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Update app.py
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app.py
CHANGED
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#!/usr/bin/env python
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import os
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import random
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import uuid
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import gradio as gr
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import numpy as np
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from PIL import Image
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import spaces
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from typing import Tuple
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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DESCRIPTION = """
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# OpenDalle V1.1
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"""
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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MAX_SEED = np.iinfo(np.int32).max
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
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MAX_SEED = np.iinfo(np.int32).max
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ENABLE_CPU_OFFLOAD = 0
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if torch.cuda.is_available():
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"dataautogpt3/OpenDalleV1.1",
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vae=vae,
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torch_dtype=torch.float16,
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use_safetensors=True,
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)
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pipe.to("cuda")
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"prompt": "{prompt}",
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"negative_prompt": "",
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},
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{
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"name": "Cinematic",
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"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
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"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
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},
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{
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"name": "Photographic",
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"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
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"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
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},
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{
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"name": "Anime",
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"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
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"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
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},
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{
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"name": "Manga",
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"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
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"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
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},
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{
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"name": "Digital Art",
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"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
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"negative_prompt": "photo, photorealistic, realism, ugly",
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},
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{
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"name": "Pixel art",
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"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
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"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
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},
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{
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"name": "Fantasy art",
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"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
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"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
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},
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{
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"name": "Neonpunk",
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"prompt": "neonpunk style {prompt} . 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": "3D Model",
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"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
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"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
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},
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]
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styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
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STYLE_NAMES = list(styles.keys())
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DEFAULT_STYLE_NAME = "(No style)"
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def
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return p.replace("{prompt}", positive), n + negative
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@spaces.GPU(
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def generate(
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prompt: str,
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negative_prompt: str = "",
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style: str = DEFAULT_STYLE_NAME,
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use_negative_prompt: bool = False,
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num_images_per_prompt: int = 2,
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seed: int = 0,
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width: int = 1024,
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height: int = 1024,
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guidance_scale: float = 3,
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randomize_seed: bool = False,
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progress=gr.Progress(track_tqdm=True),
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):
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seed = int(randomize_seed_fn(seed, randomize_seed))
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if not use_negative_prompt:
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negative_prompt = "" # type: ignore
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prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
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images = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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width=width,
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height=height,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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num_images_per_prompt=num_images_per_prompt,
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cross_attention_kwargs={"scale": 0.65},
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output_type="pil",
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).images
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image_paths = [save_image(img) for img in images]
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print(image_paths)
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return image_paths, seed
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examples = [
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"
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"
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"
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"
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"
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]
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css = '''
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visibility: hidden
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}
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'''
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with gr.Blocks(css=css
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gr.Markdown(
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value="Duplicate Space for private use",
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elem_id="duplicate-button",
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visible=False,
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)
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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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")
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result = gr.Gallery(label="Result", columns=1
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with gr.Accordion("Advanced options", open=False):
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label="Negative prompt",
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max_lines=
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placeholder="Enter a negative prompt",
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visible=True,
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)
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with gr.Row():
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num_inference_steps = gr.Slider(
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label="Steps",
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minimum=10,
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maximum=60,
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step=1,
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value=30,
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)
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with gr.Row():
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num_images_per_prompt = gr.Slider(
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label="Images",
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minimum=1,
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maximum=5,
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step=1,
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value=2,
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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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visible=True
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)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row(visible=True):
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width = gr.Slider(
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label="Width",
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minimum=512,
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maximum=
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step=
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value=1024,
