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import os | |
import random | |
import uuid | |
import json | |
import gradio as gr | |
import numpy as np | |
from PIL import Image | |
import spaces | |
import torch | |
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler | |
DESCRIPTIONx = """## REALVISXL V5 🦉 | |
""" | |
css = ''' | |
.gradio-container{max-width: 550px !important} | |
h1{text-align:center} | |
footer { | |
visibility: hidden | |
} | |
''' | |
examples = [ | |
"3d image, cute girl, in the style of Pixar --ar 1:2 --stylize 750, 4K resolution highlights, Sharp focus, octane render, ray tracing, Ultra-High-Definition, 8k, UHD, HDR, (Masterpiece:1.5), (best quality:1.5)", | |
"Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K", | |
] | |
MODEL_ID = os.getenv("MODEL_VAL_PATH", "SG161222/RealVisXL_V4.0_Lightning") | |
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "4096")) | |
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" | |
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" | |
BATCH_SIZE = int(os.getenv("BATCH_SIZE", "1")) # Allow generating multiple images at once | |
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
pipe = StableDiffusionXLPipeline.from_pretrained( | |
MODEL_ID, | |
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
use_safetensors=True, | |
add_watermarker=False, | |
).to(device) | |
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) | |
# <compile speedup > | |
if USE_TORCH_COMPILE: | |
pipe.compile() | |
if ENABLE_CPU_OFFLOAD: | |
pipe.enable_model_cpu_offload() | |
MAX_SEED = np.iinfo(np.int32).max | |
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 = "", | |
use_negative_prompt: bool = False, | |
seed: int = 1, | |
width: int = 1024, | |
height: int = 1024, | |
guidance_scale: float = 3, | |
num_inference_steps: int = 25, | |
randomize_seed: bool = False, | |
use_resolution_binning: bool = True, | |
num_images: int = 1, # Number of images to generate | |
progress=gr.Progress(track_tqdm=True), | |
): | |
seed = int(randomize_seed_fn(seed, randomize_seed)) | |
generator = torch.Generator(device=device).manual_seed(seed) | |
options = { | |
"prompt": [prompt] * num_images, | |
"negative_prompt": [negative_prompt] * num_images if use_negative_prompt else None, | |
"width": width, | |
"height": height, | |
"guidance_scale": guidance_scale, | |
"num_inference_steps": num_inference_steps, | |
"generator": generator, | |
"output_type": "pil", | |
} | |
if use_resolution_binning: | |
options["use_resolution_binning"] = True | |
images = [] | |
for i in range(0, num_images, BATCH_SIZE): | |
batch_options = options.copy() | |
batch_options["prompt"] = options["prompt"][i:i+BATCH_SIZE] | |
if "negative_prompt" in batch_options: | |
batch_options["negative_prompt"] = options["negative_prompt"][i:i+BATCH_SIZE] | |
images.extend(pipe(**batch_options).images) | |
image_paths = [save_image(img) for img in images] | |
return image_paths, seed | |
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: | |
gr.Markdown(DESCRIPTIONx) | |
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, visible=True): | |
num_images = gr.Slider( | |
label="Number of Images", | |
minimum=1, | |
maximum=4, | |
step=1, | |
value=1, | |
) | |
with gr.Row(): | |
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True) | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
max_lines=5, | |
lines=4, | |
placeholder="Enter a negative prompt", | |
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", | |
visible=True, | |
) | |
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=True): | |
width = gr.Slider( | |
label="Width", | |
minimum=512, | |
maximum=MAX_IMAGE_SIZE, | |
step=64, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=512, | |
maximum=MAX_IMAGE_SIZE, | |
step=64, | |
value=1024, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance Scale", | |
minimum=0.1, | |
maximum=6, | |
step=0.1, | |
value=3.0, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=25, | |
step=1, | |
value=23, | |
) | |
gr.Examples( | |
examples=examples, | |
inputs=prompt, | |
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, | |
num_inference_steps, | |
randomize_seed, | |
num_images | |
], | |
outputs=[result, seed], | |
api_name="run", | |
) | |
if __name__ == "__main__": | |
demo.queue(max_size=40).launch() |