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import gradio as gr |
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import requests |
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import os |
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from PIL import Image |
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from io import BytesIO |
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from tqdm import tqdm |
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import time |
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token = os.environ["HF_TOKEN"] |
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repo = "artificialguybr/TshirtDesignRedmond-V2" |
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trigger_word = "T shirt design, TshirtDesignAF, " |
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def generate_image(prompt): |
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print("Generating image with prompt:", prompt) |
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api_url = f"https://api-inference.huggingface.co/models/{repo}" |
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headers = { |
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"Authorization": f"Bearer {token}" |
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} |
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full_prompt = f"{prompt} {trigger_word}" |
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payload = { |
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"inputs": full_prompt, |
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"parameters": { |
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"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)", |
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"num_inference_steps": 30, |
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"scheduler": "DPMSolverMultistepScheduler" |
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}, |
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} |
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error_count = 0 |
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pbar = tqdm(total=None, desc="Loading model") |
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while True: |
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print("Sending request to API...") |
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response = requests.post(api_url, headers=headers, json=payload) |
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print("API response status code:", response.status_code) |
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if response.status_code == 200: |
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print("Image generation successful!") |
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return Image.open(BytesIO(response.content)) |
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elif response.status_code == 503: |
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time.sleep(1) |
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pbar.update(1) |
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elif response.status_code == 500 and error_count < 5: |
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time.sleep(1) |
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error_count += 1 |
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else: |
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print("API Error:", response.status_code) |
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raise Exception(f"API Error: {response.status_code}") |
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iface = gr.Interface( |
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fn=generate_image, |
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inputs=gr.Textbox(lines=2, placeholder="Type your prompt here..."), |
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outputs="image", |
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title="TShirt Design XL Image Generator", |
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description="Powered by Redmond.AI — This fine-tuned SDXL model generates T-shirt designs from prompts. Trigger tags: 'T shirt design, TshirtDesignAF'.", |
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examples=[["Cute Panda"], ["Skull"]] |
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) |
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print("Launching Gradio interface...") |
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iface.launch() |
