Spaces:
Runtime error
Runtime error
#!/usr/bin/env python | |
#patch 0.01 () | |
# Permission is hereby granted, free of charge, to any person obtaining a copy | |
# of this software and associated documentation files (the "Software"), to deal | |
# in the Software without restriction, including without limitation the rights | |
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
# copies of the Software, and to permit persons to whom the Software is | |
# furnished to do so, subject to the following conditions: | |
# | |
# .. | |
import os | |
import random | |
import uuid | |
from typing import Tuple | |
import gradio as gr | |
import numpy as np | |
from PIL import Image | |
import spaces | |
import torch | |
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler | |
DESCRIPTIONz= """## EPIC REALISM 🙀 | |
""" | |
def save_image(img): | |
unique_name = str(uuid.uuid4()) + ".png" | |
img.save(unique_name) | |
return unique_name | |
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
return seed | |
MAX_SEED = np.iinfo(np.int32).max | |
if not torch.cuda.is_available(): | |
DESCRIPTION += "\n<p>⚠️Running on CPU, This may not work on CPU.</p>" | |
MAX_SEED = np.iinfo(np.int32).max | |
USE_TORCH_COMPILE = 0 | |
ENABLE_CPU_OFFLOAD = 0 | |
if torch.cuda.is_available(): | |
pipe = StableDiffusionXLPipeline.from_pretrained( | |
"SG161222/RealVisXL_V4.0_Lightning", | |
torch_dtype=torch.float16, | |
use_safetensors=True, | |
) | |
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
pipe.load_lora_weights("prithivMLmods/Canopus-Realism-LoRA", weight_name="Canopus-Realism-LoRA.safetensors", adapter_name="rlms") | |
pipe.set_adapters("rlms") | |
pipe.to("cuda") | |
style_list = [ | |
{ | |
"name": "3840 x 2160", | |
"prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "2560 x 1440", | |
"prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "HD+", | |
"prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "Style Zero", | |
"prompt": "{prompt}", | |
"negative_prompt": "", | |
}, | |
] | |
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} | |
DEFAULT_STYLE_NAME = "3840 x 2160" | |
STYLE_NAMES = list(styles.keys()) | |
def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: | |
if style_name in styles: | |
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) | |
else: | |
p, n = styles[DEFAULT_STYLE_NAME] | |
if not negative: | |
negative = "" | |
return p.replace("{prompt}", positive), n + negative | |
def generate( | |
prompt: str, | |
negative_prompt: str = "", | |
use_negative_prompt: bool = False, | |
seed: int = 0, | |
width: int = 1024, | |
height: int = 1024, | |
guidance_scale: float = 3, | |
randomize_seed: bool = False, | |
style_name: str = DEFAULT_STYLE_NAME, | |
progress=gr.Progress(track_tqdm=True), | |
): | |
seed = int(randomize_seed_fn(seed, randomize_seed)) | |
positive_prompt, effective_negative_prompt = apply_style(style_name, prompt, negative_prompt) | |
if not use_negative_prompt: | |
effective_negative_prompt = "" # type: ignore | |
images = pipe( | |
prompt=positive_prompt, | |
negative_prompt=effective_negative_prompt, | |
width=width, | |
height=height, | |
guidance_scale=guidance_scale, | |
num_inference_steps=20, | |
num_images_per_prompt=1, | |
cross_attention_kwargs={"scale": 0.65}, | |
output_type="pil", | |
).images | |
image_paths = [save_image(img) for img in images] | |
print(image_paths) | |
return image_paths, seed | |
examples = [ | |
"A man in ski mask, in the style of smokey background, androgynous, imaginative prison scenes, light indigo and black, close-up, michelangelo, street-savvy --ar 125:187 --v 5.1 --style raw", | |
"Photography, front view, dynamic range, female model, upper-body, black T-shirt, dark khaki cargo pants, urban backdrop, dusk, dramatic sunlights, bokeh, cityscape, photorealism, natural, UHD --ar 9:16 --stylize 300" | |
] | |
css = ''' | |
.gradio-container{max-width: 545px !important} | |
h1{text-align:center} | |
footer { | |
visibility: hidden | |
} | |
''' | |
def load_predefined_images(): | |
predefined_images = [ | |
"assets/22222.png", | |
"assets/11111.png", | |
"assets/33333.png", | |
"assets/44444.png", | |
"assets/7.png", | |
"assets/8.png", | |
"assets/9.png", | |
"assets/10.png", | |
"assets/11.png", | |
] | |
return predefined_images | |
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: | |
gr.Markdown(DESCRIPTIONz) | |
with gr.Group(): | |
with gr.Row(): | |
prompt = gr.Text( | |
label="Prompt", | |
show_label=False, | |
max_lines=1, | |
placeholder="Enter your prompt with realism tag!", | |
container=False, | |
) | |
run_button = gr.Button("Run", scale=0) | |
result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False) | |
with gr.Accordion("Advanced options", open=False, visible=False): | |
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
lines=4, | |
max_lines=6, | |
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", | |
placeholder="Enter a negative prompt", | |
visible=True, | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
visible=True | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(visible=True): | |
width = gr.Slider( | |
label="Width", | |
minimum=512, | |
maximum=2048, | |
step=8, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=512, | |
maximum=2048, | |
step=8, | |
value=1024, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", | |
minimum=0.1, | |
maximum=20.0, | |
step=0.1, | |
value=3.0, | |
) | |
style_selection = gr.Radio( | |
show_label=True, | |
container=True, | |
interactive=True, | |
choices=STYLE_NAMES, | |
value=DEFAULT_STYLE_NAME, | |
label="Quality Style", | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=prompt, | |
outputs=[result, seed], | |
fn=generate, | |
cache_examples=False, | |
) | |
use_negative_prompt.change( | |
fn=lambda x: gr.update(visible=x), | |
inputs=use_negative_prompt, | |
outputs=negative_prompt, | |
api_name=False, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
negative_prompt.submit, | |
run_button.click, | |
], | |
fn=generate, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
use_negative_prompt, | |
seed, | |
width, | |
height, | |
guidance_scale, | |
randomize_seed, | |
style_selection, | |
], | |
outputs=[result, seed], | |
api_name="run", | |
) | |
# Adding a predefined gallery section | |
gr.Markdown("### Generated Images") | |
predefined_gallery = gr.Gallery(label="Generated Images", columns=3, show_label=False, value=load_predefined_images()) | |
gr.Markdown("**Disclaimer/Note:**") | |
gr.Markdown("🙀This space provides realistic image generation, which works better for human faces and portraits. Realistic trigger works properly, better for photorealistic trigger words, close-up shots, face diffusion, male, female characters.") | |
gr.Markdown("⚠️users are accountable for the content they generate and are responsible for ensuring it meets appropriate ethical standards.") | |
if __name__ == "__main__": | |
demo.queue(max_size=30).launch() |