kijai / comfyui-easyanimatewrapper Goto Github PK

I noticed the EasyAnimate Sampler includes a full_input_video input. However, no matter what I try, I get this error if I pass a video as input.

RuntimeError: The size of tensor a (7) must match the size of tensor b (6) at non-singleton dimension 2

I' downloaded everything as described in the structure folder the jason file is there but it gives me these errors.

Error occurred when executing DownloadAndLoadEasyAnimateModel:

K:\ComfyUI\ComfyUI_Ex\ComfyUI\models\easyanimate\common\vae\config.json does not exist

File "K:\ComfyUI\ComfyUI_Ex\ComfyUI\execution.py", line 151, in recursive_execute
output_data, output_ui = get_output_data(obj, input_data_all)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "K:\ComfyUI\ComfyUI_Ex\ComfyUI\execution.py", line 81, in get_output_data
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "K:\ComfyUI\ComfyUI_Ex\ComfyUI\execution.py", line 74, in map_node_over_list
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "K:\ComfyUI\ComfyUI_Ex\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\nodes.py", line 113, in loadmodel
vae = Choosen_AutoencoderKL.from_pretrained(common_path, subfolder="vae").to(dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "K:\ComfyUI\ComfyUI_Ex\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\easyanimate\models\autoencoder_magvit.py", line 491, in from_pretrained
raise RuntimeError(f"{config_file} does not exist")

I am a technical scumbag.Is there any solution to the error below? thx

Error occurred when executing EasyAnimateSampler:

The image to be converted to a PIL image contains values outside the range [0, 1], got [-0.07665368914604187, 1.0637787580490112] which cannot be converted to uint8.

File "D:\ComfyUI_windows_portable\ComfyUI\execution.py", line 151, in recursive_execute
output_data, output_ui = get_output_data(obj, input_data_all)
File "D:\ComfyUI_windows_portable\ComfyUI\execution.py", line 81, in get_output_data
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
File "D:\ComfyUI_windows_portable\ComfyUI\execution.py", line 74, in map_node_over_list
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
File "D:\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\nodes.py", line 227, in process
sample = pipeline(
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "D:\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\easyanimate\pipeline\pipeline_easyanimate_inpaint.py", line 999, in call
inputs = self.clip_image_processor(images=clip_image, return_tensors="pt")
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\image_processing_utils.py", line 551, in call
return self.preprocess(images, **kwargs)
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\models\clip\image_processing_clip.py", line 323, in preprocess
images = [
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\models\clip\image_processing_clip.py", line 324, in
self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\models\clip\image_processing_clip.py", line 191, in resize
return resize(
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\image_transforms.py", line 326, in resize
do_rescale = _rescale_for_pil_conversion(image)
File "D:\ComfyUI_windows_portable\python_embeded\lib\site-packages\transformers\image_transforms.py", line 150, in _rescale_for_pil_conversion
raise ValueError(

I did run it successfully but the results were very poor, so I'm wondering if there might be something wrong with the parameters?

missing keys: 0;

unexpected keys: 96;

