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dbb59b8
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Parent(s):
da1e5da
debug gpu zero
Browse files
app.py
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#!/usr/bin/env python
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import gradio as gr
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import torch
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model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")
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with gr.Accordion(label="Base model", open=False):
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with gr.Row():
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with gr.Column(scale=5):
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current_base_model = gr.Text(label="Current base model")
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with gr.Column(scale=1):
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check_base_model_button = gr.Button("Check current base model")
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with gr.Row():
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interactive=ALLOW_CHANGING_BASE_MODEL,
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)
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with gr.Column(scale=1):
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change_base_model_button = gr.Button("Change base model", interactive=ALLOW_CHANGING_BASE_MODEL)
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if not ALLOW_CHANGING_BASE_MODEL:
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gr.Markdown(
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"""The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space."""
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)
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gr.on(
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triggers=[
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fn=
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inputs=
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)
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if __name__ == "__main__":
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demo.
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import gradio as gr
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import numpy as np
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import random
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# import spaces #[uncomment to use ZeroGPU]
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from diffusers import DiffusionPipeline
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import torch
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model_repo_id = "stabilityai/sdxl-turbo" # Replace to the model you would like to use
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if torch.cuda.is_available():
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torch_dtype = torch.float16
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else:
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torch_dtype = torch.float32
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pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
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pipe = pipe.to(device)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1024
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# @spaces.GPU #[uncomment to use ZeroGPU]
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def infer(
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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progress=gr.Progress(track_tqdm=True),
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):
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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generator = torch.Generator().manual_seed(seed)
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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width=width,
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height=height,
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generator=generator,
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).images[0]
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return image, seed
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examples = [
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"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
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"An astronaut riding a green horse",
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"A delicious ceviche cheesecake slice",
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]
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css = """
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#col-container {
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margin: 0 auto;
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max-width: 640px;
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}
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"""
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with gr.Blocks(css=css) as demo:
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with gr.Column(elem_id="col-container"):
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gr.Markdown(" # Text-to-Image Gradio Template")
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with gr.Row():
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prompt = gr.Text(
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label="Prompt",
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show_label=False,
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max_lines=1,
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placeholder="Enter your prompt",
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container=False,
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)
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run_button = gr.Button("Run", scale=0, variant="primary")
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result = gr.Image(label="Result", show_label=False)
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with gr.Accordion("Advanced Settings", open=False):
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=1,
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placeholder="Enter a negative prompt",
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visible=False,
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)
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seed = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=0,
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(
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label="Width",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024, # Replace with defaults that work for your model
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)
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height = gr.Slider(
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label="Height",
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minimum=256,
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maximum=MAX_IMAGE_SIZE,
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step=32,
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value=1024, # Replace with defaults that work for your model
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)
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with gr.Row():
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guidance_scale = gr.Slider(
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label="Guidance scale",
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minimum=0.0,
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maximum=10.0,
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step=0.1,
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value=0.0, # Replace with defaults that work for your model
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)
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num_inference_steps = gr.Slider(
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label="Number of inference steps",
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minimum=1,
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maximum=50,
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step=1,
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value=2, # Replace with defaults that work for your model
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)
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gr.Examples(examples=examples, inputs=[prompt])
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[
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prompt,
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negative_prompt,
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seed,
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randomize_seed,
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width,
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height,
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guidance_scale,
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num_inference_steps,
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],
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outputs=[result, seed],
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)
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if __name__ == "__main__":
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demo.launch()
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app_.py
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#!/usr/bin/env python
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import gradio as gr
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import torch
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from app_canny import create_demo as create_demo_canny
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from model import Model
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from settings import ALLOW_CHANGING_BASE_MODEL, DEFAULT_MODEL_ID, SHOW_DUPLICATE_BUTTON
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DESCRIPTION = "# Material Authoring Demo v0.1. Under Construction"
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
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model = Model(base_model_id=DEFAULT_MODEL_ID, task_name="Canny")
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with gr.Blocks() as demo:
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gr.Markdown(DESCRIPTION)
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gr.DuplicateButton(
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value="Duplicate Space for private use",
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elem_id="duplicate-button",
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visible=SHOW_DUPLICATE_BUTTON,
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)
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with gr.Tabs():
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with gr.Tab("Canny"):
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create_demo_canny(model.process_canny)
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with gr.Tab("Texnet"):
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create_demo_canny(model.process_canny)
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with gr.Tab("Matnet"):
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create_demo_canny(model.process_canny)
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with gr.Accordion(label="Base model", open=False):
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with gr.Row():
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with gr.Column(scale=5):
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current_base_model = gr.Text(label="Current base model")
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with gr.Column(scale=1):
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check_base_model_button = gr.Button("Check current base model")
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with gr.Row():
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with gr.Column(scale=5):
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new_base_model_id = gr.Text(
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label="New base model",
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max_lines=1,
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placeholder="stable-diffusion-v1-5/stable-diffusion-v1-5",
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info="The base model must be compatible with Stable Diffusion v1.5.",
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interactive=ALLOW_CHANGING_BASE_MODEL,
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)
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with gr.Column(scale=1):
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change_base_model_button = gr.Button("Change base model", interactive=ALLOW_CHANGING_BASE_MODEL)
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if not ALLOW_CHANGING_BASE_MODEL:
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gr.Markdown(
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"""The base model is not allowed to be changed in this Space so as not to slow down the demo, but it can be changed if you duplicate the Space."""
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)
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check_base_model_button.click(
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fn=lambda: model.base_model_id,
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outputs=current_base_model,
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queue=False,
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api_name="check_base_model",
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)
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gr.on(
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triggers=[new_base_model_id.submit, change_base_model_button.click],
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fn=model.set_base_model,
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inputs=new_base_model_id,
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outputs=current_base_model,
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api_name=False,
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concurrency_id="main",
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)
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if __name__ == "__main__":
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demo.queue(max_size=20).launch()
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