RuntimeError

[syddharth]

Expected weight to be a vector of size equal to the number of channels in input, but got weight of shape [1280] and input of shape [16, 2560, 9, 9]

I have the mm_sd_v15.ckpt downloaded in stable-diffusion-webui\extensions\sd-webui-animatediff\model.

Complete log:

2023-07-18 11:19:21,548 - AnimateDiff - INFO - AnimateDiff process start with video length 2, FPS 8, motion module mm_sd_v15.ckpt.
2023-07-18 11:19:21,551 - AnimateDiff - INFO - Injecting motion module mm_sd_v15.ckpt into SD1.5 UNet input blocks.
2023-07-18 11:19:21,552 - AnimateDiff - INFO - Injecting motion module mm_sd_v15.ckpt into SD1.5 UNet output blocks.
2023-07-18 11:19:21,552 - AnimateDiff - INFO - Injection finished.
0%| | 0/30 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(ba8qq6a5sxydhr6)', 'Beautiful Scenery', '', [], 30, 16, False, False, 1, 2, 7.5, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.33, 1.5, '4x_UniversalUpscalerV2-Sharper_103000_G', 10, 0, 0, 19, '', '', [], <gradio.routes.Request object at 0x000002E7E74F5420>, 0, 0, False, 'Horizontal', '1,1', False, '0.2', False, False, 'female', True, 1, True, -1.0, [], [], [], [], False, {'ad_model': 'face_yolov8n.pt', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_min_ratio': 0, 'ad_mask_max_ratio': 1, 'ad_x_offset': 0, 'ad_y_offset': 0, 'ad_dilate_erode': 4, 'ad_mask_merge_invert': 'None', 'ad_mask_blur': 4, 'ad_denoising_strength': 0.4, 'ad_inpaint_only_masked': True, 'ad_inpaint_only_masked_padding': 32, 'ad_use_inpaint_width_height': False, 'ad_inpaint_width': 512, 'ad_inpaint_height': 512, 'ad_use_steps': False, 'ad_steps': 28, 'ad_use_cfg_scale': False, 'ad_cfg_scale': 7, 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'inpaint_global_harmonious', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': <object object at 0x000002E7E6FB5740>}, {'ad_model': 'None', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_min_ratio': 0, 'ad_mask_max_ratio': 1, 'ad_x_offset': 0, 'ad_y_offset': 0, 'ad_dilate_erode': 4, 'ad_mask_merge_invert': 'None', 'ad_mask_blur': 4, 'ad_denoising_strength': 0.4, 'ad_inpaint_only_masked': True, 'ad_inpaint_only_masked_padding': 32, 'ad_use_inpaint_width_height': False, 'ad_inpaint_width': 512, 'ad_inpaint_height': 512, 'ad_use_steps': False, 'ad_steps': 28, 'ad_use_cfg_scale': False, 'ad_cfg_scale': 7, 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'inpaint_global_harmonious', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': <object object at 0x000002E7E6FB5720>}, False, 7, 100, 'Constant', 0, 'Constant', 0, 4, True, 2, 8, 'mm_sd_v15.ckpt', <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002E7E5CB6B60>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002E7E5C86980>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002E7E750FE20>, <scripts.controlnet_ui.controlnet_ui_group.UiControlNetUnit object at 0x000002E7E750CA90>, None, False, '0', 'G:\stablediffusion\stable-diffusion-webui\extensions/sd-webui-faceswap/models\inswapper_128.onnx', 'CodeFormer', 1, '', 1, 1, False, True, False, False, 0, None, [], 0, False, [], [], False, 0, 1, False, False, 0, None, [], -2, False, [], False, 0, None, None, False, False, 0, 1, 1, 0, 0, 0, 0, False, 'Default', False, False, 'Euler a', 0.95, 0.75, 'zero', 'pos', 'linear', 0.2, 0.0, 0.75, None, 'Lanczos', 1, 0, 0, True, 0.3, 'Latent', 0.55, 0.3, 0.2, 0.2, [], False, 1.5, 1.2, False, '', '1', 'from modules.processing import process_images\n\np.width = 768\np.height = 768\np.batch_size = 2\np.steps = 10\n\nreturn process_images(p)', 2, 0, 0, 384, 384, False, False, True, True, True, 1, '', '', 8, True, 16, 'Median cut', False, None, None, '', '', '', '', 'Auto rename', {'label': 'Upload avatars config'}, 'Open outputs directory', 'Export to WebUI style', True, {'label': 'Presets'}, {'label': 'QC preview'}, '', [], 'Select', 'QC scan', 'Show pics', None, False, False, 'positive', 'comma', 0, False, False, '', 'Positive', 0, ', ', True, 32, 0, 'Median cut', 'luminance', False, 'Illustration', 'svg', True, True, False, 0.5, True, 16, True, 16, 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, 'Not set', True, True, '', '', '', '', '', 1.3, 'Not set', 'Not set', 1.3, 'Not set', 1.3, 'Not set', 1.3, 1.1, 'Not set', 1.1, 'Not set', 1.1, 'Not set', 1.1, 'Not set', 1.1, 'Not set', 1.1, 'Not set', False, 'None', 'Not set', True, False, '', '', '', '', '', 1.3, 'Not set', 'Not set', 'Not set', 1, 1.3, 'Not set', 'Not set', 'Not set', 'Not set', 'Not set', 'Not set', 1.3, 1.3, 1.3, 'Not set', 'Not set', 1.3, True, True, 'Disabled', None, None, False, None, None, False, None, None, False, None, None, False, 50) {}
Traceback (most recent call last):
File "G:\stablediffusion\stable-diffusion-webui\modules\call_queue.py", line 58, in f
res = list(func(*args, **kwargs))
File "G:\stablediffusion\stable-diffusion-webui\modules\call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\modules\txt2img.py", line 62, in txt2img
processed = processing.process_images(p)
File "G:\stablediffusion\stable-diffusion-webui\modules\processing.py", line 639, in process_images
res = process_images_inner(p)
File "G:\stablediffusion\stable-diffusion-webui\extensions\sd-webui-controlnet\scripts\batch_hijack.py", line 42, in processing_process_images_hijack
return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\modules\processing.py", line 759, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "G:\stablediffusion\stable-diffusion-webui\modules\processing.py", line 1012, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 303, in launch_sampling
return func()
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 464, in
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\utils_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_samplers_kdiffusion.py", line 183, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_hijack_utils.py", line 17, in
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_hijack_utils.py", line 28, in call
return self.__orig_func(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\modules\sd_unet.py", line 91, in UNetModel_forward
return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\repositories\stable-diffusion-stability-ai\ldm\modules\diffusionmodules\openaimodel.py", line 802, in forward
h = module(h, emb, context)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\extensions\sd-webui-animatediff\scripts\animatediff.py", line 21, in mm_tes_forward
x = layer(x, context)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\extensions\sd-webui-animatediff\motion_module.py", line 79, in forward
hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\extensions\sd-webui-animatediff\motion_module.py", line 144, in forward
hidden_states = self.norm(hidden_states)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
return forward_call(*args, **kwargs)
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\normalization.py", line 273, in forward
return F.group_norm(
File "G:\stablediffusion\stable-diffusion-webui\venv\lib\site-packages\torch\nn\functional.py", line 2530, in group_norm
return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: Expected weight to be a vector of size equal to the number of channels in input, but got weight of shape [1280] and input of shape [4, 2560, 8, 8]

