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 Settings
- 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
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: 12,157 Bytes
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from comfy_api.latest import IO, ComfyExtension, Input
from comfy_api_nodes.apis.bria import (
BriaEditImageRequest,
BriaRemoveBackgroundRequest,
BriaRemoveBackgroundResponse,
BriaRemoveVideoBackgroundRequest,
BriaRemoveVideoBackgroundResponse,
BriaImageEditResponse,
BriaStatusResponse,
InputModerationSettings,
)
from comfy_api_nodes.util import (
ApiEndpoint,
convert_mask_to_image,
download_url_to_image_tensor,
download_url_to_video_output,
poll_op,
sync_op,
upload_image_to_comfyapi,
upload_video_to_comfyapi,
validate_video_duration,
)
class BriaImageEditNode(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="BriaImageEditNode",
display_name="Bria FIBO Image Edit",
category="api node/image/Bria",
description="Edit images using Bria latest model",
inputs=[
IO.Combo.Input("model", options=["FIBO"]),
IO.Image.Input("image"),
IO.String.Input(
"prompt",
multiline=True,
default="",
tooltip="Instruction to edit image",
),
IO.String.Input("negative_prompt", multiline=True, default=""),
IO.String.Input(
"structured_prompt",
multiline=True,
default="",
tooltip="A string containing the structured edit prompt in JSON format. "
"Use this instead of usual prompt for precise, programmatic control.",
),
IO.Int.Input(
"seed",
default=1,
min=1,
max=2147483647,
step=1,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
),
IO.Float.Input(
"guidance_scale",
default=3,
min=3,
max=5,
step=0.01,
display_mode=IO.NumberDisplay.number,
tooltip="Higher value makes the image follow the prompt more closely.",
),
IO.Int.Input(
"steps",
default=50,
min=20,
max=50,
step=1,
display_mode=IO.NumberDisplay.number,
),
IO.DynamicCombo.Input(
"moderation",
options=[
IO.DynamicCombo.Option("false", []),
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input("prompt_content_moderation", default=False),
IO.Boolean.Input("visual_input_moderation", default=False),
IO.Boolean.Input("visual_output_moderation", default=True),
],
),
],
tooltip="Moderation settings",
),
IO.Mask.Input(
"mask",
tooltip="If omitted, the edit applies to the entire image.",
optional=True,
),
],
outputs=[
IO.Image.Output(),
IO.String.Output(display_name="structured_prompt"),
],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.04}""",
),
)
@classmethod
async def execute(
cls,
model: str,
image: Input.Image,
prompt: str,
negative_prompt: str,
structured_prompt: str,
seed: int,
guidance_scale: float,
steps: int,
moderation: InputModerationSettings,
mask: Input.Image | None = None,
) -> IO.NodeOutput:
if not prompt and not structured_prompt:
raise ValueError("One of prompt or structured_prompt is required to be non-empty.")
mask_url = None
if mask is not None:
mask_url = await upload_image_to_comfyapi(cls, convert_mask_to_image(mask), wait_label="Uploading mask")
response = await sync_op(
cls,
ApiEndpoint(path="proxy/bria/v2/image/edit", method="POST"),
data=BriaEditImageRequest(
instruction=prompt if prompt else None,
structured_instruction=structured_prompt if structured_prompt else None,
images=[await upload_image_to_comfyapi(cls, image, wait_label="Uploading image")],
mask=mask_url,
negative_prompt=negative_prompt if negative_prompt else None,
guidance_scale=guidance_scale,
seed=seed,
model_version=model,
steps_num=steps,
prompt_content_moderation=moderation.get("prompt_content_moderation", False),
visual_input_content_moderation=moderation.get("visual_input_moderation", False),
visual_output_content_moderation=moderation.get("visual_output_moderation", False),
),
response_model=BriaStatusResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaImageEditResponse,
)
return IO.NodeOutput(
await download_url_to_image_tensor(response.result.image_url),
response.result.structured_prompt,
)
class BriaRemoveImageBackground(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="BriaRemoveImageBackground",
display_name="Bria Remove Image Background",
category="api node/image/Bria",
description="Remove the background from an image using Bria RMBG 2.0.",
inputs=[
IO.Image.Input("image"),
IO.DynamicCombo.Input(
"moderation",
options=[
IO.DynamicCombo.Option("false", []),
IO.DynamicCombo.Option(
"true",
[
IO.Boolean.Input("visual_input_moderation", default=False),
IO.Boolean.Input("visual_output_moderation", default=True),
],
),
],
tooltip="Moderation settings",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[IO.Image.Output()],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.018}""",
),
)
@classmethod
async def execute(
cls,
image: Input.Image,
moderation: dict,
seed: int,
) -> IO.NodeOutput:
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bria/v2/image/edit/remove_background", method="POST"),
data=BriaRemoveBackgroundRequest(
image=await upload_image_to_comfyapi(cls, image, wait_label="Uploading image"),
sync=False,
visual_input_content_moderation=moderation.get("visual_input_moderation", False),
visual_output_content_moderation=moderation.get("visual_output_moderation", False),
seed=seed,
),
response_model=BriaStatusResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaRemoveBackgroundResponse,
)
return IO.NodeOutput(await download_url_to_image_tensor(response.result.image_url))
class BriaRemoveVideoBackground(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="BriaRemoveVideoBackground",
display_name="Bria Remove Video Background",
category="api node/video/Bria",
description="Remove the background from a video using Bria. ",
inputs=[
IO.Video.Input("video"),
IO.Combo.Input(
"background_color",
options=[
"Black",
"White",
"Gray",
"Red",
"Green",
"Blue",
"Yellow",
"Cyan",
"Magenta",
"Orange",
],
tooltip="Background color for the output video.",
),
IO.Int.Input(
"seed",
default=0,
min=0,
max=2147483647,
display_mode=IO.NumberDisplay.number,
control_after_generate=True,
tooltip="Seed controls whether the node should re-run; "
"results are non-deterministic regardless of seed.",
),
],
outputs=[IO.Video.Output()],
hidden=[
IO.Hidden.auth_token_comfy_org,
IO.Hidden.api_key_comfy_org,
IO.Hidden.unique_id,
],
is_api_node=True,
price_badge=IO.PriceBadge(
expr="""{"type":"usd","usd":0.14,"format":{"suffix":"/second"}}""",
),
)
@classmethod
async def execute(
cls,
video: Input.Video,
background_color: str,
seed: int,
) -> IO.NodeOutput:
validate_video_duration(video, max_duration=60.0)
response = await sync_op(
cls,
ApiEndpoint(path="/proxy/bria/v2/video/edit/remove_background", method="POST"),
data=BriaRemoveVideoBackgroundRequest(
video=await upload_video_to_comfyapi(cls, video),
background_color=background_color,
output_container_and_codec="mp4_h264",
seed=seed,
),
response_model=BriaStatusResponse,
)
response = await poll_op(
cls,
ApiEndpoint(path=f"/proxy/bria/v2/status/{response.request_id}"),
status_extractor=lambda r: r.status,
response_model=BriaRemoveVideoBackgroundResponse,
)
return IO.NodeOutput(await download_url_to_video_output(response.result.video_url))
class BriaExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
BriaImageEditNode,
BriaRemoveImageBackground,
BriaRemoveVideoBackground,
]
async def comfy_entrypoint() -> BriaExtension:
return BriaExtension()
|