Repository: Haidra-Org/hordelib (language: python)

Issue:
feat: QR Code images
- Adds a [QR workflow](https://github.com/coreyryanhanson/ComfyQR) [(AGPL)](github.com/coreyryanhanson/ComfyQR/commit/802b0d702b6f16e9cdc5b18510fffe5dfe8f3f53) and hordelib scaffolding to support it.
- Changes logging to intercept stdlib `logging` calls and rewrite them as `loguru` calls.


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index 7c0fc720..983d0d47 100644
--- a/README.md
+++ b/README.md
@@ -124,6 +124,10 @@ Custom nodes for ComfyUI providing Controlnet preprocessing capability. Licened
 
 Custom nodes for ComfyUI providing face restoration.
 
+### [ComfyQR](https://gitlab.com/sofuego-comfy-nodes/ComfyQR)
+
+Nodes for generating QR codes
+
 ## Development
 
 Requirements:
diff --git a/hordelib/comfy_horde.py b/hordelib/comfy_horde.py
index 9a59d4e0..e762f1ac 100644
--- a/hordelib/comfy_horde.py
+++ b/hordelib/comfy_horde.py
@@ -97,6 +97,39 @@
 
 
 # isort: off
+
+import logging
+
+
+class InterceptHandler(logging.Handler):
+    """
+    Add logging handler to augment python stdlib logging.
+
+    Logs which would otherwise go to stdlib logging are redirected through
+    loguru.
+    """
+
+    @logger.catch(default=True, reraise=True)
+    def emit(self, record):
+        # Get corresponding Loguru level if it exists.
+        try:
+            level = logger.level(record.levelname).name
+        except ValueError:
+            level = record.levelno
+
+        # Find caller from where originated the logged message.
+        frame, depth = logging.currentframe(), 2
+        while frame and frame.f_code.co_filename == logging.__file__:
+            frame = frame.f_back
+            depth += 1
+
+        logger.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage())
+
+
+# ComfyUI uses stdlib logging, so we need to intercept it.
+logging.basicConfig(handlers=[InterceptHandler()], level=0, force=True)
+
+
 def do_comfy_import(
     force_normal_vram_mode: bool = False,
     extra_comfyui_args: list[str] | None = None,
diff --git a/hordelib/horde.py b/hordelib/horde.py
index f5c1a232..bcc027b9 100644
--- a/hordelib/horde.py
+++ b/hordelib/horde.py
@@ -23,6 +23,7 @@
 
 from hordelib.comfy_horde import Comfy_Horde
 from hordelib.consts import MODEL_CATEGORY_NAMES
+from hordelib.nodes.comfy_qr.qr_nodes import QRByModuleSizeSplitFunctionPatterns
 from hordelib.shared_model_manager import SharedModelManager
 from hordelib.utils.dynamicprompt import DynamicPromptParser
 from hordelib.utils.image_utils import ImageUtils
@@ -145,7 +146,7 @@ class HordeLib:
         "sampler_name": {"datatype": str, "values": list(SAMPLERS_MAP.keys()), "default": "k_euler"},
         "cfg_scale": {"datatype": float, "min": 1, "max": 100, "default": 8.0},
         "denoising_strength": {"datatype": float, "min": 0.01, "max": 1.0, "default": 1.0},
-        "control_strength": {"datatype": float, "min": 0.01, "max": 1.0, "default": 1.0},
+        "control_strength": {"datatype": float, "min": 0.01, "max": 3.0, "default": 1.0},
         "seed": {"datatype": int, "default": random.randint(0, sys.maxsize)},
         "width": {"datatype": int, "min": 64, "max": 8192, "default": 512, "divisible": 64},
         "height": {"datatype": int, "min": 64, "max": 8192, "default": 512, "divisible": 64},
@@ -171,6 +172,8 @@ class HordeLib:
         "stable_cascade_stage_b": {"datatype": str, "default": None},  # Stable Cascade
         "stable_cascade_stage_c": {"datatype": str, "default": None},  # Stable Cascade
         "extra_source_images": {"datatype": list, "default": []},  # Stable Cascade Remix
+        "extra_texts": {"datatype": list, "default": []},  # QR Codes (for now)
+        "workflow": {"datatype": str, "default": "auto_detect"},
     }
 
     EXTRA_IMAGES_SCHEMA = {
@@ -178,6 +181,11 @@ class HordeLib:
         "strength": {"datatype": float, "min": 0.0, "max": 5.0, "default": 1.0},
     }
 
+    EXTRA_TEXTS_SCHEMA = {
+        "text": {"datatype": str, "default": ""},
+        "reference": {"datatype": str, "default": None},
+    }
+
     LORA_SCHEMA = {
         "name": {"datatype": str, "default": ""},
         "model": {"datatype": float, "min": -10.0, "max": 10.0, "default": 1.0},
@@ -198,6 +206,7 @@ class HordeLib:
         "sampler.cfg": "cfg_scale",
         "sampler.denoise": "denoising_strength",
         "sampler.seed": "seed",
+        "sampler.noise_seed": "seed",
         "empty_latent_image.height": "height",
         "empty_latent_image.width": "width",
         "sampler.scheduler": "scheduler",
@@ -243,6 +252,27 @@ class HordeLib:
         "2pass_sampler_stage_c.sampler_name": "sampler_name",
         "2pass_sampler_stage_c.denoise": "hires_fix_denoising_strength",
         "2pass_sampler_stage_b.sampler_name": "sampler_name",
+        # QR Codes
+        "sampler_bg.sampler_name": "sampler_name",
+        "sampler_bg.cfg": "cfg_scale",
+        "sampler_bg.denoise": "denoising_strength",
+        "sampler_bg.seed": "seed",
+        "sampler_bg.steps": "ddim_steps",
+        "sampler_bg.noise_seed": "seed",
+        "sampler_fg.sampler_name": "sampler_name",
+        "sampler_fg.cfg": "cfg_scale",
+        "sampler_fg.denoise": "denoising_strength",
+        "sampler_fg.seed": "seed",
+        "sampler_fg.steps": "ddim_steps",
+        "sampler_fg.noise_seed": "seed",
+        "controlnet_bg.strength": "control_strength",
+        "solidmask_grey.width": "width",
+        "solidmask_grey.height": "height",
+        "solidmask_white.width": "width",
+        "solidmask_white.height": "height",
+        "solidmask_black.width": "width",
+        "solidmask_black.height": "height",
+        "qr_code_split.max_image_size": "width",
     }
 
     _comfyui_callback: Callable[[str, dict, str], None] | None = None
@@ -356,6 +386,12 @@ def _validate_data_structure(self, data, schema_definition=PAYLOAD_SCHEMA):
                 data["extra_source_images"][i] = self._validate_data_structure(img, HordeLib.EXTRA_IMAGES_SCHEMA)
             data["extra_source_images"] = [x for x in data["extra_source_images"] if x.get("image")]
 
+        # Do the same for extra texts, if we have them in this data structure
+        if data.get("extra_texts"):
+            for i, img in enumerate(data.get("extra_texts")):
+                data["extra_texts"][i] = self._validate_data_structure(img, HordeLib.EXTRA_TEXTS_SCHEMA)
+            data["extra_texts"] = [x for x in data["extra_texts"] if x.get("text")]
+
         return data
 
     def _apply_aihorde_compatibility_hacks(self, payload: dict) -> tuple[dict, list[GenMetadataEntry]]:
@@ -500,6 +536,11 @@ def _apply_aihorde_compatibility_hacks(self, payload: dict) -> tuple[dict, list[
                 if not payload.get("hires_fix_denoising_strength"):
                     payload["hires_fix_denoising_strength"] = payload.get("denoising_strength")
 
+        if payload.get("workflow") == "qr_code":
+            if payload.get("source_processing") and payload.get("source_processing") != "txt2img":
+                if not payload.get("hires_fix_denoising_strength"):
+                    payload["hires_fix_denoising_strength"] = payload.get("denoising_strength")
+
         # # Remap "denoising" to "controlnet strength", historical hack
         # if payload.get("control_type"):
         #     if payload.get("denoising_strength"):
@@ -902,6 +943,92 @@ def _final_pipeline_adjustments(self, payload, pipeline_data) -> tuple[dict, lis
                         f"unclip_conditioning_{node_index}",
                     )
 
