Transform to Venom 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 transform any object to venom 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
- Prompt
- "The video begins with a anime young character with long hair. 5en3m venom transformation. Transform into a venom character transformation. Venom is depicted with his iconic black symbiote body, large white eyes with black pupils, sharp teeth, and a menacing expression. The transformation is smooth and seamless, blending the human figure with the monstrous ."
- Prompt
- The video begins with a woman wearing black clothes. 5en3m venom transformation. Transform into a venom character transformation. Venom is depicted with his iconic black symbiote body, large white eyes with black pupils, sharp teeth, and a menacing expression. The transformation is smooth and seamless, blending the human figure with the monstrous .
- Prompt
- The video begins with a man wearing a suit. 5en3m venom transformation. Transform into a venom character transformation. Venom is depicted with his iconic black symbiote body, large white eyes with black pupils, sharp teeth, and a menacing expression.The transformation is smooth and seamless, blending the human figure with the monstrous .
Model File and Inference Workflow
π₯ Download Links:
- transform2venom.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)
# Note: Choose Unipcm scheduler to generate higher quality videos for Wan
flow_shift = 3.0 # 5.0 for 720P, 3.0 for 480P
scheduler = UniPCMultistepScheduler(
prediction_type="flow_prediction",
use_flow_sigmas=True,
num_train_timesteps=1000,
flow_shift=flow_shift,
scheduler=scheduler,
)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)
pipe.to("cuda")
pipe.load_lora_weights("passenger12138/Transform2Venom")
pipe.enable_model_cpu_offload() #for low-vram environments
prompt = "The video begins with a man wearing a suit. 5en3m venom transformation. Transform into a venom character transformation. Venom is depicted with his iconic black symbiote body, large white eyes with black pupils, sharp teeth, and a menacing expression. The transformation is smooth and seamless, blending the human figure with the monstrous ."
image = load_image('./test_i2vlora_imgs/1.png')
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: 3.0
Trigger Words
The key trigger phrase is: 5en3m venom transformation.
Prompt Template
For best results, use this prompt structure:
Simply replace [object]
with whatever you want to see transform to venom!
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.
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 (40 short clips of things being squished)
- Epochs: 40
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 and RemadeAI some case!
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Model tree for passenger12138/Transform2Venom
Base model
Wan-AI/Wan2.1-I2V-14B-480P