to the get_crop_region function from https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/masking.py
-    coordinates = mask.getbbox()
-    if coordinates is not None:
-        x1, y1, x2, y2 = coordinates
-    else:
-        x1, y1, x2, y2 = mask.width, mask.height, 0, 0
-    # Apply padding
-    x1 = max(x1 - pad, 0)
-    y1 = max(y1 - pad, 0)
-    x2 = min(x2 + pad, mask.width)
-    y2 = min(y2 + pad, mask.height)
-    return fix_crop_region((x1, y1, x2, y2), (mask.width, mask.height))
-
-
-def fix_crop_region(region, image_size):
-    # Remove the extra pixel added by the get_crop_region function
-    image_width, image_height = image_size
-    x1, y1, x2, y2 = region
-    if x2 < image_width:
-        x2 -= 1
-    if y2 < image_height:
-        y2 -= 1
-    return x1, y1, x2, y2
-
-
-def expand_crop(region, width, height, target_width, target_height):
-    '''
-    Expands a crop region to a specified target size.
-    :param region: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points
-        of the rectangular region. Expected to have x2 > x1 and y2 > y1.
-    :param width: The width of the image the crop region is from.
-    :param height: The height of the image the crop region is from.
-    :param target_width: The desired width of the crop region.
-    :param target_height: The desired height of the crop region.
-    '''
-    x1, y1, x2, y2 = region
-    actual_width = x2 - x1
-    actual_height = y2 - y1
-    # target_width = math.ceil(actual_width / 8) * 8
-    # target_height = math.ceil(actual_height / 8) * 8
-
-    # Try to expand region to the right of half the difference
-    width_diff = target_width - actual_width
-    x2 = min(x2 + width_diff // 2, width)
-    # Expand region to the left of the difference including the pixels that could not be expanded to the right
-    width_diff = target_width - (x2 - x1)
-    x1 = max(x1 - width_diff, 0)
-    # Try the right again
-    width_diff = target_width - (x2 - x1)
-    x2 = min(x2 + width_diff, width)
-
-    # Try to expand region to the bottom of half the difference
-    height_diff = target_height - actual_height
-    y2 = min(y2 + height_diff // 2, height)
-    # Expand region to the top of the difference including the pixels that could not be expanded to the bottom
-    height_diff = target_height - (y2 - y1)
-    y1 = max(y1 - height_diff, 0)
-    # Try the bottom again
-    height_diff = target_height - (y2 - y1)
-    y2 = min(y2 + height_diff, height)
-
-    return (x1, y1, x2, y2), (target_width, target_height)
-
-
-def resize_region(region, init_size, resize_size):
-    # Resize a crop so that it fits an image that was resized to the given width and height
-    x1, y1, x2, y2 = region
-    init_width, init_height = init_size
-    resize_width, resize_height = resize_size
-    x1 = math.floor(x1 * resize_width / init_width)
-    x2 = math.ceil(x2 * resize_width / init_width)
-    y1 = math.floor(y1 * resize_height / init_height)
-    y2 = math.ceil(y2 * resize_height / init_height)
-    return (x1, y1, x2, y2)
-
-
-def pad_image(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
-    '''
-    Pads an image with the given number of pixels on each side and fills the padding with data from the edges.
-    :param image: A PIL image
-    :param left_pad: The number of pixels to pad on the left side
-    :param right_pad: The number of pixels to pad on the right side
-    :param top_pad: The number of pixels to pad on the top side
-    :param bottom_pad: The number of pixels to pad on the bottom side
-    :param blur: Whether to blur the padded edges
-    :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad)
-    '''
-    left_edge = image.crop((0, 1, 1, image.height - 1))
-    right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1))
-    top_edge = image.crop((1, 0, image.width - 1, 1))
-    bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height))
-    new_width = image.width + left_pad + right_pad
-    new_height = image.height + top_pad + bottom_pad
-    padded_image = Image.new(image.mode, (new_width, new_height))
-    padded_image.paste(image, (left_pad, top_pad))
-    if fill:
-        for i in range(left_pad):
-            edge = left_edge.resize(
-                (1, new_height - i * (top_pad + bottom_pad) // left_pad), resample=Image.Resampling.NEAREST)
-            padded_image.paste(edge, (i, i * top_pad // left_pad))
-        for i in range(right_pad):
-            edge = right_edge.resize(
-                (1, new_height - i * (top_pad + bottom_pad) // right_pad), resample=Image.Resampling.NEAREST)
-            padded_image.paste(edge, (new_width - 1 - i, i * top_pad // right_pad))
-        for i in range(top_pad):
-            edge = top_edge.resize(
-                (new_width - i * (left_pad + right_pad) // top_pad, 1), resample=Image.Resampling.NEAREST)
-            padded_image.paste(edge, (i * left_pad // top_pad, i))
-        for i in range(bottom_pad):
-            edge = bottom_edge.resize(
-                (new_width - i * (left_pad + right_pad) // bottom_pad, 1), resample=Image.Resampling.NEAREST)
-            padded_image.paste(edge, (i * left_pad // bottom_pad, new_height - 1 - i))
-        if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0):
-            padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE))
-            padded_image.paste(image, (left_pad, top_pad))
-    return padded_image
-
-
-def pad_image2(image, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
-    '''
-    Pads an image with the given number of pixels on each side and fills the padding with data from the edges. 
