Instructions to use vidfom/Ltx-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use vidfom/Ltx-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="vidfom/Ltx-3", filename="ComfyUI/models/text_encoders/gemma-3-12b-it-qat-UD-Q4_K_XL.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use vidfom/Ltx-3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf vidfom/Ltx-3:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf vidfom/Ltx-3:UD-Q4_K_XL
Use Docker
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- LM Studio
- Jan
- Ollama
How to use vidfom/Ltx-3 with Ollama:
ollama run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Unsloth Studio new
How to use vidfom/Ltx-3 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vidfom/Ltx-3 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vidfom/Ltx-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vidfom/Ltx-3 to start chatting
- Docker Model Runner
How to use vidfom/Ltx-3 with Docker Model Runner:
docker model run hf.co/vidfom/Ltx-3:UD-Q4_K_XL
- Lemonade
How to use vidfom/Ltx-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vidfom/Ltx-3:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ltx-3-UD-Q4_K_XL
List all available models
lemonade list
File size: 11,096 Bytes
e00eceb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 | from enum import Enum
from typing import Optional, List, Dict, Any, Union
from datetime import datetime
from pydantic import BaseModel, Field, RootModel, StrictBytes
class IdeogramColorPalette1(BaseModel):
name: str = Field(..., description='Name of the preset color palette')
class Member(BaseModel):
color: Optional[str] = Field(
None, description='Hexadecimal color code', pattern='^#[0-9A-Fa-f]{6}$'
)
weight: Optional[float] = Field(
None, description='Optional weight for the color (0-1)', ge=0.0, le=1.0
)
class IdeogramColorPalette2(BaseModel):
members: List[Member] = Field(
..., description='Array of color definitions with optional weights'
)
class IdeogramColorPalette(
RootModel[Union[IdeogramColorPalette1, IdeogramColorPalette2]]
):
root: Union[IdeogramColorPalette1, IdeogramColorPalette2] = Field(
...,
description='A color palette specification that can either use a preset name or explicit color definitions with weights',
)
class ImageRequest(BaseModel):
aspect_ratio: Optional[str] = Field(
None,
description="Optional. The aspect ratio (e.g., 'ASPECT_16_9', 'ASPECT_1_1'). Cannot be used with resolution. Defaults to 'ASPECT_1_1' if unspecified.",
)
color_palette: Optional[Dict[str, Any]] = Field(
None, description='Optional. Color palette object. Only for V_2, V_2_TURBO.'
)
magic_prompt_option: Optional[str] = Field(
None, description="Optional. MagicPrompt usage ('AUTO', 'ON', 'OFF')."
)
model: str = Field(..., description="The model used (e.g., 'V_2', 'V_2A_TURBO')")
negative_prompt: Optional[str] = Field(
None,
description='Optional. Description of what to exclude. Only for V_1, V_1_TURBO, V_2, V_2_TURBO.',
)
num_images: Optional[int] = Field(
1,
description='Optional. Number of images to generate (1-8). Defaults to 1.',
ge=1,
le=8,
)
prompt: str = Field(
..., description='Required. The prompt to use to generate the image.'
)
resolution: Optional[str] = Field(
None,
description="Optional. Resolution (e.g., 'RESOLUTION_1024_1024'). Only for model V_2. Cannot be used with aspect_ratio.",
)
seed: Optional[int] = Field(
None,
description='Optional. A number between 0 and 2147483647.',
ge=0,
le=2147483647,
)
style_type: Optional[str] = Field(
None,
description="Optional. Style type ('AUTO', 'GENERAL', 'REALISTIC', 'DESIGN', 'RENDER_3D', 'ANIME'). Only for models V_2 and above.",
)
class IdeogramGenerateRequest(BaseModel):
image_request: ImageRequest = Field(
..., description='The image generation request parameters.'
)
class Datum(BaseModel):
is_image_safe: Optional[bool] = Field(
None, description='Indicates whether the image is considered safe.'
)
prompt: Optional[str] = Field(
None, description='The prompt used to generate this image.'
)
resolution: Optional[str] = Field(
None, description="The resolution of the generated image (e.g., '1024x1024')."
)
seed: Optional[int] = Field(
None, description='The seed value used for this generation.'
)
style_type: Optional[str] = Field(
None,
description="The style type used for generation (e.g., 'REALISTIC', 'ANIME').",
)
url: Optional[str] = Field(None, description='URL to the generated image.')
class IdeogramGenerateResponse(BaseModel):
created: Optional[datetime] = Field(
None, description='Timestamp when the generation was created.'
)
data: Optional[List[Datum]] = Field(
None, description='Array of generated image information.'
