app.py

from diffusers import StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipeline
import torch
from PIL import Image, ImageDraw
import os
import numpy as np
from scipy.io.wavfile import read
import gradio as gr


os.system('git clone https://github.com/hmartiro/riffusion-inference.git riffusion')

from riffusion.riffusion.riffusion_pipeline import RiffusionPipeline
from riffusion.riffusion.datatypes import PromptInput, InferenceInput
from riffusion.riffusion.audio import wav_bytes_from_spectrogram_image
from PIL import Image
import struct
import random

repo_id = "riffusion/riffusion-model-v1"

model = RiffusionPipeline.from_pretrained(
      repo_id,
      revision="main",
      torch_dtype=torch.float16,
      safety_checker=lambda images, **kwargs: (images, False),
  )

if torch.cuda.is_available():
  model.to("cuda")

pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, safety_checker=lambda images, **kwargs: (images, False),)
pipe_inpaint.scheduler = DPMSolverMultistepScheduler.from_config(pipe_inpaint.scheduler.config)

# pipe_inpaint.enable_xformers_memory_efficient_attention()

if torch.cuda.is_available():
    pipe_inpaint = pipe_inpaint.to("cuda")


def get_init_image(image, overlap, feel):

    width, height = image.size
    init_image = Image.open(f"riffusion/seed_images/{feel}.png").convert("RGB")
    # Crop the right side of the original image with `overlap_width`
    cropped_img = image.crop((width - int(width*overlap), 0, width, height))
    init_image.paste(cropped_img, (0, 0))

    return init_image

def get_mask(image, overlap):

    width, height = image.size

    mask = Image.new("RGB", (width, height), color="white")
    draw = ImageDraw.Draw(mask)
    draw.rectangle((0, 0, int(overlap * width), height), fill="black")
    return mask

def i2i(prompt, steps, feel, seed):
#   return pipe_i2i(
#       prompt,
#       num_inference_steps=steps,
#       image=Image.open(f"riffusion/seed_images/{feel}.png").convert("RGB"),
#       ).images[0]

    prompt_input_start = PromptInput(prompt=prompt, seed=seed)
    prompt_input_end = PromptInput(prompt=prompt, seed=seed)

    return model.riffuse(
        inputs=InferenceInput(
            start=prompt_input_start,
            end=prompt_input_end,
            alpha=1.0,
            num_inference_steps=steps),
        init_image=Image.open(f"riffusion/seed_images/{feel}.png").convert("RGB")
    )

def outpaint(prompt, init_image, mask, steps):
  return pipe_inpaint(
      prompt,
      num_inference_steps=steps,
      image=init_image,
      mask_image=mask,
      ).images[0]


def generate(prompt, steps, num_iterations, feel, seed):

    if seed == 0:
        seed = random.randint(0,4294967295)

    num_images = num_iterations
    overlap = 0.5
    image_width, image_height = 512, 512  # dimensions of each output image
    total_width = num_images * image_width - (num_images - 1) * int(overlap * image_width)  # total width of the stitched image

    # Create a blank image with the desired dimensions
    stitched_image = Image.new("RGB", (total_width, image_height), color="white")

    # Initialize the x position for pasting the next image
    x_pos = 0

    image = i2i(prompt, steps, feel, seed)

    for i in range(num_images):
        # Generate the prompt, initial image, and mask for this iteration
        init_image = get_init_image(image, overlap, feel)
        mask = get_mask(init_image, overlap)
        
        # Run the outpaint function to generate the output image
        steps = 25
        image = outpaint(prompt, init_image, mask, steps)

        # Paste the output image onto the stitched image
        stitched_image.paste(image, (x_pos, 0))
        
        # Update the x position for the next iteration
        x_pos += int((1 - overlap) * image_width)

    wav_bytes, duration_s = wav_bytes_from_spectrogram_image(stitched_image)

