mirror of https://github.com/hpcaitech/ColossalAI
37 lines
1.3 KiB
Python
37 lines
1.3 KiB
Python
import os
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import sys
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from copy import deepcopy
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import yaml
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from datetime import datetime
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from diffusers import StableDiffusionPipeline
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import torch
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from ldm.util import instantiate_from_config
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from main import get_parser
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if __name__ == "__main__":
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with torch.no_grad():
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yaml_path = "../../train_colossalai.yaml"
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with open(yaml_path, 'r', encoding='utf-8') as f:
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config = f.read()
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base_config = yaml.load(config, Loader=yaml.FullLoader)
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unet_config = base_config['model']['params']['unet_config']
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diffusion_model = instantiate_from_config(unet_config).to("cuda:0")
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pipe = StableDiffusionPipeline.from_pretrained(
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"/data/scratch/diffuser/stable-diffusion-v1-4"
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).to("cuda:0")
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dif_model_2 = pipe.unet
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random_input_ = torch.rand((4, 4, 32, 32)).to("cuda:0")
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random_input_2 = torch.clone(random_input_).to("cuda:0")
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time_stamp = torch.randint(20, (4,)).to("cuda:0")
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time_stamp2 = torch.clone(time_stamp).to("cuda:0")
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context_ = torch.rand((4, 77, 768)).to("cuda:0")
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context_2 = torch.clone(context_).to("cuda:0")
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out_1 = diffusion_model(random_input_, time_stamp, context_)
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out_2 = dif_model_2(random_input_2, time_stamp2, context_2)
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print(out_1.shape)
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print(out_2['sample'].shape) |