lint(api): join all paths
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@ -47,9 +47,9 @@ max_height = 512
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max_width = 512
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max_width = 512
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# paths
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# paths
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model_path = environ.get('ONNX_WEB_MODEL_PATH', '../models')
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model_path = environ.get('ONNX_WEB_MODEL_PATH', path.join('..', 'models'))
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output_path = environ.get('ONNX_WEB_OUTPUT_PATH', '../outputs')
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output_path = environ.get('ONNX_WEB_OUTPUT_PATH', path.join('..', 'outputs'))
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params_path = environ.get('ONNX_WEB_PARAMS_PATH', './params.json')
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params_path = environ.get('ONNX_WEB_PARAMS_PATH', 'params.json')
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# pipeline caching
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# pipeline caching
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@ -1,11 +1,15 @@
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from diffusers import OnnxStableDiffusionPipeline
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from diffusers import OnnxStableDiffusionPipeline
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from os import path
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cfg = 8
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cfg = 8
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steps = 22
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steps = 22
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height = 512
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height = 512
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width = 512
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width = 512
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prompt = "an astronaut eating a hamburger"
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pipe = OnnxStableDiffusionPipeline.from_pretrained("../models/stable-diffusion-onnx-v1-5", provider="DmlExecutionProvider", safety_checker=None)
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model = path.join('..', 'models', 'stable-diffusion-onnx-v1-5')
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prompt = 'an astronaut eating a hamburger'
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output = path.join('..', 'outputs', 'test.png')
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pipe = OnnxStableDiffusionPipeline.from_pretrained(model, provider='DmlExecutionProvider', safety_checker=None)
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image = pipe(prompt, height, width, num_inference_steps=steps, guidance_scale=cfg).images[0]
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image = pipe(prompt, height, width, num_inference_steps=steps, guidance_scale=cfg).images[0]
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image.save("../test.png")
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image.save(output)
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