import os
import random
import autocuda
from pyabsa.utils.pyabsa_utils import fprint
from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, \
DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
import utils
import datetime
import time
import psutil
from Waifu2x.magnify import ImageMagnifier
magnifier = ImageMagnifier()
start_time = time.time()
is_colab = utils.is_google_colab()
CUDA_VISIBLE_DEVICES = ''
device = autocuda.auto_cuda()
dtype = torch.float16 if device != 'cpu' else torch.float32
class Model:
def __init__(self, name, path="", prefix=""):
self.name = name
self.path = path
self.prefix = prefix
self.pipe_t2i = None
self.pipe_i2i = None
models = [
Model("DnD Cover Art", "sd-dreambooth-library/dndcoverart-v1", "Use the token 'dndcoverart'"),
Model("test2", "Jackflack09/mrsrm", "testing2"),
]
# Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
# Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
# Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
# Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy ")
# Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
# Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
# Model("Robo Diffusion", "nousr/robo-diffusion", ""),
scheduler = DPMSolverMultistepScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
trained_betas=None,
predict_epsilon=True,
thresholding=False,
algorithm_type="dpmsolver++",
solver_type="midpoint",
lower_order_final=True,
)
custom_model = None
if is_colab:
models.insert(0, Model("Custom model"))
custom_model = models[0]
last_mode = "txt2img"
current_model = models[1] if is_colab else models[0]
current_model_path = current_model.path
if is_colab:
pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=dtype, scheduler=scheduler,
safety_checker=lambda images, clip_input: (images, False))
else: # download all models
print(f"{datetime.datetime.now()} Downloading vae...")
vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=dtype)
for model in models:
try:
print(f"{datetime.datetime.now()} Downloading {model.name} model...")
unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=dtype)
model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae,
torch_dtype=dtype, scheduler=scheduler,
safety_checker=None)
model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae,
torch_dtype=dtype,
scheduler=scheduler, safety_checker=None)
except Exception as e:
print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
models.remove(model)
pipe = models[0].pipe_t2i
# model.pipe_i2i = torch.compile(model.pipe_i2i)
# model.pipe_t2i = torch.compile(model.pipe_t2i)
if torch.cuda.is_available():
pipe = pipe.to(device)
# device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
def error_str(error, title="Error"):
return f"""#### {title}
{error}""" if error else ""
def custom_model_changed(path):
models[0].path = path
global current_model
current_model = models[0]
def on_model_change(model_name):
prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name),
None) + "\" is prefixed automatically" if model_name != models[
0].name else "Don't forget to use the custom model prefix in the prompt!"
return gr.update(visible=model_name == models[0].name), gr.update(placeholder=prefix)
def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5,
neg_prompt="", scale_factor=2):
fprint(psutil.virtual_memory()) # print memory usage
prompt = 'detailed fingers, beautiful hands,' + prompt
fprint(f"Prompt: {prompt}")
global current_model
for model in models:
if model.name == model_name:
current_model = model
model_path = current_model.path
generator = torch.Generator(device).manual_seed(seed) if seed != 0 else None
try:
if img is not None:
return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height,
generator, scale_factor), None
else:
return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator,
scale_factor), None
except Exception as e:
return None, error_str(e)
# if img is not None:
# return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height,
# generator, scale_factor), None
# else:
# return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor), None
def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator, scale_factor):
print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
global last_mode
global pipe
global current_model_path
if model_path != current_model_path or last_mode != "txt2img":
current_model_path = model_path
if is_colab or current_model == custom_model:
pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=dtype,
scheduler=scheduler,
safety_checker=lambda images, clip_input: (images, False))
else:
# pipe = pipe.to("cpu")
pipe = current_model.pipe_t2i
if torch.cuda.is_available():
pipe = pipe.to(device)
last_mode = "txt2img"
prompt = current_model.prefix + prompt
result = pipe(
prompt,
negative_prompt=neg_prompt,
# num_images_per_prompt=n_images,
num_inference_steps=int(steps),
guidance_scale=guidance,
width=width,
height=height,
generator=generator)
result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor)
