Image-to-Video
Diffusers
text-to-video
image-text-to-video
video-to-video
text-to-audio-video
image-to-audio-video
image-text-to-audio-video
video-to-audio-video
audio-to-audio-video
audio-video-generation
multimodal
synchronized-audio-video
reference-to-audio-video
Instructions to use rzgar/minimax_h3_fl2va_fp8_e4m3fn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rzgar/minimax_h3_fl2va_fp8_e4m3fn with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rzgar/minimax_h3_fl2va_fp8_e4m3fn", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
lora
#5
by ludashi666 - opened
Is this a model with LoRA integrated into it, or is it a fully fine-tuned model? Also, will a LoRA version be released?
It’s quantized FP8 with some layers kept in higher precision. against pruned FP8 in full-action scenarios, it generate better quality.
It’s a 33b model, training a distilled LoRA for it would take months, or you'd need a multi-GPU cluster. they might release a distilled LoRA or a Lightx2V-style variant
but right now it’s faster than WanN 2.2
from my tests if there isn’t much action and motion like talking-head shots, the 'heun' sampler was the fastest with 3 steps. But when the character is running, you need to increase the shift to 10 and steps to 8 as a minimum.