    # mask = Image.new("RGB", (512, 512), color="white")
    # bg_image = outpaint(prompt, init_image, mask, steps)
    # bg_image.save("bg_image.png")
    init_image.save("bg_image.png")

    # return read(wav_bytes)
    with open("output.wav", "wb") as f:
        f.write(wav_bytes.read())

    return gr.make_waveform("output.wav", bg_image="bg_image.png", bar_count=int(duration_s*25))


###############################################

def riffuse(steps, feel, init_image, prompt_start, seed_start, denoising_start=0.75, guidance_start=7.0, prompt_end=None, seed_end=None, denoising_end=0.75, guidance_end=7.0, alpha=0.5):

  prompt_input_start = PromptInput(prompt=prompt_start, seed=seed_start, denoising=denoising_start, guidance=guidance_start)
    
  prompt_input_end = PromptInput(prompt=prompt_end, seed=seed_end, denoising=denoising_end, guidance=guidance_end)

  input = InferenceInput(
      start=prompt_input_start,
      end=prompt_input_end,
      alpha=alpha,
      num_inference_steps=steps,
      seed_image_id=feel,
      # mask_image_id="mask_beat_lines_80.png"
  )

  image = model.riffuse(inputs=input, init_image=init_image)

  wav_bytes, duration_s = wav_bytes_from_spectrogram_image(image)

  return wav_bytes, image

def generate_riffuse(prompt_start, steps, num_iterations, feel, prompt_end=None, seed_start=None, seed_end=None, denoising_start=0.75, denoising_end=0.75, guidance_start=7.0, guidance_end=7.0):
    """Generate a WAV file of length seconds using the Riffusion model.

    Args:
        length (int): Length of the WAV file in seconds, must be divisible by 5.
        prompt_start (str): Prompt to start with.
        prompt_end (str, optional): Prompt to end with. Defaults to prompt_start.
        overlap (float, optional): Overlap between audio clips as a fraction of the image size. Defaults to 0.2.
        """

    # open the initial image and convert it to RGB
    init_image = Image.open(f"riffusion/seed_images/{feel}.png").convert("RGB")

    if prompt_end is None:
      prompt_end = prompt_start
    if seed_start is 0:
      seed_start = random.randint(0,4294967295)
    if seed_end is None:
      seed_end = seed_start

    # one riffuse() generates 5 seconds of audio
    wav_list = []

    for i in range(int(num_iterations)):

        alpha = i / (num_iterations - 1)
        print(alpha)
        wav_bytes, image = riffuse(steps, feel, init_image, prompt_start, seed_start, denoising_start, guidance_start, prompt_end, seed_end, denoising_end, guidance_end, alpha=alpha)
        wav_list.append(wav_bytes)

        init_image = image

        seed_start = seed_end
        seed_end = seed_start + 1

    # return read(wav_bytes)
    # return wav_list_to_wav(wav_list)

    # mask = Image.new("RGB", (512, 512), color="white")
    # bg_image = outpaint(f"{prompt_start} and {prompt_end}", init_image, mask, steps)
    # bg_image.save("bg_image.png")
    init_image.save("bg_image.png")

    with open("output.wav", "wb") as f:
        f.write(wav_list_to_wav(wav_list))

    return gr.make_waveform("output.wav", bg_image="bg_image.png")


def wav_list_to_wav(wav_list):

  # remove headers from the WAV files
  data = [wav.read()[44:] for wav in wav_list]

  # concatenate the data
  concatenated_data = b"".join(data)

  # create a new RIFF header
  channels = 1
  sample_rate = 44100
  bytes_per_second = channels * sample_rate
  new_header = struct.pack("<4sI4s4sIHHIIHH4sI", b"RIFF", len(concatenated_data) + 44 - 8, b"WAVE", b"fmt ", 16, 1, channels, sample_rate, bytes_per_second, 2, 16, b"data", len(concatenated_data))

  # combine the header and data to create the final WAV file
  final_wav = new_header + concatenated_data
  return final_wav

