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---
base_model: THUDM/CogVideoX-5b
datasets: finetrainers/crush-smol
library_name: diffusers
license: other
license_link: https://huggingface.co/THUDM/CogVideoX-5b/blob/main/LICENSE
instance_prompt: PIKA_CAKEIFY A red tea cup is placed on a wooden surface. Suddenly, a knife appears and slices through the cup, revealing a cake inside. The cake turns into a hyper-realistic prop cake, showcasing the creative transformation of everyday objects into something unexpected and delightful.
widget:
- text: PIKA_CAKEIFY A blue soap is placed on a modern table. Suddenly, a knife appears and slices through the soap, revealing a cake inside. The soap turns into a hyper-realistic prop cake, showcasing the creative transformation of everyday objects into something unexpected and delightful.
output:
url: "./assets/output_0.mp4"
- text: PIKA_CAKEIFY On a gleaming glass display stand, a sleek black purse quietly commands attention. Suddenly, a knife appears and slices through the shoe, revealing a fluffy vanilla sponge at its core. Immediately, it turns into a hyper-realistic prop cake, delighting the senses with its playful juxtaposition of the everyday and the extraordinary.
output:
url: "./assets/output_1.mp4"
- text: PIKA_CAKEIFY A red tea cup is placed on a wooden surface. Suddenly, a knife appears and slices through the cup, revealing a cake inside. The cake turns into a hyper-realistic prop cake, showcasing the creative transformation of everyday objects into something unexpected and delightful.
output:
url: "./assets/output_2.mp4"
tags:
- text-to-video
- diffusers-training
- diffusers
- cogvideox
- cogvideox-diffusers
- template:sd-lora
---
<Gallery />
This is a fine-tune of the [THUDM/CogVideoX-5b](https://huggingface.co/THUDM/CogVideoX-5b) model on the
[finetrainers/crush-smol](https://huggingface.co/datasets/finetrainers/crush-smol) dataset. We also provide
a LoRA variant of the params. Check it out [here](#lora).
Code: https://github.com/a-r-r-o-w/finetrainers
> [!IMPORTANT]
> This is an experimental checkpoint and its poor generalization is well-known.
Inference code:
```py
from diffusers import CogVideoXTransformer3DModel, DiffusionPipeline
from diffusers.utils import export_to_video
import torch
transformer = CogVideoXTransformer3DModel.from_pretrained(
"finetrainers/crush-smol-v0", torch_dtype=torch.bfloat16
)
pipeline = DiffusionPipeline.from_pretrained(
"THUDM/CogVideoX-5b", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
prompt = """
DIFF_crush A thick burger is placed on a dining table, and a large metal cylinder descends from above, crushing the burger as if it were under a hydraulic press. The bulb is crushed, leaving a pile of debris around it.
"""
negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
video = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=81,
height=512,
width=768,
num_inference_steps=50
).frames[0]
export_to_video(video, "output.mp4", fps=25)
```
Training logs are available on WandB [here](https://wandb.ai/sayakpaul/finetrainers-cogvideox/runs/ngcsyhom).
## LoRA
We extracted a 64-rank LoRA from the finetuned checkpoint
(script [here](https://github.com/huggingface/diffusers/blob/main/scripts/extract_lora_from_model.py)).
[This LoRA](./extracted_crush_smol_lora_64.safetensors) can be used to emulate the same kind of effect:
<details>
<summary>Code</summary>
```py
from diffusers import DiffusionPipeline
from diffusers.utils import export_to_video
import torch
pipeline = DiffusionPipeline.from_pretrained("THUDM/CogVideoX-5b", torch_dtype=torch.bfloat16).to("cuda")
pipeline.load_lora_weights("finetrainers/cakeify-v0", weight_name="extracted_crush_smol_lora_64.safetensors")
prompt = """
DIFF_crush A thick burger is placed on a dining table, and a large metal cylinder descends from above, crushing the burger as if it were under a hydraulic press. The bulb is crushed, leaving a pile of debris around it.
"""
negative_prompt = "inconsistent motion, blurry motion, worse quality, degenerate outputs, deformed outputs"
video = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_frames=81,
height=512,
width=768,
num_inference_steps=50
).frames[0]
export_to_video(video, "output_lora.mp4", fps=25)
```
</details> |