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Remade-AI / Squish
Model's Last Updated: May 26 2025
image-to-video
Introduction of Squish
Model Details of Squish
Squish Effect LoRA for Wan2.1 14B I2V 480p
Overview
This LoRA is trained on the Wan2.1 14B I2V 480p model and allows you to squish any object in an image. The effect works on a wide variety of objects, from animals to vehicles to people!
Features
- Transform any image into a video of it being squished
- Trained on the Wan2.1 14B 480p I2V base model
- Consistent results across different object types
- Simple prompt structure that's easy to adapt
Community
- Discord: Join our community to generate videos with this LoRA for free
- Request LoRAs: We're training and open-sourcing Wan2.1 LoRAs for free - join our Discord to make requests!
- Prompt
- Prompt
- Prompt
- Prompt
- Prompt
Model File and Inference Workflow
📥 Download Links:
- squish_18.safetensors - LoRA Model File
- wan_img2video_lora_workflow.json - Wan I2V with LoRA Workflow for ComfyUI
Using with Diffusers
pip install git+https://github.com/huggingface/diffusers.git
import torch
from diffusers.utils import export_to_video, load_image
from diffusers import AutoencoderKLWan, WanImageToVideoPipeline
from transformers import CLIPVisionModel
import numpy as np
model_id = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
image_encoder = CLIPVisionModel.from_pretrained(model_id, subfolder="image_encoder", torch_dtype=torch.float32)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe.load_lora_weights("Remade/Squish")
pipe.enable_model_cpu_offload() #for low-vram environments
prompt = "In the video, a miniature cat toy is presented. The cat toy is held in a person's hands. The person then presses on the cat toy, causing a sq41sh squish effect. The person keeps pressing down on the cat toy, further showing the sq41sh squish effect."
image = load_image("https://huggingface.co/datasets/diffusers/cat_toy_example/resolve/main/1.jpeg")
max_area = 480 * 832
aspect_ratio = image.height / image.width
mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1]
height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value
width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value
image = image.resize((width, height))
output = pipe(
image=image,
prompt=prompt,
height=height,
width=width,
num_frames=81,
guidance_scale=5.0,
num_inference_steps=28
).frames[0]
export_to_video(output, "output.mp4", fps=16)
Recommended Settings
- LoRA Strength: 1.0
- Embedded Guidance Scale: 6.0
- Flow Shift: 5.0
Trigger Words
The key trigger phrase is:
sq41sh squish effect
Prompt Template
For best results, use this prompt structure:
In the video, a miniature [object] is presented. The [object] is held in a person's hands. The person then presses on the [object], causing a sq41sh squish effect. The person keeps pressing down on the [object], further showing the sq41sh squish effect.
Simply replace
[object]
with whatever you want to see squished!
ComfyUI Workflow
This LoRA works with a modified version of Kijai's Wan Video Wrapper workflow . The main modification is adding a Wan LoRA node connected to the base model.
See the Downloads section above for the modified workflow.
Model Information
The model weights are available in Safetensors format. See the Downloads section above.
Training Details
- Base Model: Wan2.1 14B I2V 480p
- Training Data: 1.5 minutes of video (20 short clips of things being squished)
- Epochs: 18
Additional Information
Training was done using Diffusion Pipe for Training
Acknowledgments
Special thanks to Kijai for the ComfyUI Wan Video Wrapper and tdrussell for the training scripts!
Runs of Remade-AI Squish on huggingface.co
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More Information About Squish huggingface.co Model
Url of Squish
Squish huggingface.co Url
Provider of Squish huggingface.co
Other API from Remade-AI
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