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| import gradio as gr | |
| import replicate | |
| from openai import OpenAI | |
| from PIL import Image | |
| import requests | |
| from io import BytesIO | |
| import os | |
| OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') | |
| REPLICATE_API_TOKEN = os.getenv('REPLICATE_API_TOKEN') | |
| def generate_image_openai(prompt): | |
| client = OpenAI() | |
| response = client.images.generate( | |
| model="dall-e-3", | |
| prompt=prompt, | |
| size="1024x1024", | |
| n=1, | |
| ) | |
| return response.data[0].url, response.data[0].revised_prompt | |
| def style_transfer(input_image_path, style_image_path, revised_prompt): | |
| input = { | |
| # "image": open(input_image_path, "rb"), | |
| "image": input_image_path, | |
| "image_style": open(style_image_path, "rb"), | |
| "style_strength": 0.4, | |
| "structure_strength":1.2, | |
| "prompt": " natural light, natural bright colors, low quality, candid, grainy, instagram photo, phone camera, candid, blurry home video, high iso noisy" , | |
| "seed": 42, | |
| } | |
| output = replicate.run( | |
| "prakharsaxena24/2d-to-real-style:c0e1e612a11a13d1d57a6d647e7665ad850bc73715337c1f499bb7b52404c35a", | |
| input=input | |
| ) | |
| return output[0] | |
| def infer(text,title): | |
| prompt = f"""Please create a simple suitable image to accompany the following text as part of an article with the title "{title}". The objects in the image must have realistic proportions. Text: "{text}" | |
| Please make sure not to include text in the image.""" | |
| image_url_openai, revised_prompt = generate_image_openai(prompt) | |
| style_image_url = style_transfer(image_url_openai, f'./Style.png',revised_prompt) | |
| response = requests.get(style_image_url) | |
| img = Image.open(BytesIO(response.content)) | |
| return img | |
| examples = [ | |
| # "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k", | |
| # "An astronaut riding a green horse", | |
| # "A delicious ceviche cheesecake slice", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f""" | |
| # Text-to-Image and style transfer. | |
| """) | |
| with gr.Row(): | |
| text = gr.Text( | |
| label="Text", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| title = gr.Text( | |
| label="Title", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| # with gr.Accordion("Advanced Settings", open=False): | |
| # negative_prompt = gr.Text( | |
| # label="Negative prompt", | |
| # max_lines=1, | |
| # placeholder="Enter a negative prompt", | |
| # visible=False, | |
| # ) | |
| # seed = gr.Slider( | |
| # label="Seed", | |
| # minimum=0, | |
| # maximum=100000, | |
| # step=1, | |
| # value=0, | |
| # ) | |
| # randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
| # with gr.Row(): | |
| # width = gr.Slider( | |
| # label="Width", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=512, | |
| # ) | |
| # height = gr.Slider( | |
| # label="Height", | |
| # minimum=256, | |
| # maximum=MAX_IMAGE_SIZE, | |
| # step=32, | |
| # value=512, | |
| # ) | |
| # with gr.Row(): | |
| # guidance_scale = gr.Slider( | |
| # label="Guidance scale", | |
| # minimum=0.0, | |
| # maximum=10.0, | |
| # step=0.1, | |
| # value=0.0, | |
| # ) | |
| # num_inference_steps = gr.Slider( | |
| # label="Number of inference steps", | |
| # minimum=1, | |
| # maximum=12, | |
| # step=1, | |
| # value=2, | |
| # ) | |
| # gr.Examples( | |
| # examples = examples, | |
| # inputs = [prompt] | |
| # ) | |
| run_button.click( | |
| fn = infer, | |
| inputs = [text, title], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() |