app.py

import gradio as gr
import os 
hf_token = os.environ.get('HF_TOKEN')

lpmc_client = gr.load("seungheondoh/LP-Music-Caps-demo", src="spaces")

from gradio_client import Client

client = Client("https://fffiloni-test-llama-api.hf.space/", hf_token=hf_token)
lyrics_client = Client("https://fffiloni-music-to-lyrics.hf.space/")


from compel import Compel, ReturnedEmbeddingsType
from diffusers import DiffusionPipeline
import torch

pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0",
                                         torch_dtype=torch.float16, 
                                         use_safetensors=True, 
                                         variant="fp16")
pipe.to("cuda")

compel = Compel(
    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
    requires_pooled=[False, True]
)

#pipe.enable_model_cpu_offload()

# if using torch < 2.0
# pipe.enable_xformers_memory_efficient_attention()

from pydub import AudioSegment

def cut_audio(input_path, output_path, max_duration=30000):
    audio = AudioSegment.from_file(input_path)

    if len(audio) > max_duration:
        audio = audio[:max_duration]

    audio.export(output_path, format="mp3")

    return output_path

def get_text_after_colon(input_text):
    # Find the first occurrence of ":"
    colon_index = input_text.find(":")
    
    # Check if ":" exists in the input_text
    if colon_index != -1:
        # Extract the text after the colon
        result_text = input_text[colon_index + 1:].strip()
        return result_text
    else:
        # Return the original text if ":" is not found
        return input_text


def solo_xd(prompt):
    images = pipe(prompt=prompt).images[0]
    return images

def infer(audio_file, has_lyrics):
    print("NEW INFERENCE ...")

    truncated_audio = cut_audio(audio_file, "trunc_audio.mp3")

    print("Calling LP Music Caps...")
    cap_result = lpmc_client(
    				truncated_audio,	# str (filepath or URL to file) in 'audio_path' Audio component
    				api_name="predict"
    )
    print(f"MUSIC DESC: {cap_result}")

    if has_lyrics == "Yes" : 
        print("""———
        Getting Lyrics ...
        """)
        lyrics_result = lyrics_client.predict(
        				audio_file,	# str (filepath or URL to file) in 'Song input' Audio component
        				fn_index=0
        )
        print(f"LYRICS: {lyrics_result}")
    
        llama_q = f"""
        I'll give you a music description + the lyrics of the song. 
        Give me an image description that would fit well with the music description, reflecting the lyrics too. 
        Be creative, do not do list, just an image description as required. Try to think about human characters first.
        Your image description must fit well for a stable diffusion prompt.
    
        Here's the music description :
    
        « {cap_result} »
    
        And here are the lyrics : 
    
        « {lyrics_result} »
        
        """
    elif has_lyrics == "No" : 
        
        llama_q = f"""
        I'll give you a music description. 
        Give me an image description that would fit well with the music description. 
        Be creative, do not do list, just an image description as required. Try to think about human characters first.
        Your image description must fit well for a stable diffusion prompt.
    
        Here's the music description :
    
        « {cap_result} »
        """
    print("""———
    Calling Llama2 ...
    """)
    result = client.predict(
    				llama_q,	# str in 'Message' Textbox component
    				api_name="/predict"
    )    
    
    result = get_text_after_colon(result)

    print(f"Llama2 result: {result}")

    # ———
    print("""———
    Calling SD-XL ...
    """)
    prompt = result
    conditioning, pooled = compel(prompt)
    images = pipe(prompt_embeds=conditioning, pooled_prompt_embeds=pooled).images[0]

    print("Finished")
    
    #return cap_result, result, images
    return images, result, gr.update(visible=True)

css = """
#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
"""
with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
                <div
                style="
                    display: inline-flex;
                    align-items: center;
                    gap: 0.8rem;
                    font-size: 1.75rem;
                "
                >
                <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
                    Music To Image
                </h1>
                </div>
                <p style="margin-bottom: 10px; font-size: 94%">
                Sends an audio into <a href="https://huggingface.co/spaces/seungheondoh/LP-Music-Caps-demo" target="_blank">LP-Music-Caps</a>
                to generate a audio caption which is then translated to an illustrative image description with Llama2, and finally run through 
                Stable Diffusion XL to generate an image from the audio ! <br /><br />
                Note: Only the first 30 seconds of your audio will be used for inference.
                </p>
            </div>""")
        audio_input = gr.Audio(label="Music input", type="filepath", source="upload")
        has_lyrics = gr.Radio(label="Does your audio has lyrics ?", choices=["Yes", "No"], value="No", info="If yes, the image should reflect the lyrics, but be aware that because we add a step (getting lyrics), inference will take more time.")
        infer_btn = gr.Button("Generate Image from Music")
        #lpmc_cap = gr.Textbox(label="Lp Music Caps caption")
        llama_trans_cap = gr.Textbox(label="Llama translation", visible=False)
        img_result = gr.Image(label="Image Result")
        tryagain_btn = gr.Button("Try another image ?", visible=False)

        gr.Examples(examples=[["./examples/electronic.mp3", "No"],["./examples/folk.wav", "No"], ["./examples/orchestra.wav", "No"]],
                    fn=infer,
                    inputs=[audio_input, has_lyrics],
                    outputs=[img_result, llama_trans_cap, tryagain_btn],
                    cache_examples=True
                   )

    #infer_btn.click(fn=infer, inputs=[audio_input], outputs=[lpmc_cap, llama_trans_cap, img_result])
    infer_btn.click(fn=infer, inputs=[audio_input, has_lyrics], outputs=[img_result, llama_trans_cap, tryagain_btn])
    tryagain_btn.click(fn=solo_xd, inputs=[llama_trans_cap], outputs=[img_result])

demo.queue(max_size=20).launch()