+        # If we have a qr code request, we check for extra texts such as the generation url
+        if payload.get("workflow") == "qr_code":
+            original_width = pipeline_params.get("empty_latent_image.width", 512)
+            original_height = pipeline_params.get("empty_latent_image.height", 512)
+            pipeline_params["qr_code_split.max_image_size"] = max(original_width, original_height)
+            pipeline_params["qr_code_split.text"] = "https://haidra.net"
+            for text in payload.get("extra_texts"):
+                if text["reference"] == "qr_text":
+                    pipeline_params["qr_code_split.text"] = text["text"]
+                if text["reference"] == "protocol" and text["text"].lower() in ["https", "http"]:
+                    pipeline_params["qr_code_split.protocol"] = text["text"].capitalize()
+                if text["reference"] == "module_drawer" and text["text"].lower() in [
+                    "square",
+                    "gapped square",
+                    "circle",
+                    "rounded",
+                    "vertical bars",
+                    "horizontal bars",
+                ]:
+                    pipeline_params["qr_code_split.module_drawer"] = text["text"].capitalize()
+                if text["reference"] == "function_layer_prompt":
+                    pipeline_params["function_layer_prompt.text"] = text["text"]
+                if text["reference"] == "x_offset" and text["text"].lstrip("-").isdigit():
+                    x_offset = int(text["text"])
+                    if x_offset < 0:
+                        x_offset = 10
+                    pipeline_params["qr_flattened_composite.x"] = x_offset
+                if text["reference"] == "y_offset" and text["text"].lstrip("-").isdigit():
+                    y_offset = int(text["text"])
+                    if y_offset < 0:
+                        y_offset = 10
+                    pipeline_params["qr_flattened_composite.y"] = y_offset
+                if text["reference"] == "qr_border" and text["text"].lstrip("-").isdigit():
+                    border = int(text["text"])
+                    if border < 0:
+                        border = 10
+                    pipeline_params["qr_code_split.border"] = border
+            if not pipeline_params.get("qr_code_split.protocol"):
+                pipeline_params["qr_code_split.protocol"] = "None"
+            if not pipeline_params.get("function_layer_prompt.text"):
+                pipeline_params["function_layer_prompt.text"] = payload["prompt"]
+            try:
+                test_qr = QRByModuleSizeSplitFunctionPatterns()
+                _, _, _, _, _, qr_size = test_qr.generate_qr(
+                    protocol=pipeline_params.get("qr_code_split.protocol"),
+                    text=pipeline_params["qr_code_split.text"],
+                    module_size=16,
+                    max_image_size=pipeline_params["qr_code_split.max_image_size"],
+                    fill_hexcolor="#FFFFFF",
+                    back_hexcolor="#000000",
+                    error_correction="High",
+                    border=1,
+                    module_drawer="Square",
+                )
+            except RuntimeError as err:
+                logger.error(err)
+                pipeline_params["qr_code_split.text"] = "This QR Code is too large for this image"
+                test_qr = QRByModuleSizeSplitFunctionPatterns()
+                qr_size = 624
+
+            if not pipeline_params.get("qr_flattened_composite.x"):
+                x_offset = int((original_width / 2) - qr_size / 2)
+                # I don't know why but through trial and error I've discovered that the QR codes
+                # are more legible when they're placed in an offset which is a multiple of 64
+                x_offset = x_offset - (x_offset % 64) if x_offset % 64 != 0 else x_offset
+                pipeline_params["qr_flattened_composite.x"] = x_offset
+            if pipeline_params.get("qr_flattened_composite.x", 0) > int((original_width) - qr_size):
+                pipeline_params["qr_flattened_composite.x"] = int((original_width) - qr_size) - 10
+            if not pipeline_params.get("qr_flattened_composite.y"):
+                y_offset = int((original_height / 2) - qr_size / 2)
+                y_offset = y_offset - (y_offset % 64) if y_offset % 64 != 0 else y_offset
+                pipeline_params["qr_flattened_composite.y"] = y_offset
+            if pipeline_params.get("qr_flattened_composite.y", 0) > int((original_height) - qr_size):
+                pipeline_params["qr_flattened_composite.y"] = int((original_height) - qr_size) - 10
+            pipeline_params["module_layer_composite.x"] = pipeline_params["qr_flattened_composite.x"]
+            pipeline_params["module_layer_composite.y"] = pipeline_params["qr_flattened_composite.y"]
+            pipeline_params["function_layer_composite.x"] = pipeline_params["qr_flattened_composite.x"]
+            pipeline_params["function_layer_composite.y"] = pipeline_params["qr_flattened_composite.y"]
+            pipeline_params["mask_composite.x"] = pipeline_params["qr_flattened_composite.x"]
+            pipeline_params["mask_composite.y"] = pipeline_params["qr_flattened_composite.y"]
+            if SharedModelManager.manager.compvis:
+                model_details = SharedModelManager.manager.compvis.get_model_reference_info(payload["model_name"])
+                if model_details and model_details.get("baseline") == "stable diffusion 1":
+                    pipeline_params["controlnet_qr_model_loader.control_net_name"] = (
+                        "control_v1p_sd15_qrcode_monster_v2.safetensors"
+                    )
         return pipeline_params, faults
 
     def _get_appropriate_pipeline(self, params):
@@ -921,8 +1048,11 @@ def _get_appropriate_pipeline(self, params):
         #     stable_cascade
         #       stable_cascade_remix
         #       stable_cascade_2pass
+        #     qr_code
 
