Repository: stable-diffusion-webui
Issue: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument weight in method wrapper_CUDA___slow_conv2d_forward)
Labels: bug-report

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What happened?

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument weight in method wrapper_CUDA___slow_conv2d_forward)
提示：Python 运行时抛出了一个异常。请检查疑难解答页面。

### Steps to reproduce the problem

normal use

### What should have happened?

use  Animatediff  and  controlnet  

### Sysinfo

python: 3.10.11  •  torch: 2.0.0+cu118  •  xformers: 0.0.17  •  gradio: 3.41.2 

### What browsers do you use to access the UI ?

_No response_

### Console logs

```Shell
Startup time: 46.8s (prepare environment: 25.9s, import torch: 4.7s, import gradio: 1.0s, setup paths: 0.5s, initialize shared: 0.2s, other imports: 0.5s, setup codeformer: 0.3s, load scripts: 8.4s, create ui: 4.1s, gradio launch: 0.6s, app_started_callback: 0.5s).
Loading VAE weights specified in settings: E:\sd-webui-aki\sd-webui-aki-v4\models\VAE\vae-ft-mse-840000-ema-pruned.safetensors
Applying attention optimization: xformers... done.
Model loaded in 6.5s (load weights from disk: 0.6s, create model: 0.9s, apply weights to model: 4.3s, load VAE: 0.4s, calculate empty prompt: 0.1s).
refresh_ui
Restoring base VAE
Applying attention optimization: xformers... done.
VAE weights loaded.
2023-11-25 18:37:19,315 - ControlNet - INFO - Loading model: control_v11f1p_sd15_depth [cfd03158]
2023-11-25 18:37:19,995 - ControlNet - INFO - Loaded state_dict from [E:\sd-webui-aki\sd-webui-aki-v4\models\ControlNet\control_v11f1p_sd15_depth.pth]
2023-11-25 18:37:19,996 - ControlNet - INFO - controlnet_default_config
2023-11-25 18:37:22,842 - ControlNet - INFO - ControlNet model control_v11f1p_sd15_depth [cfd03158] loaded.
2023-11-25 18:37:23,008 - ControlNet - INFO - Loading preprocessor: depth
2023-11-25 18:37:23,010 - ControlNet - INFO - preprocessor resolution = 896
2023-11-25 18:37:27,343 - ControlNet - INFO - ControlNet Hooked - Time = 8.458001852035522

0: 640x384 1 face, 78.0ms
Speed: 4.0ms preprocess, 78.0ms inference, 29.0ms postprocess per image at shape (1, 3, 640, 384)
2023-11-25 18:37:50,189 - ControlNet - INFO - Loading model from cache: control_v11f1p_sd15_depth [cfd03158]
2023-11-25 18:37:50,192 - ControlNet - INFO - Loading preprocessor: depth
2023-11-25 18:37:50,192 - ControlNet - INFO - preprocessor resolution = 896
2023-11-25 18:37:50,279 - ControlNet - INFO - ControlNet Hooked - Time = 0.22900152206420898
2023-11-25 18:38:30,791 - AnimateDiff - INFO - AnimateDiff process start.
2023-11-25 18:38:30,791 - AnimateDiff - INFO - Loading motion module mm_sd_v15_v2.ckpt from E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\model\mm_sd_v15_v2.ckpt
2023-11-25 18:38:31,574 - AnimateDiff - INFO - Guessed mm_sd_v15_v2.ckpt architecture: MotionModuleType.AnimateDiffV2
2023-11-25 18:38:33,296 - AnimateDiff - WARNING - Missing keys <All keys matched successfully>
2023-11-25 18:38:34,243 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet middle block.
2023-11-25 18:38:34,245 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet input blocks.
2023-11-25 18:38:34,245 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet output blocks.
2023-11-25 18:38:34,246 - AnimateDiff - INFO - Setting DDIM alpha.
2023-11-25 18:38:34,254 - AnimateDiff - INFO - Injection finished.
2023-11-25 18:38:34,254 - AnimateDiff - INFO - Hacking loral to support motion lora
2023-11-25 18:38:34,254 - AnimateDiff - INFO - Hacking CFGDenoiser forward function.
2023-11-25 18:38:34,254 - AnimateDiff - INFO - Hacking ControlNet.
*** Error completing request
*** Arguments: ('task(8jna2axn6nwg2d4)', '1 sex girl, big breasts, solo, high heels, skirt, thigh strap, squatting, black footwear, long hair, closed eyes, multicolored hair, red hair, black shirt, sleeveless, black skirt, full body, shirt, lips, brown hair, black hair, sleeveless shirt, bare shoulders, crop top, midriff, grey background, simple background, ', 'bad hands, normal quality, ((monochrome)), ((grayscale)), ((strabismus)), ng_deepnegative_v1_75t, (bad-hands-5:1.3), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad_prompt, badhandv4, EasyNegative, ', [], 20, 'Euler a', 1, 1, 7, 1600, 896, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], <gradio.routes.Request object at 0x0000024A8CCB4670>, 0, False, '', 0.8, -1, False, -1, 0, 0, 0, True, False, {'ad_model': 