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import streamlit as st
import os
import requests
from PIL import Image
from io import BytesIO
# Set up environment variables for API keys
os.environ['CLIPDROP_API_KEY'] = 'sk-GBmsWR78MmCSAWGkkC1CFgWgE6GPgV00pNLJlxlyZWyT3QQO'
os.environ['STABILITY_API_KEY'] = '1143a102dbe21628248d4bb992b391a49dc058c584181ea72e17c2ccd49be9ca69ccf4a2b97fc82c89ff1029578abbea'
os.environ['REPLICATE_API_TOKEN'] = 'r8_3V5WKOBwbbuL0DQGMliP0972IAVIBo62Lmi8I'
# Importing Replicate and Stability SDK libraries
import replicate
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
def generate_image(prompt):
# Make a POST request to the ClipDrop text-to-image API
url = 'https://clipdrop-api.co/text-to-image/v1'
headers = {'x-api-key': os.environ['CLIPDROP_API_KEY']}
data = {'prompt': prompt}
response = requests.post(url, headers=headers, data=data)
if response.status_code == 200:
# Get the generated image from the response
img = Image.open(BytesIO(response.content))
return img
else:
st.error('Failed to generate image from text prompt.')
def upscale_image(img):
# Set up Stability API connection
stability_api = replicate.StabilityInference(
key=os.environ['STABILITY_API_KEY'],
upscale_engine="stable-diffusion-x4-latent-upscaler"
)
# Upscale the image using the Stability API
upscale_responses = stability_api.upscale(init_image=img)
if upscale_responses:
# Get the upscaled image from the response
upscaled_img = None
for resp in upscale_responses:
for artifact in resp.artifacts:
if artifact.type == generation.ARTIFACT_IMAGE:
upscaled_img = Image.open(BytesIO(artifact.binary))
break
if upscaled_img:
break
return upscaled_img
else:
st.error('Failed to upscale the image.')
def gfpgan_upscale_image(img):
# Set up Replicate API connection
replicate_api = replicate.ReplicateAPI(token=os.environ['REPLICATE_API_TOKEN'])
# Upscale the image using GFPGAN model
output = replicate_api.run(
"tencentarc/gfpgan:9283608cc6b7be6b65a8e44983db012355fde4132009bf99d976b2f0896856a3",
input={"img": img, "version": "v1.4", "scale": 16}
)
if output:
# Get the upscaled image URI from the output
response = requests.get(output)
upscaled_img = Image.open(BytesIO(response.content))
return upscaled_img
else:
st.error('Failed to upscale the image using GFPGAN.')
def main():
st.title("Image Upscaling")
st.write("Enter a text prompt to generate and upscale an image.")
# Get user input for the text prompt
prompt = st.text_input("Enter a text prompt:", max_chars=1000)
if st.button("Generate and Upscale"):
# Generate the image from text prompt using ClipDrop API
generated_img = generate_image(prompt)
if generated_img:
st.image(generated_img, caption='Generated Image', use_column_width=True)
# Upscale the generated image using Stability API
upscaled_img = upscale_image(generated_img)
if upscaled_img:
st.image(upscaled_img, caption='Upscaled Image (Stability API)', use_column_width=True)
# Further upscale the image using GFPGAN
gfpgan_upscaled_img = gfpgan_upscale_image(upscaled_img)
if gfpgan_upscaled_img:
st.image(gfpgan_upscaled_img, caption='Upscaled Image (GFPGAN)', use_column_width=True)
st.success("Image generation and upscaling completed successfully.")
else:
st.error("Failed to further upscale the image using GFPGAN.")
else:
st.error("Failed to upscale the generated image.")
if __name__ == "__main__":
main()
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