import gradio as gr
import torch
from PIL import Image
import numpy as np
from spectro import wav_bytes_from_spectrogram_image
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
from share_btn import community_icon_html, loading_icon_html, share_js
device = "cuda"
MODEL_ID = "spaceinvader/fb"
pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
pipe = pipe.to(device)
pipe2 = StableDiffusionImg2ImgPipeline.from_pretrained(MODEL_ID, torch_dtype=torch.float16)
pipe2 = pipe2.to(device)
spectro_from_wav = gr.Interface.load("spaces/fffiloni/audio-to-spectrogram")
def dummy_checker(images, **kwargs): return images, False
def predict(prompt, negative_prompt, audio_input, duration):
# if audio_input == None :
return classic(prompt, negative_prompt, duration)
# else :
# return style_transfer(prompt, negative_prompt, audio_input)
def classic(prompt, negative_prompt, duration):
pipe.safety_checker = dummy_checker
spec = pipe(prompt, negative_prompt=negative_prompt, height=512, width=512).images[0]
print(spec)
wav = wav_bytes_from_spectrogram_image(spec)
with open("output.wav", "wb") as f:
f.write(wav[0].getbuffer())
return spec, 'output.wav', gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
def style_transfer(prompt, negative_prompt, audio_input):
# spec = spectro_from_wav(audio_input)
# Open the image
im = Image.open('rootfart-1.jpg')
# im = Image.open(spec)
# Open the image
# im = image_from_spectrogram(im, 1)
new_spectro = pipe2(prompt=prompt, image=im, strength=0.5, guidance_scale=7).images
wav = wav_bytes_from_spectrogram_image(new_spectro[0])
with open("output.wav", "wb") as f:
f.write(wav[0].getbuffer())
return new_spectro[0], 'output.wav', gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
def image_from_spectrogram(
spectrogram: np.ndarray, max_volume: float = 50, power_for_image: float = 0.25
) -> Image.Image:
"""
Compute a spectrogram image from a spectrogram magnitude array.
"""
# Apply the power curve
data = np.power(spectrogram, power_for_image)
# Rescale to 0-255
data = data * 255 / max_volume
# Invert
data = 255 - data
# Convert to a PIL image
image = Image.fromarray(data.astype(np.uint8))
# Flip Y
image = image.transpose(Image.FLIP_TOP_BOTTOM)
# Convert to RGB
image = image.convert("RGB")
return image
title = """
"""
article = """
About the model: Riffusion is a latent text-to-image diffusion model capable of generating spectrogram images given any text input. These spectrograms can be converted into audio clips.
—
The Riffusion model was created by fine-tuning the Stable-Diffusion-v1-5 checkpoint.
—
The model is intended for research purposes only. Possible research areas and tasks include
generation of artworks, audio, and use in creative processes, applications in educational or creative tools, research on generative models.
Do you need faster results ? You can skip the queue by duplicating this space:
"""
css = '''
#col-container, #col-container-2 {max-width: 510px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
div#record_btn > .mt-6 {
margin-top: 0!important;
}
div#record_btn > .mt-6 button {
width: 100%;
height: 40px;
}
.footer {
margin-bottom: 45px;
margin-top: 10px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
'''
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
prompt_input = gr.Textbox(placeholder="describe your fart", label="Prompt", elem_id="prompt-in")
audio_input = gr.Audio(source="upload", type="filepath", visible=False)
with gr.Row():
negative_prompt = gr.Textbox(label="Negative prompt")
duration_input = gr.Slider(label="Duration in seconds", minimum=5, maximum=10, step=1, value=8, elem_id="duration-slider", visible=False)
send_btn = gr.Button(value="Generate fart! ", elem_id="submit-btn")
with gr.Column(elem_id="col-container-2"):
spectrogram_output = gr.Image(label="spectrogram image result", elem_id="img-out")
sound_output = gr.Audio(type='filepath', label="spectrogram sound", elem_id="music-out")
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=False)
loading_icon = gr.HTML(loading_icon_html, visible=False)
share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
gr.HTML(article)
send_btn.click(predict, inputs=[prompt_input, negative_prompt, audio_input, duration_input], outputs=[spectrogram_output, sound_output, share_button, community_icon, loading_icon])
share_button.click(None, [], [], _js=share_js)
demo.queue(max_size=250).launch(debug=True)