'face_yolov8n.pt', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, {'ad_model': 'None', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, False, 'keyword prompt', 'keyword1, keyword2', 'None', 'textual inversion first', 'None', '0.7', 'None', False, 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, <animatediff_utils.py.AnimateDiffProcess object at 0x0000024A8CC58940>, False, False, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, None, 'Refresh models', <scripts.animatediff_ui.AnimateDiffProcess object at 0x0000024A3BD73F10>, UiControlNetUnit(enabled=True, module='depth_midas', model='control_v11f1p_sd15_depth [cfd03158]', weight=1, image={'image': array([[[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        ...,
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]]], dtype=uint8), 'mask': array([[[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        ...,
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]]], dtype=uint8)}, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=True, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, '', 0.5, True, False, '', 'Lerp', False, False, 1, 0.15, False, 'OUT', ['OUT'], 5, 0, 'Bilinear', False, 'Bilinear', False, 'Lerp', '', '', False, False, None, True, '🔄', False, False, 'Matrix', 'Columns', 'Mask', 'Prompt', '1,1', '0.2', False, False, False, 'Attention', [False], '0', '0', '0.4', None, '0', '0', False, False, False, 0, None, [], 0, False, [], [], False, 0, 1, False, False, 0, None, [], -2, False, [], False, 0, None, None, False, False, False, False, False, False, False, False, '1:1,1:2,1:2', '0:0,0:0,0:1', '0.2,0.8,0.8', 20, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, False, 5, 'all', 'all', 'all', '', '', '', '1', 'none', False, '', '', 'comma', '', True, '', '20', 'all', 'all', 'all', 'all', 0, '', 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, None, None, False, None, None, False, None, None, False, 50, [], 30, '', 4, [], 1, '', '', '', '') {}
    Traceback (most recent call last):
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 57, in f
        res = list(func(*args, **kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 36, in f
        res = func(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\txt2img.py", line 55, in txt2img
        processed = processing.process_images(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-prompt-history\lib_history\image_process_hijacker.py", line 21, in process_images
        res = original_function(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 732, in process_images
        res = process_images_inner(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\scripts\animatediff_cn.py", line 77, in hacked_processing_process_images_hijack
        assert global_input_frames, 'No input images found for ControlNet module'
    AssertionError: No input images found for ControlNet module
提示：Python 运行时抛出了一个异常。请检查疑难解答页面。

---
2023-11-25 18:39:52,611 - AnimateDiff - INFO - AnimateDiff process start.
2023-11-25 18:39:52,611 - AnimateDiff - INFO - Motion module already injected. Trying to restore.
2023-11-25 18:39:52,612 - AnimateDiff - INFO - Restoring DDIM alpha.
2023-11-25 18:39:52,612 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet input blocks.
2023-11-25 18:39:52,612 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet output blocks.
2023-11-25 18:39:52,612 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet middle block.
2023-11-25 18:39:52,612 - AnimateDiff - INFO - Removal finished.
2023-11-25 18:39:52,650 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet middle block.
2023-11-25 18:39:52,650 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet input blocks.
2023-11-25 18:39:52,650 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet output blocks.
2023-11-25 18:39:52,650 - AnimateDiff - INFO - Setting DDIM alpha.
2023-11-25 18:39:52,656 - AnimateDiff - INFO - Injection finished.
2023-11-25 18:39:52,656 - AnimateDiff - INFO - AnimateDiff LoRA already hacked
2023-11-25 18:39:52,656 - AnimateDiff - INFO - CFGDenoiser already hacked
2023-11-25 18:39:52,657 - AnimateDiff - INFO - Hacking ControlNet.
2023-11-25 18:39:52,657 - AnimateDiff - INFO - BatchHijack already hacked.
2023-11-25 18:39:52,657 - AnimateDiff - INFO - ControlNet Main Entry already hacked.
2023-11-25 18:39:52,697 - ControlNet - INFO - Loading model from cache: control_v11f1p_sd15_depth [cfd03158]
2023-11-25 18:39:55,575 - ControlNet - INFO - Loading preprocessor: depth
