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| import gradio as gr | |
| import requests | |
| import os | |
| from PIL import Image | |
| from io import BytesIO | |
| from tqdm import tqdm | |
| import time | |
| import random | |
| # Defining the repository information and the trigger word | |
| repo = "stabilityai/stable-diffusion-xl-base-1.0" | |
| trigger_word = "T shirt design, TshirtDesignAF, " | |
| # Directory to save images | |
| output_dir = "saved_designs" | |
| os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist | |
| def generate_images(prompt): | |
| print("Generating 10 unique images with prompt:", prompt) | |
| api_url = f"https://api-inference.huggingface.co/models/{repo}" | |
| #token = os.getenv("API_TOKEN") # Uncomment and use your Hugging Face API token | |
| headers = { | |
| #"Authorization": f"Bearer {token}" | |
| } | |
| images = [] | |
| for i in range(10): | |
| # Add a unique seed to each prompt to ensure different images | |
| unique_seed = random.randint(1000, 9999) | |
| full_prompt = f"{prompt} {trigger_word} seed_{unique_seed}" | |
| payload = { | |
| "inputs": full_prompt, | |
| "parameters": { | |
| "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)", | |
| "num_inference_steps": 30, | |
| "scheduler": "DPMSolverMultistepScheduler" | |
| }, | |
| } | |
| error_count = 0 | |
| pbar = tqdm(total=None, desc=f"Loading model {i+1}/10") | |
| while True: | |
| print(f"Sending request to API for image {i+1}...") | |
| response = requests.post(api_url, headers=headers, json=payload) | |
| print("API response status code:", response.status_code) | |
| if response.status_code == 200: | |
| print(f"Image {i+1} generation successful!") | |
| img = Image.open(BytesIO(response.content)) | |
| images.append(img) | |
| # Save the image to the output directory | |
| img_filename = os.path.join(output_dir, f"{prompt.replace(' ', '_')}_design_{i+1}.png") | |
| img.save(img_filename) | |
| print(f"Image saved as: {img_filename}") | |
| break | |
| elif response.status_code == 503: | |
| time.sleep(1) | |
| pbar.update(1) | |
| elif response.status_code == 500 and error_count < 5: | |
| time.sleep(1) | |
| error_count += 1 | |
| else: | |
| print(f"API Error for image {i+1}: {response.status_code}") | |
| raise Exception(f"API Error: {response.status_code}") | |
| return images | |
| iface = gr.Interface( | |
| fn=generate_images, | |
| inputs=gr.Textbox(lines=2, placeholder="Type your prompt here..."), | |
| outputs=gr.Gallery(label="Generated Images", columns=5), # Display images in a grid of 5 columns | |
| title="Design by rahul7star", | |
| description="Make designs for your clothes", | |
| examples=[["Cute Panda"], ["Skull"]] | |
| ) | |
| print("Launching Gradio interface...") | |
| iface.launch() | |