app.py

# imports from gradio_demo.py
import gradio as gr 
import spaces
import numpy as np
from PIL import Image
import torch 
from torchvision.transforms import ToTensor, ToPILImage
import sys
import os
from midi_player import MIDIPlayer
from midi_player.stylers import basic, cifka_advanced, dark
import numpy as np 
from time import sleep
from subprocess import call
import pandas as pd

# imports from sample.py
import argparse
from pathlib import Path
import accelerate
import safetensors.torch as safetorch
#import torch
from tqdm import trange, tqdm
#from PIL import Image
from torchvision import transforms
import k_diffusion as K

# test natten import:
import natten
import accelerate

from sample import zero_wrapper

zero = torch.Tensor([0]).cuda()
print("Zero Device = ",zero.device," <-- this probably says cpu") # <-- 'cpu' 🤔


from pom.pianoroll import regroup_lines, img_file_2_midi_file, square_to_rect, rect_to_square
from pom.square_to_rect import square_to_rect

CT_HOME = '.'

@spaces.GPU
def infer_mask_from_init_img(img, mask_with='grey'):
    "note, this works whether image is normalized on 0..1 or -1..1, but not 0..255"
    assert mask_with in ['blue','white','grey']
    "given an image with mask areas marked, extract the mask itself"
    print("\n in infer_mask_from_init_img: ")
    if not torch.is_tensor(img):
        img = ToTensor()(img)
    print("    img.shape: ", img.shape)
    # shape of mask should be img shape without the channel dimension
    if len(img.shape) == 3:
        mask = torch.zeros(img.shape[-2:])
    elif len(img.shape) == 2:
        mask = torch.zeros(img.shape)
    print("    mask.shape: ", mask.shape)
    if mask_with == 'white':
        mask[ (img[0,:,:]==1) & (img[1,:,:]==1) & (img[2,:,:]==1)] = 1
    elif mask_with == 'blue':
        mask[img[2,:,:]==1] = 1  # blue
    if mask_with == 'grey':
        mask[ (img[0,:,:] != 0) & (img[0,:,:]==img[1,:,:])  & (img[2,:,:]==img[1,:,:])] = 1
    return mask*1.0


@spaces.GPU
def count_notes_in_mask(img, mask):
    "counts the number of new notes in the mask"
    img_t = ToTensor()(img)
    new_notes = (mask * (img_t[1,:,:] > 0)).sum() # green channel
    return new_notes.item()


@spaces.GPU
def grab_dense_gen(init_img, 
                PREFIX, 
                num_to_gen=64, 
                busyness=100, # after ranking images by how many notes were in mask, which one should we grab?
                ):
    df = None
    mask = infer_mask_from_init_img(init_img, mask_with='grey')
    for num in range(num_to_gen):
        filename = f'{PREFIX}_{num:05d}.png'
        gen_img = Image.open(filename)
        gen_img_rect = square_to_rect(gen_img)
        new_notes = count_notes_in_mask(gen_img, mask)
        if df is None:
            df = pd.DataFrame([[filename, new_notes, gen_img_rect]], columns=['filename', 'new_notes', 'img_rect'])
        else:
            df = pd.concat([df, pd.DataFrame([[filename, new_notes, gen_img_rect]], columns=['filename', 'new_notes', 'img_rect'])], ignore_index=True)

    # sort df by new_notes column, 
    df = df.sort_values(by='new_notes', ascending=True)
    grab_index = (len(df)-1)*busyness//100
    print("grab_index = ", grab_index)
    dense_filename = df.iloc[grab_index]['filename']
    print("Grabbing filename = ", dense_filename)
    return dense_filename

# dummy class to make an args-like object
class Args:
    def __init__(self, **kwargs):
        for key, value in kwargs.items():
            setattr(self, key, value)


@spaces.GPU
def process_image(image, repaint, busyness):

    print("Process Image: Zero Device = ",zero.device)
    accelerator = accelerate.Accelerator()
    device = accelerator.device
    print('Accelerator device:', device, flush=True)

