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# import gradio as gr
# import numpy as np
# import random
# #import spaces #[uncomment to use ZeroGPU]
# from diffusers import DiffusionPipeline
# import torch
#
# device = "cuda" if torch.cuda.is_available() else "cpu"
# model_repo_id = "stabilityai/sdxl-turbo" #Replace to the model you would like to use
#
# if torch.cuda.is_available():
# torch_dtype = torch.float16
# else:
# torch_dtype = torch.float32
#
# pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
# pipe = pipe.to(device)
#
# MAX_SEED = np.iinfo(np.int32).max
# MAX_IMAGE_SIZE = 1024
#
# #@spaces.GPU #[uncomment to use ZeroGPU]
# def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
#
# if randomize_seed:
# seed = random.randint(0, MAX_SEED)
#
# generator = torch.Generator().manual_seed(seed)
#
# image = pipe(
# prompt = prompt,
# negative_prompt = negative_prompt,
# guidance_scale = guidance_scale,
# num_inference_steps = num_inference_steps,
# width = width,
# height = height,
# generator = generator
# ).images[0]
#
# return image, seed
#
# examples = [
# "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
# "An astronaut riding a green horse",
# "A delicious ceviche cheesecake slice",
# ]
#
# css="""
# #col-container {
# margin: 0 auto;
# max-width: 640px;
# }
# """
#
# with gr.Blocks(css=css) as demo:
#
# with gr.Column(elem_id="col-container"):
# gr.Markdown(f"""
# # Text-to-Image Gradio Template
# """)
#
# with gr.Row():
#
# prompt = gr.Text(
# label="Prompt",
# show_label=False,
# max_lines=1,
# placeholder="Enter your prompt",
# container=False,
# )
#
# run_button = gr.Button("Run", scale=0)
#
# result = gr.Image(label="Result", show_label=False)
#
# with gr.Accordion("Advanced Settings", open=False):
#
# negative_prompt = gr.Text(
# label="Negative prompt",
# max_lines=1,
# placeholder="Enter a negative prompt",
# visible=False,
# )
#
# seed = gr.Slider(
# label="Seed",
# minimum=0,
# maximum=MAX_SEED,
# step=1,
# value=0,
# )
#
# randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
#
# with gr.Row():
#
# width = gr.Slider(
# label="Width",
# minimum=256,
# maximum=MAX_IMAGE_SIZE,
# step=32,
# value=1024, #Replace with defaults that work for your model
# )
#
# height = gr.Slider(
# label="Height",
# minimum=256,
# maximum=MAX_IMAGE_SIZE,
# step=32,
# value=1024, #Replace with defaults that work for your model
# )
#
# with gr.Row():
#
# guidance_scale = gr.Slider(
# label="Guidance scale",
# minimum=0.0,
# maximum=10.0,
# step=0.1,
# value=0.0, #Replace with defaults that work for your model
# )
#
# num_inference_steps = gr.Slider(
# label="Number of inference steps",
# minimum=1,
# maximum=50,
# step=1,
# value=2, #Replace with defaults that work for your model
# )
#
# gr.Examples(
# examples = examples,
# inputs = [prompt]
# )
# gr.on(
# triggers=[run_button.click, prompt.submit],
# fn = infer,
# inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
# outputs = [result, seed]
# )
#
# demo.queue().launch()
import gradio as gr
gr.load("models/nerijs/dark-fantasy-illustration-flux").launch()
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