)
class StyleCode(RootModel[str]):
root: str = Field(..., pattern='^[0-9A-Fa-f]{8}$')
class Datum1(BaseModel):
is_image_safe: Optional[bool] = None
prompt: Optional[str] = None
resolution: Optional[str] = None
seed: Optional[int] = None
style_type: Optional[str] = None
url: Optional[str] = None
class IdeogramV3IdeogramResponse(BaseModel):
created: Optional[datetime] = None
data: Optional[List[Datum1]] = None
class RenderingSpeed1(str, Enum):
TURBO = 'TURBO'
DEFAULT = 'DEFAULT'
QUALITY = 'QUALITY'
class IdeogramV3ReframeRequest(BaseModel):
color_palette: Optional[Dict[str, Any]] = None
image: Optional[StrictBytes] = None
num_images: Optional[int] = Field(None, ge=1, le=8)
rendering_speed: Optional[RenderingSpeed1] = None
resolution: str
seed: Optional[int] = Field(None, ge=0, le=2147483647)
style_codes: Optional[List[str]] = None
style_reference_images: Optional[List[StrictBytes]] = None
class MagicPrompt(str, Enum):
AUTO = 'AUTO'
ON = 'ON'
OFF = 'OFF'
class StyleType(str, Enum):
AUTO = 'AUTO'
GENERAL = 'GENERAL'
REALISTIC = 'REALISTIC'
DESIGN = 'DESIGN'
class IdeogramV3RemixRequest(BaseModel):
aspect_ratio: Optional[str] = None
color_palette: Optional[Dict[str, Any]] = None
image: Optional[StrictBytes] = None
image_weight: Optional[int] = Field(50, ge=1, le=100)
magic_prompt: Optional[MagicPrompt] = None
negative_prompt: Optional[str] = None
num_images: Optional[int] = Field(None, ge=1, le=8)
prompt: str
rendering_speed: Optional[RenderingSpeed1] = None
resolution: Optional[str] = None
seed: Optional[int] = Field(None, ge=0, le=2147483647)
style_codes: Optional[List[str]] = None
style_reference_images: Optional[List[StrictBytes]] = None
style_type: Optional[StyleType] = None
class IdeogramV3ReplaceBackgroundRequest(BaseModel):
color_palette: Optional[Dict[str, Any]] = None
image: Optional[StrictBytes] = None
magic_prompt: Optional[MagicPrompt] = None
num_images: Optional[int] = Field(None, ge=1, le=8)
prompt: str
rendering_speed: Optional[RenderingSpeed1] = None
seed: Optional[int] = Field(None, ge=0, le=2147483647)
style_codes: Optional[List[str]] = None
style_reference_images: Optional[List[StrictBytes]] = None
class ColorPalette(BaseModel):
name: str = Field(..., description='Name of the color palette', examples=['PASTEL'])
class MagicPrompt2(str, Enum):
ON = 'ON'
OFF = 'OFF'
class StyleType1(str, Enum):
AUTO = 'AUTO'
GENERAL = 'GENERAL'
REALISTIC = 'REALISTIC'
DESIGN = 'DESIGN'
FICTION = 'FICTION'
class RenderingSpeed(str, Enum):
DEFAULT = 'DEFAULT'
TURBO = 'TURBO'
QUALITY = 'QUALITY'
class IdeogramV3EditRequest(BaseModel):
color_palette: Optional[IdeogramColorPalette] = None
image: Optional[StrictBytes] = Field(
None,
description='The image being edited (max size 10MB); only JPEG, WebP and PNG formats are supported at this time.',
)
magic_prompt: Optional[str] = Field(
None,
description='Determine if MagicPrompt should be used in generating the request or not.',
)
mask: Optional[StrictBytes] = Field(
None,
description='A black and white image of the same size as the image being edited (max size 10MB). Black regions in the mask should match up with the regions of the image that you would like to edit; only JPEG, WebP and PNG formats are supported at this time.',
)
num_images: Optional[int] = Field(
None, description='The number of images to generate.'
)
prompt: str = Field(
..., description='The prompt used to describe the edited result.'
)
rendering_speed: RenderingSpeed
seed: Optional[int] = Field(
None, description='Random seed. Set for reproducible generation.'
)
style_codes: Optional[List[StyleCode]] = Field(
None,
description='A list of 8 character hexadecimal codes representing the style of the image. Cannot be used in conjunction with style_reference_images or style_type.',
)
style_reference_images: Optional[List[StrictBytes]] = Field(
None,
description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.',
)
character_reference_images: Optional[List[str]] = Field(
None,
description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
)
character_reference_images_mask: Optional[List[str]] = Field(
None,
description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
)
class IdeogramV3Request(BaseModel):
aspect_ratio: Optional[str] = Field(
None, description='Aspect ratio in format WxH', examples=['1x3']
)
color_palette: Optional[ColorPalette] = None
magic_prompt: Optional[MagicPrompt2] = Field(
None, description='Whether to enable magic prompt enhancement'
)
negative_prompt: Optional[str] = Field(
None, description='Text prompt specifying what to avoid in the generation'
)
num_images: Optional[int] = Field(
None, description='Number of images to generate', ge=1
)
prompt: str = Field(..., description='The text prompt for image generation')
rendering_speed: RenderingSpeed
resolution: Optional[str] = Field(
None, description='Image resolution in format WxH', examples=['1280x800']
)
seed: Optional[int] = Field(
None, description='Seed value for reproducible generation'
)
style_codes: Optional[List[StyleCode]] = Field(
None, description='Array of style codes in hexadecimal format'
)
style_reference_images: Optional[List[str]] = Field(
None, description='Array of reference image URLs or identifiers'
)
style_type: Optional[StyleType1] = Field(
None, description='The type of style to apply'
)
character_reference_images: Optional[List[str]] = Field(
None,
description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
)
character_reference_images_mask: Optional[List[str]] = Field(
None,
description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
)
|