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)
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height = gr.Slider(
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label="Height",
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minimum=512,
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maximum=
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step=
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value=1024,
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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=0.1,
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maximum=
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step=0.1,
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value=
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)
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value=DEFAULT_STYLE_NAME,
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label="Image Style",
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)
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gr.Examples(
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examples=examples,
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inputs=prompt,
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outputs=[result, seed],
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fn=generate,
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cache_examples=
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)
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use_negative_prompt.change(
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outputs=negative_prompt,
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api_name=False,
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)
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gr.on(
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triggers=[
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inputs=[
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prompt,
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negative_prompt,
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style_selection,
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use_negative_prompt,
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num_inference_steps,
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num_images_per_prompt,
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seed,
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width,
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height,
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guidance_scale,
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randomize_seed,
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],
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outputs=[result, seed],
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api_name="run",
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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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import os
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import random
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import uuid
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import json
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import gradio as gr
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import numpy as np
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from PIL import Image
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import spaces
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import torch
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from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
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if not torch.cuda.is_available():
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
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MAX_SEED = np.iinfo(np.int32).max
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096"))
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():
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pipe = StableDiffusionXLPipeline.from_pretrained(
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"dataautogpt3/OpenDalleV1.1",
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torch_dtype=torch.float16,
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use_safetensors=True,
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add_watermarker=False
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)
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pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
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pipe.to("cuda")
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def save_image(img):
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unique_name = str(uuid.uuid4()) + ".png"
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img.save(unique_name)
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return unique_name
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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@spaces.GPU(duration=30, queue=False)
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def generate(
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prompt: str,
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negative_prompt: str = "",
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use_negative_prompt: bool = False,
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seed: int = 1,
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width: int = 1024,
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height: int = 1024,
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guidance_scale: float = 3,
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num_inference_steps: int = 30,
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randomize_seed: bool = False,
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use_resolution_binning: bool = True,
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progress=gr.Progress(track_tqdm=True),
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):
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pipe.to(device)
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seed = int(randomize_seed_fn(seed, randomize_seed))
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generator = torch.Generator().manual_seed(seed)
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options = {
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"prompt":prompt,
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"negative_prompt":negative_prompt,
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"width":width,
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"height":height,
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"guidance_scale":guidance_scale,
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"num_inference_steps":num_inference_steps,
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"generator":generator,
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"use_resolution_binning":use_resolution_binning,
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"output_type":"pil",
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}
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images = pipe(**options).images
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image_paths = [save_image(img) for img in images]
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return image_paths, seed
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examples = [
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"a cat eating a piece of cheese",
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"a ROBOT riding a BLUE horse on Mars, photorealistic, 4k",
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"Ironman VS Hulk, ultrarealistic",
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"Astronaut in a jungle, cold color palette, oil pastel, detailed, 8k",
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"An alien holding sign board contain word 'Flash', futuristic, neonpunk",
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"Kids going to school, Anime style"
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]
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css = '''
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visibility: hidden
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}
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'''
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with gr.Blocks(css=css) as demo:
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gr.Markdown("""# SDXL Flash
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### First Image processing takes time then images generate faster.""")
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with gr.Group():
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with gr.Row():
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prompt = gr.Text(
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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)
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result = gr.Gallery(label="Result", columns=1)
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with gr.Accordion("Advanced options", open=False):
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with gr.Row():
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
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negative_prompt = gr.Text(
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label="Negative prompt",
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max_lines=5,
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lines=4,
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placeholder="Enter a negative prompt",
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value="(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
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visible=True,
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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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|
129 |
)
|
130 |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
|
131 |
with gr.Row(visible=True):
|
132 |
width = gr.Slider(
|
133 |
label="Width",
|
134 |
minimum=512,
|
135 |
+
maximum=MAX_IMAGE_SIZE,
|
136 |
+
step=64,
|
137 |
value=1024,
|
138 |
)
|
139 |
height = gr.Slider(
|
140 |
label="Height",
|
141 |
minimum=512,
|
142 |
+
maximum=MAX_IMAGE_SIZE,
|
143 |
+
step=64,
|
144 |
value=1024,
|
145 |
)
|
146 |
with gr.Row():
|
147 |
guidance_scale = gr.Slider(
|
148 |
label="Guidance Scale",
|
149 |
minimum=0.1,
|
150 |
+
maximum=6,
|
151 |
step=0.1,
|
152 |
+
value=3.0,
|
153 |
)
|
154 |
+
num_inference_steps = gr.Slider(
|
155 |
+
label="Number of inference steps",
|
156 |
+
minimum=1,
|
157 |
+
maximum=15,
|
158 |
+
step=1,
|
159 |
+
value=8,
|
|
|
|
|
160 |
)
|
|
|
161 |
|
162 |
gr.Examples(
|
163 |
examples=examples,
|
164 |
inputs=prompt,
|
165 |
outputs=[result, seed],
|
166 |
fn=generate,
|
167 |
+
cache_examples=CACHE_EXAMPLES,
|
168 |
)
|
169 |
|
170 |
use_negative_prompt.change(
|
|
|
173 |
outputs=negative_prompt,
|
174 |
api_name=False,
|
175 |
)
|
|
|
176 |
|
177 |
gr.on(
|
178 |
triggers=[
|
|
|
184 |
inputs=[
|
185 |
prompt,
|
186 |
negative_prompt,
|
|
|
187 |
use_negative_prompt,
|
|
|
|
|
188 |
seed,
|
189 |
width,
|
190 |
height,
|
191 |
guidance_scale,
|
192 |
+
num_inference_steps,
|
193 |
randomize_seed,
|
194 |
],
|
195 |
outputs=[result, seed],
|
196 |
api_name="run",
|
197 |
)
|
198 |
+
|
199 |
if __name__ == "__main__":
|
200 |
+
demo.queue(max_size=20).launch()
|