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aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
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12 lines
351 B
12 lines
351 B
import torch |
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from diffusers import DiffusionPipeline |
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model_id = "<Your Model Path>" |
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print(f"Loading model... from{model_id}") |
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pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") |
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prompt = "A photo of an apple." |
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image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] |
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image.save("output.png")
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