2023-11-25 18:39:55,576 - ControlNet - INFO - preprocessor resolution = 448
2023-11-25 18:40:03,478 - ControlNet - INFO - ControlNet Hooked - Time = 10.787747859954834
*** Error completing request
*** Arguments: ('task(pud1kvh3lzj0ocs)', '1 sex girl, big breasts, solo, high heels, skirt, thigh strap, squatting, black footwear, long hair, closed eyes, multicolored hair, red hair, black shirt, sleeveless, black skirt, full body, shirt, lips, brown hair, black hair, sleeveless shirt, bare shoulders, crop top, midriff, grey background, simple background, ', 'bad hands, normal quality, ((monochrome)), ((grayscale)), ((strabismus)), ng_deepnegative_v1_75t, (bad-hands-5:1.3), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad_prompt, badhandv4, EasyNegative, ', [], 20, 'Euler a', 1, 1, 7, 800, 448, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], <gradio.routes.Request object at 0x0000024A8CCB7DC0>, 0, False, '', 0.8, -1, False, -1, 0, 0, 0, True, False, {'ad_model': 'face_yolov8n.pt', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, {'ad_model': 'None', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, False, 'keyword prompt', 'keyword1, keyword2', 'None', 'textual inversion first', 'None', '0.7', 'None', False, 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, <animatediff_utils.py.AnimateDiffProcess object at 0x0000024A8CC58940>, False, False, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, None, 'Refresh models', <scripts.animatediff_ui.AnimateDiffProcess object at 0x0000024AB1539DB0>, UiControlNetUnit(enabled=True, module='depth_midas', model='control_v11f1p_sd15_depth [cfd03158]', weight=1, image={'image': array([[[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        ...,
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]]], dtype=uint8), 'mask': array([[[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        ...,
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]]], dtype=uint8)}, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=True, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, '', 0.5, True, False, '', 'Lerp', False, False, 1, 0.15, False, 'OUT', ['OUT'], 5, 0, 'Bilinear', False, 'Bilinear', False, 'Lerp', '', '', False, False, None, True, '🔄', False, False, 'Matrix', 'Columns', 'Mask', 'Prompt', '1,1', '0.2', False, False, False, 'Attention', [False], '0', '0', '0.4', None, '0', '0', False, False, False, 0, None, [], 0, False, [], [], False, 0, 1, False, False, 0, None, [], -2, False, [], False, 0, None, None, False, False, False, False, False, False, False, False, '1:1,1:2,1:2', '0:0,0:0,0:1', '0.2,0.8,0.8', 20, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, False, 5, 'all', 'all', 'all', '', '', '', '1', 'none', False, '', '', 'comma', '', True, '', '20', 'all', 'all', 'all', 'all', 0, '', 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, None, None, False, None, None, False, None, None, False, 50, [], 30, '', 4, [], 1, '', '', '', '') {}
    Traceback (most recent call last):
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 57, in f
        res = list(func(*args, **kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 36, in f
        res = func(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\txt2img.py", line 55, in txt2img
        processed = processing.process_images(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-prompt-history\lib_history\image_process_hijacker.py", line 21, in process_images
        res = original_function(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 732, in process_images
        res = process_images_inner(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\scripts\animatediff_cn.py", line 118, in hacked_processing_process_images_hijack
        return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 867, 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 "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 420, in process_sample
        return process.sample_before_CN_hack(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 1140, in sample
        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_kdiffusion.py", line 235, in sample
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_common.py", line 261, in launch_sampling
        return func()
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_kdiffusion.py", line 235, in <lambda>
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
