新增音频生成图像（Audio-to-Image Generation） gradio demo (#365)

完成pipeline验证有效

#258
#272

applications/Audio2Img/gradio_demo.py

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+# 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.
+
+import argparse
+from types import SimpleNamespace
+
+import gradio as gr
+import paddle
+
+from paddlemix import ImageBindModel, ImageBindProcessor
+from paddlemix.utils.log import logger
+from ppdiffusers import StableUnCLIPImg2ImgPipeline
+
+ModalityType = SimpleNamespace(
+    VISION="vision",
+    TEXT="text",
+    AUDIO="audio",
+    THERMAL="thermal",
+    DEPTH="depth",
+    IMU="imu",
+)
+
+
+class Predictor:
+    def __init__(self, model_args):
+        self.processor = ImageBindProcessor.from_pretrained(model_args.model_name_or_path)
+        self.predictor = ImageBindModel.from_pretrained(model_args.model_name_or_path)
+        self.predictor.eval()
+
+    def run(self, inputs):
+        with paddle.no_grad():
+            embeddings = self.predictor(inputs)
+        return embeddings
+
+
+def model_init(model_args):
+    predictor = Predictor(model_args)
+    return predictor
+
+
+def infer(input_image, input_audio, input_text):
+
+    global predictor
+    image_pil = input_image
+
+    encoding = predictor.processor(images=image_pil, text="", audios=input_audio, return_tensors="pd")
+    inputs = {}
+
+    if image_pil is not None:
+        image_processor = encoding["pixel_values"]
+        inputs.update({ModalityType.VISION: image_processor})
+
+    if input_audio is not None:
+        audio_processor = encoding["audio_values"]
+        inputs.update({ModalityType.AUDIO: audio_processor})
+    else:
+        pass
+
+    embeddings = predictor.run(inputs)
+    image_proj_embeds = embeddings[ModalityType.AUDIO]
+
+    if image_pil is not None:
+        logger.info("Generate vision embedding: {}".format(embeddings[ModalityType.VISION]))
+        image_proj_embeds += embeddings[ModalityType.VISION]
+
+    logger.info("Generate audio embedding: {}".format(embeddings[ModalityType.AUDIO]))
+
+    if input_text is not None:
+        prompt = input_text
+    else:
+        prompt = ""
+
+    pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(model_args.stable_unclip_model_name_or_path)
+    pipe.set_progress_bar_config(disable=None)
+    output = pipe(image_embeds=image_proj_embeds, prompt=prompt)
+
+    return output.images[0]
+
+
+def parse_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        default="imagebind-1.2b/",
+        help="Path to pretrained model or model identifier",
+    )
+    parser.add_argument(
+        "--stable_unclip_model_name_or_path",
+        type=str,
+        default="stabilityai/stable-diffusion-2-1-unclip",
+        help="Path to pretrained model or model identifier in stable_unclip_model_name_or_path",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default="GPU",
+        choices=["CPU", "GPU", "XPU"],
+        help="Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU.",
+    )
+    return parser.parse_args()
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("音频生成图像（Audio-to-Image Generation）")
+    with gr.Row():
+        with gr.Column():
+            input_audio = gr.Audio(label="input audio", type="filepath")
+            with gr.Tab(label="input text（可选）") as txttab:
+                input_text = gr.Textbox(label="input text")
+            with gr.Tab(label="input image（可选）") as imgtab:
+                input_image = gr.Image(label="input image")
+            infer_button = gr.Button("推理")
+        output_image = gr.Image(label="result")
+        txttab.select(fn=lambda: None, outputs=input_image)
+        imgtab.select(fn=lambda: None, outputs=input_text)
+        infer_button.click(fn=infer, inputs=[input_image, input_audio, input_text], outputs=[output_image])
+if __name__ == "__main__":
+
+    model_args = parse_arguments()
+    assert model_args.device in ["CPU", "GPU", "XPU", "NPU"], "device should be CPU, GPU, XPU or NPU"
+    predictor = model_init(model_args)
+
+    demo.launch()