[] ['loss.discriminator.main.0.bias', 'loss.discriminator.main.0.weight', 'loss.discriminator.main.11.bias', 'loss.discriminator.main.11.weight', 'loss.discriminator.main.2.weight', 'loss.discriminator.main.3.bias', 'loss.discriminator.main.3.num_batches_tracked', 'loss.discriminator.main.3.running_mean', 'loss.discriminator.main.3.running_var', 'loss.discriminator.main.3.weight', 'loss.discriminator.main.5.weight', 'loss.discriminator.main.6.bias', 'loss.discriminator.main.6.num_batches_tracked', 'loss.discriminator.main.6.running_mean', 'loss.discriminator.main.6.running_var', 'loss.discriminator.main.6.weight', 'loss.discriminator.main.8.weight', 'loss.discriminator.main.9.bias', 'loss.discriminator.main.9.num_batches_tracked', 'loss.discriminator.main.9.running_mean', 'loss.discriminator.main.9.running_var', 'loss.discriminator.main.9.weight', 'loss.discriminator3d.blocks.0.conv1.bias', 'loss.discriminator3d.blocks.0.conv1.weight', 'loss.discriminator3d.blocks.0.conv2.bias', 'loss.discriminator3d.blocks.0.conv2.weight', 'loss.discriminator3d.blocks.0.downsampler.filt', 'loss.discriminator3d.blocks.0.norm1.bias', 'loss.discriminator3d.blocks.0.norm1.weight', 'loss.discriminator3d.blocks.0.norm2.bias', 'loss.discriminator3d.blocks.0.norm2.weight', 'loss.discriminator3d.blocks.0.shortcut.0.filt', 'loss.discriminator3d.blocks.0.shortcut.1.bias', 'loss.discriminator3d.blocks.0.shortcut.1.weight', 'loss.discriminator3d.blocks.1.conv1.bias', 'loss.discriminator3d.blocks.1.conv1.weight', 'loss.discriminator3d.blocks.1.conv2.bias', 'loss.discriminator3d.blocks.1.conv2.weight', 'loss.discriminator3d.blocks.1.downsampler.filt', 'loss.discriminator3d.blocks.1.norm1.bias', 'loss.discriminator3d.blocks.1.norm1.weight', 'loss.discriminator3d.blocks.1.norm2.bias', 'loss.discriminator3d.blocks.1.norm2.weight', 'loss.discriminator3d.blocks.1.shortcut.0.filt', 'loss.discriminator3d.blocks.1.shortcut.1.bias', 'loss.discriminator3d.blocks.1.shortcut.1.weight', 'loss.discriminator3d.blocks.2.conv1.bias', 'loss.discriminator3d.blocks.2.conv1.weight', 'loss.discriminator3d.blocks.2.conv2.bias', 'loss.discriminator3d.blocks.2.conv2.weight', 'loss.discriminator3d.blocks.2.norm1.bias', 'loss.discriminator3d.blocks.2.norm1.weight', 'loss.discriminator3d.blocks.2.norm2.bias', 'loss.discriminator3d.blocks.2.norm2.weight', 'loss.discriminator3d.blocks.2.shortcut.0.bias', 'loss.discriminator3d.blocks.2.shortcut.0.weight', 'loss.discriminator3d.conv_in.bias', 'loss.discriminator3d.conv_in.weight', 'loss.discriminator3d.conv_norm_out.bias', 'loss.discriminator3d.conv_norm_out.weight', 'loss.discriminator3d.conv_out.bias', 'loss.discriminator3d.conv_out.weight', 'loss.logvar', 'loss.perceptual_loss.lin0.model.1.weight', 'loss.perceptual_loss.lin1.model.1.weight', 'loss.perceptual_loss.lin2.model.1.weight', 'loss.perceptual_loss.lin3.model.1.weight', 'loss.perceptual_loss.lin4.model.1.weight', 'loss.perceptual_loss.net.slice1.0.bias', 'loss.perceptual_loss.net.slice1.0.weight', 'loss.perceptual_loss.net.slice1.2.bias', 'loss.perceptual_loss.net.slice1.2.weight', 'loss.perceptual_loss.net.slice2.5.bias', 'loss.perceptual_loss.net.slice2.5.weight', 'loss.perceptual_loss.net.slice2.7.bias', 'loss.perceptual_loss.net.slice2.7.weight', 'loss.perceptual_loss.net.slice3.10.bias', 'loss.perceptual_loss.net.slice3.10.weight', 'loss.perceptual_loss.net.slice3.12.bias', 'loss.perceptual_loss.net.slice3.12.weight', 'loss.perceptual_loss.net.slice3.14.bias', 'loss.perceptual_loss.net.slice3.14.weight', 'loss.perceptual_loss.net.slice4.17.bias', 'loss.perceptual_loss.net.slice4.17.weight', 'loss.perceptual_loss.net.slice4.19.bias', 'loss.perceptual_loss.net.slice4.19.weight', 'loss.perceptual_loss.net.slice4.21.bias', 'loss.perceptual_loss.net.slice4.21.weight', 'loss.perceptual_loss.net.slice5.24.bias', 'loss.perceptual_loss.net.slice5.24.weight', 'loss.perceptual_loss.net.slice5.26.bias', 'loss.perceptual_loss.net.slice5.26.weight', 'loss.perceptual_loss.net.slice5.28.bias', 'loss.perceptual_loss.net.slice5.28.weight', 'loss.perceptual_loss.scaling_layer.scale', 'loss.perceptual_loss.scaling_layer.shift']

Error occurred when executing DownloadAndLoadEasyAnimateModel:

C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\models\easyanimate\common\vae\diffusion_pytorch_model.bin does not exist

File "C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\execution.py", line 152, in recursive_execute
output_data, output_ui = get_output_data(obj, input_data_all)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\execution.py", line 82, in get_output_data
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\execution.py", line 75, in map_node_over_list
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\nodes.py", line 105, in loadmodel
vae = AutoencoderKLMagvit.from_pretrained(common_path, subfolder="vae").to(dtype)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\ComfyUI\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-EasyAnimateWrapper\easyanimate\models\autoencoder_magvit.py", line 504, in from_pretrained
raise RuntimeError(f"{model_file} does not exist")

I don't know if some of the code comes from you. I got the original code from a friend, and since I was writing Comfyui code for the first time, I made modifications directly from that code.
I will add the source of the original code.

Hi, thanks for your great node!
Does this support long video generation? I noticed that the maximum frame adjustment in the comfyui node is 144 frames (6 seconds), while the official gradio provides 1440 frames (60 seconds, though I haven't tested it yet). I have used the comfyui node you created, which quickly generated the video, whereas the official gradio's generation speed is very slow and inefficient. Your node has greatly improved the efficiency.