         # controlnet, controlnet_hires_fix controlnet_annotator
+        if params.get("workflow") == "qr_code":
+            return "qr_code"
         if params.get("model_name"):
             model_details = SharedModelManager.manager.compvis.get_model_reference_info(params["model_name"])
             if model_details.get("baseline") == "stable_cascade":
diff --git a/hordelib/nodes/comfy_qr/LICENSE b/hordelib/nodes/comfy_qr/LICENSE
new file mode 100644
index 00000000..127684a4
--- /dev/null
+++ b/hordelib/nodes/comfy_qr/LICENSE
@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 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.
+
+                            Preamble
+
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diff --git a/hordelib/nodes/comfy_qr/README.md b/hordelib/nodes/comfy_qr/README.md
new file mode 100644
index 00000000..c054c53a
--- /dev/null
+++ b/hordelib/nodes/comfy_qr/README.md
@@ -0,0 +1,146 @@
+# ComfyQR
+
+A set of ComfyUI nodes to dynamically create QR image layers for generative QR art without the hassle of leaving the webui.
+
+Everything in this pack is primarily focused on the creation of patterns. While the `Mask QR Errors` does some basic statistics on the differences between pixels, a more definative node on determining scannability can be found in the companion project [ComfyQR-scanning-nodes](https://gitlab.com/sofuego-comfy-nodes/ComfyQR-scanning-nodes) which uses additional dependencies for the reading of QRs.
+
+This repository is managed publicly on [Gitlab](https://gitlab.com/sofuego-comfy-nodes/ComfyQR), but also mirrored on [Github](https://github.com/coreyryanhanson/ComfyQR). Please submit any [issues](https://gitlab.com/sofuego-comfy-nodes/ComfyQR/-/issues) or [pull requests](https://gitlab.com/sofuego-comfy-nodes/ComfyQR/-/merge_requests) to the gitlab repo.
+
+![QR code with repo link](example_generations/main_qr_inpainted.png)
+
+## Getting started
+
+This project currently contains two nodes for generating QR codes.
+
+### QR Code and QR Code (Split)
+
+A dynamically sized QR code without any resampling scaled by the text length, the module size, and the error correction level. Use the `QR Code `for simple workflows and the `QR Code (Split)` if you want to build more advanced pipelines with additional outputs for the `MODULE_LAYER`, `FINDER_LAYER`, or `FINDER_MASK`.
+
+![Split QR Code Node Example](img/node-qr-code-split.png)
+
+#### Inputs
+
+* `protocol` - If enabled this will prefix the textbox input with a preset to represent the internet protocol. This is included both for convenience and as a workaround for the textbox clipping strings with this character combination.
+  * `Http` - Adds "http://" before the text.
+  * `Https` - Adds "https://" before the text.
+  * `None` - Uses only the contents of the `text` box.
+* `text` - What text to build your QR code with. If `protocol` is specified, this textbox will be combined it with the selected option.
+* `module_size` - The pixel width of the smallest unit of a QR code.
+* `max_image_size` - The maximum size of the resulting QR code. If the combination of `text`, `module_size`, and `error_correction` create dimensions that exceed this, an error will halt the pipeline.
+* `fill_hexcolor` - A string of characters representing the hex value of the QR units. Can be 3 or 6 hexadecimal characters with an optional # before.
+* `back_hexcolor` - A string of characters representing the hex value of the space around the QR units. Can be 3 or 6 hexadecimal characters with an optional # before.
+* `error_correction` - The level of error correction to apply.
+  * `Low` - 7% error correction.
+  * `Medium` - 15% error correction.
+  * `Quartile` - 25% error correction.
+  * `High` - 30% error correction.
+* `border` - The border size (In multiples of `module` widths)
+* `module_drawer` - The shape the QR code modules should be. The default is `square`, but see [Alternate Module Drawers](#alternate-module-drawers) below for new examples.
+
+#### Outputs
+
+* `QR_CODE` - The QR code
+* `MODULE_LAYER` - The QR code with the `back_hexcolor` filling in the function patterns (to remove their influence from the early diffusion steps).
+* `FINDER_LAYER` - The isolated finder patterns over the `back_hexcolor`.
+* `FINDER_MASK` - A mask that isolates the locations of the 3 finder patterns.
+* `QR_VERSION` - An integer of the QR version that was selected based on the text length and error correction level.
+* `IMAGE_SIZE` - An integer based on the pixel width of the resulting QR code.
+
+### QR Code (Conformed to Image Size)
+
+![Conformed QR Code Node Example](img/node-qr-code-conformed.png)
+
+A QR code fixed to specific output dimensions through image resampling.
+
+#### Inputs
+
+* `protocol` - If enabled this will prefix the textbox input with a preset to represent the internet protocol. This is included both for convenience and as a workaround for the textbox clipping strings with this character combination.
+  * `Http` - Adds "http://" before the text.
+  * `Https` - Adds "https://" before the text.
+  * `None` - Uses only the contents of the `text` box.
+* `text` - What text to build your QR code with. If `protocol` is specified, this textbox will be combined it with the selected option.
+* `image_size` - The pixel dimensions to conform the QR code to.
+* `fill_hexcolor` - A string of characters representing the hex value of the QR units. Can be 3 or 6 hexadecimal characters with an optional # before.
+* `back_hexcolor` - A string of characters representing the hex value of the space around the QR units. Can be 3 or 6 hexadecimal characters with an optional # before.
+* `error_correction` - The level of error correction to apply.
+  * `Low` - 7% error correction.
+  * `Medium` - 15% error correction.
+  * `Quartile` - 25% error correction.
+  * `High` - 30% error correction.
+* `border` - The border size (In multiples of `module` widths)
+* `resampling` - The resampling algorithm to use when rescaling the QR code.
+  * `Bicubic` - Bicubic interpolation
+  * `Bilinear` - Bilinear interpolation
+  * `Box` - Box interpolation
+  * `Hamming` - Hamming interpolation
+  * `Lanczos` - Lanczos interpolation
+  * `Nearest` - Nearest Neighbor interpolation
+* `module_drawer` - The shape the QR code modules should be. The default is `square`, but see [Alternate Module Drawers](#alternate-module-drawers) below for new examples.
+
+#### Outputs
+
+* `QR_CODE` - The QR code
+* `QR_VERSION` - An integer of the QR version that was selected based on the text length and error correction level.
+
+### Mask QR Errors
+
+A node that that will analyze the differences between a modified QR and create a mask of the estimated errors.
+
+![Mask QR Errors Node Example](img/node-mask-qr-errors.png)
+
+**Note**: _This is an extremly powerful node but relies on several assumptions in order to be used._
+1. A QR with a fixed module size that has not been resampled irregularly or distorted.
+2. Shared allignment between the `source_qr` and `modified_qr`.
+3. Fill and back colors must be perfectly white and black (`#FFFFFF`, `#000000`) for the `source_qr`.
+4. The canvas can be bigger than the QR code, but on the `source_qr`, there should be no pixels the same color as the module color outside of the QR exterior.
+
+#### Inputs
+
+* `source_qr` - The original QR code used to generate the image (with the same placement and dimensions as `modified_qr`).
+* `modified_qr` - The generated QR art to evaluate (with the same placement and dimensions as `source_qr`).
+* `module_size` - The size in pixels of each QR module (the individual blocks that make up a qr code) in the provided images.
+* `grayscale_method` - How to convert the RGB color channels to a single grayscale value.
+  * `mean` - A simple average of the RGB values.
+  * `luminance` - Converts colors to grayscale using a [more advanced formula](https://en.wikipedia.org/wiki/Grayscale#Colorimetric_(perceptual_luminance-preserving)_conversion_to_grayscale) to better match percieved brightness.
+* `aggregate_method` - How to aggregate the many pixels that make up a QR module into a single value.
+  * `mean` - Within each module a simple average is taken of all constituent pixels.
+* `evaluate` - Which part of the QR to examine.
+  * `full_qr` - The entire QR code is evaluated.
+  * `module_pattern` - Has the node focus only on the modules while excluding the finder pattern.
+  * `finder_pattern` - Ignores the modules while examining only the 3 finder patterns at the corners of the QR.
+* `error_difficulty` - It is possible that while using the same reader a QR code could be easily scannable on one monitor, but not on another. The way the colors are displayed can have a major effect on unsability. By increasing this threshold, you are simulating inability to distinguish midrange pixels for both the `PERCENT_ERROR` and `QR_ERROR_MASK` outputs. It ranges from 0 to 1 where 0 only automatically fails perfect grays while 1 accepts only exactly matched modules that are perfectly black or white.
+* `inverted_pattern` - If set to `True` this assumes that the QR code has inverted colors (a black background with white modules.)
+* `gamma` - If using the `grayscale` method based on `luminance`, this will influence the formula by appending the gamma expansion and compression formulas before and after the color conversion. A gamma of 2.2 will use the [formula specific to srgb](qr_nodes.py#0.04045), while other gamma values will simply use the gamma amount as an exponent.
+
+#### Outputs
+
+* `QR_ERROR_MASK` - A mask with white areas falling on locations of modules with errors (precision set by `error_difficulty`). This mask can be used for streamlined img2img operations to salvage unscannable QRs.
+* `PERCENT_ERROR` - Number of modules that do not fall into the acceptable threshold (set by `error_difficulty`) divided by the total number of modules.
+* `CORRELATION` - The Pearson correlation coefficient between the QR patterns on the `source_qr` and `modified_qr`
+* `RMSE` - The root mean squared error between both QR codes. In this case, a value of 0 indicates an identical match to the `source_qr`. A value of 1 indicates a perfect inversion where each expected color is the complete opposite.
+
+## Examples
+
+There is an assortment of workflow examples that can be found in the `examples` directory or in the metadata of the images in the `example_generations` folder. For example taking this image.
+
+![Bad Scan Example](example_generations/unscannable_00001_.png)
+
+And then aggressively increasing the strength of the ControlNet on only the error pixels to make something previously completely unscannable more reliably across different monitors and QR readers.
+
+![Fixed Scan Example](example_generations/unscannable_00001_fixed_.png)
+
+Any workflow in the example that ends with "validated" (and a few image examples) assume the installation of the [scanning pack](https://gitlab.com/sofuego-comfy-nodes/ComfyQR-scanning-nodes) as well.
+
+## Alternate Module Drawers
+
+The QR generation nodes now support alternate module styles. Experiment using different ones for greater flexibility during generation.
+
+| Square | Gapped Square | Circle | Rounded | Vertical bars | Horizontal bars |
+|:---: | :---: | :---: | :---: | :---: | :---: |
+| ![Example QR](/img/square.png) | ![Example QR](img/gapped_square.png) | ![Example QR](img/circle.png) | ![Example QR](img/rounded.png) | ![Example QR](img/vertical-bars.png) | ![Example QR](img/horizontal-bars.png) |
+
+**Note** _Even unaltered non square styles will inherently register small deviations from RMSE values and possible minute deviations for correlations when using the [Mask QR Errors](#mask-qr-errors) node. When information is extracted, the `source_qr` values are rounded so that they can only be 0 or 1 (to allow alternate drawers to be used as inputs) and matched against the unrounded aggregation of the `modified_qr` values._
+
+## Future updates
+
+If the solution is clean enough and if it can definitively improve scannability, there may be additional plans for the seperation of alignment patterns (based on `module_size`, `border`, and `QR_VERSION`) for more advanced workflows.
diff --git a/hordelib/nodes/comfy_qr/__init__.py b/hordelib/nodes/comfy_qr/__init__.py
new file mode 100644
index 00000000..8eca1e0e
--- /dev/null
+++ b/hordelib/nodes/comfy_qr/__init__.py
@@ -0,0 +1,3 @@
+from hordelib.nodes.comfy_qr.qr_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
diff --git a/hordelib/nodes/comfy_qr/qr_nodes.py b/hordelib/nodes/comfy_qr/qr_nodes.py
new file mode 100644
index 00000000..ac24431c
--- /dev/null
+++ b/hordelib/nodes/comfy_qr/qr_nodes.py
@@ -0,0 +1,645 @@
+import numpy as np
+import qrcode
+from qrcode.image.styles.moduledrawers import (GappedSquareModuleDrawer,
+                                               CircleModuleDrawer,
+                                               RoundedModuleDrawer,
+                                               VerticalBarsDrawer,
+                                               HorizontalBarsDrawer)
+from qrcode.image.styledpil import StyledPilImage
+from qrcode.image.styles.colormasks import SolidFillColorMask
+from qrcode.compat.pil import Image
+import torch
+import torch.nn.functional as F
+
+
+class QRBase:
+    def __init__(self):
+        self.text = ""
+        self.fill = None
+        self.back = None
+
+    FUNCTION = "generate_qr"
+    CATEGORY = "ComfyQR"
+
+    def _get_error_correction_constant(self, error_correction_string):
+        if error_correction_string == "Low":
+            return qrcode.constants.ERROR_CORRECT_L
+        if error_correction_string == "Medium":
+            return qrcode.constants.ERROR_CORRECT_M
+        if error_correction_string == "Quartile":
+            return qrcode.constants.ERROR_CORRECT_Q
+        return qrcode.constants.ERROR_CORRECT_H
+
+    def _img_to_tensor(self, img):
+        out_image = np.array(img, dtype=np.uint8).astype(np.float32) / 255
+        return torch.from_numpy(out_image).unsqueeze(0)
+
+    def _make_qr(self, qr, fill_hexcolor, back_hexcolor, module_drawer):
+        self.fill = self._parse_hexcolor_string(fill_hexcolor, "fill_hexcolor")
+        self.back = self._parse_hexcolor_string(back_hexcolor, "back_hexcolor")
+        qr.make(fit=True)
+        if module_drawer == "Square":
+            # Keeps using Square QR generation the old way for faster speeds.
+            return qr.make_image(fill_color=self.fill, back_color=self.back)
+        color_mask = SolidFillColorMask(back_color=self.back,
+                                        front_color=self.fill)
+        module_drawing_method = self._select_module_drawer(module_drawer)
+        return qr.make_image(image_factory=StyledPilImage,
+                             color_mask=color_mask,
+                             module_drawer=module_drawing_method)
+
+    def _parse_hexcolor_string(self, s, parameter):
+        if s.startswith("#"):
+            s = s[1:]
+        if len(s) == 3:
+            rgb = (c + c for c in s)
+        elif len(s) == 6:
+            rgb = (s[i] + s[i+1] for i in range(0, 6, 2))
+        else:
+            raise ValueError(f"{parameter} must be 3 or 6 characters long")
+        try:
+            return tuple(int(channel, 16) for channel in rgb)
+        except ValueError:
+            raise ValueError(f"{parameter} contains invalid hexadecimal "
+                             f"characters")
+
+    def _validate_qr_size(self, size, max_size):
+        if size > max_size:
+            raise RuntimeError(f"QR dimensions of {size} exceed max size of "
+                               f"{max_size}.")
+
+    def _select_module_drawer(self, module_drawer_string):
+        """Square is not included in the results, for a speed optimization
+        applying color masks. Current version of python-qr code suffers a
+        slowdown when using custom colors combined with custom module drawers.
+        By bypassing square QRs, non standard colors will load faster."""
+        if module_drawer_string == "Gapped square":
+            return GappedSquareModuleDrawer()
+        if module_drawer_string == "Circle":
+            return CircleModuleDrawer()
+        if module_drawer_string == "Rounded":
+            return RoundedModuleDrawer()
+        if module_drawer_string == "Vertical bars":
+            return VerticalBarsDrawer()
+        if module_drawer_string == "Horizontal bars":
+            return HorizontalBarsDrawer()
+        raise ValueError(f"Module drawing method of {module_drawer_string} "
+                         f"not supported")
+
+    def update_text(self, protocol, text):
+        """This function takes input from a text box and a chosen internet
+        protocol and stores a full address within an instance variable.
+        Backslashes will invalidate text box input and this acts as a
+        workaround to be able to use them when required in QR strings.
+
+        Args:
+            protocol: A categorical variable of one of the available internet
+                protocols.
+            text: The input from the text box.
+        """
+        if protocol == "Https":
+            prefix = "https://"
+        elif protocol == "Http":
+            prefix = "http://"
+        elif protocol == "None":
+            prefix = ""
+        self.text = prefix + text
+
+
+class QRByImageSize(QRBase):
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "protocol": (["Http", "Https", "None"], {"default": "Https"}),
+                "text": ("STRING", {"multiline": True}),
+                "image_size": ("INT", {"default": 512,
+                                       "min": 64,
+                                       "max": 4096,
+                                       "step": 64}),
+                "fill_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#000000"}),
+                "back_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#FFFFFF"}),
+                "error_correction": (["Low", "Medium", "Quartile", "High"],
+                                     {"default": "High"}),
+                "border": ("INT", {"default": 1,
+                                   "min": 0,
+                                   "max": 100,
+                                   "step": 1}),
+                "resampling": (["Bicubic",
+                                "Bilinear",
+                                "Box",
+                                "Hamming",
+                                "Lanczos",
+                                "Nearest"
+                                ], {"default": "Nearest"}),
+                "module_drawer": (["Square",
+                                   "Gapped square",
+                                   "Circle",
+                                   "Rounded",
+                                   "Vertical bars",
+                                   "Horizontal bars"
+                                   ], {"default": "Square"})
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT")
+    RETURN_NAMES = ("QR_CODE", "QR_VERSION")
+
+    def _select_resampling_method(self, resampling_string):
+        if resampling_string == "Nearest":
+            return Image.NEAREST
+        if resampling_string == "Bicubic":
+            return Image.BICUBIC
+        if resampling_string == "Bilinear":
+            return Image.BILINEAR
+        if resampling_string == "Lanczos":
+            return Image.LANCZOS
+        if resampling_string == "Box":
+            return Image.BOX
+        if resampling_string == "Hamming":
+            return Image.HAMMING
+        raise ValueError(f"Resampling method of {resampling_string} not "
+                         f"supported")
+
+    def generate_qr(
+            self,
+            protocol,
+            text,
+            image_size,
+            fill_hexcolor,
+            back_hexcolor,
+            error_correction,
+            border,
+            resampling,
+            module_drawer
+            ):
+        resampling_method = self._select_resampling_method(resampling)
+        error_level = self._get_error_correction_constant(error_correction)
+        self.update_text(protocol, text)
+        qr = qrcode.QRCode(
+                error_correction=error_level,
+                box_size=16,
+                border=border)
+        qr.add_data(self.text)
+        img = self._make_qr(qr, fill_hexcolor, back_hexcolor, module_drawer)
+        img = img.resize((image_size, image_size), resample=resampling_method)
+        return (self._img_to_tensor(img), qr.version)
+
+
+class QRByModuleSize(QRBase):
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "protocol": (["Http", "Https", "None"], {"default": "Https"}),
+                "text": ("STRING", {"multiline": True}),
+                "module_size": ("INT", {"default": 16,
+                                        "min": 1,
+                                        "max": 64,
+                                        "step": 1}),
+                "max_image_size": ("INT", {"default": 512,
+                                           "min": 64,
+                                           "max": 4096,
+                                           "step": 64}),
+                "fill_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#000000"}),
+                "back_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#FFFFFF"}),
+                "error_correction": (["Low", "Medium", "Quartile", "High"],
+                                     {"default": "High"}),
+                "border": ("INT", {"default": 1,
+                                   "min": 0,
+                                   "max": 100,
+                                   "step": 1}),
+                "module_drawer": (["Square",
+                                   "Gapped square",
+                                   "Circle",
+                                   "Rounded",
+                                   "Vertical bars",
+                                   "Horizontal bars"
+                                   ], {"default": "Square"})
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "INT")
+    RETURN_NAMES = ("QR_CODE", "QR_VERSION", "IMAGE_SIZE")
+
+    def generate_qr(
+            self,
+            protocol,
+            text,
+            module_size,
+            max_image_size,
+            fill_hexcolor,
+            back_hexcolor,
+            error_correction,
+            border,
+            module_drawer
+            ):
+        self.update_text(protocol, text)
+        error_level = self._get_error_correction_constant(error_correction)
+        qr = qrcode.QRCode(
+                error_correction=error_level,
+                box_size=module_size,
+                border=border)
+        qr.add_data(self.text)
+        img = self._make_qr(qr, fill_hexcolor, back_hexcolor, module_drawer)
+        self._validate_qr_size(img.pixel_size, max_image_size)
+        return (self._img_to_tensor(img), qr.version, img.pixel_size)
+
+
+class QRByModuleSizeSplitFunctionPatterns(QRBase):
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "protocol": (["Http", "Https", "None"], {"default": "Https"}),
+                "text": ("STRING", {"multiline": True}),
+                "module_size": ("INT", {"default": 16,
+                                        "min": 1,
+                                        "max": 64,
+                                        "step": 1}),
+                "max_image_size": ("INT", {"default": 512,
+                                           "min": 64,
+                                           "max": 4096,
+                                           "step": 64}),
+                "fill_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#000000"}),
+                "back_hexcolor": ("STRING", {"multiline": False,
+                                             "default": "#FFFFFF"}),
+                "error_correction": (["Low", "Medium", "Quartile", "High"],
+                                     {"default": "High"}),
+                "border": ("INT", {"default": 1,
+                                   "min": 0,
+                                   "max": 100,
+                                   "step": 1}),
+                "module_drawer": (["Square",
+                                   "Gapped square",
+                                   "Circle",
+                                   "Rounded",
+                                   "Vertical bars",
+                                   "Horizontal bars"
+                                   ], {"default": "Square"})
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "INT", "INT")
+    RETURN_NAMES = ("QR_CODE",
+                    "MODULE_LAYER",
+                    "FINDER_LAYER",
+                    "FINDER_MASK",
+                    "QR_VERSION",
+                    "IMAGE_SIZE")
+
+    def _generate_finder_pattern_ranges(self, module_size, border_size):
+        outer = module_size * border_size
+        inner = 7 * module_size + outer
+        # Alternate behavior is required to prevent bugs from 0 border_size.
+        far_outer = -outer if border_size else None
+        return [
+            (outer, inner, outer, inner),
+            (outer, inner, -inner, far_outer),
+            (-inner, far_outer, outer, inner)
+        ]
+
+    def _generate_finder_pattern_mask(self,
+                                      pixel_size,
+                                      module_size,
+                                      border_size):
+        mask = np.zeros((pixel_size, pixel_size), dtype=bool)
+        for (x_min,
+             x_max,
+             y_min,
+             y_max) in self._generate_finder_pattern_ranges(module_size,
+                                                            border_size):
+            mask[y_min:y_max, x_min:x_max] = True
+        return mask
+
+    def _apply_fill_to_mask(self, img, mask):
+        array = np.array(img).copy()
+        indices = np.nonzero(mask)
+        array[indices[0], indices[1], :] = self.back
+        return Image.fromarray(array)
+
+    def _mask_to_tensor(self, mask):
+        out_image = mask.astype(np.float32)
+        return torch.from_numpy(out_image).unsqueeze(0)
+
+    def generate_qr(
+            self,
+            protocol,
+            text,
+            module_size,
+            max_image_size,
+            fill_hexcolor,
+            back_hexcolor,
+            error_correction,
+            border,
+            module_drawer
+            ):
+        self.update_text(protocol, text)
+        error_level = self._get_error_correction_constant(error_correction)
+        qr = qrcode.QRCode(
+                error_correction=error_level,
+                box_size=module_size,
+                border=border)
+        qr.add_data(self.text)
+        img = self._make_qr(qr, fill_hexcolor, back_hexcolor, module_drawer)
+        pixel_size = img.pixel_size
+        self._validate_qr_size(pixel_size, max_image_size)
+        mask = self._generate_finder_pattern_mask(pixel_size,
+                                                  module_size,
+                                                  border)
+        module_image = self._apply_fill_to_mask(img, mask)
+        function_image = self._apply_fill_to_mask(img, ~mask)
+        return (
+            self._img_to_tensor(img),
+            self._img_to_tensor(module_image),
+            self._img_to_tensor(function_image),
+            self._mask_to_tensor(mask),
+            qr.version,
+            pixel_size,
+            )
+
+
+class QRErrorMasker:
+    def __init__(self):
+        self.module_size = None
+        self.canvas_shape = None
+        self.qr_bounds = None
+
+    FUNCTION = "find_qr_errors"
+    CATEGORY = "ComfyQR"
+    RETURN_TYPES = ("MASK", "FLOAT", "FLOAT", "FLOAT")
+    RETURN_NAMES = ("QR_ERROR_MASK", "PERCENT_ERROR", "CORRELATION", "RMSE")
+    OUTPUT_IS_LIST = (False, True, True, True)
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "source_qr": ("IMAGE",),
+                "modified_qr": ("IMAGE",),
+                "module_size": ("INT", {"default": 16,
+                                        "min": 1,
+                                        "max": 64,
