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Update app.py
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app.py
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@@ -1,10 +1,10 @@
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import os
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import spaces
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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import gradio as gr
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import random
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import tqdm
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# Enable TQDM progress tracking
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tqdm.monitor_interval = 0
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@@ -19,7 +19,6 @@ pipe = StableDiffusionXLPipeline.from_pretrained(
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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# Function to generate an image
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@spaces.GPU() # Adjust the duration as needed
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def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
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pipe.to('cuda') # Move the model to GPU when the function is called
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@@ -103,4 +102,8 @@ with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/[email protected]") as d
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]
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)
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import os
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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import gradio as gr
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import random
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import tqdm
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from pyngrok import ngrok
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# Enable TQDM progress tracking
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tqdm.monitor_interval = 0
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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# Function to generate an image
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def generate_image(prompt, negative_prompt, use_defaults, resolution, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
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pipe.to('cuda') # Move the model to GPU when the function is called
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]
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)
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# Set up ngrok
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public_url = ngrok.connect(api_key=NGROK_API_KEY)
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print(f"Public URL: {public_url}")
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demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)
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