End of training
Browse files- README.md +77 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scheduler.bin +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: THUDM/CogVideoX-5b
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library_name: diffusers
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license: other
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tags:
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- text-to-video
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- diffusers-training
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- diffusers
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- lora
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- cogvideox
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- cogvideox-diffusers
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- template:sd-lora
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widget: []
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# CogVideoX LoRA Finetune
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<Gallery />
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## Model description
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This is a lora finetune of the CogVideoX model `THUDM/CogVideoX-5b`.
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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).
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## Download model
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[Download LoRA](sayakpaul/optimizer_adamw_steps_1000_lr-schedule_cosine_with_restarts_learning-rate_1e-4/tree/main) in the Files & Versions tab.
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## Usage
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Requires the [🧨 Diffusers library](https://github.com/huggingface/diffusers) installed.
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```py
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import torch
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from diffusers import CogVideoXPipeline
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from diffusers.utils import export_to_video
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pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
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pipe.load_lora_weights("sayakpaul/optimizer_adamw_steps_1000_lr-schedule_cosine_with_restarts_learning-rate_1e-4", weight_name="pytorch_lora_weights.safetensors", adapter_name="cogvideox-lora")
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# The LoRA adapter weights are determined by what was used for training.
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# In this case, we assume `--lora_alpha` is 32 and `--rank` is 64.
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# It can be made lower or higher from what was used in training to decrease or amplify the effect
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# of the LoRA upto a tolerance, beyond which one might notice no effect at all or overflows.
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pipe.set_adapters(["cogvideox-lora"], [32 / 64])
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video = pipe("None", guidance_scale=6, use_dynamic_cfg=True).frames[0]
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export_to_video(video, "output.mp4", fps=8)
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```
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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.
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## License
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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).
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-1000/pytorch_lora_weights.safetensors
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checkpoint-1000/random_states_0.pkl
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pytorch_lora_weights.safetensors
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