+                                        "step": 1}),
+                "grayscale_method": (["mean", "luminance"],
+                                     {"default": "luminance"}),
+                "aggregate_method": (["mean",], {"default": "mean"}),
+                "evaluate": (["full_qr", "module_pattern", "finder_pattern"],
+                             {"default": "module_pattern"}),
+                "error_difficulty": ("FLOAT", {"default": 0,
+                                               "min": 0,
+                                               "max": 1,
+                                               "step": .01}),
+                "inverted_pattern": ("BOOLEAN", {"default": False}),
+                "gamma": ("FLOAT", {"default": 2.2,
+                                    "min": .1,
+                                    "max": 2.8,
+                                    "step": .1}),
+            },
+        }
+
+    def _get_qr_bounds(self, tensor, invert):
+        module_color = 1.0 if invert else 0.0
+        module_pixels = (tensor == module_color)
+        indices = torch.nonzero(module_pixels, as_tuple=True)
+        # The viewer patterns will guarentee a module pixel in the upper left
+        # The bottom right does not have that guarentee so max is used.
+        return (indices[0][0],
+                indices[0].max() + 1,
+                indices[1][0], indices[1].max() + 1)
+
+    def _extract_pattern_from_bounds(self, tensor):
+        return tensor[self.qr_bounds[0]: self.qr_bounds[1],
+                      self.qr_bounds[2]: self.qr_bounds[3]]
+
+    def _trim_to_qr_area(self, source_qr, modified_qr, inverted_pattern):
+        self.qr_bounds = self._get_qr_bounds(source_qr, inverted_pattern)
+        self._check_bounds_and_module_size()
+        source_qr = self._extract_pattern_from_bounds(source_qr)
+        modified_qr = self._extract_pattern_from_bounds(modified_qr)
+        return source_qr, modified_qr
+
+    def _reshape_tensor_to_modules(self, tensor):
+        if len(tensor.shape) != 2:
+            raise RuntimeError("Module reshaping requires a 2 dimensional "
+                               "array.")
+        length = tensor.shape[0] // self.module_size
+        reshaped_tensor = tensor.view(length,
+                                      self.module_size,
+                                      length,
+                                      self.module_size)
+        rehaped_tensor = reshaped_tensor.permute(0, 2, 1, 3).contiguous()
+        return rehaped_tensor.view(length, length, self.module_size ** 2)
+
+    def _check_bounds_and_module_size(self):
+        height = self.qr_bounds[1] - self.qr_bounds[0]
+        width = self.qr_bounds[3] - self.qr_bounds[2]
+        color_warning = "Make sure that qr_fill and back colors have exact "
+        "#FFFFFFF and #000000 values (and that module color values do not "
+        "occur outside the QR) and invert is set correctly."
+        if width != height:
+            raise RuntimeError(f"Source QR dimensions are {width} x {height}. "
+                               f"They must be a perfect square. "
+                               f"{color_warning}")
+        if width % self.module_size:
+            raise RuntimeError(f"QR width of {width} does not fit module_size "
+                               f"of {self.module_size}. It must be perfectly "
+                               f"divisible. {color_warning}")
+
+    def _check_equal_shape(self, source_qr, modified_qr):
+        if source_qr.shape != modified_qr.shape:
+            raise ValueError("Source and modified QR must have the same batch "
+                             "size and dimensions.")
+
+    def _squeeze_by_mean(self, tensor):
+        return torch.mean(tensor, dim=-1)
+
+    def _gamma_expansion(self, tensor, gamma):
+        if gamma == 1:
+            return tensor
+        if gamma == 2.2:
+            return torch.where(tensor <= 0.04045,
+                               tensor / 12.92,
+                               (tensor + 0.055) / 1.055) ** 2.4
+        return tensor ** gamma
+
+    def _gamma_compression(self, tensor, gamma):
+        if gamma == 1:
+            return tensor
+        if gamma == 2.2:
+            return torch.where(tensor <= .0031308,
+                               tensor * 12.92,
+                               1.055 * tensor ** (1/2.4) - 0.055)
+        return tensor ** (1/gamma)
+
+    def _grayscale_by_luminance(self, tensor, gamma):
+        weights = torch.tensor([0.2125, 0.7154, 0.0721], dtype=torch.float32)
+        tensor = self._gamma_expansion(tensor, gamma)
+        tensor = tensor @ weights
+        if gamma != 1:
+            tensor = tensor ** gamma
+        return self._gamma_compression(tensor, gamma)
+
+    def _squeeze_to_modules(self, tensor, method):
+        tensor = self._reshape_tensor_to_modules(tensor)
+        if method == "mean":
+            return self._squeeze_by_mean(tensor)
+        raise RuntimeError("Module aggregation currently only supports the "
+                           "mean.")
+
+    def _reduce_to_modules(
+            self,
+            source_qr,
+            modified_qr,
+            module_size,
+            grayscale_method,
+            aggregate_method,
+            inverted_pattern,
+            gamma
+            ):
+        self.module_size = module_size
+        self.canvas_shape = (source_qr.shape[0], source_qr.shape[1])
+        # Processed first for simplified indexing of QR bounds.
+        source_qr = self._squeeze_by_mean(source_qr)
+        source_qr, modified_qr = self._trim_to_qr_area(source_qr,
+                                                       modified_qr,
+                                                       inverted_pattern
+                                                       )
+        if grayscale_method == "mean":
+            modified_qr = self._squeeze_by_mean(modified_qr)
+        elif grayscale_method == "luminance":
+            modified_qr = self._grayscale_by_luminance(modified_qr, gamma)
+        else:
+            raise ValueError("Currently only mean is supported for rgb to "
+                             "grayscale conversion.")
+        source_qr = torch.round(self._squeeze_to_modules(source_qr, "mean"))
+        modified_qr = self._squeeze_to_modules(modified_qr, aggregate_method)
+        return source_qr, modified_qr
+
+    def _create_finder_pattern_mask(self, width, inverted):
+        mask = torch.zeros((width, width), dtype=torch.bool)
+        # When borders are trimmed and QR code has module size of 1, results
+        #  are consistent.
+        finder_coords = [[0, 7, 0, 7], [0, 7, -7, None], [-7, None, 0, 7]]
+        for x_min, x_max, y_min, y_max in finder_coords:
+            mask[y_min:y_max, x_min:x_max] = True
+        return ~mask if inverted else mask
+
+    def _create_qr_mask(self, tensor, evaluate):
+        if evaluate == "module_pattern":
+            return self._create_finder_pattern_mask(tensor, True)
+        if evaluate == "finder_pattern":
+            return self._create_finder_pattern_mask(tensor, False)
+        return None
+
+    def _bin_tensor_to_threshold(self, tensor, contrast_difficulty):
+        tensor = tensor.clone()
+        threshold = contrast_difficulty / 2
+        # Since we are only interested in value matches and there is a clear
+        # stable dividing line of .5, bringing in the other array is
+        # unneccessary and the binning process can be simplified.
+        bin_condition = (tensor + threshold <= .5) & (tensor != .5)
+        tensor[bin_condition] = 0.0
+        bin_condition = (tensor - threshold >= .5) & (tensor != .5)
+        tensor[bin_condition] = 1.0
+        return tensor
+
+    def _replace_qr_to_canvas(self, tensor):
+        length = tensor.shape[0] * self.module_size
+        bounds = self.qr_bounds
+        tensor = F.interpolate(tensor.unsqueeze(0).unsqueeze(0),
+                               size=(length, length),
+                               mode='nearest')
+        canvas = torch.zeros(self.canvas_shape, dtype=torch.float32)
+        canvas[bounds[0]:bounds[1], bounds[2]:bounds[3]] = tensor.squeeze()
+        return canvas
+
+    def _compare_modules(
+            self,
+            source_qr,
+            modified_qr,
+            mask,
+            error_difficulty
+            ):
+        modified_qr = self._bin_tensor_to_threshold(modified_qr,
+                                                    error_difficulty)
+        error = source_qr != modified_qr
+        percent_error = error[mask].sum().item() / error[mask].numel()
+        if mask is not None:
+            error[~mask] = False
+        return (self._replace_qr_to_canvas((error).to(torch.float32)),
+                percent_error)
+
+    def _qr_correlation(self, source_qr, modified_qr, mask):
+        source_qr = source_qr[mask].numpy().reshape((-1))
+        modified_qr = modified_qr[mask].numpy().reshape((-1))
+        return np.corrcoef(source_qr, modified_qr)[0, 1]
+
+    def _qr_rmse(self, source_qr, modified_qr, mask):
+        diff = source_qr[mask].numpy() - modified_qr[mask].numpy()
+        return np.sqrt((diff ** 2).mean())
+
+    def find_qr_errors(
+            self,
+            source_qr,
+            modified_qr,
+            module_size,
+            grayscale_method,
+            aggregate_method,
+            evaluate,
+            error_difficulty,
+            inverted_pattern,
+            gamma,
+            ):
+        self._check_equal_shape(source_qr, modified_qr)
+        error_masks, error_percents, correlations, rmses = [], [], [], []
+        for i in range(source_qr.shape[0]):
+            qr_s, qr_m = source_qr[i], modified_qr[i]
+            qr_s, qr_m = self._reduce_to_modules(qr_s,
+                                                 qr_m,
+                                                 module_size,
+                                                 grayscale_method,
+                                                 aggregate_method,
+                                                 inverted_pattern,
+                                                 gamma
+                                                 )
+            mask = self._create_qr_mask(qr_s.shape[0], evaluate)
+            error_mask, percent_error = self._compare_modules(qr_s,
+                                                              qr_m,
+                                                              mask,
+                                                              error_difficulty
+                                                              )
+            correlation = self._qr_correlation(qr_s, qr_m, mask)
+            rmse = self._qr_rmse(qr_s, qr_m, mask)
+            error_masks.append(error_mask)
+            error_percents.append(percent_error)
+            correlations.append(correlation)
+            rmses.append(rmse)
+        error_masks = torch.stack(error_masks, dim=0)
+        return (error_masks, error_percents, correlations, rmses)
+
+
+NODE_CLASS_MAPPINGS = {
+                       "comfy-qr-by-module-size": QRByModuleSize,
+                       "comfy-qr-by-image-size": QRByImageSize,
+                       "comfy-qr-by-module-split":
+                       QRByModuleSizeSplitFunctionPatterns,
+                       "comfy-qr-mask_errors": QRErrorMasker,
+                       }
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+                              "comfy-qr-by-module-size": "QR Code",
+                              "comfy-qr-by-image-size": "QR Code (Conformed "
+                              "to Image Size)",
+                              "comfy-qr-by-module-split": "QR Code (Split)",
+                              "comfy-qr-mask_errors": "Mask QR Errors",
+                              }
diff --git a/hordelib/nodes/comfy_qr/requirements.txt b/hordelib/nodes/comfy_qr/requirements.txt
new file mode 100644
index 00000000..38f035e9
--- /dev/null
+++ b/hordelib/nodes/comfy_qr/requirements.txt
@@ -0,0 +1,1 @@
+qrcode >= 7.1
diff --git a/hordelib/pipeline_designs/pipeline_qr_code.json b/hordelib/pipeline_designs/pipeline_qr_code.json
new file mode 100644
index 00000000..435cdc27
--- /dev/null
+++ b/hordelib/pipeline_designs/pipeline_qr_code.json
@@ -0,0 +1,550 @@
+{
+  "6": {
+    "inputs": {
+      "ckpt_name": "albedo_base_xl.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "model_loader"
+    }
+  },
+  "8": {
+    "inputs": {
+      "text": "worst quality, bad lighting, deformed, ugly, low contrast",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "negative_prompt"
+    }
+  },
+  "12": {
+    "inputs": {
+      "filename_prefix": "qr",
+      "images": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  },
+  "13": {
+    "inputs": {
+      "samples": [
+        "30",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "17": {
+    "inputs": {
+      "strength": 1,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "90",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "43",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_bg"
+    }
+  },
+  "18": {
+    "inputs": {
+      "control_net_name": "control_v1p_sdxl_qrcode_monster.safetensors"
+    },
+    "class_type": "ControlNetLoader",
+    "_meta": {
+      "title": "controlnet_qr_model_loader"
+    }
+  },
+  "23": {
+    "inputs": {
+      "strength": 6,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "57",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_fg"
+    }
+  },
+  "24": {
+    "inputs": {
+      "add_noise": "enable",
+      "noise_seed": 246981983966927,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler_ancestral",
+      "scheduler": "normal",
+      "start_at_step": 0,
+      "end_at_step": 11,
+      "return_with_leftover_noise": "enable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "17",
+        0
+      ],
+      "negative": [
+        "17",
+        1
+      ],
+      "latent_image": [
+        "63",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler_bg"
+    }
+  },
+  "27": {
+    "inputs": {
+      "add_noise": "enable",
+      "noise_seed": 816363190951797,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler",
+      "scheduler": "normal",
+      "start_at_step": 1,
+      "end_at_step": 11,
+      "return_with_leftover_noise": "enable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "23",
+        0
+      ],
+      "negative": [
+        "23",
+        1
+      ],
+      "latent_image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler_fg"
+    }
+  },
+  "29": {
+    "inputs": {
+      "x": 0,
+      "y": 0,
+      "resize_source": false,
+      "destination": [
+        "24",
+        0
+      ],
+      "source": [
+        "27",
+        0
+      ],
+      "mask": [
+        "88",
+        0
+      ]
+    },
+    "class_type": "LatentCompositeMasked",
+    "_meta": {
+      "title": "LatentCompositeMasked"
+    }
+  },
+  "30": {
+    "inputs": {
+      "add_noise": "disable",
+      "noise_seed": 219905100267874,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler_ancestral",
+      "scheduler": "normal",
+      "start_at_step": 11,
+      "end_at_step": 10000,
+      "return_with_leftover_noise": "disable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "38",
+        0
+      ],
+      "negative": [
+        "38",
+        1
+      ],
+      "latent_image": [
+        "29",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler"
+    }
+  },
+  "36": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE Encode (FG)"
+    }
+  },
+  "38": {
+    "inputs": {
+      "strength": 1.5,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "90",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "42",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_combined"
+    }
+  },
+  "39": {
+    "inputs": {
+      "protocol": "Https",
+      "text": "aihorde.net",
+      "module_size": 16,
+      "max_image_size": 1024,
+      "fill_hexcolor": "#000000",
+      "back_hexcolor": "#FFFFFF",
+      "error_correction": "High",
+      "border": 1,
+      "module_drawer": "Square"
+    },
+    "class_type": "comfy-qr-by-module-split",
+    "_meta": {
+      "title": "qr_code_split"
+    }
+  },
+  "40": {
+    "inputs": {
+      "value": 0.5,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_grey"
+    }
+  },
+  "41": {
+    "inputs": {
+      "mask": [
+        "40",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (Grey)"
+    }
+  },
+  "42": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "41",
+        0
+      ],
+      "source": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "QR Flattened Composite"
+    }
+  },
+  "43": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "41",
+        0
+      ],
+      "source": [
+        "39",
+        1
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "Module Layer Composite"
+    }
+  },
+  "44": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "65",
+        0
+      ],
+      "source": [
+        "39",
+        2
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "Function Layer Composite"
+    }
+  },
+  "47": {
+    "inputs": {
+      "mask": [
+        "39",
+        3
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Convert Mask to Image"
+    }
+  },
+  "49": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "operation": "add",
+      "destination": [
+        "50",
+        0
+      ],
+      "source": [
+        "39",
+        3
+      ]
+    },
+    "class_type": "MaskComposite",
+    "_meta": {
+      "title": "MaskComposite"
+    }
+  },
+  "50": {
+    "inputs": {
+      "value": 0,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_black"
+    }
+  },
+  "57": {
+    "inputs": {
+      "text": "Lucid Creations,a brain cells  ethereal, dreamlike, surreal, beautiful, illustration, incredible detail, 8k, abstract",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "function_layer_prompt"
+    }
+  },
+  "63": {
+    "inputs": {
+      "pixels": [
+        "43",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE Encode (BG)"
+    }
+  },
+  "64": {
+    "inputs": {
+      "value": 1,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_white"
+    }
+  },
+  "65": {
+    "inputs": {
+      "mask": [
+        "64",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (White)"
+    }
+  },
+  "67": {
+    "inputs": {
+      "mask": [
+        "50",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (Black)"
+    }
+  },
+  "83": {
+    "inputs": {
+      "expand": 8,
+      "tapered_corners": false,
+      "mask": [
+        "49",
+        0
+      ]
+    },
+    "class_type": "GrowMask",
+    "_meta": {
+      "title": "GrowMask"
+    }
+  },
+  "86": {
+    "inputs": {
+      "mask": [
+        "83",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Convert Mask to Image"
+    }
+  },
+  "87": {
+    "inputs": {
+      "blur_radius": 16,
+      "sigma": 3.0000000000000018,
+      "image": [
+        "86",
+        0
+      ]
+    },
+    "class_type": "ImageBlur",
+    "_meta": {
+      "title": "image_blur"
+    }
+  },
+  "88": {
+    "inputs": {
+      "channel": "red",
+      "image": [
+        "87",
+        0
+      ]
+    },
+    "class_type": "ImageToMask",
+    "_meta": {
+      "title": "Convert Image to Mask"
+    }
+  },
+  "90": {
+    "inputs": {
+      "text": "Lucid Creations, a human brain, ethereal, dreamlike, surreal, beautiful, illustration, incredible detail, 8k, abstract",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "prompt"
+    }
+  }
+}
diff --git a/hordelib/pipelines/pipeline_qr_code.json b/hordelib/pipelines/pipeline_qr_code.json
new file mode 100644
index 00000000..671f7a8b
--- /dev/null
+++ b/hordelib/pipelines/pipeline_qr_code.json
@@ -0,0 +1,550 @@
+{
+  "6": {
+    "inputs": {
+      "ckpt_name": "albedo_base_xl.safetensors"
+    },
+    "class_type": "CheckpointLoaderSimple",
+    "_meta": {
+      "title": "model_loader"
+    }
+  },
+  "8": {
+    "inputs": {
+      "text": "worst quality, bad lighting, deformed, ugly, low contrast",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "negative_prompt"
+    }
+  },
+  "12": {
+    "inputs": {
+      "filename_prefix": "qr",
+      "images": [
+        "13",
+        0
+      ]
+    },
+    "class_type": "SaveImage",
+    "_meta": {
+      "title": "Save Image"
+    }
+  },
+  "13": {
+    "inputs": {
+      "samples": [
+        "30",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEDecode",
+    "_meta": {
+      "title": "VAE Decode"
+    }
+  },
+  "17": {
+    "inputs": {
+      "strength": 1,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "90",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "43",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_bg"
+    }
+  },
+  "18": {
+    "inputs": {
+      "control_net_name": "control_v1p_sdxl_qrcode_monster.safetensors"
+    },
+    "class_type": "ControlNetLoader",
+    "_meta": {
+      "title": "controlnet_qr_model_loader"
+    }
+  },
+  "23": {
+    "inputs": {
+      "strength": 6,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "57",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "44",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_fg"
+    }
+  },
+  "24": {
+    "inputs": {
+      "add_noise": "enable",
+      "noise_seed": 246981983966927,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler_ancestral",
+      "scheduler": "normal",
+      "start_at_step": 0,
+      "end_at_step": 11,
+      "return_with_leftover_noise": "enable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "17",
+        0
+      ],
+      "negative": [
+        "17",
+        1
+      ],
+      "latent_image": [
+        "63",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler_bg"
+    }
+  },
+  "27": {
+    "inputs": {
+      "add_noise": "enable",
+      "noise_seed": 816363190951797,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler",
+      "scheduler": "normal",
+      "start_at_step": 1,
+      "end_at_step": 11,
+      "return_with_leftover_noise": "enable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "23",
+        0
+      ],
+      "negative": [
+        "23",
+        1
+      ],
+      "latent_image": [
+        "36",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler_fg"
+    }
+  },
+  "29": {
+    "inputs": {
+      "x": 0,
+      "y": 0,
+      "resize_source": false,
+      "destination": [
+        "24",
+        0
+      ],
+      "source": [
+        "27",
+        0
+      ],
+      "mask": [
+        "88",
+        0
+      ]
+    },
+    "class_type": "LatentCompositeMasked",
+    "_meta": {
+      "title": "LatentCompositeMasked"
+    }
+  },
+  "30": {
+    "inputs": {
+      "add_noise": "disable",
+      "noise_seed": 219905100267874,
+      "steps": 25,
+      "cfg": 8,
+      "sampler_name": "euler_ancestral",
+      "scheduler": "normal",
+      "start_at_step": 11,
+      "end_at_step": 10000,
+      "return_with_leftover_noise": "disable",
+      "model": [
+        "6",
+        0
+      ],
+      "positive": [
+        "38",
+        0
+      ],
+      "negative": [
+        "38",
+        1
+      ],
+      "latent_image": [
+        "29",
+        0
+      ]
+    },
+    "class_type": "KSamplerAdvanced",
+    "_meta": {
+      "title": "sampler"
+    }
+  },
+  "36": {
+    "inputs": {
+      "pixels": [
+        "44",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE Encode (FG)"
+    }
+  },
+  "38": {
+    "inputs": {
+      "strength": 1.5,
+      "start_percent": 0,
+      "end_percent": 1,
+      "positive": [
+        "90",
+        0
+      ],
+      "negative": [
+        "8",
+        0
+      ],
+      "control_net": [
+        "18",
+        0
+      ],
+      "image": [
+        "42",
+        0
+      ]
+    },
+    "class_type": "ControlNetApplyAdvanced",
+    "_meta": {
+      "title": "controlnet_combined"
+    }
+  },
+  "39": {
+    "inputs": {
+      "protocol": "Https",
+      "text": "aihorde.net",
+      "module_size": 16,
+      "max_image_size": 1024,
+      "fill_hexcolor": "#000000",
+      "back_hexcolor": "#FFFFFF",
+      "error_correction": "High",
+      "border": 1,
+      "module_drawer": "Square"
+    },
+    "class_type": "comfy-qr-by-module-split",
+    "_meta": {
+      "title": "qr_code_split"
+    }
+  },
+  "40": {
+    "inputs": {
+      "value": 0.5,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_grey"
+    }
+  },
+  "41": {
+    "inputs": {
+      "mask": [
+        "40",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (Grey)"
+    }
+  },
+  "42": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "41",
+        0
+      ],
+      "source": [
+        "39",
+        0
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "qr_flattened_composite"
+    }
+  },
+  "43": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "41",
+        0
+      ],
+      "source": [
+        "39",
+        1
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "module_layer_composite"
+    }
+  },
+  "44": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "resize_source": false,
+      "destination": [
+        "65",
+        0
+      ],
+      "source": [
+        "39",
+        2
+      ]
+    },
+    "class_type": "ImageCompositeMasked",
+    "_meta": {
+      "title": "function_layer_composite"
+    }
+  },
+  "47": {
+    "inputs": {
+      "mask": [
+        "39",
+        3
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Convert Mask to Image"
+    }
+  },
+  "49": {
+    "inputs": {
+      "x": 256,
+      "y": 256,
+      "operation": "add",
+      "destination": [
+        "50",
+        0
+      ],
+      "source": [
+        "39",
+        3
+      ]
+    },
+    "class_type": "MaskComposite",
+    "_meta": {
+      "title": "mask_composite"
+    }
+  },
+  "50": {
+    "inputs": {
+      "value": 0,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_black"
+    }
+  },
+  "57": {
+    "inputs": {
+      "text": "Lucid Creations,a brain cells  ethereal, dreamlike, surreal, beautiful, illustration, incredible detail, 8k, abstract",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "function_layer_prompt"
+    }
+  },
+  "63": {
+    "inputs": {
+      "pixels": [
+        "43",
+        0
+      ],
+      "vae": [
+        "6",
+        2
+      ]
+    },
+    "class_type": "VAEEncode",
+    "_meta": {
+      "title": "VAE Encode (BG)"
+    }
+  },
+  "64": {
+    "inputs": {
+      "value": 1,
+      "width": 1024,
+      "height": 1024
+    },
+    "class_type": "SolidMask",
+    "_meta": {
+      "title": "solidmask_white"
+    }
+  },
+  "65": {
+    "inputs": {
+      "mask": [
+        "64",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (White)"
+    }
+  },
+  "67": {
+    "inputs": {
+      "mask": [
+        "50",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Solid (Black)"
+    }
+  },
+  "83": {
+    "inputs": {
+      "expand": 8,
+      "tapered_corners": false,
+      "mask": [
+        "49",
+        0
+      ]
+    },
+    "class_type": "GrowMask",
+    "_meta": {
+      "title": "GrowMask"
+    }
+  },
+  "86": {
+    "inputs": {
+      "mask": [
+        "83",
+        0
+      ]
+    },
+    "class_type": "MaskToImage",
+    "_meta": {
+      "title": "Convert Mask to Image"
+    }
+  },
+  "87": {
+    "inputs": {
+      "blur_radius": 16,
+      "sigma": 3.0000000000000018,
+      "image": [
+        "86",
+        0
+      ]
+    },
+    "class_type": "ImageBlur",
+    "_meta": {
+      "title": "image_blur"
+    }
+  },
+  "88": {
+    "inputs": {
+      "channel": "red",
+      "image": [
+        "87",
+        0
+      ]
+    },
+    "class_type": "ImageToMask",
+    "_meta": {
+      "title": "Convert Image to Mask"
+    }
+  },
+  "90": {
+    "inputs": {
+      "text": "Lucid Creations, a human brain, ethereal, dreamlike, surreal, beautiful, illustration, incredible detail, 8k, abstract",
+      "clip": [
+        "6",
+        1
+      ]
+    },
+    "class_type": "CLIPTextEncode",
+    "_meta": {
+      "title": "prompt"
+    }
+  }
+}
diff --git a/hordelib/utils/image_utils.py b/hordelib/utils/image_utils.py
index 947af7d0..19c0dd23 100644
--- a/hordelib/utils/image_utils.py
+++ b/hordelib/utils/image_utils.py
@@ -12,7 +12,6 @@
 
