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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() | |