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Merge pull request PaddlePaddle#126 from luyao-cv/develop
[AppFlow] fix audio2img readme and add missing files
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,71 @@ | ||
| # Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| from ppdiffusers import AudioLDMPipeline | ||
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| from .apptask import AppTask | ||
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| class AudioLDMPipelineTask(AppTask): | ||
| def __init__(self, task, model, **kwargs): | ||
| super().__init__(task=task, model=model, **kwargs) | ||
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| # Default to static mode | ||
| self._static_mode = False | ||
| self._construct_model(model) | ||
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| def _construct_model(self, model): | ||
| """ | ||
| Construct the inference model for the predictor. | ||
| """ | ||
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| # bulid model | ||
| model_instance = AudioLDMPipeline.from_pretrained(model) | ||
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| self._model = model_instance | ||
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| def _preprocess(self, inputs): | ||
| """ """ | ||
| prompt = inputs.get("prompt", None) | ||
| assert prompt is not None, "The prompt is None" | ||
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| return inputs | ||
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| def _run_model(self, inputs): | ||
| """ | ||
| Run the task model from the outputs of the `_preprocess` function. | ||
| """ | ||
| _num_inference_steps = inputs.get("num_inference_steps", 10) | ||
| _audio_length_in_s = inputs.get("audio_length_in_s", 5.0) | ||
| tmp = inputs["prompt"] | ||
| print(tmp) | ||
| result = self._model( | ||
| prompt=inputs["prompt"], | ||
| num_inference_steps=_num_inference_steps, | ||
| audio_length_in_s=_audio_length_in_s, | ||
| ).audios[0] | ||
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| inputs.pop("prompt", None) | ||
| inputs.pop("_num_inference_steps", None) | ||
| inputs.pop("_audio_length_in_s", None) | ||
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| inputs["result"] = result | ||
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| return inputs | ||
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| def _postprocess(self, inputs): | ||
| """ | ||
| The model output is tag ids, this function will convert the model output to raw text. | ||
| """ | ||
| return inputs |