from diffusers import StableDiffusionImageVariationPipeline from PIL import Image device = "cuda:0" sd_pipe = StableDiffusionImageVariationPipeline.from_pretrained( "lambdalabs/sd-image-variations-diffusers", revision="main", ) sd_pipe = sd_pipe.to(device) im = Image.open("path/to/image.jpg") tform = transforms.Compose([ transforms.ToTensor(), transforms.Resize( (224, 224), interpolation=transforms.InterpolationMode.BICUBIC, antialias=False, ), transforms.Normalize( [0.48145466, 0.4578275, 0.40821073], [0.26862954, 0.26130258, 0.27577711]), ]) inp = tform(im).to(device) out = sd_pipe(inp, guidance_scale=3) 

Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.8/dist-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py", line 367, in __call__ image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance) File "/usr/local/lib/python3.8/dist-packages/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py", line 180, in _encode_image image_embeddings = self.image_encoder(image).image_embeds File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/clip/modeling_clip.py", line 1299, in forward vision_outputs = self.vision_model( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/clip/modeling_clip.py", line 854, in forward hidden_states = self.embeddings(pixel_values) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/usr/local/lib/python3.8/dist-packages/transformers/models/clip/modeling_clip.py", line 195, in forward embeddings = torch.cat([class_embeds, patch_embeds], dim=1) RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 3 but got size 1024 for tensor number 1 in the list.