python3 -m demo.animate_cli error

[wangxr1999]

/mnt/home/code/workspace/AnimateAnyone-unofficial/demo/gradio_pipeline.py:96: FutureWarning: The configuration file of this scheduler: DDIMScheduler { "_class_name": "DDIMScheduler", "_diffusers_version": "0.21.4", "beta_end": 0.012, "beta_schedule": "linear", "beta_start": 0.00085, "clip_sample": true, "clip_sample_range": 1.0, "dynamic_thresholding_ratio": 0.995, "num_train_timesteps": 1000, "prediction_type": "epsilon", "rescale_betas_zero_snr": false, "sample_max_value": 1.0, "set_alpha_to_one": true, "steps_offset": 1, "thresholding": false, "timestep_spacing": "leading", "trained_betas": null } has not set the configuration clip_sample. clip_sample should be set to False in the configuration file. Please make sure to update the config accordingly as not setting clip_sample in the config might lead to incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for the scheduler/scheduler_config.json file deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) Initialization Done! /mnt/home/code/workspace/AnimateAnyone-unofficial/demo/gradio_pipeline.py:479: FutureWarning: Accessing config attribute in_channels directly via 'UNet3DConditionModel' object attribute is deprecated. Please access 'in_channels' over 'UNet3DConditionModel's config object instead, e.g. 'unet.config.in_channels'. num_channels_latents = self.unet.in_channels Traceback (most recent call last): File "/opt/conda/envs/animate/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/opt/conda/envs/animate/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/mnt/home/code/workspace/AnimateAnyone-unofficial/demo/animate_cli.py", line 37, in animate_images(args) File "/mnt/home/code/workspace/AnimateAnyone-unofficial/demo/animate_cli.py", line 20, in animate_images animation_path = animator(reference_image, motion_sequence, seed, steps, guidance_scale, size) File "/mnt/home/code/workspace/AnimateAnyone-unofficial/demo/animate.py", line 112, in call sample = self.pipeline( File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, kwargs) File "/mnt/home/code/workspace/AnimateAnyone-unofficial/demo/gradio_pipeline.py", line 512, in call image_embeddings = clip_image_encoder(clip_ref_image).unsqueeze(1).to(device=latents.device,dtype=latents.dtype) File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, *kwargs) File "/mnt/home/code/workspace/AnimateAnyone-unofficial/models/ReferenceEncoder.py", line 23, in forward outputs = self.model(pixel_values) File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(input, kwargs) File "/opt/conda/envs/animate/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 941, in forward return self.vision_model( File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, *kwargs) File "/opt/conda/envs/animate/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 866, in forward hidden_states = self.embeddings(pixel_values) File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(input, kwargs) File "/opt/conda/envs/animate/lib/python3.8/site-packages/transformers/models/clip/modeling_clip.py", line 195, in forward patch_embeds = self.patch_embedding(pixel_values) # shape = [, width, grid, grid] File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(input, kwargs) File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 463, in forward return self._conv_forward(input, self.weight, self.bias) File "/opt/conda/envs/animate/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 459, in _conv_forward return F.conv2d(input, weight, bias, self.stride, RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.cuda.HalfTensor) should be the same