First commit
Browse files- .gitignore +3 -0
- README.md +1 -1
- app.py +85 -0
- imagen.png +0 -0
- requirements.txt +14 -0
.gitignore
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venv/
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__pycache__/
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*.py[cod]
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README.md
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 4.
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app_file: app.py
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pinned: false
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---
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colorFrom: purple
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colorTo: pink
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sdk: gradio
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sdk_version: 4.7.1
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app_file: app.py
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pinned: false
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---
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app.py
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import gradio as gr
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import torch
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import modin.pandas as pd
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import numpy as np
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from diffusers import DiffusionPipeline
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from transformers import pipeline
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pipe = pipeline('text-generation', model='daspartho/prompt-extend')
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def extend_prompt(prompt):
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return pipe(prompt+',', num_return_sequences=1)[0]["generated_text"]
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def text_it(inputs):
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return extend_prompt(inputs)
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def load_pipeline(use_cuda):
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device = "cuda" if use_cuda and torch.cuda.is_available() else "cpu"
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if device == "cuda":
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torch.cuda.max_memory_allocated(device=device)
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torch.cuda.empty_cache()
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
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pipe.enable_xformers_memory_efficient_attention()
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pipe = pipe.to(device)
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torch.cuda.empty_cache()
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else:
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pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
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pipe = pipe.to(device)
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return pipe
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def genie(prompt="sexy woman", use_details=True,steps=2, seed=398231747038484200, use_cuda=False):
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pipe = load_pipeline(use_cuda)
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generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
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if use_details:
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extended_prompt = extend_prompt(prompt)
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else:
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extended_prompt=prompt
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int_image = pipe(prompt=extended_prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
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return int_image, extended_prompt
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# Custom HTML for the interface
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html_code = '''
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<style>
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body {
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background-color: #F0F0F0;
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}
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</style>
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<h1 style="color:black; text-align:center;">Stable Diffusion Turbo with GPT</h1>
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'''
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with gr.Blocks() as myface:
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gr.HTML(html_code) # Add the custom HTML
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with gr.Row():
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input_text = gr.Textbox(label='Text prompt.', lines=1)
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with gr.Row():
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details_checkbox = gr.Checkbox(label="details", info="Generate Details?")
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steps_slider = gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations')
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seed_slider = gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=False, value=398231747038484200)
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cuda_checkbox = gr.Checkbox(label="cuda", info="Do you have cuda?")
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with gr.Row():
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generate_button = gr.Button("Generate")
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with gr.Row():
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output_image1 = gr.Image()
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output_image2 = gr.Image()
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with gr.Row():
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output_text1 = gr.Textbox(label="Generated Text", lines=2)
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output_text2 = gr.Textbox(label="Generated Text", lines=2)
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with gr.Row():
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output_image3 = gr.Image()
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output_image4 = gr.Image()
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with gr.Row():
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output_text3 = gr.Textbox(label="Generated Text", lines=2)
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output_text4 = gr.Textbox(label="Generated Text", lines=2)
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generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image1, output_text1], concurrency_limit=10)
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generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image2, output_text2], concurrency_limit=10)
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generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image3, output_text3], concurrency_limit=10)
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generate_button.click(genie, inputs=[input_text, details_checkbox, steps_slider, seed_slider, cuda_checkbox], outputs=[output_image4, output_text4], concurrency_limit=10)
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myface.launch(inline=True, show_api=False, max_threads=200)
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imagen.png
ADDED
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requirements.txt
ADDED
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diffusers==0.23.1
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transformers
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gradio==4.7.1
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--extra-index-url https://download.pytorch.org/whl/cu121
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torch==2.1.0
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fastapi==0.104.0
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uvicorn==0.23.2
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Pillow==10.1.0
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accelerate==0.24.0
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compel==2.0.2
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controlnet-aux==0.0.7
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peft==0.6.0
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xformers
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hf_transfer
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