# save image
result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
return replace_nsfw_images(result)
def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator, scale_factor):
fprint(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
global last_mode
global pipe
global current_model_path
if model_path != current_model_path or last_mode != "img2img":
current_model_path = model_path
if is_colab or current_model == custom_model:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=dtype,
scheduler=scheduler,
safety_checker=lambda images, clip_input: (
images, False))
else:
# pipe = pipe.to("cpu")
pipe = current_model.pipe_i2i
if torch.cuda.is_available():
pipe = pipe.to(device)
last_mode = "img2img"
prompt = current_model.prefix + prompt
ratio = min(height / img.height, width / img.width)
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
result = pipe(
prompt,
negative_prompt=neg_prompt,
# num_images_per_prompt=n_images,
image=img,
num_inference_steps=int(steps),
strength=strength,
guidance_scale=guidance,
# width=width,
# height=height,
generator=generator)
result.images[0] = magnifier.magnify(result.images[0], scale_factor=scale_factor)
# save image
result.images[0].save("imgs/result-{}.png".format(datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
return replace_nsfw_images(result)
def replace_nsfw_images(results):
if is_colab:
return results.images[0]
if hasattr(results, "nsfw_content_detected") and results.nsfw_content_detected:
for i in range(len(results.images)):
if results.nsfw_content_detected[i]:
results.images[i] = Image.open("nsfw.png")
return results.images[0]
css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
if not os.path.exists('imgs'):
os.mkdir('imgs')
gr.Markdown('# Super Resolution Anime Diffusion')
gr.Markdown(
"## Author: [yangheng95](https://github.com/yangheng95) Github:[Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion)")
gr.Markdown("### This demo is running on a CPU, so it will take at least 20 minutes. "
"If you have a GPU, you can clone from [Github](https://github.com/yangheng95/SuperResolutionAnimeDiffusion) and run it locally.")
gr.Markdown("### FYI: to generate a 512*512 image and magnify 4x, it only takes 5~8 seconds on a RTX 2080 GPU")
gr.Markdown(
"### You can duplicate this demo on HuggingFace Spaces, click [here](https://huggingface.co/spaces/yangheng/Super-Resolution-Anime-Diffusion?duplicate=true)")
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
gr.Markdown("Text to image")
model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
with gr.Box(visible=False) as custom_model_group:
custom_model_path = gr.Textbox(label="Custom model path",
placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion",
interactive=True)
gr.HTML(
"<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,
placeholder="Enter prompt. Style applied automatically").style(container=False)
with gr.Row():
generate = gr.Button(value="Generate")
with gr.Row():
with gr.Group():
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
image_out = gr.Image(height=512)
# gallery = gr.Gallery(
# label="Generated images", show_label=False, elem_id="gallery"
# ).style(grid=[1], height="auto")
error_output = gr.Markdown()
with gr.Column(scale=45):
with gr.Group():
gr.Markdown("Image to Image")
with gr.Row():
with gr.Group():
image = gr.Image(label="Image", height=256, tool="editor", type="pil")
strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01,
value=0.5)
with gr.Row():
with gr.Group():
# n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
with gr.Row():
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
steps = gr.Slider(label="Steps", value=15, minimum=2, maximum=75, step=1)
with gr.Row():
width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
with gr.Row():
scale_factor = gr.Slider(1, 8, label='Scale factor (to magnify image) (1, 2, 4, 8)',
value=2,
step=1)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
if is_colab:
model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
# n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
gr.Markdown("### based on [Anything V3](https://huggingface.co/Linaqruf/anything-v3.0)")
inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, scale_factor]
outputs = [image_out, error_output]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs, api_name="generate")
prompt_keys = [
'girl', 'lovely', 'cute', 'beautiful eyes', 'cumulonimbus clouds', 'detailed fingers',
random.choice(['dress']),
random.choice(['white hair']),
random.choice(['blue eyes']),
random.choice(['flower meadow']),
random.choice(['Elif', 'Angel'])
]
prompt.value = ','.join(prompt_keys)
ex = gr.Examples([
[models[0].name, prompt.value, 7.5, 15],
], inputs=[model_name, prompt, guidance, steps, seed], outputs=outputs, fn=inference, cache_examples=False)
print(f"Space built in {time.time() - start_time:.2f} seconds")
if not is_colab:
demo.queue(concurrency_count=2)
demo.launch(debug=is_colab, enable_queue=True, share=is_colab)