---

[continue-revolution]

Please screenshot your webui

[syddharth]

I got the extension to run, had to restart.
Though, the gifs created are static and do not animate.

[continue-revolution]

1. check your terminal and see if you have successfully downloaded model weights
2. Gradio cannot show gif properly. You need to download the gif or go to output/txt2img-images/AnimateDiff

[syddharth]

Model weights were manually downloaded.

This is what the terminal shows:

2023-07-18 11:44:03,725 - AnimateDiff - INFO - AnimateDiff process start with video length 8, FPS 8, motion module mm_sd_v15.ckpt.
2023-07-18 11:44:03,729 - AnimateDiff - INFO - Injecting motion module mm_sd_v15.ckpt into SD1.5 UNet input blocks.
2023-07-18 11:44:03,729 - AnimateDiff - INFO - Injecting motion module mm_sd_v15.ckpt into SD1.5 UNet output blocks.
2023-07-18 11:44:03,729 - AnimateDiff - INFO - Injection finished.
Data shape for DDIM sampling is (8, 4, 64, 64), eta 0.0
Running DDIM Sampling with 31 timesteps
DDIM Sampler: 100%|███████████████| 31/31 [00:11<00:00, 2.75it/s]
2023-07-18 11:44:17,809 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet input blocks.
2023-07-18 11:44:17,809 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet output blocks.
2023-07-18 11:44:17,810 - AnimateDiff - INFO - Removal finished.
2023-07-18 11:44:17,810 - AnimateDiff - INFO - Merging images into GIF.
2023-07-18 11:44:18,429 - AnimateDiff - INFO - AnimateDiff process end.

I did check out the outputs\txt2img-images\AnimateDiff folder.
All the gifs seem to have just one frame.

Screenshot of webui

[continue-revolution]

Huh, it seems that it is not your problem. How about setting frame number to 16? They trained their motion modules when the frame number is 16.

Also, there is no need to change batch size. It will be replaced by the video frame numbers internally.

[syddharth]

I tried 16 too, same results. The gif seems to be made of just 1frame.

[RyanzW0521]

Same problem. Each time when I restart Webui it runs, but then the second time the error appears.
And when it runs correctly it can only generate 1 frame too

[continue-revolution]

Weird. You both can also observe txt2img-images/{today’s date}/ and read the images that look similar to your GIF. Are there 8/16 images?

@RyanzW0521 please post your terminal log with error message.

[RyanzW0521]

Wierd. You both can also observe txt2img-images/{today’s date}/ and read the images that look similar to your GIF. Are there 8/16 images?

@RyanzW0521 please post your terminal log with error message.

