mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-03-13 00:49:48 +00:00
Animagine XL 3.1 Official User Interface
This commit is contained in:
layerdiffusion
400
extensions-builtin/forge_space_animagine_xl_31/forge_app.py
Normal file
400
extensions-builtin/forge_space_animagine_xl_31/forge_app.py
Normal file
@@ -0,0 +1,400 @@
|
||||
import spaces
|
||||
import os
|
||||
import gc
|
||||
import gradio as gr
|
||||
import numpy as np
|
||||
import torch
|
||||
import json
|
||||
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
|
||||
|
||||
|
||||
with spaces.GPUObject() as gpu_object:
|
||||
pipe = load_pipeline(MODEL)
|
||||
logger.info("Loaded on Device!")
|
||||
|
||||
|
||||
spaces.automatically_move_pipeline_components(pipe)
|
||||
spaces.change_attention_from_diffusers_to_forge(pipe.unet)
|
||||
spaces.change_attention_from_diffusers_to_forge(pipe.vae)
|
||||
|
||||
|
||||
@spaces.GPU(gpu_objects=[gpu_object], manual_load=True)
|
||||
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),
|
||||
):
|
||||
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
|
||||
metadata["Model"] = {
|
||||
"Model": DESCRIPTION,
|
||||
"Model hash": "e3c47aedb0",
|
||||
}
|
||||
|
||||
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:
|
||||
image_paths = [
|
||||
utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
|
||||
for image in images
|
||||
]
|
||||
|
||||
for image_path in image_paths:
|
||||
logger.info(f"Image saved as {image_path} with metadata")
|
||||
|
||||
return image_paths, 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()
|
||||
|
||||
|
||||
|
||||
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(spaces.convert_root_path() + "wildcard")
|
||||
|
||||
with gr.Blocks(css="style.css", theme="NoCrypt/miku@1.2.1") 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(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():
|
||||
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():
|
||||
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,
|
||||
height='100%',
|
||||
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,
|
||||
)
|
||||
|
||||
gr.on(
|
||||
triggers=[
|
||||
prompt.submit,
|
||||
negative_prompt.submit,
|
||||
run_button.click,
|
||||
],
|
||||
fn=utils.randomize_seed_fn,
|
||||
inputs=[seed, randomize_seed],
|
||||
outputs=seed,
|
||||
queue=False,
|
||||
api_name=False,
|
||||
).then(
|
||||
fn=generate,
|
||||
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,
|
||||
],
|
||||
outputs=[result, gr_metadata],
|
||||
api_name="run",
|
||||
)
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo.queue(max_size=20).launch(debug=IS_COLAB, share=IS_COLAB)
|
||||
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"tag": "General Image Generation",
|
||||
"title": "Animagine XL 3.1 Official User Interface (8GB VRAM)",
|
||||
"repo_id": "cagliostrolab/animagine-xl-3.1",
|
||||
"revision": "f240016348c54945299cfb4163fbc514fba1c2ed"
|
||||
}
|
||||
Reference in New Issue
Block a user