-    Faster than pad_image, but only pads with edge data in straight lines.
-    :param image: A PIL image
-    :param left_pad: The number of pixels to pad on the left side
-    :param right_pad: The number of pixels to pad on the right side
-    :param top_pad: The number of pixels to pad on the top side
-    :param bottom_pad: The number of pixels to pad on the bottom side
-    :param blur: Whether to blur the padded edges
-    :return: A PIL image with size (image.width + left_pad + right_pad, image.height + top_pad + bottom_pad)
-    '''
-    left_edge = image.crop((0, 1, 1, image.height - 1))
-    right_edge = image.crop((image.width - 1, 1, image.width, image.height - 1))
-    top_edge = image.crop((1, 0, image.width - 1, 1))
-    bottom_edge = image.crop((1, image.height - 1, image.width - 1, image.height))
-    new_width = image.width + left_pad + right_pad
-    new_height = image.height + top_pad + bottom_pad
-    padded_image = Image.new(image.mode, (new_width, new_height))
-    padded_image.paste(image, (left_pad, top_pad))
-    if fill:
-        if left_pad > 0:
-            padded_image.paste(left_edge.resize((left_pad, new_height), resample=Image.Resampling.NEAREST), (0, 0))
-        if right_pad > 0:
-            padded_image.paste(right_edge.resize((right_pad, new_height),
-                               resample=Image.Resampling.NEAREST), (new_width - right_pad, 0))
-        if top_pad > 0:
-            padded_image.paste(top_edge.resize((new_width, top_pad), resample=Image.Resampling.NEAREST), (0, 0))
-        if bottom_pad > 0:
-            padded_image.paste(bottom_edge.resize((new_width, bottom_pad),
-                               resample=Image.Resampling.NEAREST), (0, new_height - bottom_pad))
-        if blur and not (left_pad == right_pad == top_pad == bottom_pad == 0):
-            padded_image = padded_image.filter(ImageFilter.GaussianBlur(BLUR_KERNEL_SIZE))
-            padded_image.paste(image, (left_pad, top_pad))
-    return padded_image
-
-
-def pad_tensor(tensor, left_pad, right_pad, top_pad, bottom_pad, fill=False, blur=False):
-    '''
-    Pads an image tensor with the given number of pixels on each side and fills the padding with data from the edges.
-    :param tensor: A tensor of shape [B, H, W, C]
-    :param left_pad: The number of pixels to pad on the left side
-    :param right_pad: The number of pixels to pad on the right side
-    :param top_pad: The number of pixels to pad on the top side
-    :param bottom_pad: The number of pixels to pad on the bottom side
-    :param blur: Whether to blur the padded edges
-    :return: A tensor of shape [B, H + top_pad + bottom_pad, W + left_pad + right_pad, C]
-    '''
-    batch_size, channels, height, width = tensor.shape
-    h_pad = left_pad + right_pad
-    v_pad = top_pad + bottom_pad
-    new_width = width + h_pad
-    new_height = height + v_pad
-
-    # Create empty image
-    padded = torch.zeros((batch_size, channels, new_height, new_width), dtype=tensor.dtype)
-
-    # Copy the original image into the centor of the padded tensor
-    padded[:, :, top_pad:top_pad + height, left_pad:left_pad + width] = tensor
-
-    # Duplicate the edges of the original image into the padding
-    if top_pad > 0:
-        padded[:, :, :top_pad, :] = padded[:, :, top_pad:top_pad + 1, :]  # Top edge
-    if bottom_pad > 0:
-        padded[:, :, -bottom_pad:, :] = padded[:, :, -bottom_pad - 1:-bottom_pad, :]  # Bottom edge
-    if left_pad > 0:
-        padded[:, :, :, :left_pad] = padded[:, :, :, left_pad:left_pad + 1]  # Left edge
-    if right_pad > 0:
-        padded[:, :, :, -right_pad:] = padded[:, :, :, -right_pad - 1:-right_pad]  # Right edge
-
-    return padded
-
-
-def resize_and_pad_image(image, width, height, fill=False, blur=False):
-    '''
-    Resizes an image to the given width and height and pads it to the given width and height.
