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Duplicate from phenomenon1981/DreamlikeArt-Diffusion-1.0

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  1. .gitattributes +34 -0
  2. README.md +19 -0
  3. app.py +192 -0
  4. requirements.txt +2 -0
.gitattributes ADDED
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
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+ ---
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+ title: DreamlikeArt-Diffusion 1.0
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+ emoji: 📈
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+ colorFrom: blue
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+ colorTo: yellow
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+ sdk: gradio
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+ sdk_version: 3.16.1
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+ app_file: app.py
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+ pinned: false
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+ duplicated_from: phenomenon1981/DreamlikeArt-Diffusion-1.0
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+ ---
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+ ---
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+ title: DreamlikeArt-Diffusion .0
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+ emoji: 📈
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+ colorFrom: blue
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+ colorTo: yellow
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+ sdk: gradio
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+ sdk_version: 3.16.1
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+ app_file: app.py
app.py ADDED
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+ import gradio as gr
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+ import os
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+ import sys
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+ from pathlib import Path
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+ import random
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+ import string
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+ import time
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+ from queue import Queue
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+ queue = Queue()
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+
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+ text_gen=gr.Interface.load("spaces/Omnibus/MagicPrompt-Stable-Diffusion")
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+ proc1=gr.Interface.load("models/dreamlike-art/dreamlike-photoreal-2.0")
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+ proc5=gr.Interface.load("models/dreamlike-art/dreamlike-diffusion-1.0")
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+
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+ def reset_queue_periodically():
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+ start_time = time.time()
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+ while True:
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+ if time.time() - start_time > 300: # 300 seconds = 5 minutes
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+ queue.queue.clear()
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+ start_time = time.time()
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+ time.sleep(1)
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+
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+ def add_random_noise(prompt, noise_level=0.07):
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+ # Get the percentage of characters to add as noise
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+ percentage_noise = noise_level * 5
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+ # Get the number of characters to add as noise
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+ num_noise_chars = int(len(prompt) * (percentage_noise/100))
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+ # Get the indices of the characters to add noise to
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+ noise_indices = random.sample(range(len(prompt)), num_noise_chars)
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+ # Add noise to the selected characters
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+ prompt_list = list(prompt)
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+ for index in noise_indices:
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+ prompt_list[index] = random.choice(string.ascii_letters + string.punctuation)
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+ return "".join(prompt_list)
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+
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+ queue_length_counter = 0
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+
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+ def send_it1(inputs, noise_level, proc1=proc1):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output1 = proc1(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output1
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+ time.sleep(1)
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+
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+ def send_it2(inputs, noise_level, proc1=proc1):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output2 = proc1(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output2
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+ time.sleep(1)
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+
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+ def send_it3(inputs, noise_level, proc1=proc1):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output3 = proc1(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output3
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+ time.sleep(1)
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+
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+ def send_it4(inputs, noise_level, proc1=proc1):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output4 = proc1(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output4
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+ time.sleep(1)
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+
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+ def send_it5(inputs, noise_level, proc5=proc5):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output5 = proc5(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output5
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+ time.sleep(1)
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+
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+ def send_it6(inputs, noise_level, proc5=proc5):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output6 = proc5(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output6
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+ time.sleep(1)
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+
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+ def send_it7(inputs, noise_level, proc5=proc5):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output7 = proc5(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output7
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+ time.sleep(1)
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+
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+ def send_it8(inputs, noise_level, proc5=proc5):
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+ global queue_length_counter
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+ prompt_with_noise = add_random_noise(inputs, noise_level)
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output8 = proc5(prompt_with_noise)
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+ queue_length_counter += 1
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+ return output8
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+ time.sleep(1)
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+
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+
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+
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+ def get_prompts(prompt_text):
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+ global queue_length_counter
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+ if queue_length_counter >= 15:
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+ if not queue.empty():
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+ queue.queue.clear()
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+ queue_length_counter = 0
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+ output = text_gen(prompt_text)
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+ queue_length_counter += 1
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+ return output
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+ time.sleep(1)
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+
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+
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+ with gr.Blocks() as myface:
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+ with gr.Row():
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+
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+ input_text=gr.Textbox(label="Short Prompt")
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+ see_prompts=gr.Button("Magic Prompt")
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+ with gr.Row():
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+
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+ prompt=gr.Textbox(label="Enter Prompt")
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+ noise_level=gr.Slider(minimum=0.1, maximum=3, step=0.1, label="Noise Level: Controls how much randomness is added to the input before it is sent to the model. Higher noise level produces more diverse outputs, while lower noise level produces similar outputs.")
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+ run=gr.Button("Generate")
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+
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+ with gr.Row():
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+ like_message = gr.Button("❤️ Press the Like Button if you enjoy my space! ❤️")
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+ with gr.Row():
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+ output1=gr.Image(label="Dreamlike Photoreal 2.0")
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+ output2=gr.Image(label="Dreamlike Photoreal 2.0")
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+ with gr.Row():
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+ output3=gr.Image(label="Dreamlike Photoreal 2.0")
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+ output4=gr.Image(label="Dreamlike Photoreal 2.0")
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+ with gr.Row():
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+ output5=gr.Image(label="Dreamlike Diffusion 1.0")
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+ output6=gr.Image(label="Dreamlike Diffusion 1.0")
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+ with gr.Row():
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+ output7=gr.Image(label="Dreamlike Diffusion 1.0")
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+ output8=gr.Image(label="Dreamlike Diffusion 1.0")
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+
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+
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+ run.click(send_it1, inputs=[prompt, noise_level], outputs=[output1])
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+ run.click(send_it2, inputs=[prompt, noise_level], outputs=[output2])
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+ run.click(send_it3, inputs=[prompt, noise_level], outputs=[output3])
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+ run.click(send_it4, inputs=[prompt, noise_level], outputs=[output4])
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+ run.click(send_it5, inputs=[prompt, noise_level], outputs=[output5])
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+ run.click(send_it6, inputs=[prompt, noise_level], outputs=[output6])
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+ run.click(send_it7, inputs=[prompt, noise_level], outputs=[output7])
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+ run.click(send_it8, inputs=[prompt, noise_level], outputs=[output8])
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+ see_prompts.click(get_prompts, inputs=[input_text], outputs=[prompt])
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+
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+
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+
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+ myface.queue(concurrency_count=8)
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+ myface.launch(enable_queue=True, inline=True)
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+ while True:
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+ if queue.qsize() >= 20:
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+ queue.queue.clear()
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+ time.sleep(30)
requirements.txt ADDED
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+ accelerate==0.12.0
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+ gradio