Instructions to use hgjc/ltx-ugc-bundle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use hgjc/ltx-ugc-bundle with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download hgjc/ltx-ugc-bundle --local-dir models/ltx-ugc-bundle hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/ltx-ugc-bundle/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/ltx-ugc-bundle/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/ltx-ugc-bundle/<checkpoint>.safetensors \ --distilled-lora models/ltx-ugc-bundle/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/ltx-ugc-bundle/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
File size: 4,002 Bytes
46dc982 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 | from abc import ABC, abstractmethod
from typing import Any, Mapping, Sequence, Tuple
SDXL_SUPPORTED_RESOLUTIONS = [
(1024, 1024, 1.0),
(1152, 896, 1.2857142857142858),
(896, 1152, 0.7777777777777778),
(1216, 832, 1.4615384615384615),
(832, 1216, 0.6842105263157895),
(1344, 768, 1.75),
(768, 1344, 0.5714285714285714),
(1536, 640, 2.4),
(640, 1536, 0.4166666666666667),
]
SDXL_EXTENDED_RESOLUTIONS = [
(512, 2048, 0.25),
(512, 1984, 0.26),
(512, 1920, 0.27),
(512, 1856, 0.28),
(576, 1792, 0.32),
(576, 1728, 0.33),
(576, 1664, 0.35),
(640, 1600, 0.4),
(640, 1536, 0.42),
(704, 1472, 0.48),
(704, 1408, 0.5),
(704, 1344, 0.52),
(768, 1344, 0.57),
(768, 1280, 0.6),
(832, 1216, 0.68),
(832, 1152, 0.72),
(896, 1152, 0.78),
(896, 1088, 0.82),
(960, 1088, 0.88),
(960, 1024, 0.94),
(1024, 1024, 1.0),
(1024, 960, 1.8),
(1088, 960, 1.14),
(1088, 896, 1.22),
(1152, 896, 1.30),
(1152, 832, 1.39),
(1216, 832, 1.47),
(1280, 768, 1.68),
(1344, 768, 1.76),
(1408, 704, 2.0),
(1472, 704, 2.10),
(1536, 640, 2.4),
(1600, 640, 2.5),
(1664, 576, 2.90),
(1728, 576, 3.0),
(1792, 576, 3.12),
(1856, 512, 3.63),
(1920, 512, 3.76),
(1984, 512, 3.89),
(2048, 512, 4.0),
]
class Resolution(ABC):
@classmethod
@abstractmethod
def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ...
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {
"required": {
"resolution": ([f"{res[0]}x{res[1]}" for res in cls.resolutions()],)
}
}
RETURN_TYPES = ("INT", "INT")
RETURN_NAMES = ("width", "height")
FUNCTION = "op"
CATEGORY = "math/graphics"
def op(self, resolution: str) -> tuple[int, int]:
width, height = resolution.split("x")
return (int(width), int(height))
class NearestResolution(ABC):
@classmethod
@abstractmethod
def resolutions(cls) -> Sequence[Tuple[int, int, float]]: ...
@classmethod
def INPUT_TYPES(cls) -> Mapping[str, Any]:
return {"required": {"image": ("IMAGE",)}}
RETURN_TYPES = ("INT", "INT")
RETURN_NAMES = ("width", "height")
FUNCTION = "op"
CATEGORY = "math/graphics"
def op(self, image) -> tuple[int, int]:
image_width = image.size()[2]
image_height = image.size()[1]
print(f"Input image resolution: {image_width}x{image_height}")
image_ratio = image_width / image_height
differences = [
(abs(image_ratio - resolution[2]), resolution)
for resolution in self.resolutions()
]
smallest = None
for difference in differences:
if smallest is None:
smallest = difference
else:
if difference[0] < smallest[0]:
smallest = difference
if smallest is not None:
width = smallest[1][0]
height = smallest[1][1]
else:
width = 1024
height = 1024
print(f"Selected resolution: {width}x{height}")
return (width, height)
class SDXLResolution(Resolution):
@classmethod
def resolutions(cls):
return SDXL_SUPPORTED_RESOLUTIONS
class SDXLExtendedResolution(Resolution):
@classmethod
def resolutions(cls):
return SDXL_EXTENDED_RESOLUTIONS
class NearestSDXLResolution(NearestResolution):
@classmethod
def resolutions(cls):
return SDXL_SUPPORTED_RESOLUTIONS
class NearestSDXLExtendedResolution(NearestResolution):
@classmethod
def resolutions(cls):
return SDXL_EXTENDED_RESOLUTIONS
NODE_CLASS_MAPPINGS = {
"CM_SDXLResolution": SDXLResolution,
"CM_NearestSDXLResolution": NearestSDXLResolution,
"CM_SDXLExtendedResolution": SDXLExtendedResolution,
"CM_NearestSDXLExtendedResolution": NearestSDXLExtendedResolution,
}
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