Instructions to use Hinorix/yaoi_NEW_LTX_4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Hinorix/yaoi_NEW_LTX_4k with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Hinorix/yaoi_NEW_LTX_4k", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
yaoi_NEW_LTX_4k
Trained with the LTX LoRA Trainer β powered by LTX-2.
This is a Image-to-Video LoRA fine-tuned from ltx-2.3-22b-dev.safetensors on custom data.
Model Details
- Base Model:
ltx-2.3-22b-dev.safetensors - LoRA type: Image-to-Video LoRA
- Training Type: LoRA fine-tuning
- Training Steps: 4000
- Learning Rate: 0.0001
- Batch Size: 1
Sample Outputs
Usage
𧨠Diffusers
LTX-2.3 support is currently on the diffusers
mainbranch:pip install git+https://github.com/huggingface/diffusers.git
import torch
from diffusers import LTX2ImageToVideoPipeline
from diffusers.utils import load_image
from diffusers.pipelines.ltx2.export_utils import encode_video
from diffusers.pipelines.ltx2.utils import DEFAULT_NEGATIVE_PROMPT
pipe = LTX2ImageToVideoPipeline.from_pretrained(
"diffusers/LTX-2.3-Diffusers", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
# Load this LoRA
pipe.load_lora_weights("Hinorix/yaoi_NEW_LTX_4k", weight_name="lora_weights_step_04000.safetensors", adapter_name="lora")
pipe.set_adapters("lora", 1.0)
video, audio = pipe(
prompt="<your prompt>",
negative_prompt=DEFAULT_NEGATIVE_PROMPT,
image=load_image("first_frame.png"),
width=768, height=512, num_frames=49, frame_rate=25.0,
num_inference_steps=30, guidance_scale=4.0,
output_type="np", return_dict=False,
)
encode_video(video[0], fps=25.0, output_path="output.mp4")
For the full reference implementation and ComfyUI workflows, see the official LTX-2 repository.
π Using Trained LoRAs in ComfyUI
In order to use the trained LoRA in ComfyUI, follow these steps:
- Copy your trained LoRA checkpoint (
.safetensorsfile) to themodels/lorasfolder in your ComfyUI installation. - In your ComfyUI workflow:
- Add the "Load LoRA" node to choose your LoRA file
- Connect it to the "Load Checkpoint" node to apply the LoRA to the base model
You can find reference Text-to-Video (T2V) and Image-to-Video (I2V) workflows in the official LTX-2 repository.
Example Prompts
This model inherits the license of the base model (ltx-2.3-22b-dev.safetensors).
Acknowledgments
- Base model: Lightricks
- Trainer: LTX-2
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