 
 class ImageUtils:
-
     @classmethod
     def resize_image_dimensions(
         cls,
diff --git a/hordelib/utils/ioredirect.py b/hordelib/utils/ioredirect.py
index 9b4a66f9..a51dec0f 100644
--- a/hordelib/utils/ioredirect.py
+++ b/hordelib/utils/ioredirect.py
@@ -59,86 +59,94 @@ def __init__(self, *, comfyui_progress_callback: Callable[[ComfyUIProgress, str]
         self.comfyui_progress_callback = comfyui_progress_callback
         self.start_time = perf_counter()
 
+        self.pattern_it_per_s = regex.compile(r"(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d]+\.?\d*)it/s]")
+        self.pattern_s_per_it = regex.compile(r"(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d]+\.?\d*)s/it]")
+        self.pattern_pipe = regex.compile(r"\|.*?\|")
+        self.pattern_whitespace = regex.compile(r"\s{5,}")
+        self.pattern_double_space = regex.compile(r"\s{2,}")
+
     def write(self, message: str):
-        if message != "\n" and "MemoryEfficientCrossAttention." not in message:
-            message = message.strip()
-            if not message:
+        message = message.strip()
+        if not message:
+            return
+
+        # If the has a percent followed by a pipe, it's a progress bar;
+        # log at (approximately) 0%, 25%, 50%, 75%, 100%
+        if "%|" in message:
+            matches = None
+            is_iterations_per_second = None
+            if "DDIM Sampler:" in message:
+                message = message[len("DDIM Sampler:") :]
+                message = message.strip()
+            if "it/s]" in message:
+                matches = self.pattern_it_per_s.match(message)
+                is_iterations_per_second = True
+            elif "s/it]" in message:
+                matches = self.pattern_s_per_it.match(message)
+                is_iterations_per_second = False
+
+            if not matches:
+                logger.debug(f"Unknown progress bar format?: {message}")
+                self.capture_deque.append(message)
                 return
 
