Instructions to use Remade-AI/Squish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Remade-AI/Squish 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("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Remade-AI/Squish") prompt = "In the video, a miniature dog is presented. The dog is held in a person's hands. The person then presses on the dog, causing a sq41sh squish effect. The person keeps pressing down on the dog, further showing the sq41sh squish effect." input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png") output = pipe(image=input_image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
Update README.md
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README.md
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This LoRA works with a modified version of [Kijai's Wan Video Wrapper workflow](https://github.com/kijai/ComfyUI-WanVideoWrapper/blob/main/example_workflows/wanvideo_480p_I2V_example_02.json). The main modification is adding a Wan LoRA node connected to the base model.
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. The main modification is adding a Wan LoRA node connected to the base model.
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You can download my modified workflow here:
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[Download Squish Effect Workflow](./workflow/wan_img2video_lora_workflow.json)
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## Download
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Weights for this model are available in Safetensors format.
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[Download squish_18.safetensors](./squish_18.safetensors) from the Files & versions tab.
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## Community
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