    # get image ready and execute sampler
    print("image = ",image)
    image = image['composite']
    # if image is a numpy array convert to PIL 
    if isinstance(image, np.ndarray):
        image = ToPILImage()(image)
    image = image.convert("RGB").crop((0, 0, 512, 128))
    image = rect_to_square( image )
    #mask = infer_mask_from_init_img( image )
    masked_img_file = 'gradio_masked_image.png' # TODO: could allow for clobber at scale
    print("Saving masked image file to ", masked_img_file)
    image.save(masked_img_file)
    num = 64 # number of images to generate; we'll take the one with the most notes in the masked region
    bs = num
    repaint = repaint
    seed_scale = 1.0
    CT_HOME = '.'
    CKPT = f'ckpt/256_chords_00130000.pth'
    PREFIX = 'gradiodemo'
    # !echo {DEVICES} {CT_HOME} {CKPT} {PREFIX} {masked_img_file}
    print("Reading init image from ", masked_img_file,", repaint = ",repaint) 
    # cmd = f'{sys.executable} {CT_HOME}/sample.py --batch-size {bs} --checkpoint {CKPT} --config {CT_HOME}/configs/config_pop909_256x256_chords.json -n {num} --prefix {PREFIX} --init-image {masked_img_file} --steps=100 --repaint={repaint}'    
    # print("Will run command: ", cmd)
    # args = cmd.split(' ')
    # #call(cmd, shell=True)
    # print("Calling: ", args,"\n")
    # return_value = call(args)
    # print("Return value = ", return_value)
    args = Args(batch_size=bs, checkpoint=CKPT, config=f'{CT_HOME}/configs/config_pop909_256x256_chords.json', n=num, prefix=PREFIX, init_image=masked_img_file, steps=100, repaint=repaint)
    print(" Now calling zero_wrapper with args = ",args,"\n")
    zero_wrapper(args, accelerator, device)

    # find gen'd image and convert to midi piano roll 
    #gen_file = f'{PREFIX}_00000.png'
    gen_file = grab_dense_gen(image, PREFIX, num_to_gen=num)
    gen_image = square_to_rect(Image.open(gen_file))
    midi_file = img_file_2_midi_file(gen_file)
    srcdoc = MIDIPlayer(midi_file, 300, styler=dark).html
    srcdoc = srcdoc.replace("\"", "'")
    html = f'''<iframe srcdoc="{srcdoc}" height="500" width="100%" title="Iframe Example"></iframe>'''


    # convert the midi to audio too 
    audio_file = 'gradio_demo_out.mp3'
    cmd = f'timidity {midi_file} -Ow -o {audio_file}'
    print("Converting midi to audio with: ", cmd)
    return_value = call(cmd.split(' '))
    print("Return value = ", return_value)

    return gen_image, html, audio_file



make_dict = lambda x: {'background':x, 'composite':x, 'layers':[x]}



demo = gr.Interface(fn=process_image,
    inputs=[gr.ImageEditor(sources=["upload",'clipboard'], label="Input Piano Roll Image (White = Gen Notes Here)", value=make_dict('all_black.png'), brush=gr.Brush(colors=["#FFFFFF","#000000"])),
        gr.Slider(minimum=1, maximum=10, step=1, value=2, label="RePaint (Larger = More Notes, But Crazier. Also Slower.)"),
        gr.Slider(minimum=1, maximum=100, step=1, value=100, label="Busy-ness Percentile (Based on Notes Generated)")],
    outputs=[gr.Image(width=512, height=128, label='Generated Piano Roll Image'), 
             gr.HTML(label="MIDI Player"),
             gr.Audio(label="MIDI as Audio")],
    examples=   [[make_dict(y),1,100] for y in ['all_white.png','all_black.png','init_img_melody.png','init_img_accomp.png','init_img_cont.png',]]+
                [[make_dict(x),2,100] for x in ['584_TOTAL_crop.png', '780_TOTAL_crop_bg.png', '780_TOTAL_crop_draw.png','loop_middle_2.png']]+
                [[make_dict(z),3,100] for z in ['584_TOTAL_crop_draw.png','loop_middle.png']] + 
                [[make_dict('ismir_mask_2.png'),6,100]],
    )
demo.queue().launch()