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Markus-Pobitzer/
wlp-Wan2.2-TI2V-5B-lora

Name: wlp-Wan2.2-TI2V-5B-lora
Availability: InStock

art

image-text-to-video

2511.17344

Wan-AI/Wan2.2-TI2V-5B-Diffusers

finetune

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Loomis Painter: Reconstructing the painting process

_{Generated Video}

_Input

Base Model Inference

Before running the code make sure to have installed torch, diffusers, transformers, huggingface_hub, and pillow. You can also install the dependencies from the offical Loomis Portrait repo link.

python

import torch
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan, WanTransformer3DModel, UniPCMultistepScheduler
from diffusers.utils import export_to_video, load_image
from huggingface_hub import hf_hub_download
from typing import List, Tuple, Union
from PIL import Image, ImageOps


def pil_resize(
    image: Image.Image,
    target_size: Tuple[int, int],
    pad_input: bool = False,
    padding_color: Union[str, int, Tuple[int, ...]] = "white",
) -> Image.Image:
    """Resizing it to the target size.

    Args:
        image: Input image to be processed.
        target_size: Target size (width, height).
        pad_input: If set resizes the image while keeping the aspect ratio and pads the unfilled part.
        padding_color: The color for the padded pixels.

    Returns:
        The resized image
    """
    if pad_input:
        # Resize image, keep aspect ratio
        image = ImageOps.contain(image, size=target_size)
        # Pad while keeping image in center
        image = ImageOps.pad(image, size=target_size, color=padding_color)
    else:
        image = image.resize(target_size)
    return image


def undo_pil_resize(
    image: Image.Image,
    target_size: Tuple[int, int],
) -> Image.Image:
    """Undo the resizing and padding of the input image to the a new image with size target_size.

    Args:
        image: Input image to be processed.
        target_size: Target size (width, height).

    Returns:
        The resized image
    """
    tmp_img = Image.new(mode="RGB", size=target_size)
    # Get the resized image size
    tmp_img = ImageOps.contain(tmp_img, size=image.size)

    # Undo padding by center cropping
    width, height = image.size
    tmp_width, tmp_height = tmp_img.size

    left = int(round((width - tmp_width) / 2.0))
    top = int(round((height - tmp_height) / 2.0))
    right = left + tmp_width
    bottom = top + tmp_height
    cropped = image.crop((left, top, right, bottom))

    # Undo resizing
    ret = cropped.resize(target_size)
    return ret

# Set to True to save VRAM, slower inference
enable_sequential_cpu_offload = True

# Download the LoRA file
lora_path = hf_hub_download(repo_id="Markus-Pobitzer/wlp-Wan2.2-TI2V-5B-lora", filename="base.safetensors")
print(f"LoRA path: {lora_path}")

# Loads the pipeline
model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, dtype=torch.bfloat16)

# Load LoRA
pipe.load_lora_weights(lora_path)
pipe.fuse_lora()

# Either offload or directly to GPU
if enable_sequential_cpu_offload:
    pipe.enable_sequential_cpu_offload()
else:
    pipe.to("cuda")


### INFERENCE ###
image = load_image(
    "https://uploads3.wikiart.org/images/claude-monet/haystacks-at-giverny.jpg"
)
og_size = image.size
height = 480
width = 832
# Resize and pad
ref_image = pil_resize(image, target_size=(width, height), pad_input=True)
prompt = "Painting process step by step."

output = pipe(
    image=ref_image,
    prompt=prompt,
    height=height,
    width=width,
    num_frames=81,
    output_type="pil",
    guidance_scale=1.0,
).frames[0]
# To original image size
output = [undo_pil_resize(img, og_size) for img in output][::-1]
# Save video
export_to_video(output, "output.mp4", fps=3)

Citation

If you use this work, please cite:

bibtex

@misc{pobitzer2025loomispainter,
      title={Loomis Painter: Reconstructing the Painting Process},
      author={Markus Pobitzer and Chang Liu and Chenyi Zhuang and Teng Long and Bin Ren and Nicu Sebe},
      year={2025},
      eprint={2511.17344},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.17344},
}