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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,
) |