-    :param image: A PIL image
-    :param width: The width of the resized image
-    :param height: The height of the resized image
-    :param fill: Whether to fill the padding with data from the edges
-    :param blur: Whether to blur the padded edges
-    :return: A PIL image of size (width, height)
-    '''
-    width_ratio = width / image.width
-    height_ratio = height / image.height
-    if height_ratio > width_ratio:
-        resize_ratio = width_ratio
-    else:
-        resize_ratio = height_ratio
-    resize_width = round(image.width * resize_ratio)
-    resize_height = round(image.height * resize_ratio)
-    resized = image.resize((resize_width, resize_height), resample=Image.Resampling.LANCZOS)
-    # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize
-    horizontal_pad = (width - resize_width) // 2
-    vertical_pad = (height - resize_height) // 2
-    result = pad_image2(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur)
-    result = result.resize((width, height), resample=Image.Resampling.LANCZOS)
-    return result, (horizontal_pad, vertical_pad)
-
-
-def resize_and_pad_tensor(tensor, width, height, fill=False, blur=False):
-    '''
-    Resizes an image tensor to the given width and height and pads it to the given width and height.
-    :param tensor: A tensor of shape [B, H, W, C]
-    :param width: The width of the resized image
-    :param height: The height of the resized image
-    :param fill: Whether to fill the padding with data from the edges
-    :param blur: Whether to blur the padded edges
-    :return: A tensor of shape [B, height, width, C]
-    '''
-    # Resize the image to the closest size that maintains the aspect ratio
-    width_ratio = width / tensor.shape[3]
-    height_ratio = height / tensor.shape[2]
-    if height_ratio > width_ratio:
-        resize_ratio = width_ratio
-    else:
-        resize_ratio = height_ratio
-    resize_width = round(tensor.shape[3] * resize_ratio)
-    resize_height = round(tensor.shape[2] * resize_ratio)
-    resized = F.interpolate(tensor, size=(resize_height, resize_width), mode='nearest-exact')
-    # Pad the sides of the image to get the image to the desired size that wasn't covered by the resize
-    horizontal_pad = (width - resize_width) // 2
-    vertical_pad = (height - resize_height) // 2
-    result = pad_tensor(resized, horizontal_pad, horizontal_pad, vertical_pad, vertical_pad, fill, blur)
-    result = F.interpolate(result, size=(height, width), mode='nearest-exact')
-    return result
-
-
-def crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
-    if "control" not in cond_dict:
-        return
-    c = cond_dict["control"]
-    controlnet = c.copy()
-    cond_dict["control"] = controlnet
-    while c is not None:
-        # hint is shape (B, C, H, W)
-        hint = controlnet.cond_hint_original
-        resized_crop = resize_region(region, canvas_size, hint.shape[:-3:-1])
-        hint = crop_tensor(hint.movedim(1, -1), resized_crop).movedim(-1, 1)
-        hint = resize_tensor(hint, tile_size[::-1])
-        controlnet.cond_hint_original = hint
-        c = c.previous_controlnet
-        controlnet.set_previous_controlnet(c.copy() if c is not None else None)
-        controlnet = controlnet.previous_controlnet
-
-
-def region_intersection(region1, region2):
-    """
-    Returns the coordinates of the intersection of two rectangular regions.
-    :param region1: A tuple of the form (x1, y1, x2, y2) denoting the upper left and the lower right points 
-        of the first rectangular region. Expected to have x2 > x1 and y2 > y1.
-    :param region2: The second rectangular region with the same format as the first.
-    :return: A tuple of the form (x1, y1, x2, y2) denoting the rectangular intersection. 
-        None if there is no intersection.