###############################################

def on_submit(prompt_1, prompt_2, steps, num_iterations, feel, seed):
    if prompt_1 == "":
        return None, gr.update(value="First prompt is required.")
    if prompt_2 == "":
        return generate(prompt_1, steps, num_iterations, feel, seed), None
    else:
        return generate_riffuse(prompt_1, steps, num_iterations, feel, prompt_end=prompt_2, seed_start=seed), None


def on_num_iterations_change(n, prompt_2):
    if n is None:
        return gr.update(value="")

    if prompt_2 != "":
        total_length = 5 * n
    else:
        total_length = 2.5 + 2.5 * n
    return gr.update(value=f"Total length: {total_length:.2f} seconds")

with gr.Blocks() as app:
    gr.Markdown("## Riffusion Demo")
    gr.Markdown("""Generate audio using the [Riffusion](https://huggingface.co/riffusion/riffusion-model-v1) model.<br>
                In single prompt mode you can generate up to ~1 minute of audio with smooth transitions between sections. (beta)<br>
                Bi-prompt mode interpolates between two prompts. It can generate up to ~2 minutes of audio, but transitions between sections are more abrupt.""")
    gr.Markdown(f"""Running on {"**GPU 🔥**" if torch.cuda.is_available() else f"**CPU 🥶**. For faster inference it is recommended to **upgrade to GPU in space's Settings**"}<br>
                [![Duplicate Space](https://bit.ly/3gLdBN6)](https://huggingface.co/spaces/$space_id?duplicate=true)""")
    
    with gr.Row():
        with gr.Group():
            with gr.Row():
                prompt_1 = gr.Textbox(lines=1, label="Start from", placeholder="Starting prompt")
                prompt_2 = gr.Textbox(lines=1, label="End with (optional)", placeholder="Prompt to shift towards at the end")
            with gr.Row():
                steps = gr.Slider(minimum=1, maximum=100, value=25, label="Steps per section")
                num_iterations = gr.Slider(minimum=2, maximum=25, value=2, step=1, label="Number of sections")
            with gr.Row():
                feel = gr.Dropdown(["og_beat", "agile", "vibes", "motorway", "marim"], value="og_beat", label="Feel")
                seed = gr.Slider(minimum=0, maximum=4294967295, value=0, step=1, label="Seed (0 for random)")

            btn_generate = gr.Button(value="Generate").style(full_width=True)
            info = gr.Markdown()
        with gr.Column():
            video = gr.Video()

    inputs = [prompt_1, prompt_2, steps, num_iterations, feel, seed]
    outputs = [video, info]

    num_iterations.change(on_num_iterations_change, [num_iterations, prompt_2], [info])
    prompt_1.submit(on_submit, inputs, outputs)
    prompt_2.submit(on_submit, inputs, outputs)
    btn_generate.click(on_submit, inputs, outputs)

    examples = gr.Examples(
        examples=[
            ["typing", "dance beat", "og_beat", 10],
            ["synthwave", "jazz", "agile", 10],
            ["rap battle freestyle", "", "og_beat", 10],
            ["techno club banger", "", "og_beat", 10],
            ["reggae dub beat", "sunset chill", "og_beat", 10],
            ["acoustic folk ballad", "", "agile", 10],
            ["blues guitar riff", "", "agile", 5],
            ["jazzy trumpet solo", "", "og_beat", 5],
            ["classical symphony orchestra", "", "vibes", 10],
            ["rock and roll power chord", "", "motorway", 5],
            ["soulful R&B love song", "", "marim", 10],
            ["country western twangy guitar", "", "agile", 10]],
        inputs=[prompt_1, prompt_2, feel, num_iterations])
    
    gr.HTML("""
    <div style="border-top: 1px solid #303030;">
      <br>
      <p>Space by:<br>
      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br>
      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 24px !important;width: 81px !important;" ></a><br><br>
      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.riffusion-demo" alt="visitors"></p>
    </div>
    """)

app.launch()