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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 = "Animagine XL 3.1"
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/cagliostrolab/animagine-xl-3.1/blob/main/animagine-xl-3.1.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,
)
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 = 28,
sampler: str = "Euler a",
aspect_ratio_selector: str = "896 x 1152",
style_selector: str = "(None)",
quality_selector: str = "Standard v3.1",
use_upscaler: bool = False,
upscaler_strength: float = 0.55,
upscale_by: float = 1.5,
add_quality_tags: bool = True,
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,
)
prompt = utils.add_wildcard(prompt, wildcard_files)
prompt, negative_prompt = utils.preprocess_prompt(
quality_prompt, quality_selector, prompt, negative_prompt, add_quality_tags
)
prompt, negative_prompt = utils.preprocess_prompt(
styles, style_selector, prompt, negative_prompt
)
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,
"sdxl_style": style_selector,
"add_quality_tags": add_quality_tags,
"quality_tags": quality_selector,
}
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)
image = 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[0]
else:
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]
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
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.style_list}
quality_prompt = {
k["name"]: (k["prompt"], k["negative_prompt"]) for k in config.quality_prompt_list
}
wildcard_files = utils.load_wildcard_files("wildcard")
with gr.Blocks(css="style.css", theme="NoCrypt/[email protected]") as demo:
title = gr.HTML(
f"""<h1><span>{DESCRIPTION}</span></h1>""",
elem_id="title",
)
gr.Markdown(
f"""Gradio demo for [cagliostrolab/animagine-xl-3.1](https://huggingface.co/cagliostrolab/animagine-xl-3.1)""",
elem_id="subtitle",
)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Row():
with gr.Column(scale=2):
with gr.Tab("Txt2img"):
with gr.Group():
prompt = gr.Text(
label="Prompt",
max_lines=5,
placeholder="Enter your prompt",
)
negative_prompt = gr.Text(
label="Negative Prompt",
max_lines=5,
placeholder="Enter a negative prompt",
)
with gr.Accordion(label="Quality Tags", open=True):
add_quality_tags = gr.Checkbox(
label="Add Quality Tags", value=True
)
quality_selector = gr.Dropdown(
label="Quality Tags Presets",
interactive=True,
choices=list(quality_prompt.keys()),
value="Standard v3.1",
)
with gr.Tab("Advanced Settings"):
with gr.Group():
style_selector = gr.Radio(
label="Style Preset",
container=True,
interactive=True,
choices=list(styles.keys()),
value="(None)",
)
with gr.Group():
aspect_ratio_selector = gr.Radio(
label="Aspect Ratio",
choices=config.aspect_ratios,
value="896 x 1152",
container=True,
)
with gr.Group():
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,
)
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,
)
with gr.Group():
sampler = gr.Dropdown(
label="Sampler",
choices=config.sampler_list,
interactive=True,
value="Euler a",
)
with gr.Group():
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.Column(scale=3):
with gr.Blocks():
run_button = gr.Button("Generate", variant="primary")
result = gr.Gallery(
label="Result",
columns=1,
preview=True,
show_label=False
)
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,
style_selector,
quality_selector,
use_upscaler,
upscaler_strength,
upscale_by,
add_quality_tags,
]
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)
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