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import gradio as gr |
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from diffusers import DiffusionPipeline |
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import torch |
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def get_device(): |
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if torch.cuda.is_available(): |
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return "cuda" |
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else: |
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return "cpu" |
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def generate_image(prompt): |
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pipe_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE" |
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pipe = DiffusionPipeline.from_pretrained(pipe_id, torch_dtype=torch.float16).to("cuda") |
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pipe.load_lora_weights("timdpaep/t1m") |
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prompt = "professional photo, closeup photo of t1mLora, wearing black sweater, nature, gloomy, cloudy weather, bokeh <lora:t1m01:1>" |
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lora_scale= 0.9 |
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image = pipe( |
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prompt, num_inference_steps=10, cross_attention_kwargs={"scale": lora_scale}, generator=torch.manual_seed(0) |
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).to(get_device()).images[0] |
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return image |
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iface = gr.Interface(fn=generate_image, inputs="textbox", outputs="image") |
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iface.launch() |