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Update app.py
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import os
import gc
import gradio as gr
import numpy as np
import torch
import json
import spaces
import config
import utils
import logging
from PIL import Image, PngImagePlugin
from datetime import datetime
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
DESCRIPTION = "Juggernaut XI FOR SD XLXL"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. </p>"
IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
HF_TOKEN = os.getenv("HF_TOKEN")
CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
MIN_IMAGE_SIZE = int(os.getenv("MIN_IMAGE_SIZE", "512"))
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "2048"))
USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./outputs")
MODEL = os.getenv(
"MODEL",
"https://huggingface.co/RunDiffusion/Juggernaut-XI-v11/blob/main/Juggernaut-XI-byRunDiffusion.safetensors",
)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def load_pipeline(model_name):
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16,
)
pipeline = (
StableDiffusionXLPipeline.from_single_file
if MODEL.endswith(".safetensors")
else StableDiffusionXLPipeline.from_pretrained
)
pipe = pipeline(
model_name,
vae=vae,
torch_dtype=torch.float16,
custom_pipeline="lpw_stable_diffusion_xl",
use_safetensors=True,
add_watermarker=False,
use_auth_token=HF_TOKEN,
variant="fp16",
)
pipe.to(device)
return pipe
@spaces.GPU
def generate(
prompt: str,
negative_prompt: str = "",
seed: int = 0,
custom_width: int = 1024,
custom_height: int = 1024,
guidance_scale: float = 7.0,
num_inference_steps: int = 30,
sampler: str = "DPM++ 2M SDE Karras",
aspect_ratio_selector: str = "1024 x 1024",
use_upscaler: bool = False,
upscaler_strength: float = 0.55,
upscale_by: float = 1.5,
progress=gr.Progress(track_tqdm=True),
) -> Image:
generator = utils.seed_everything(seed)
width, height = utils.aspect_ratio_handler(
aspect_ratio_selector,
custom_width,
custom_height,
)
width, height = utils.preprocess_image_dimensions(width, height)
backup_scheduler = pipe.scheduler
pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
if use_upscaler:
upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
metadata = {
"prompt": prompt,
"negative_prompt": negative_prompt,
"resolution": f"{width} x {height}",
"guidance_scale": guidance_scale,
"num_inference_steps": num_inference_steps,
"seed": seed,
"sampler": sampler,
}
if use_upscaler:
new_width = int(width * upscale_by)
new_height = int(height * upscale_by)
metadata["use_upscaler"] = {
"upscale_method": "nearest-exact",
"upscaler_strength": upscaler_strength,
"upscale_by": upscale_by,
"new_resolution": f"{new_width} x {new_height}",
}
else:
metadata["use_upscaler"] = None
logger.info(json.dumps(metadata, indent=4))
try:
if use_upscaler:
latents = 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="latent",
).images
upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
images = upscaler_pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=upscaled_latents,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
strength=upscaler_strength,
generator=generator,
output_type="pil",
).images
else:
images = 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
if images and IS_COLAB:
for image in images:
filepath = utils.save_image(image, metadata, OUTPUT_DIR)
logger.info(f"Image saved as {filepath} with metadata")
return images, metadata
except Exception as e:
logger.exception(f"An error occurred: {e}")
raise
finally:
if use_upscaler:
del upscaler_pipe
pipe.scheduler = backup_scheduler
utils.free_memory()
if torch.cuda.is_available():
pipe = load_pipeline(MODEL)
logger.info("Loaded on Device!")
else:
pipe = None
with gr.Blocks(css="style.css") as demo:
title = gr.HTML(
f"""<h1><span>{DESCRIPTION}</span></h1>""",
elem_id="title",
)
gr.Markdown(
f"""Gradio demo for [Juggernaut XI V11 XL](https://huggingface.co/RunDiffusion/Juggernaut-XI-v11/)""",
elem_id="subtitle",
)
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=5,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button(
"Generate",
variant="primary",
scale=0
)
result = gr.Gallery(
label="Result",
columns=1,
preview=True,
show_label=False
)
with gr.Accordion(label="Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative Prompt",
max_lines=5,
placeholder="Enter a negative prompt",
value="(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
)
aspect_ratio_selector = gr.Radio(
label="Aspect Ratio",
choices=config.aspect_ratios,
value="1024 x 1024",
container=True,
)
with gr.Group(visible=False) as custom_resolution:
with gr.Row():
custom_width = gr.Slider(
label="Width",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
custom_height = gr.Slider(
label="Height",
minimum=MIN_IMAGE_SIZE,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
use_upscaler = gr.Checkbox(label="Use Upscaler", value=False)
with gr.Row() as upscaler_row:
upscaler_strength = gr.Slider(
label="Strength",
minimum=0,
maximum=1,
step=0.05,
value=0.55,
visible=False,
)
upscale_by = gr.Slider(
label="Upscale by",
minimum=1,
maximum=1.5,
step=0.1,
value=1.5,
visible=False,
)
sampler = gr.Dropdown(
label="Sampler",
choices=config.sampler_list,
interactive=True,
value="DPM++ 2M SDE Karras",
)
with gr.Row():
seed = gr.Slider(
label="Seed", minimum=0, maximum=utils.MAX_SEED, step=1, value=0
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Group():
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=1,
maximum=12,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
with gr.Accordion(label="Generation Parameters", open=False):
gr_metadata = gr.JSON(label="Metadata", show_label=False)
gr.Examples(
examples=config.examples,
inputs=prompt,
outputs=[result, gr_metadata],
fn=lambda *args, **kwargs: generate(*args, use_upscaler=True, **kwargs),
cache_examples=CACHE_EXAMPLES,
)
use_upscaler.change(
fn=lambda x: [gr.update(visible=x), gr.update(visible=x)],
inputs=use_upscaler,
outputs=[upscaler_strength, upscale_by],
queue=False,
api_name=False,
)
aspect_ratio_selector.change(
fn=lambda x: gr.update(visible=x == "Custom"),
inputs=aspect_ratio_selector,
outputs=custom_resolution,
queue=False,
api_name=False,
)
inputs = [
prompt,
negative_prompt,
seed,
custom_width,
custom_height,
guidance_scale,
num_inference_steps,
sampler,
aspect_ratio_selector,
use_upscaler,
upscaler_strength,
upscale_by,
]
prompt.submit(
fn=utils.randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name="run",
)
negative_prompt.submit(
fn=utils.randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=result,
api_name=False,
)
run_button.click(
fn=utils.randomize_seed_fn,
inputs=[seed, randomize_seed],
outputs=seed,
queue=False,
api_name=False,
).then(
fn=generate,
inputs=inputs,
outputs=[result, gr_metadata],
api_name=False,
)
demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)