End of training

README.md

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+---
+base_model: THUDM/CogVideoX-5b
+library_name: diffusers
+license: other
+tags:
+- text-to-video
+- diffusers-training
+- diffusers
+- lora
+- cogvideox
+- cogvideox-diffusers
+- template:sd-lora
+widget: []
+---
+
+<!-- This model card has been generated automatically according to the information the training script had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+
+# CogVideoX LoRA Finetune
+
+<Gallery />
+
+## Model description
+
+This is a lora finetune of the CogVideoX model `THUDM/CogVideoX-5b`.
+
+The model was trained using [CogVideoX Factory](https://github.com/a-r-r-o-w/cogvideox-factory) - a repository containing memory-optimized training scripts for the CogVideoX family of models using [TorchAO](https://github.com/pytorch/ao) and [DeepSpeed](https://github.com/microsoft/DeepSpeed). The scripts were adopted from [CogVideoX Diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/cogvideo/train_cogvideox_lora.py).
+
+## Download model
+
+[Download LoRA](sayakpaul/optimizer_adamw_steps_1000_lr-schedule_cosine_with_restarts_learning-rate_5e-4/tree/main) in the Files & Versions tab.
+
+## Usage
+
+Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.
+
+```py
+import torch
+from diffusers import CogVideoXPipeline
+from diffusers.utils import export_to_video
+
+pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
+pipe.load_lora_weights("sayakpaul/optimizer_adamw_steps_1000_lr-schedule_cosine_with_restarts_learning-rate_5e-4", 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]
+export_to_video(video, "output.mp4", fps=8)
+```
+
+For more details, including weighting, merging and fusing LoRAs, check the [documentation](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) on loading LoRAs in diffusers.
+
+## License
+
+Please adhere to the licensing terms as described [here](https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE) and [here](https://huggingface.co/THUDM/CogVideoX-2b/blob/main/LICENSE).
+
+
+## Intended uses & limitations
+
+#### How to use
+
+```python
+# 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]

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