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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x80 in position 7: invalid start byte
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1815, in _prepare_split_single
for _, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 73, in _generate_tables
batch = f.read(self.config.chunksize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 813, in read_with_retries
out = read(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "<frozen codecs>", line 322, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 7: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1339, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 972, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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MIT License
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Copyright (c) 2024 plugcrypt
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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yolov8m-face.pt https://github.com/akanametov/yolo-face?tab=readme-ov-file#models
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face_yolov8n-seg2_60.pt https://huggingface.co/jags/yolov8_model_segmentation-set/blob/main/face_yolov8n-seg2_60.pt
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sigclip_vision_384 https://huggingface.co/Comfy-Org/sigclip_vision_384/blob/main/sigclip_vision_patch14_384.safetensors
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FLUX.1-dev-ControlNet-Union-Pro https://huggingface.co/Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro/tree/main
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Flux.1-dev-Controlnet-Upscaler https://huggingface.co/jasperai/Flux.1-dev-Controlnet-Upscaler/tree/main
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FLUX SUPER
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PONY SUPER
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1. Resolution
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This is a utility node that standardizes image dimensions. By providing a target length for the longest side and selecting from a list of common aspect ratios (like 3:2 or 16:9), the node calculates and provides the corresponding width and height, making it easy to maintain consistent image proportions.
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2. Simple Toggle & K (Simple Knob)
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These are compact UI utility nodes.
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Simple Toggle provides a simple True/False value, visualized as an intuitive on/off switch in the interface.
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K (Simple Knob) offers a float value through a space-saving rotary knob widget, serving as a compact alternative to a full slider.
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3. SamplerSchedulerSelector
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A convenience node that centralizes the selection of ComfyUI's samplers and schedulers. It provides two dropdown menus containing all available options and outputs the selected names as strings for use in other sampler nodes.
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4. Boolean Transform Node
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This node acts as a simple logic gate, converting an input string into a boolean and an integer. It interprets any non-zero number as True (and 1), while zero or non-numeric strings become False (and 0).
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5. FancyNoteNode
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A purely visual tool for workflow annotation, this node displays highly customizable, stylized text directly on the graph. Users can adjust font size, colors, and glow effects to create clear, visually appealing notes for complex workflows.
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6. LoraLoaderStr
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This is an enhanced LoRA loader with a convenient on/off switch. It applies a selected LoRA to the model and CLIP and also generates a descriptive string that can optionally include the strength settings, which is useful for tracking metadata.
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7. RemoveTrailingCommaNode
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A specific string utility node whose sole function is to remove a single comma from the very end of a string, if present. This is often used to clean up dynamically generated lists of keywords or block weights.
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8. Toggle Lora Unet Blocks L1 / L2
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These two nodes offer incredibly granular control over LoRA application. Each provides 38 boolean toggles, corresponding to specific blocks in the UNet architecture. Activating toggles generates a comma-separated string that can be fed into advanced LoRA nodes to apply weights to very specific parts of the model's structure. L1 targets the first linear layer in each block, while L2 targets the second.
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9. Video Duration Calculator
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A straightforward calculator that determines the total duration of a video clip in seconds, based on its frames per second (FPS) and total frame count.
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10. Smart Style Model Apply & Clear Cache
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This pair of nodes provides an efficient way to apply style models (like those used with FLUX).
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The main node's key feature is its ability to bypass all computation if the style strength is zero, saving significant resources. It also includes an optional cache to speed up repeated generations with the same settings.
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The accompanying Clear Style Model Cache node provides a simple way to manually clear this cache.
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11. Flux Tiled Sampler (Advanced)
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A powerful, self-contained tiled sampler designed specifically for high-resolution generation with FLUX models. It works by dividing an image or latent into a grid of overlapping tiles, running the sampling process on each tile individually, and then seamlessly stitching them back together using a blended mask. This technique allows for creating images at resolutions that would normally cause out-of-memory errors.
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12. MaskEmptyFloatNode
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A conditional utility node that outputs a float value based on a mask's content. If the input mask is completely empty (all black), it outputs 0.0; otherwise, it outputs a user-specified value.
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13. FluxSemanticEncoder https://www.youtube.com/watch?v=it35sOMFxho
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An extremely advanced text conditioning node for FLUX models that offers powerful, creative control over prompts through several modes:
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Positional EQ: Functions like an audio EQ for prompts, allowing you to boost or cut the influence of tokens based on their position in the sentence using parametric bands (center, gain, Q-factor).
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Semantic Shift: Uses a pre-computed "map" of related words to replace tokens with semantically similar ones. A distance parameter controls how far the meaning can drift.
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