Spaces:
Runtime error
Runtime error
File size: 3,039 Bytes
59864f6 3c7ffed 59864f6 3c7ffed 59864f6 3c7ffed 59864f6 3c7ffed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import streamlit as st
import requests
from PIL import Image
from io import BytesIO
import getpass, os
import warnings
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
import replicate
# API keys
api_key = '1143a102dbe21628248d4bb992b391a49dc058c584181ea72e17c2ccd49be9ca69ccf4a2b97fc82c89ff1029578abbea'
os.environ['STABILITY_KEY'] = 'sk-GBmsWR78MmCSAWGkkC1CFgWgE6GPgV00pNLJlxlyZWyT3QQO'
os.environ['REPLICATE_API_TOKEN'] = 'r8_0a77UG8yfrNXOS6xHhUTLh80dJQ5kxO0CTLmq' # Replace with your actual API token
# Increase the pixel limit
Image.MAX_IMAGE_PIXELS = None
# Establish connection to Stability API
stability_api = client.StabilityInference(
key=os.environ['STABILITY_KEY'],
upscale_engine="esrgan-v1-x2plus",
verbose=True,
)
# ClipDrop API function
def generate_image(prompt):
headers = {'x-api-key': api_key}
body_params = {'prompt': (None, prompt, 'text/plain')}
response = requests.post('https://clipdrop-api.co/text-to-image/v1', files=body_params, headers=headers)
if response.status_code == 200:
return Image.open(BytesIO(response.content))
else:
st.write(f"Request failed with status code {response.status_code}")
return None
# Stability API function
def upscale_image_stability(img):
answers = stability_api.upscale(init_image=img)
for resp in answers:
for artifact in resp.artifacts:
if artifact.finish_reason == generation.FILTER:
warnings.warn(
"Your request activated the API's safety filters and could not be processed."
"Please submit a different image and try again.")
if artifact.type == generation.ARTIFACT_IMAGE:
return Image.open(io.BytesIO(artifact.binary))
# GFPGAN function
def upscale_image_gfpgan(image_path):
with open(image_path, "rb") as img_file:
output = replicate.run(
"tencentarc/gfpgan:9283608cc6b7be6b65a8e44983db012355fde4132009bf99d976b2f0896856a3",
input={"img": img_file, "version": "v1.4", "scale": 16}
)
response = requests.get(output)
return Image.open(BytesIO(response.content))
# Streamlit UI
st.title("Image Generator and Upscaler")
prompt = st.text_input("Enter a prompt for the image generation")
if st.button("Generate and Upscale"):
if prompt:
img1 = generate_image(prompt)
if img1:
st.image(img1, caption="Generated Image", use_column_width=True)
img1.save('generated_image.png')
img2 = upscale_image_stability(img1)
st.image(img2, caption="Upscaled Image (Stability API)", use_column_width=True)
img2.save('upscaled_image_stability.png')
img3 = upscale_image_gfpgan('upscaled_image_stability.png')
st.image(img3, caption="Upscaled Image (GFPGAN)", use_column_width=True)
img3.save('upscaled_image_gfpgan.png')
else:
st.write("Please enter a prompt")
|