        return func(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
        denoised = model(x, sigmas[i] * s_in, **extra_args)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\scripts\animatediff_infv2v.py", line 269, in mm_cfg_forward
        x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\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 "E:\sd-webui-aki\sd-webui-aki-v4\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
        return self.inner_model.apply_model(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_utils.py", line 17, in <lambda>
        setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_utils.py", line 26, in __call__
        return self.__sub_func(self.__orig_func, *args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_unet.py", line 48, in apply_model
        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
        x_recon = self.model(x_noisy, t, **cond)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1335, in forward
        out = self.diffusion_model(x, t, context=cc)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 827, in forward_webui
        raise e
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 824, in forward_webui
        return forward(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 561, in forward
        control = param.control_model(x=x_in, hint=hint, timesteps=timesteps, context=context, y=y)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\cldm.py", line 31, in forward
        return self.control_model(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\cldm.py", line 300, in forward
        guided_hint = self.input_hint_block(hint, emb, context)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 102, in forward
        x = layer(x)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions-builtin\Lora\networks.py", line 444, in network_Conv2d_forward
        return originals.Conv2d_forward(self, input)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
        return self._conv_forward(input, self.weight, self.bias)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
        return F.conv2d(input, weight, bias, self.stride,
    RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument weight in method wrapper_CUDA___slow_conv2d_forward)
提示：Python 运行时抛出了一个异常。请检查疑难解答页面。

---
2023-11-25 18:43:26,500 - AnimateDiff - INFO - AnimateDiff process start.
2023-11-25 18:43:26,501 - AnimateDiff - INFO - Motion module already injected. Trying to restore.
2023-11-25 18:43:26,501 - AnimateDiff - INFO - Restoring DDIM alpha.
2023-11-25 18:43:26,502 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet input blocks.
2023-11-25 18:43:26,503 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet output blocks.
2023-11-25 18:43:26,503 - AnimateDiff - INFO - Removing motion module from SD1.5 UNet middle block.
2023-11-25 18:43:26,503 - AnimateDiff - INFO - Removal finished.
2023-11-25 18:43:26,519 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet middle block.
2023-11-25 18:43:26,519 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet input blocks.
2023-11-25 18:43:26,519 - AnimateDiff - INFO - Injecting motion module mm_sd_v15_v2.ckpt into SD1.5 UNet output blocks.
2023-11-25 18:43:26,520 - AnimateDiff - INFO - Setting DDIM alpha.
2023-11-25 18:43:26,522 - AnimateDiff - INFO - Injection finished.
2023-11-25 18:43:26,522 - AnimateDiff - INFO - AnimateDiff LoRA already hacked
2023-11-25 18:43:26,522 - AnimateDiff - INFO - CFGDenoiser already hacked
2023-11-25 18:43:26,522 - AnimateDiff - INFO - Hacking ControlNet.
2023-11-25 18:43:26,522 - AnimateDiff - INFO - BatchHijack already hacked.
2023-11-25 18:43:26,522 - AnimateDiff - INFO - ControlNet Main Entry already hacked.
2023-11-25 18:43:26,736 - ControlNet - INFO - Loading model from cache: control_v11f1p_sd15_depth [cfd03158]
2023-11-25 18:43:28,938 - ControlNet - INFO - Loading preprocessor: depth
2023-11-25 18:43:28,939 - ControlNet - INFO - preprocessor resolution = 448
2023-11-25 18:43:38,425 - ControlNet - INFO - ControlNet Hooked - Time = 11.88900351524353
