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import gradio as gr | |
from diffusers import StableDiffusionPipeline | |
pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") | |
pipe = pipe.to("cuda") | |
client = Client('abidlabs/GFPGAN') | |
# setting=Tom Hanks nightwing in real life camera, high-def, high-res, 4k, dark night, raining, thunder | |
def predict(celebrity, movie,setting): | |
prompt=f" Create a movie poster featuring {celebrity} along with the movie title. The poster should include the title of the movie as {movie},and in a given settings as {setting}" | |
image = pipe(prompt).images[0] | |
tmpFileName = 'tmp_image.png' | |
image.save(tmpFileName, 'PNG') | |
gfpGanFileName = client.predict(tmpFileName, 2) | |
return Image.open(gfpGanFileName) | |
import gradio as gr | |
import os | |
HF_TOKEN = os.getenv('HF_TOKEN') | |
hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "movie-poster") | |
gr.Interface( | |
fn = predict, | |
inputs=[ | |
gr.inputs.Textbox(label="celebrity"), | |
gr.inputs.Dropdown(label="Movies", choices=["Batman", "Gladiator", "Mission Impossible"]), | |
gr.inputs.Textbox(label="Setting") | |
], | |
outputs= gr.outputs.Image(label="Generated movie poster",type='pil'), | |
description="Generate a movie poster with the given information", | |
title = "Movie Poster Generator", | |
allow_flagging="manual", | |
flagging_options=["Good", "Better", "Worst"], | |
flagging_callback=hf_writer | |
).launch(share=True) | |