-    """
-    x1, y1, x2, y2 = region1
-    x1_, y1_, x2_, y2_ = region2
-    x1 = max(x1, x1_)
-    y1 = max(y1, y1_)
-    x2 = min(x2, x2_)
-    y2 = min(y2, y2_)
-    if x1 >= x2 or y1 >= y2:
-        return None
-    return (x1, y1, x2, y2)
-
-
-def crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
-    if "gligen" not in cond_dict:
-        return
-    type, model, cond = cond_dict["gligen"]
-    if type != "position":
-        from warnings import warn
-        warn(f"Unknown gligen type {type}")
-        return
-    cropped = []
-    for c in cond:
-        emb, h, w, y, x = c
-        # Get the coordinates of the box in the upscaled image
-        x1 = x * 8
-        y1 = y * 8
-        x2 = x1 + w * 8
-        y2 = y1 + h * 8
-        gligen_upscaled_box = resize_region((x1, y1, x2, y2), init_size, canvas_size)
-
-        # Calculate the intersection of the gligen box and the region
-        intersection = region_intersection(gligen_upscaled_box, region)
-        if intersection is None:
-            continue
-        x1, y1, x2, y2 = intersection
-
-        # Offset the gligen box so that the origin is at the top left of the tile region
-        x1 -= region[0]
-        y1 -= region[1]
-        x2 -= region[0]
-        y2 -= region[1]
-
-        # Add the padding
-        x1 += w_pad
-        y1 += h_pad
-        x2 += w_pad
-        y2 += h_pad
-
-        # Set the new position params
-        h = (y2 - y1) // 8
-        w = (x2 - x1) // 8
-        x = x1 // 8
-        y = y1 // 8
-        cropped.append((emb, h, w, y, x))
-
-    cond_dict["gligen"] = (type, model, cropped)
-
-
-def crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
-    if "area" not in cond_dict:
-        return
-
-    # Resize the area conditioning to the canvas size and confine it to the tile region
-    h, w, y, x = cond_dict["area"]
-    w, h, x, y = 8 * w, 8 * h, 8 * x, 8 * y
-    x1, y1, x2, y2 = resize_region((x, y, x + w, y + h), init_size, canvas_size)
-    intersection = region_intersection((x1, y1, x2, y2), region)
-    if intersection is None:
-        del cond_dict["area"]
-        del cond_dict["strength"]
-        return
-    x1, y1, x2, y2 = intersection
-
-    # Offset origin to the top left of the tile
-    x1 -= region[0]
-    y1 -= region[1]
-    x2 -= region[0]
-    y2 -= region[1]
-
-    # Add the padding
-    x1 += w_pad
-    y1 += h_pad
-    x2 += w_pad
-    y2 += h_pad
-
-    # Set the params for tile
-    w, h = (x2 - x1) // 8, (y2 - y1) // 8
-    x, y = x1 // 8, y1 // 8
-
-    cond_dict["area"] = (h, w, y, x)
-
-
-def crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad):
-    if "mask" not in cond_dict:
-        return
-    mask_tensor = cond_dict["mask"]  # (B, H, W)
-    masks = []
-    for i in range(mask_tensor.shape[0]):
-        # Convert to PIL image
-        mask = tensor_to_pil(mask_tensor, i)  # W x H
-
-        # Resize the mask to the canvas size
-        mask = mask.resize(canvas_size, Image.Resampling.BICUBIC)
-
-        # Crop the mask to the region
-        mask = mask.crop(region)
-
-        # Add padding
-        mask, _ = resize_and_pad_image(mask, tile_size[0], tile_size[1], fill=True)
-
-        # Resize the mask to the tile size
-        if tile_size != mask.size:
-            mask = mask.resize(tile_size, Image.Resampling.BICUBIC)
-
-        # Convert back to tensor
-        mask = pil_to_tensor(mask)  # (1, H, W, 1)
-        mask = mask.squeeze(-1)  # (1, H, W)
-        masks.append(mask)
-
-    cond_dict["mask"] = torch.cat(masks, dim=0)  # (B, H, W)
-
-
-def crop_cond(cond, region, init_size, canvas_size, tile_size, w_pad=0, h_pad=0):
-    cropped = []
-    for emb, x in cond:
-        cond_dict = x.copy()
-        n = [emb, cond_dict]
-        crop_controlnet(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
-        crop_gligen(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
-        crop_area(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
-        crop_mask(cond_dict, region, init_size, canvas_size, tile_size, w_pad, h_pad)
-        cropped.append(n)
-    return cropped