On the operation screen, I can upload any still image to Reference Image on the left. However, when I try to upload the Motion Sequence created with DensePose_colab on the right, an error is displayed. Is there any better way?

Please check the project. it doesn't work

上传的模板都报错。

(enhancement)

It would be great if the Colab featured an indicator of what stage of the computation the code is in, or how many minutes(/hours? days?) are left. e.g., a percentage output or progess bar.

(The Gradio "progress bar" doesn't work when you render something for the first time.)

I don't have my power supply on me, and started the Colab run 18 minutes ago with 20% battery. It's now down to 5% battery, and I have no idea whether the render will complete in time for me to see it, or if I should just give up until I have a chance to plug in.

how to get motion file

This is very strange. Exactly the same error message as your other Google Colab notebooks.
camenduru/styletts-colab#2

Edge, Chrome (Win11)
Edge, Chrome, Firefox (Win10)

I made Densepose video by DensePose_colab and tried to upload this video in MagicAnimate_colab but failed.

How do I upload DensePose video whici is made by DensePose_colab?

More than 20 minutes have passed and the examples have not been run?

/content
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Building wheel for antlr4-python3-runtime (setup.py) ... done
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ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
lida 0.0.10 requires kaleido, which is not installed.
llmx 0.0.15a0 requires cohere, which is not installed.
llmx 0.0.15a0 requires openai, which is not installed.
llmx 0.0.15a0 requires tiktoken, which is not installed.
tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.
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The following additional packages will be installed:
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The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling transformers.utils.move_cache().
0it [00:00, ?it/s]
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2023-12-18 10:07:28.958284: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.1.0+cu118 with CUDA 1108 (you have 2.1.0+cu121)
Python 3.10.13 (you have 3.10.12)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
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Initializing MagicAnimate Pipeline...
loaded temporal unet's pretrained weights from /content/MagicAnimate-hf/stable-diffusion-v1-5/unet ...

missing keys: 560;

unexpected keys: 0;

Temporal Module Parameters: 417.1376 M

The config attributes {'addition_embed_type': None, 'addition_embed_type_num_heads': 64, 'addition_time_embed_dim': None, 'conditioning_channels': 3, 'encoder_hid_dim': None, 'encoder_hid_dim_type': None, 'global_pool_conditions': False, 'num_attention_heads': None, 'transformer_layers_per_block': 1} were passed to ControlNetModel, but are not expected and will be ignored. Please verify your config.json configuration file.
It is recommended to provide attention_head_dim when calling get_down_block. Defaulting attention_head_dim to 8.
It is recommended to provide attention_head_dim when calling get_down_block. Defaulting attention_head_dim to 8.
It is recommended to provide attention_head_dim when calling get_down_block. Defaulting attention_head_dim to 8.
It is recommended to provide attention_head_dim when calling get_down_block. Defaulting attention_head_dim to 8.
Traceback (most recent call last):
File "/content/MagicAnimate-hf/app.py", line 26, in
animator = MagicAnimate()
File "/content/MagicAnimate-hf/demo/animate.py", line 81, in init
unet.enable_xformers_memory_efficient_attention()
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 287, in enable_xformers_memory_efficient_attention
self.set_use_memory_efficient_attention_xformers(True, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 251, in set_use_memory_efficient_attention_xformers
fn_recursive_set_mem_eff(module)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 247, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 247, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 247, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 244, in fn_recursive_set_mem_eff
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 251, in set_use_memory_efficient_attention_xformers
fn_recursive_set_mem_eff(module)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 247, in fn_recursive_set_mem_eff
fn_recursive_set_mem_eff(child)
File "/usr/local/lib/python3.10/dist-packages/diffusers/models/modeling_utils.py", line 244, in fn_recursive_set_mem_eff
module.set_use_memory_efficient_attention_xformers(valid, attention_op)
File "/content/MagicAnimate-hf/magicanimate/models/attention.py", line 270, in set_use_memory_efficient_attention_xformers
raise e
File "/content/MagicAnimate-hf/magicanimate/models/attention.py", line 264, in set_use_memory_efficient_attention_xformers
_ = xformers.ops.memory_efficient_attention(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py", line 223, in memory_efficient_attention
return _memory_efficient_attention(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py", line 321, in _memory_efficient_attention
return _memory_efficient_attention_forward(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/init.py", line 337, in _memory_efficient_attention_forward
op = _dispatch_fw(inp, False)
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 120, in _dispatch_fw
return _run_priority_list(
File "/usr/local/lib/python3.10/dist-packages/xformers/ops/fmha/dispatch.py", line 63, in _run_priority_list
raise NotImplementedError(msg)
NotImplementedError: No operator found for memory_efficient_attention_forward with inputs:
query : shape=(1, 2, 1, 40) (torch.float32)
key : shape=(1, 2, 1, 40) (torch.float32)
value : shape=(1, 2, 1, 40) (torch.float32)
attn_bias : <class 'NoneType'>
p : 0.0
decoderF is not supported because:
xFormers wasn't build with CUDA support
attn_bias type is <class 'NoneType'>
operator wasn't built - see python -m xformers.info for more info
[email protected] is not supported because:
xFormers wasn't build with CUDA support
requires device with capability > (8, 0) but your GPU has capability (7, 5) (too old)
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
operator wasn't built - see python -m xformers.info for more info
tritonflashattF is not supported because:
xFormers wasn't build with CUDA support
requires device with capability > (8, 0) but your GPU has capability (7, 5) (too old)
dtype=torch.float32 (supported: {torch.bfloat16, torch.float16})
operator wasn't built - see python -m xformers.info for more info
triton is not available
requires GPU with sm80 minimum compute capacity, e.g., A100/H100/L4
Only work on pre-MLIR triton for now
cutlassF is not supported because:
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
smallkF is not supported because:
max(query.shape[-1] != value.shape[-1]) > 32
xFormers wasn't build with CUDA support
operator wasn't built - see python -m xformers.info for more info
unsupported embed per head: 40
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