-            # If the has a percent followed by a pipe, it's a progress bar;
-            # log at (approximately) 0%, 25%, 50%, 75%, 100%
-            if "%|" in message:
-                matches = None
-                is_iterations_per_second = None
-                if "DDIM Sampler:" in message:
-                    message = message[len("DDIM Sampler:") :]
-                    message = message.strip()
-                if "it/s]" in message:
-                    matches = regex.match(r"(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d]+\.?\d*)it/s]", message)
-                    is_iterations_per_second = True
-                elif "s/it]" in message:
-                    matches = regex.match(r"(\d+)%.*\s(\d+)/(\d+)\s.*\s([\?\d]+\.?\d*)s/it]", message)
-                    is_iterations_per_second = False
-
-                if not matches:
-                    logger.debug(f"Unknown progress bar format?: {message}")
-                    self.capture_deque.append(message)
-                    return
-
-                # Remove everything in between '|' and '|'
-                message = regex.sub(r"\|.*?\|", "", message)
-
-                # Remove all cases of more than 5 whitespace characters in a row
-                message = regex.sub(r"\s{5,}", " ", message)
-
-                # Add a timestamp to the log
-                message = f"{message} ({perf_counter() - self.start_time:.2f} seconds in ComfyUI)"
-
-                # found_percent_number = int(matches.group(1))
-                found_current_step = int(matches.group(2))
-                found_total_steps = int(matches.group(3))
-                iteration_rate = matches.group(4)
-
-                if (
-                    self.slow_message_count < 5
-                    and iteration_rate != "?"
-                    and (not is_iterations_per_second or float(iteration_rate) < 1.2)
-                ):
-                    self.slow_message_count += 1
-                    if self.slow_message_count == 5:
-                        logger.warning("Suppressing further slow job warnings. Please investigate.")
-                    else:
-                        rate_unit = "iterations per second" if is_iterations_per_second else "*seconds per iterations*"
-                        logger.warning(f"Job Slowdown: Job is running at {iteration_rate} {rate_unit}.")
-
-                if found_current_step == 0:
-                    logger.info("Job will show progress for the first three steps, and then every 10 steps.")
-
-                # Log the first 2 steps, then every 10 steps, then the last step
-                if (
-                    found_current_step in [1, 2, 3]
-                    or found_current_step % 10 == 0
-                    or found_current_step == found_total_steps
-                ):
-                    logger.info(message)
-
-                if self.comfyui_progress_callback:
-                    self.comfyui_progress_callback(
-                        ComfyUIProgress(
-                            percent=int(matches.group(1)),
-                            current_step=found_current_step,
-                            total_steps=found_total_steps,
-                            rate=float(iteration_rate) if iteration_rate != "?" else -1.0,
-                            rate_unit=(
-                                ComfyUIProgressUnit.ITERATIONS_PER_SECOND
-                                if is_iterations_per_second
-                                else ComfyUIProgressUnit.SECONDS_PER_ITERATION
-                            ),
+            # Remove everything in between '|' and '|'
+            message = self.pattern_pipe.sub("", message)
+
+            # Remove all cases of more than 5 whitespace characters in a row
+            message = self.pattern_whitespace.sub(" ", message)
+
+            # Add a timestamp to the log
+            message = f"{message} ({perf_counter() - self.start_time:.2f} seconds in ComfyUI)"
+
+            # found_percent_number = int(matches.group(1))
+            found_current_step = int(matches.group(2))
+            found_total_steps = int(matches.group(3))
+            iteration_rate = matches.group(4)
+
+            # Remove any double spaces
+            message = self.pattern_double_space.sub(" ", message)
+
+            if (
+                self.slow_message_count < 5
+                and iteration_rate != "?"
+                and (not is_iterations_per_second or float(iteration_rate) < 1.2)
+            ):
+                self.slow_message_count += 1
+                if self.slow_message_count == 5:
+                    logger.warning("Suppressing further slow job warnings. Please investigate.")
+                else:
+                    rate_unit = "iterations per second" if is_iterations_per_second else "*seconds per iterations*"
+                    logger.warning(f"Job Slowdown: Job is running at {iteration_rate} {rate_unit}.")
+
+            if found_current_step == 0:
+                logger.info("Job will show progress for the first three steps, and then every 10 steps.")
+
+            # Log the first 3 steps, then every 10 steps, then the last step
+            if (
+                found_current_step in [1, 2, 3]
+                or found_current_step % 10 == 0
+                or found_current_step == found_total_steps
+            ):
+                logger.info(message)
+
+            if self.comfyui_progress_callback:
+                self.comfyui_progress_callback(
+                    ComfyUIProgress(
+                        percent=int(matches.group(1)),
+                        current_step=found_current_step,
+                        total_steps=found_total_steps,
+                        rate=float(iteration_rate) if iteration_rate != "?" else -1.0,
+                        rate_unit=(
+                            ComfyUIProgressUnit.ITERATIONS_PER_SECOND
+                            if is_iterations_per_second
+                            else ComfyUIProgressUnit.SECONDS_PER_ITERATION
                         ),
-                        message,
-                    )
+                    ),
+                    message,
+                )
 
