[wan2.2] add 5b i2v

[yiyixuxu]

import torch
import numpy as np
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan, ModularPipeline
from diffusers.utils import export_to_video


model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
dtype = torch.bfloat16
device = "cuda:2"

vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload(device=device)

# use default wan image processor to resize and crop the image
image_processor = ModularPipeline.from_pretrained("YiYiXu/WanImageProcessor", trust_remote_code=True)
image = image_processor(
    image="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
    max_area=1280*704, output="processed_image")

height, width = image.height, image.width
print(f"height: {height}, width: {width}")
num_frames = 121
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

output = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=num_frames,
    guidance_scale=guidance_scale,
    num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "yiyi_test_6_ti2v_5b_output.mp4", fps=24)

[HuggingFaceDocBuilderDev]

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[nitinmukesh]

Thank you @yiyixuxu .

1 query, is this fixed value or need to be reversed as per input image,
max_area=1280*704

portrait=1280*704 
landscape=704*1280

[a-r-r-o-w]

Thanks!

[yiyixuxu]

@nitinmukesh
it's atuallyint input so it's fixed value

[zhaoyun0071]

import torch
import numpy as np
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan, ModularPipeline
from diffusers.utils import export_to_video


model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
dtype = torch.bfloat16
device = "cuda:2"

vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload(device=device)

# use default wan image processor to resize and crop the image
image_processor = ModularPipeline.from_pretrained("YiYiXu/WanImageProcessor", trust_remote_code=True)
image = image_processor(
    image="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
    max_area=1280*704, output="processed_image")

height, width = image.height, image.width
print(f"height: {height}, width: {width}")
num_frames = 121
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

output = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=num_frames,
    guidance_scale=guidance_scale,
    num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "yiyi_test_6_ti2v_5b_output.mp4", fps=24)

进度跑满后，最后关头占用显存直接飙升到30G左右，导致非常慢，我是3090显卡

[JoeGaffney]

Hey,

With Wan2.1 we was able to pass just a RGB PIl image. With 2.2 i get

def _conv_forward(self, input: Tensor, weight: Tensor, bias: Optional[Tensor]):
        if self.padding_mode != "zeros":
            return F.conv3d(
                F.pad(
                    input, self._reversed_padding_repeated_twice, mode=self.padding_mode
                ),
                weight,
                bias,
                self.stride,
                _triple(0),
                self.dilation,
                self.groups,
            )
>       return F.conv3d(
            input, weight, bias, self.stride, self.padding, self.dilation, self.groups
        )
E       RuntimeError: Given groups=1, weight of size [160, 12, 3, 3, 3], expected input[1, 3, 3, 258, 258] to have 12 channels, but got 3 channels instead

/opt/conda/lib/python3.11/site-packages/torch/nn/modules/conv.py:720: RuntimeError

Cheers,
Joe

[yiyixuxu]

hi @JoeGaffney
there is no code attached so i'm not 100% sure I understand the issue here, but the error might be caused by the fact that with wan 2.2 there is a patchify/unpatchify step

diffusers/src/diffusers/models/autoencoders/autoencoder_kl_wan.py

Line 1182 in 20e0740

x = patchify(x, patch_size=self.config.patch_size)

[chensongkui]

import torch
import numpy as np
from diffusers import WanImageToVideoPipeline, AutoencoderKLWan, ModularPipeline
from diffusers.utils import export_to_video


model_id = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
dtype = torch.bfloat16
device = "cuda:2"

vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload(device=device)

# use default wan image processor to resize and crop the image
image_processor = ModularPipeline.from_pretrained("YiYiXu/WanImageProcessor", trust_remote_code=True)
image = image_processor(
    image="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
    max_area=1280*704, output="processed_image")

height, width = image.height, image.width
print(f"height: {height}, width: {width}")
num_frames = 121
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

output = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=num_frames,
    guidance_scale=guidance_scale,
    num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "yiyi_test_6_ti2v_5b_output.mp4", fps=24)

do you run this code successfully? I run this code fail. I met some problems, like lacking image processor and image encoder, i have solved these problems. now i met a new problem, as the picture showed above. I think this phenomenon is unreasonable. Theoretically, this code should be run successfully.

[JoeGaffney]

hi @JoeGaffney there is no code attached so i'm not 100% sure I understand the issue here, but the error might be caused by the fact that with wan 2.2 there is a patchify/unpatchify step

diffusers/src/diffusers/models/autoencoders/autoencoder_kl_wan.py

Line 1182 in 20e0740

x = patchify(x, patch_size=self.config.patch_size)

Hey @yiyixuxu it was resolved in the other ticket it was vae.enable_tiling()

[nitinmukesh]

@JoeGaffney

Which ticket fixed enable_tiling. I'm getting OOM even after installing diffusers from source.