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Create stivker.py
Browse files- stivker.py +115 -0
stivker.py
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from diffusers import DiffusionPipeline
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import torch
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import streamlit as st
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sdxl_base_model_path = ("../Models/models--stabilityai--stable-diffusion-xl-base-1.0/snapshots"
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"/462165984030d82259a11f4367a4eed129e94a7b")
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color_book_lora_path = "../Models/Loras/ColoringBookRedmond-ColoringBook-ColoringBookAF (1).safetensors"
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sticker_lora_path = "../Models/Loras/SDXL-StickerSheet-Lora (1).safetensors"
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color_book_trigger =", ColoringBookAF, Coloring Book"
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sticker_trigger =", StickerSheet"
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@st.cache_resource
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def load_pipeline(lora):
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# pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0",
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# torch_dtype=torch.float16 if device == "cuda" else torch.float32,
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# use_safetensors=True,
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# variant="fp16" if device =="cuda" else None)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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pipe = DiffusionPipeline.from_pretrained(sdxl_base_model_path,
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torch_dtype=torch.float16 if device == "cuda" else torch.float32,
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use_safetensors=True,
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variant="fp16" if device == "cuda" else None)
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if lora == "Coloring Book":
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pipe.load_lora_weights(color_book_lora_path)
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if lora == "Sticker":
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pipe.load_lora_weights(sticker_lora_path)
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if device == "cuda":
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pipe.to(device)
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else:
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pipe.enable_model_cpu_offload()
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return pipe
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def image_generation(pipe, prompt, negative_prompt):
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try:
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image = pipe(
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prompt = prompt,
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negative_prompt = "blurred, ugly, watermark, low resolution" + negative_prompt,
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num_inference_steps= 20,
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guidance_scale=9.0
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).images[0]
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return image
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except Exception as e:
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st.error(f"Error generating image: {str(e)}")
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return None
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import streamlit as st
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# Define the table as a list of dictionaries with the provided data
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table = [
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{
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"name": "sai-neonpunk",
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"prompt": "neonpunk style . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
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"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured"
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},
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{
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"name": "futuristic-retro cyberpunk",
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"prompt": "retro cyberpunk. 80's inspired, synthwave, neon, vibrant, detailed, retro futurism",
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"negative_prompt": "modern, desaturated, black and white, realism, low contrast"
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},
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{
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"name": "Dark Fantasy",
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"prompt": "Dark Fantasy Art, dark, moody, dark fantasy style",
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, bright, sunny"
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},
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{
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"name": "Double Exposure",
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"prompt": "Double Exposure Style, double image ghost effect, image combination, double exposure style",
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast"
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},
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{
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"name": "None",
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"prompt": "8K ",
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"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured"
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}
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]
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# Convert the list of dictionaries to a dictionary with 'name' as key for easy lookup
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styles_dict = {entry["name"]: entry for entry in table}
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st.title("Project 13: @GenAILearniverse Sticker Generator")
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prompt = st.text_input("Enter your Prompt", value="A cute Lion")
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select_lora = st.selectbox("Select your lora", options=["Coloring Book", "Sticker", "None"])
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# Dropdown for selecting a style
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style_name = st.selectbox("Select a Style", options=list(styles_dict.keys()))
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# Display the selected style's prompt and negative prompt
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if style_name:
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selected_entry = styles_dict[style_name]
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selected_style_prompt = selected_entry["prompt"];
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selected_style_negative_prompt = selected_entry["negative_prompt"]
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if st.button("Generate Awesome Image"):
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with st.spinner("Generating your awesome image..."):
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pipeline = load_pipeline(select_lora)
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if select_lora == "None":
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image =image_generation(pipeline,prompt + selected_style_prompt, selected_style_negative_prompt)
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else:
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if select_lora == "Coloring Book":
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image = image_generation(pipeline, prompt + selected_style_prompt + color_book_trigger, selected_style_negative_prompt)
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else:
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image = image_generation(pipeline, prompt + selected_style_prompt + sticker_trigger,
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selected_style_negative_prompt)
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if image:
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st.image(image)
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