-            self.capture_deque.append(message)
+        self.capture_deque.append(message)
 
     def set_size(self, size):
         while len(self.capture_deque) > size:
diff --git a/images_expected/qr_code.png b/images_expected/qr_code.png
new file mode 100644
index 00000000..5ca125ab
Binary files /dev/null and b/images_expected/qr_code.png differ
diff --git a/images_expected/qr_code_out_of_bounds.png b/images_expected/qr_code_out_of_bounds.png
new file mode 100644
index 00000000..2d35251d
Binary files /dev/null and b/images_expected/qr_code_out_of_bounds.png differ
diff --git a/images_expected/qr_code_size.png b/images_expected/qr_code_size.png
new file mode 100644
index 00000000..b5bdcbaa
Binary files /dev/null and b/images_expected/qr_code_size.png differ
diff --git a/images_expected/qr_code_strength.png b/images_expected/qr_code_strength.png
new file mode 100644
index 00000000..47acbb56
Binary files /dev/null and b/images_expected/qr_code_strength.png differ
diff --git a/images_expected/qr_code_texts.png b/images_expected/qr_code_texts.png
new file mode 100644
index 00000000..ff1a6b7d
Binary files /dev/null and b/images_expected/qr_code_texts.png differ
diff --git a/images_expected/qr_code_too_long_text.png b/images_expected/qr_code_too_long_text.png
new file mode 100644
index 00000000..2a854ecf
Binary files /dev/null and b/images_expected/qr_code_too_long_text.png differ
diff --git a/images_expected/qr_code_xl.png b/images_expected/qr_code_xl.png
new file mode 100644
index 00000000..19107bcf
Binary files /dev/null and b/images_expected/qr_code_xl.png differ
diff --git a/pyproject.toml b/pyproject.toml
index 32dfba9b..14809ae8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -69,6 +69,7 @@ exclude = '''
     ComfyUI
   | \.tox
   | comfy_controlnet_preprocessors
+  | comfy_qr
   | facerestore
   | build
 )/
@@ -76,7 +77,7 @@ exclude = '''
 
