Conditionally add random generator to inference pipeline
Browse files- handler.py +18 -7
handler.py
CHANGED
@@ -36,13 +36,13 @@ class EndpointHandler():
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width = params.pop("width", None)
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manual_seed = params.pop("manual_seed", -1)
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if manual_seed != -1:
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generator.manual_seed(manual_seed)
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out = self.pipe(prompt,
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generator=generator,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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@@ -50,7 +50,18 @@ class EndpointHandler():
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negative_prompt=negative_prompt,
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height=height,
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width=width
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return out.images[0]
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width = params.pop("width", None)
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manual_seed = params.pop("manual_seed", -1)
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out = None
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if manual_seed != -1:
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generator = torch.Generator(device='cuda')
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generator.manual_seed(manual_seed)
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# run inference pipeline
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out = self.pipe(prompt,
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generator=generator,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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negative_prompt=negative_prompt,
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height=height,
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width=width
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)
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else:
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# run inference pipeline
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out = self.pipe(prompt,
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num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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num_images_per_prompt=1,
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negative_prompt=negative_prompt,
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height=height,
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width=width
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)
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# return first generated PIL image
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return out.images[0]
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