*** Error completing request
*** Arguments: ('task(ugw097u8h16i04e)', '1 sex girl, big breasts, solo, high heels, skirt, thigh strap, squatting, black footwear, long hair, closed eyes, multicolored hair, red hair, black shirt, sleeveless, black skirt, full body, shirt, lips, brown hair, black hair, sleeveless shirt, bare shoulders, crop top, midriff, grey background, simple background, ', 'bad hands, normal quality, ((monochrome)), ((grayscale)), ((strabismus)), ng_deepnegative_v1_75t, (bad-hands-5:1.3), (worst quality:2), (low quality:2), (normal quality:2), lowres, bad anatomy, bad_prompt, badhandv4, EasyNegative, ', [], 20, 'Euler a', 1, 1, 7, 800, 448, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', '', '', [], <gradio.routes.Request object at 0x0000024A8CC5E950>, 0, False, '', 0.8, -1, False, -1, 0, 0, 0, True, False, {'ad_model': 'face_yolov8n.pt', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, {'ad_model': 'None', 'ad_prompt': '', 'ad_negative_prompt': '', 'ad_confidence': 0.3, 'ad_mask_k_largest': 0, '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_checkpoint': False, 'ad_checkpoint': 'Use same checkpoint', 'ad_use_vae': False, 'ad_vae': 'Use same VAE', 'ad_use_sampler': False, 'ad_sampler': 'Euler a', 'ad_use_noise_multiplier': False, 'ad_noise_multiplier': 1, 'ad_use_clip_skip': False, 'ad_clip_skip': 1, 'ad_restore_face': False, 'ad_controlnet_model': 'None', 'ad_controlnet_module': 'None', 'ad_controlnet_weight': 1, 'ad_controlnet_guidance_start': 0, 'ad_controlnet_guidance_end': 1, 'is_api': ()}, False, 'keyword prompt', 'keyword1, keyword2', 'None', 'textual inversion first', 'None', '0.7', 'None', False, 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, <animatediff_utils.py.AnimateDiffProcess object at 0x0000024A8CC58940>, False, False, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, 'LoRA', 'None', 1, 1, None, 'Refresh models', <scripts.animatediff_ui.AnimateDiffProcess object at 0x0000024A8CC59F60>, UiControlNetUnit(enabled=True, module='depth_midas', model='control_v11f1p_sd15_depth [cfd03158]', weight=1, image={'image': array([[[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        [[183, 187, 189],
***         [183, 187, 189],
***         [183, 187, 189],
***         ...,
***         [185, 189, 191],
***         [185, 189, 191],
***         [185, 189, 191]],
*** 
***        ...,
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]],
*** 
***        [[223, 224, 227],
***         [223, 224, 227],
***         [223, 224, 227],
***         ...,
***         [227, 227, 227],
***         [227, 227, 227],
***         [227, 227, 227]]], dtype=uint8), 'mask': array([[[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        ...,
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]],
*** 
***        [[0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0],
***         ...,
***         [0, 0, 0],
***         [0, 0, 0],
***         [0, 0, 0]]], dtype=uint8)}, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=-1, threshold_b=-1, guidance_start=0, guidance_end=1, pixel_perfect=True, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), UiControlNetUnit(enabled=False, module='none', model='None', weight=1, image=None, resize_mode='Crop and Resize', low_vram=False, processor_res=512, threshold_a=64, threshold_b=64, guidance_start=0, guidance_end=1, pixel_perfect=False, control_mode='Balanced', save_detected_map=True), False, '', 0.5, True, False, '', 'Lerp', False, False, 1, 0.15, False, 'OUT', ['OUT'], 5, 0, 'Bilinear', False, 'Bilinear', False, 'Lerp', '', '', False, False, None, True, '🔄', False, False, 'Matrix', 'Columns', 'Mask', 'Prompt', '1,1', '0.2', False, False, False, 'Attention', [False], '0', '0', '0.4', None, '0', '0', False, False, False, 0, None, [], 0, False, [], [], False, 0, 1, False, False, 0, None, [], -2, False, [], False, 0, None, None, False, False, False, False, False, False, False, False, '1:1,1:2,1:2', '0:0,0:0,0:1', '0.2,0.8,0.8', 20, False, False, 'positive', 'comma', 0, False, False, '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, 0, False, 5, 'all', 'all', 'all', '', '', '', '1', 'none', False, '', '', 'comma', '', True, '', '20', 'all', 'all', 'all', 'all', 0, '', 1.6, 0.97, 0.4, 0, 20, 0, 12, '', True, False, False, False, 512, False, True, ['Face'], False, '{\n    "face_detector": "RetinaFace",\n    "rules": {\n        "then": {\n            "face_processor": "img2img",\n            "mask_generator": {\n                "name": "BiSeNet",\n                "params": {\n                    "fallback_ratio": 0.1\n                }\n            }\n        }\n    }\n}', 'None', 40, None, None, False, None, None, False, None, None, False, 50, [], 30, '', 4, [], 1, '', '', '', '') {}
    Traceback (most recent call last):
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 57, in f
