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import gradio as gr | |
import os | |
import torch | |
from torch import autocast | |
from diffusers import StableDiffusionPipeline | |
from PIL import Image | |
from styles import css, header_html, footer_html | |
from examples import examples | |
from transformers import pipeline | |
ars_model = pipeline("automatic-speech-recognition") | |
model_id = "CompVis/stable-diffusion-v1-4" | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
# If you are running this code locally, you need to either do a 'huggingface-cli login` or paste your User Access Token from here https://huggingface.co/settings/tokens into the use_auth_token field below. | |
pipe = StableDiffusionPipeline.from_pretrained( | |
model_id, use_auth_token=os.environ.get('auth_token'), revision="fp16", torch_dtype=torch.float16) | |
pipe = pipe.to(device) | |
def transcribe(audio): | |
text = ars_model(audio)["text"] | |
return text | |
def infer(audio, samples, steps, scale, seed): | |
prompt = transcribe(audio) | |
generator = torch.Generator(device=device).manual_seed(seed) | |
# If you are running locally with CPU, you can remove the `with autocast("cuda")` | |
if device == "cuda": | |
with autocast("cuda"): | |
images_list = pipe( | |
[prompt] * samples, | |
num_inference_steps=steps, | |
guidance_scale=scale, | |
generator=generator, | |
) | |
else: | |
images_list = pipe( | |
[prompt] * samples, | |
num_inference_steps=steps, | |
guidance_scale=scale, | |
generator=generator, | |
) | |
images = [] | |
safe_image = Image.open(r"unsafe.png") | |
for i, image in enumerate(images_list["sample"]): | |
if(images_list["nsfw_content_detected"][i]): | |
images.append(safe_image) | |
else: | |
images.append(image) | |
return images | |
block = gr.Blocks(css=css) | |
with block: | |
gr.HTML(header_html) | |
with gr.Group(): | |
with gr.Box(): | |
with gr.Row().style(mobile_collapse=False, equal_height=True): | |
audio = gr.Audio( | |
label="Describe a prompt", | |
source="microphone", | |
type="filepath" | |
# ).style( | |
# border=(True, False, True, True), | |
# rounded=(True, False, False, True), | |
# container=False, | |
) | |
btn = gr.Button("Generate image").style( | |
margin=False, | |
rounded=(False, True, True, False), | |
) | |
gallery = gr.Gallery( | |
label="Generated images", show_label=False, elem_id="gallery" | |
).style(grid=[2], height="auto") | |
advanced_button = gr.Button("Advanced options", elem_id="advanced-btn") | |
with gr.Row(elem_id="advanced-options"): | |
samples = gr.Slider(label="Images", minimum=1, | |
maximum=4, value=4, step=1) | |
steps = gr.Slider(label="Steps", minimum=1, | |
maximum=50, value=45, step=1) | |
scale = gr.Slider( | |
label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1 | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=2147483647, | |
step=1, | |
randomize=True, | |
) | |
# ex = gr.Examples(fn=infer, inputs=[ | |
# audio, samples, steps, scale, seed], outputs=gallery) | |
# ex.dataset.headers = [""] | |
# audio.submit(infer, inputs=[audio, samples, | |
# steps, scale, seed], outputs=gallery) | |
btn.click(infer, inputs=[audio, samples, steps, | |
scale, seed], outputs=gallery) | |
advanced_button.click( | |
None, | |
[], | |
audio, | |
_js=""" | |
() => { | |
const options = document.querySelector("body > gradio-app").querySelector("#advanced-options"); | |
options.style.display = ["none", ""].includes(options.style.display) ? "flex" : "none"; | |
}""", | |
) | |
gr.HTML(footer_html) | |
block.queue(max_size=25).launch() |