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| import os | |
| import time | |
| import logging | |
| import gradio as gr | |
| import random | |
| import requests | |
| import json | |
| logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
| NUM_IMAGES = 1 | |
| REFRESH_INTERVAL_S = 1.0 | |
| MIN_IMAGE_SIZE = 768 | |
| MAX_IMAGE_SIZE = 1024 | |
| MAX_SEED = 100 | |
| API_KEY = os.getenv('DREAMLOOK_API_KEY') | |
| API_ENDPOINT = os.getenv('DREAMLOOK_API_ENDPOINT', default='https://api.dreamlook.ai') | |
| DREAMLOOK_API_ENDPOINT_IMAGE_CREATE_PATH = os.getenv('DREAMLOOK_API_ENDPOINT_IMAGE_CREATE_PATH', 'image_gen') | |
| DREAMLOOK_API_MODEL_CHECKPOINT = os.getenv('DREAMLOOK_API_MODEL_CHECKPOINT') | |
| API_CREATE_ENDPOINT = f"{API_ENDPOINT}/{DREAMLOOK_API_ENDPOINT_IMAGE_CREATE_PATH}" | |
| API_STATUS_ENDPOINT = f"{API_ENDPOINT}/jobs/image-gen/" | |
| examples = [ | |
| "A screen-printed poster from 1975 with bold, expressive contours features a pensive figure in red and green, set against a dark, moody background. \"Kunstmuseum Bern Sammlung im Obersteg\"", | |
| "Colorful offset-printed poster from 1990 featuring animated, cartoonish characters with exaggerated expressions. Background features a gradient of blue and light orange hues. Bold, handwritten-style text. \"NOTRE ÉGLISE\" \"TOUTE UNE VIE À SOUTENIR\" \"ÉGLISE NATIONALE PROTESTANTE DE GENÈVE\"", | |
| "Offset printed poster in black and white from 1983 featuring a photo of a joyful couple embracing in front of a train, with bold text above and below the image. \"J'ai un cours du soir en ville.\" \"Et chaque heure un train.\" \"Vos CFF\"", | |
| "Lithographic poster from 1911, showcasing three well-dressed men in trench coats and top hats with rich, muted colors and stylized outlining, emphasizing modern men's fashion. \"PKZ\" \"Moderne Herrenbekleidung\" \"Burger & Zimmermann\" \"Luzern - Reußbrücke - Kramgasse 2\"", | |
| ] | |
| NEGATIVE_PROMPT = "lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, text, signature, watermark, username, blurry" | |
| def infer(prompt, negative_prompt, seed, randomize_seed, guidance_scale): | |
| if randomize_seed: | |
| seed = random.randint(0, MAX_SEED) | |
| headers = { | |
| "content-type": "application/json", | |
| "authorization": f"Bearer {API_KEY}" | |
| } | |
| payload = { | |
| # "base_model": "stable-diffusion-xl-v1-0" | |
| "checkpoint_id": DREAMLOOK_API_MODEL_CHECKPOINT, | |
| "prompt": prompt[:500], | |
| "negative_prompt": NEGATIVE_PROMPT, | |
| "num_samples": NUM_IMAGES, | |
| "width": 704, | |
| "height": 1024, | |
| "num_inference_steps": 30, | |
| "enable_hrf": False, | |
| "scheduler_type": "dpmpp-2m-karras", | |
| "seed": seed, | |
| "model_type": "sdxl-v1", | |
| "guidance_scale": guidance_scale, | |
| } | |
| logging.debug(f"Payload: {json.dumps(payload, indent=2)}") | |
| response = requests.post(API_CREATE_ENDPOINT, headers=headers, data=json.dumps(payload)) | |
| logging.debug(f"API response status code: {response.status_code}") | |
| logging.debug(f"API response: {response.text}") | |
| if response.status_code == 200: | |
| job_id = response.json()['job_id'] | |
| logging.info(f"Job submitted successfully. Job ID: {job_id}") | |
| image_url = None | |
| while True: | |
| status_response = requests.get(f"{API_STATUS_ENDPOINT}{job_id}", headers=headers) | |
| status_data = status_response.json() | |
| state = status_data.get('state') | |
| yield state, image_url | |
| logging.info(f"Job state: {state}") | |
| if state == 'success': | |
| image_url = status_data['image_gen_results'][0]['generated_images'][0]['url'] | |
| logging.info(f"Image generation succeeded. Image URL: {image_url}") | |
| yield state, image_url | |
| return | |
| elif state == 'failed': | |
| failure_reason = status_data.get('failure_reason', 'Unknown') | |
| logging.error(f"Job failed with reason: {failure_reason}") | |
| return f"Error: Job failed with reason: {failure_reason}" | |
| time.sleep(REFRESH_INTERVAL_S) | |
| else: | |
| return f"Error: {response.status_code}, {response.text}" | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Perspectives on AI") | |
| gr.Markdown( | |
| "In collaboration with the Alliance Graphique Internationale [\"Perspectives on AI\"](https://agi-open.com) exhibition, [dreamlook.ai](https://dreamlook.ai) has created an image generation model trained from thousands of posters from the [Basel Poster Collection](https://www.recherche-plakatsammlungbasel.ch/objects) to enable its exploration through prompts.") | |
| # gr.Markdown("Write a prompt then click on \"Generate image\"") | |
| gr.Markdown("[Read more...](https://dreamlook.ai/perspectives-on-ai)") | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox( | |
| label="Prompt", | |
| info="Describe the image you want to generate, or click on a sample prompt below.", | |
| placeholder="Enter your prompt", | |
| max_lines=10, | |
| ) | |
| negative_prompt = gr.Textbox( | |
| label="Negative Prompt", | |
| placeholder="Enter a negative prompt (optional)", | |
| visible=False | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| info="The random number used to create your image. Change it to explore variants.", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| value=42, | |
| visible=True | |
| ) | |
| randomize_seed = gr.Checkbox( | |
| label="Randomize Seed", | |
| value=False, | |
| visible=False | |
| ) | |
| guidance_scale = gr.Slider( | |
| label="Guidance Scale", | |
| info="Defined how much your prompt is taken into account when generating the image.", | |
| minimum=5.0, | |
| maximum=15.0, | |
| value=10.0 | |
| ) | |
| # num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=10, maximum=30, value=20) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[prompt], | |
| label="Sample prompts", | |
| ) | |
| run_button = gr.Button( | |
| "Generate image", | |
| ) | |
| with gr.Column(): | |
| status_box = gr.Textbox(label="Status", value="", interactive=False) | |
| result = gr.Image(label="Result") | |
| run_button.click( | |
| fn=infer, | |
| inputs=[prompt, negative_prompt, seed, randomize_seed, guidance_scale], | |
| outputs=[status_box, result] | |
| ) | |
| demo.launch() | |