        res = list(func(*args, **kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\call_queue.py", line 36, in f
        res = func(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\txt2img.py", line 55, in txt2img
        processed = processing.process_images(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-prompt-history\lib_history\image_process_hijacker.py", line 21, in process_images
        res = original_function(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 732, in process_images
        res = process_images_inner(p)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\scripts\animatediff_cn.py", line 118, in hacked_processing_process_images_hijack
        return getattr(processing, '__controlnet_original_process_images_inner')(p, *args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 867, 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 "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 420, in process_sample
        return process.sample_before_CN_hack(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\processing.py", line 1140, in sample
        samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_kdiffusion.py", line 235, in sample
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_common.py", line 261, in launch_sampling
        return func()
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_samplers_kdiffusion.py", line 235, in <lambda>
        samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\utils\_contextlib.py", line 115, in decorate_context
        return func(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\k-diffusion\k_diffusion\sampling.py", line 145, in sample_euler_ancestral
        denoised = model(x, sigmas[i] * s_in, **extra_args)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-animatediff\scripts\animatediff_infv2v.py", line 269, in mm_cfg_forward
        x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\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 "E:\sd-webui-aki\sd-webui-aki-v4\repositories\k-diffusion\k_diffusion\external.py", line 138, in get_eps
        return self.inner_model.apply_model(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_utils.py", line 17, in <lambda>
        setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_utils.py", line 26, in __call__
        return self.__sub_func(self.__orig_func, *args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\modules\sd_hijack_unet.py", line 48, in apply_model
        return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 858, in apply_model
        x_recon = self.model(x_noisy, t, **cond)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\stable-diffusion-stability-ai\ldm\models\diffusion\ddpm.py", line 1335, in forward
        out = self.diffusion_model(x, t, context=cc)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 827, in forward_webui
        raise e
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 824, in forward_webui
        return forward(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\hook.py", line 561, in forward
        control = param.control_model(x=x_in, hint=hint, timesteps=timesteps, context=context, y=y)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\cldm.py", line 31, in forward
        return self.control_model(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions\sd-webui-controlnet\scripts\cldm.py", line 300, in forward
        guided_hint = self.input_hint_block(hint, emb, context)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\repositories\generative-models\sgm\modules\diffusionmodules\openaimodel.py", line 102, in forward
        x = layer(x)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl
        return forward_call(*args, **kwargs)
      File "E:\sd-webui-aki\sd-webui-aki-v4\extensions-builtin\Lora\networks.py", line 444, in network_Conv2d_forward
        return originals.Conv2d_forward(self, input)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 463, in forward
        return self._conv_forward(input, self.weight, self.bias)
      File "E:\sd-webui-aki\sd-webui-aki-v4\py310\lib\site-packages\torch\nn\modules\conv.py", line 459, in _conv_forward
        return F.conv2d(input, weight, bias, self.stride,
    RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! (when checking argument for argument weight in method wrapper_CUDA___slow_conv2d_forward)
提示：Python 运行时抛出了一个异常。请检查疑难解答页面。

---
```


### Additional information

_No response_