 [tool.ruff] # XXX this isn't part of CI yet
 line-length=119
-exclude=["ComfyUI", "comfy_controlnet_preprocessors", "facerestore", "build"]
+exclude=["ComfyUI", "comfy_controlnet_preprocessors", "facerestore", "comfy_qr", "build"]
 ignore=[
     # "F401", # imported but unused
     "E402", # Module level import not at top of file
diff --git a/requirements.txt b/requirements.txt
index 4ff5caa0..bbc3bedf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -46,3 +46,4 @@ unidecode
 fuzzywuzzy
 strenum
 kornia
+qrcode


Test patch:
diff --git a/tests/conftest.py b/tests/conftest.py
index bd7a27ad..d8e4ca9e 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -164,6 +164,11 @@ def sdxl_1_0_base_model_name(shared_model_manager: type[SharedModelManager]) ->
     return "SDXL 1.0"
 
 
+@pytest.fixture(scope="session")
+def sdxl_refined_model_name(shared_model_manager: type[SharedModelManager]) -> str:
+    return "AlbedoBase XL (SDXL)"
+
+
 @pytest.fixture(scope="session")
 def stable_cascade_base_model_name(shared_model_manager: type[SharedModelManager]) -> str:
     return "Stable Cascade 1.0"
@@ -252,6 +257,8 @@ def pytest_collection_modifyitems(items):
     MODULES_TO_RUN_LAST = [
         "tests.test_horde_inference",
         "tests.test_horde_inference_img2img",
+        "tests.test_horde_inference_qrcode",
+        "tests.test_horde_inference_cascade",
         "tests.test_horde_samplers",
         "tests.test_horde_ti",
         "tests.test_horde_lcm",
diff --git a/tests/test_horde_inference_qr_code.py b/tests/test_horde_inference_qr_code.py
new file mode 100644
index 00000000..50df141e
--- /dev/null
+++ b/tests/test_horde_inference_qr_code.py
@@ -0,0 +1,408 @@
+# test_horde.py
+
+import pytest
+from PIL import Image
+
+from hordelib.horde import HordeLib
+from hordelib.shared_model_manager import SharedModelManager
+from tests.testing_shared_functions import check_single_inference_image_similarity
+
+
+class TestHordeInferenceQRCode:
+    @pytest.fixture(autouse=True)
+    def setup_and_teardown(self, shared_model_manager: type[SharedModelManager]):
+        assert shared_model_manager.manager.controlnet is not None
+        for preproc in HordeLib.CONTROLNET_IMAGE_PREPROCESSOR_MAP.keys():
+            shared_model_manager.manager.controlnet.download_control_type(preproc, ["stable diffusion 1"])
+
+    def test_qr_code_inference(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        stable_diffusion_model_name_for_testing: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1312,
+            "height": 768,
+            "width": 768,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": (
+                "drawing of two witches performing a seanse around a cauldron. Wispy and Ethereal, sepia colors"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "ddim_steps": 30,
+            "n_iter": 1,
+            "model": stable_diffusion_model_name_for_testing,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "https://aihorde.net",
+                    "reference": "qr_text",
+                },
+                {
+                    "text": "256",
+                    "reference": "x_offset",
+                },
+                {
+                    "text": "256",
+                    "reference": "y_offset",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_inference_out_of_bounds(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        stable_diffusion_model_name_for_testing: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1312,
+            "height": 768,
+            "width": 768,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": (
+                "drawing of two witches performing a seanse around a cauldron. Wispy and Ethereal, sepia colors"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "ddim_steps": 30,
+            "n_iter": 1,
+            "model": stable_diffusion_model_name_for_testing,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "https://aihorde.net",
+                    "reference": "qr_text",
+                },
+                {
+                    "text": "-256",
+                    "reference": "x_offset",
+                },
+                {
+                    "text": "800",
+                    "reference": "y_offset",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_out_of_bounds.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_inference_xl(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        sdxl_refined_model_name: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1312,
+            "height": 1024,
+            "width": 1024,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": (
+                "Lucid Creations, a human brain, ethereal, dreamlike, surreal, "
+                "beautiful, illustration, incredible detail, 8k, abstract"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "control_strength": 1.2,
+            "ddim_steps": 25,
+            "n_iter": 1,
+            "model": sdxl_refined_model_name,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "https://aihorde.net",
+                    "reference": "qr_text",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_xl.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_inference_too_large_text(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        sdxl_refined_model_name: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1111,
+            "height": 1024,
+            "width": 1024,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": "The Scream, 1893 by Edvard Munch",
+            "ddim_steps": 25,
+            "n_iter": 1,
+            "model": sdxl_refined_model_name,
+            "workflow": "qr_code",
+            "control_strength": 1.4,
+            "extra_texts": [
+                {
+                    "text": """
+Sint eos pariatur architecto repellat nihil sed distinctio aut.
+Aut quae modi libero. Est ea nobis blanditiis in ut quam.
+Blanditiis expedita minus tenetur dolorum.
+Nisi recusandae blanditiis quia mollitia. Voluptatibus omnis non eos.
+Aut voluptatum ut aspernatur minima omnis.
+Quia officia est nisi exercitationem sint.
+Adipisci mollitia sunt dignissimos fugiat sint magnam et sit. Cumque ipsum ullam et molestiae.
+Consequatur saepe occaecati amet odio molestias odio est doloremque.
+Sapiente reprehenderit qui adipisci officia delectus quas totam.
+Maxime commodi rerum quod voluptas dolor ducimus. Non quibusdam ut assumenda ipsum voluptatem sit a necessitatibus.
+Provident est culpa delectus nemo. Dolorem velit assumenda labore ut.
+Voluptatum corporis modi id dolores necessitatibus voluptatibus at voluptate.
+Rerum sed incidunt commodi quo quo. Sit sint accusantium modi eligendi molestiae maxime.
+                """,
+                    "reference": "qr_text",
+                },
+                {
+                    "text": "48",
+                    "reference": "x_offset",
+                },
+                {
+                    "text": "48",
+                    "reference": "y_offset",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_too_long_text.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_control_strength(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        sdxl_refined_model_name: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1312,
+            "height": 1024,
+            "width": 1024,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "control_strength": 0.5,
+            "prompt": (
+                "Lucid Creations, a human brain, ethereal, dreamlike, surreal, "
+                "beautiful, illustration, incredible detail, 8k, abstract"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "ddim_steps": 25,
+            "n_iter": 1,
+            "model": sdxl_refined_model_name,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "https://aihorde.net",
+                    "reference": "qr_text",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_strength.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_control_non_square(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        sdxl_refined_model_name: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 11312,
+            "height": 1280,
+            "width": 768,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": (
+                "Lucid Creations, a human brain, ethereal, dreamlike, surreal, "
+                "beautiful, illustration, incredible detail, 8k, abstract"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "control_strength": 1,
+            "ddim_steps": 25,
+            "n_iter": 1,
+            "model": sdxl_refined_model_name,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "https://aihorde.net",
+                    "reference": "qr_text",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_size.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )
+
+    def test_qr_code_control_qr_texts(
+        self,
+        shared_model_manager: type[SharedModelManager],
+        hordelib_instance: HordeLib,
+        sdxl_refined_model_name: str,
+    ):
+        data = {
+            "sampler_name": "k_euler",
+            "cfg_scale": 7.5,
+            "denoising_strength": 1.0,
+            "seed": 1312,
+            "height": 1024,
+            "width": 1024,
+            "karras": False,
+            "tiling": False,
+            "hires_fix": False,
+            "clip_skip": 1,
+            "prompt": (
+                "Lucid Creations, a human brain, ethereal, dreamlike, surreal, "
+                "beautiful, illustration, incredible detail, 8k, abstract"
+                "###worst quality, bad lighting, deformed, ugly, low contrast"
+            ),
+            "ddim_steps": 25,
+            "n_iter": 1,
+            "model": sdxl_refined_model_name,
+            "workflow": "qr_code",
+            "extra_texts": [
+                {
+                    "text": "haidra.net",
+                    "reference": "qr_text",
+                },
+                {
+                    "text": "Circle",
+                    "reference": "module_drawer",
+                },
+                {
+                    "text": "https",
+                    "reference": "protocol",
+                },
+                {
+                    "text": "Lucid Creations, ethereal brain cells",
+                    "reference": "function_layer_prompt",
+                },
+                {
+                    "text": "48",
+                    "reference": "x_offset",
+                },
+                {
+                    "text": "48",
+                    "reference": "y_offset",
+                },
+            ],
+        }
+        assert hordelib_instance is not None
+        assert shared_model_manager.manager.controlnet is not None
+
+        pil_image = hordelib_instance.basic_inference_single_image(data).image
+        assert pil_image is not None
+        assert isinstance(pil_image, Image.Image)
+
+        img_filename = "qr_code_texts.png"
+        pil_image.save(f"images/{img_filename}", quality=100)
+
+        assert check_single_inference_image_similarity(
+            f"images_expected/{img_filename}",
+            pil_image,
+        )