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| from shap_e.diffusion.sample import sample_latents | |
| from shap_e.diffusion.gaussian_diffusion import diffusion_from_config as diffusion_from_config_shape | |
| from shap_e.models.download import load_model, load_config | |
| from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget | |
| from shap_e.util.notebooks import decode_latent_mesh | |
| import torch | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| xm = load_model('transmitter', device=device) | |
| model = load_model('text300M', device=device) | |
| # model.load_state_dict(torch.load('./load/shapE_finetuned_with_330kdata.pth', map_location=device)['model_state_dict']) | |
| diffusion = diffusion_from_config_shape(load_config('diffusion')) | |
| del xm | |
| del model | |
| del diffusion | |
| from diffusers import DDIMScheduler, DDPMScheduler, StableDiffusionPipeline | |
| pipe_kwargs = { | |
| "tokenizer": None, | |
| "safety_checker": None, | |
| "feature_extractor": None, | |
| "requires_safety_checker": False, | |
| "torch_dtype": torch.float16 , | |
| } | |
| pipe = StableDiffusionPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-2-1-base", | |
| **pipe_kwargs, | |
| ) |