Zlikwidcogvideoxlora

🚀 CogVideoX LoRA - Zlikwid/ZlikwidCogVideoXLoRa

These are LoRA weights for THUDM/CogVideoX-2b, enabling text - to - video generation.

🚀 Quick Start

Download model

You can Download the *.safetensors LoRA in the Files & versions tab.

Use it with the 🧨 diffusers library

from diffusers import CogVideoXPipeline
import torch

pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("Zlikwid/ZlikwidCogVideoXLoRa", weight_name="pytorch_lora_weights.safetensors", adapter_name=["cogvideox-lora"])

# The LoRA adapter weights are determined by what was used for training.
# In this case, we assume `--lora_alpha` is 32 and `--rank` is 64.
# It can be made lower or higher from what was used in training to decrease or amplify the effect
# of the LoRA upto a tolerance, beyond which one might notice no effect at all or overflows.
pipe.set_adapters(["cogvideox-lora"], [32 / 64])

video = pipe("None", guidance_scale=6, use_dynamic_cfg=True).frames[0]

For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers

✨ Features

These are Zlikwid/ZlikwidCogVideoXLoRa LoRA weights for THUDM/CogVideoX-2b. The weights were trained using the CogVideoX Diffusers trainer. Was LoRA for the text encoder enabled? No.

📚 Documentation

Model description

These are Zlikwid/ZlikwidCogVideoXLoRa LoRA weights for THUDM/CogVideoX-2b. The weights were trained using the CogVideoX Diffusers trainer. Was LoRA for the text encoder enabled? No.

Intended uses & limitations

How to use

# TODO: add an example code snippet for running this diffusion pipeline

Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

Training details

[TODO: describe the data used to train the model]

📄 License

Please adhere to the licensing terms as described here and here.

📦 Information Table