reupload Ultimate SD Upscale

ComfyUI_UltimateSDUpscale/.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

ComfyUI_UltimateSDUpscale/.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
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ComfyUI_UltimateSDUpscale/.gitmodules

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+[submodule "repositories/ultimate_sd_upscale"]
+	path = repositories/ultimate_sd_upscale
+	url = https://github.com/Coyote-A/ultimate-upscale-for-automatic1111

ComfyUI_UltimateSDUpscale/LICENSE

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+GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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ComfyUI_UltimateSDUpscale/README.md

@@ -0,0 +1,34 @@
+# ComfyUI_UltimateSDUpscale
+
+ [ComfyUI](https://github.com/comfyanonymous/ComfyUI) nodes for the [Ultimate Stable Diffusion Upscale script by Coyote-A](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111). This is a wrapper for the script used in the A1111 extension.
+
+## Installation
+
+Enter the following command from the commandline starting in ComfyUI/custom_nodes/
+```
+git clone https://github.com/ssitu/ComfyUI_UltimateSDUpscale --recursive
+```
+
+## Usage
+
+Nodes can be found in the node menu under `image/upscaling`:
+
+|Node|Description|
+| --- | --- |
+| Ultimate SD Upscale | The primary node that has the most of the inputs as the original extension script. |
+| Ultimate SD Upscale <br>(No Upscale) | Same as the primary node, but without the upscale inputs and assumes that the input image is already upscaled. Use this if you already have an upscaled image or just want to do the tiled sampling. |
+
+---
+
+Details about most of the parameters can be found [here](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111/wiki/FAQ#parameters-descriptions).
+
+Parameters not found in the original repository:
+
+* `upscale_by` The number to multiply the width and height of the image by. If you want to specify an exact width and height, use the "No Upscale" version of the node and perform the upscaling separately (e.g., ImageUpscaleWithModel -> ImageScale -> UltimateSDUpscaleNoUpscale).
+* `force_uniform_tiles` If enabled, tiles that would be cut off by the edges of the image will expand the tile using the rest of the image to keep the same tile size determined by `tile_width` and `tile_height`, which is what the A1111 Web UI does. If disabled, the minimal size for tiles will be used, which may make the sampling faster but may cause artifacts due to irregular tile sizes.
+
+## Examples
+
+#### Using the ControlNet tile model:
+
+![image](https://github.com/ssitu/ComfyUI_UltimateSDUpscale/assets/57548627/64f8d3b2-10ae-45ee-9f8a-40b798a51655)

ComfyUI_UltimateSDUpscale/__init__.py

@@ -0,0 +1,38 @@
+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 existing modules if using a1111 web ui
+modules_used = [
+    "modules",
+    "modules.devices",
+    "modules.images",
+    "modules.processing",
+    "modules.scripts",
+    "modules.shared",
+    "modules.upscaler"
+]
+original_webui_modules = {}
+for module in modules_used:
+    if module in sys.modules:
+        original_webui_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_webui_modules)

ComfyUI_UltimateSDUpscale/gradio.py

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

ComfyUI_UltimateSDUpscale/modules/devices.py

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

ComfyUI_UltimateSDUpscale/modules/images.py

@@ -0,0 +1,8 @@
+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")

ComfyUI_UltimateSDUpscale/modules/processing.py

@@ -0,0 +1,156 @@
+from PIL import Image, ImageFilter
+import torch
+import math
+from nodes import common_ksampler, VAEEncode, VAEDecode
+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):
+        # 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
+
+        # 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 == "enable":
+        # 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
+    vae_encoder = VAEEncode()
+    batched_tiles = torch.cat([pil_to_tensor(tile) for tile in tiles], dim=0)
+    (latent,) = 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
+    vae_decoder = VAEDecode()
+    (decoded,) = vae_decoder.decode(p.vae, samples)
+
+    # 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

ComfyUI_UltimateSDUpscale/modules/scripts.py

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

ComfyUI_UltimateSDUpscale/modules/shared.py

@@ -0,0 +1,24 @@
+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

ComfyUI_UltimateSDUpscale/modules/upscaler.py

@@ -0,0 +1,30 @@
+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()

ComfyUI_UltimateSDUpscale/nodes.py

@@ -0,0 +1,190 @@
+# 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": 1.0, "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", (["enable", "disable"], ))
+    ]
+
+
+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):
+        #
+        # 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
+        )
+
+        #
+        # 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):
+
+        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
+        )
+
+        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)"
+}

ComfyUI_UltimateSDUpscale/repositories/__init__.py

@@ -0,0 +1,14 @@
+import os
+import sys
+import importlib.util
+
+repositories_path = os.path.dirname(os.path.realpath(__file__))
+
+# Import the script
+script_name = "scripts/ultimate-upscale"
+repo_name = "ultimate_sd_upscale"
+script_path = f"{repositories_path}/{repo_name}/{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)

ComfyUI_UltimateSDUpscale/usdu_patch.py

@@ -0,0 +1,66 @@
+# 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

ComfyUI_UltimateSDUpscale/utils.py

@@ -0,0 +1,460 @@
+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