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Zero
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 | |
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) |