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#!/usr/bin/env python
from __future__ import annotations
import os
import random
import gradio as gr
import numpy as np
import PIL.Image
import torch
from diffusers import AutoencoderKL, StableDiffusionXLPipeline
import uuid
DESCRIPTION = '''
'''
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "1") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
style_list = [
{
"name": "(No style)",
"prompt": "{prompt}",
"negative_prompt": "",
},
{
"name": "Cinematic",
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
},
{
"name": "Photographic",
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
},
{
"name": "Anime",
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
},
{
"name": "Manga",
"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
},
{
"name": "Digital Art",
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
"negative_prompt": "photo, photorealistic, realism, ugly",
},
{
"name": "Pixel art",
"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
},
{
"name": "Fantasy art",
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
"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",
},
{
"name": "Neonpunk",
"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",
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
},
{
"name": "3D Model",
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
},
]
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
DEFAULT_STYLE_NAME = "Cinematic"
def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
if not negative:
negative = ""
return p.replace("{prompt}", positive), n + negative
if torch.cuda.is_available():
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLPipeline.from_pretrained(
"segmind/SSD-1B",
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
if ENABLE_REFINER:
refiner = DiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-refiner-1.0",
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True,
variant="fp16",
)
if ENABLE_CPU_OFFLOAD:
pipe.enable_model_cpu_offload()
if ENABLE_REFINER:
refiner.enable_model_cpu_offload()
else:
pipe.to(device)
if ENABLE_REFINER:
refiner.to(device)
print("Loaded on Device!")
if USE_TORCH_COMPILE:
pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
if ENABLE_REFINER:
refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
print("Model Compiled!")
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
def generate(
prompt: str,
negative_prompt: str = "",
style: str = DEFAULT_STYLE_NAME,
prompt_2: str = "",
negative_prompt_2: str = "",
use_negative_prompt: bool = False,
use_prompt_2: bool = False,
use_negative_prompt_2: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale_base: float = 5.0,
guidance_scale_refiner: float = 5.0,
num_inference_steps_base: int = 25,
num_inference_steps_refiner: int = 25,
apply_refiner: bool = False,
randomize_seed: bool = False,
progress = gr.Progress(track_tqdm=True)
):
seed = randomize_seed_fn(seed, randomize_seed)
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
if not use_prompt_2:
prompt_2 = None # type: ignore
if not use_negative_prompt_2:
negative_prompt_2 = None # type: ignore
prompt, negative_prompt = apply_style(style, prompt, negative_prompt)
if not apply_refiner:
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type="pil",
).images[0]
else:
latents = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
width=width,
height=height,
guidance_scale=guidance_scale_base,
num_inference_steps=num_inference_steps_base,
generator=generator,
output_type="latent",
).images
image = refiner(
prompt=prompt,
negative_prompt=negative_prompt,
prompt_2=prompt_2,
negative_prompt_2=negative_prompt_2,
guidance_scale=guidance_scale_refiner,
num_inference_steps=num_inference_steps_refiner,
image=latents,
generator=generator,
).images[0]
image_path = save_image(image)
print(image_path)
return [image_path], seed
examples = ['3d digital art of an adorable ghost, glowing within, holding a heart shaped pumpkin, Halloween, super cute, spooky haunted house background', 'beautiful lady, freckles, big smile, blue eyes, short ginger hair, dark makeup, wearing a floral blue vest top, soft light, dark grey background', 'professional portrait photo of an anthropomorphic cat wearing fancy gentleman hat and jacket walking in autumn forest.', 'an astronaut sitting in a diner, eating fries, cinematic, analog film', 'Albert Einstein in a surrealist Cyberpunk 2077 world, hyperrealistic', 'cinematic film still of Futuristic hero with golden dark armour with machine gun, muscular body']
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Group():
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Gallery(label="Result", columns=1, show_label=False)
with gr.Accordion("Advanced options", open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
style_selection = gr.Radio(
show_label=True, container=True, interactive=True,
choices=STYLE_NAMES,
value=DEFAULT_STYLE_NAME,
label='Image Style'
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
prompt_2 = gr.Text(
label="Prompt 2",
max_lines=1,
placeholder="Enter your prompt",
visible=False,
)
negative_prompt_2 = gr.Text(
label="Negative prompt 2",
max_lines=1,
placeholder="Enter a negative prompt",
visible=False,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row(visible=False):
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
with gr.Row():
guidance_scale_base = gr.Slider(
label="Guidance scale for base",
minimum=1,
maximum=20,
step=0.1,
value=9.0,
)
num_inference_steps_base = gr.Slider(
label="Number of inference steps for base",
minimum=10,
maximum=100,
step=1,
value=25,
)
with gr.Row(visible=False) as refiner_params:
guidance_scale_refiner = gr.Slider(
label="Guidance scale for refiner",
minimum=1,
maximum=20,
step=0.1,
value=5.0,
)
num_inference_steps_refiner = gr.Slider(
label="Number of inference steps for refiner",
minimum=10,
maximum=100,
step=1,
value=25,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=[result, seed],
fn=generate,
cache_examples=CACHE_EXAMPLES,
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
queue=False,
api_name=False,
)
use_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_prompt_2,
outputs=prompt_2,
queue=False,
api_name=False,
)
use_negative_prompt_2.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt_2,
outputs=negative_prompt_2,
queue=False,
api_name=False,
)
apply_refiner.change(
fn=lambda x: gr.update(visible=x),
inputs=apply_refiner,
outputs=refiner_params,
queue=False,
api_name=False,
)
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
prompt_2.submit,
negative_prompt_2.submit,
run_button.click,
],
fn=generate,
inputs=[
prompt,
negative_prompt,
style_selection,
prompt_2,
negative_prompt_2,
use_negative_prompt,
use_prompt_2,
use_negative_prompt_2,
seed,
width,
height,
guidance_scale_base,
guidance_scale_refiner,
num_inference_steps_base,
num_inference_steps_refiner,
apply_refiner,
randomize_seed
],
outputs=[result, seed],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()