Unfortunately, in my folder it only has one image.
And here is the log

 0%|                                                                                                                                                                                                   | 0/20 [00:00<?, ?it/s]
Error completing request
Arguments: ('task(c3l341tdjvpcs5t)', 'best quality, masterpiece, 1girl, cloudy sky, dandelion, contrapposto, alternate hairstyle', '', [], 20, 0, False, False, 1, 1, 7, -1.0, -1.0, 0, 0, 0, False, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, [], 0, True, 16, 8, 'mm_sd_v15.ckpt', False, False, 'none', 'None', 1, None, False, 'Scale to Fit (Inner Fit)', False, False, 64, 64, 64, 0, 1, False, False, False, 'positive', 'comma', 0, False, False, '', 1, '', 0, '', 0, '', True, False, False, False, 0, None, 50) {}
Traceback (most recent call last):
  File "/stable-diffusion-webui/modules/call_queue.py", line 56, in f
    res = list(func(*args, **kwargs))
  File "/stable-diffusion-webui/modules/call_queue.py", line 37, in f
    res = func(*args, **kwargs)
  File "/stable-diffusion-webui/modules/txt2img.py", line 56, in txt2img
    processed = process_images(p)
  File "/stable-diffusion-webui/modules/processing.py", line 486, in process_images
    res = process_images_inner(p)
  File "/stable-diffusion-webui/modules/processing.py", line 632, in process_images_inner
    samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
  File "/stable-diffusion-webui/modules/processing.py", line 832, in sample
    samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
  File "/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 349, in sample
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 225, in launch_sampling
    return func()
  File "/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 349, in <lambda>
    samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/sampling.py", line 145, in sample_euler_ancestral
    denoised = model(x, sigmas[i] * s_in, **extra_args)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 117, in forward
    x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]})
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 112, in forward
    eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
  File "/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 138, in get_eps
    return self.inner_model.apply_model(*args, **kwargs)
  File "/stable-diffusion-webui/modules/sd_hijack_utils.py", line 17, in <lambda>
    setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
  File "/stable-diffusion-webui/modules/sd_hijack_utils.py", line 28, in __call__
    return self.__orig_func(*args, **kwargs)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 858, in apply_model
    x_recon = self.model(x_noisy, t, **cond)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 1329, in forward
    out = self.diffusion_model(x, t, context=cc)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/extensions/sd-webui-controlnet/scripts/hook.py", line 212, in forward2
    return forward(*args, **kwargs)
  File "/stable-diffusion-webui/extensions/sd-webui-controlnet/scripts/hook.py", line 201, in forward
    h = module(h, emb, context)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/extensions/sd-webui-animatediff/scripts/animatediff.py", line 19, in mm_tes_forward
    x = layer(x, emb)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 249, in forward
    return checkpoint(
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py", line 114, in checkpoint
    return CheckpointFunction.apply(func, len(inputs), *args)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py", line 129, in forward
    output_tensors = ctx.run_function(*ctx.input_tensors)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 262, in _forward
    h = self.in_layers(x)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 204, in forward
    input = module(input)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl
    return forward_call(*input, **kwargs)
  File "/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/util.py", line 219, in forward
    return super().forward(x.float()).type(x.dtype)
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/normalization.py", line 273, in forward
    return F.group_norm(
  File "/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/functional.py", line 2528, in group_norm
    return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: Expected weight to be a vector of size equal to the number of channels in input, but got weight of shape [2560] and input of shape [2, 5120, 8, 8]

---

And I found that after the error reported, even though you cancel the use, before you restart you can still not generate other images

[continue-revolution]

@RyanzW0521 Please also post a screenshot of your webui that produce this error. I will look into the reason tomorrow.

[RyanzW0521]

@RyanzW0521 Please also post a screenshot of your webui that produce this error. I will look into the reason tomorrow.

thanks!

[continue-revolution]

For everyone who come to here: I am not sure which features on WebUI are not compatible with AnimateDiff. Please feel free to post your terminal logs and a screenshot. Please do not use xformers at this time. The very first thing you can do is to try reproducing my demo.

https://civitai.com/models/30240?modelVersionId=78775

masterpiece, best quality, 1girl, solo, cherry blossoms, hanami, pink flower, white flower, spring season, wisteria, petals, flower, plum blossoms, outdoors, falling petals, white hair, black eyes,

badhandv4,easynegative,ng_deepnegative_v1_75t,verybadimagenegative_v1.3, bad-artist, bad_prompt_version2-neg, teeth

The majority of negative prompts are textual embeddings from civitai. Please manually download them.

[JPW0080]

Working on Win 11/3090, AUTOMATIC1111 1.4.0.
The generated GIF plays when clicked on my side.

[robbsaber]

Not working. Same error as above. Win 10/3090

[legarth]

Same error here. Downloaded models manually.
Windows 11. RTX4090
A1111 v1.4.0

File "C:\Users\x\stable-diffusion-webui\venv\lib\site-packages\torch\nn\functional.py", line 2530, in group_norm
return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
RuntimeError: Expected weight to be a vector of size equal to the number of channels in input, but got weight of shape [1280] and input of shape [32, 2560, 8, 8]