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import json |
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import requests |
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import gradio as gr |
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def generate_image(prompt, negative_prompt, width, height, samples, num_inference_steps, safety_checker, enhance_prompt, seed, guidance_scale, multi_lingual, panorama, self_attention, upscale, embeddings, lora, webhook, track_id): |
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url = "https://modelslab.com/api/v6/images/text2img" |
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payload = json.dumps({ |
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"key": "sHj15HTjxiCkFtV3PHmSeehjaVGdpNotsb1iMbIpniNzfTsjgbN7Z9RFB8Wu", |
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"model_id": "juggernaut-xl-v8", |
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"prompt": prompt, |
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"negative_prompt": negative_prompt, |
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"width": width, |
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"height": height, |
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"samples": samples, |
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"num_inference_steps": num_inference_steps, |
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"safety_checker": safety_checker, |
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"enhance_prompt": enhance_prompt, |
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"seed": seed, |
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"guidance_scale": guidance_scale, |
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"multi_lingual": multi_lingual, |
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"panorama": panorama, |
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"self_attention": self_attention, |
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"upscale": upscale, |
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"embeddings": embeddings, |
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"lora": lora, |
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"webhook": webhook, |
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"track_id": track_id |
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}) |
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headers = { |
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'Content-Type': 'application/json' |
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} |
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response = requests.request("POST", url, headers=headers, data=payload) |
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return response.text |
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iface = gr.Interface(fn=generate_image, |
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inputs=["text", "text", "text", "text", "text", "text", "text", "text", "text", "number", "text", "text", "text", "text", "text", "text", "text", "text", "text"], |
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outputs="text", |
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title="Text to Image Generation", |
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description="Generate an image based on text prompts.", |
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article="Enter your prompts and settings and click 'Generate Image'.") |
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iface.launch() |
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