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#!/usr/bin/env python | |
from __future__ import annotations | |
import os | |
import random | |
import uuid | |
import gradio as gr | |
import numpy as np | |
import PIL.Image | |
import torch | |
from diffusers import AutoencoderKL, PixArtAlphaPipeline | |
DESCRIPTION = """![Logo](https://raw.githubusercontent.com/PixArt-alpha/PixArt-alpha.github.io/master/static/images/logo.png) | |
# PixArt-Alpha 1024px | |
#### [PixArt-Alpha 1024px](https://github.com/PixArt-alpha/PixArt-alpha) is a transformer-based text-to-image diffusion system trained on text embeddings from T5. This demo uses the [PixArt-alpha/PixArt-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-XL-2-1024-MS) checkpoint. | |
#### English prompts ONLY; 提示词仅限英文 | |
Don't want to queue? Try [Google Colab Demo](https://colab.research.google.com/drive/1jZ5UZXk7tcpTfVwnX33dDuefNMcnW9ME?usp=sharing). It's slower but still free. | |
""" | |
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", "0") == "1" | |
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "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 = "(No style)" | |
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(): | |
pipe = PixArtAlphaPipeline.from_pretrained( | |
"PixArt-alpha/PixArt-XL-2-1024-MS", | |
torch_dtype=torch.float16, | |
variant="fp16", | |
use_safetensors=True, | |
) | |
if ENABLE_CPU_OFFLOAD: | |
pipe.enable_model_cpu_offload() | |
else: | |
pipe.to(device) | |
print("Loaded on Device!") | |
# speed-up T5 | |
pipe.text_encoder.to_bettertransformer() | |
if USE_TORCH_COMPILE: | |
pipe.transformer = torch.compile( | |
pipe.transformer, 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, | |
use_negative_prompt: bool = False, | |
seed: int = 0, | |
width: int = 1024, | |
height: int = 1024, | |
guidance_scale: float = 4.5, | |
num_inference_steps: int = 20, | |
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 | |
prompt, negative_prompt = apply_style(style, prompt, negative_prompt) | |
image = pipe( | |
prompt=prompt, | |
negative_prompt=negative_prompt, | |
width=width, | |
height=height, | |
guidance_scale=guidance_scale, | |
num_inference_steps=num_inference_steps, | |
generator=generator, | |
output_type="pil", | |
).images[0] | |
image_path = save_image(image) | |
print(image_path) | |
return [image_path], seed | |
examples = [ | |
"A small cactus with a happy face in the Sahara desert.", | |
"Pirate ship trapped in a cosmic maelstrom nebula, rendered in cosmic beach whirlpool engine, volumetric lighting, spectacular, ambient lights, light pollution, cinematic atmosphere, art nouveau style, illustration art artwork by SenseiJaye, intricate detail.", | |
"stars, water, brilliantly, gorgeous large scale scene, a little girl, in the style of dreamy realism, light gold and amber, blue and pink, brilliantly illuminated in the background.", | |
"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", | |
] | |
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) | |
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, | |
) | |
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, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=1, | |
maximum=20, | |
step=0.1, | |
value=4.5, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=10, | |
maximum=100, | |
step=1, | |
value=20, | |
) | |
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, | |
api_name=False, | |
) | |
gr.on( | |
triggers=[ | |
prompt.submit, | |
negative_prompt.submit, | |
run_button.click, | |
], | |
fn=generate, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
style_selection, | |
use_negative_prompt, | |
seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
randomize_seed, | |
], | |
outputs=[result, seed], | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
# demo.queue(max_size=20).launch() | |
demo.launch(share=True) | |