from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
from diffusers import EulerAncestralDiscreteScheduler
import gradio as gr
import torch
import os
from PIL import Image
import datetime
import time
import psutil
from share_btn import community_icon_html, loading_icon_html, share_js
def is_google_colab():
try:
import google.colab
return True
except:
return False
is_colab = is_google_colab()
start_time = time.time()
if torch.cuda.is_available():
torchfloat = torch.float16
else:
torchfloat = 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("Cyberpunk Anime Diffusion", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
Model("Guan Yu Diffusion", "DGSpitzer/Guan-Yu-Diffusion", "")
]
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=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
else:
eulera = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = StableDiffusionPipeline.from_pretrained(
current_model.path,
torch_dtype=torch.float16,
scheduler=eulera
)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
#pipe.enable_xformers_memory_efficient_attention()
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, neg_prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, n_images=1):
print("Generated image with prompt: " + prompt)
if neg_prompt != "":
print("Negative prompt: " + neg_prompt)
print(psutil.virtual_memory()) # print memory usage
global current_model
for model in models:
if model.name == model_name:
current_model = model
model_path = current_model.path
generator = torch.Generator('cuda').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, n_images, generator), None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
else:
return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, n_images, generator), None, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
except Exception as e:
return None, error_str(e), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
def inference_example(model_name, prompt, neg_prompt, guidance, steps, width=512, height=512):
_image, _error, _bool1, _bool2, _bool3 = inference(model_name, prompt, neg_prompt, guidance, steps, width, height)
return _image
def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, n_images, generator):
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=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
safety_checker=lambda images, clip_input: (images, False)
)
else:
eulera = EulerAncestralDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
pipe = StableDiffusionPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=eulera
)
# pipe = pipe.to("cpu")
# pipe = current_model.pipe_t2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
# pipe.enable_xformers_memory_efficient_attention()
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)
return replace_nsfw_images(result)
def img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, n_images, generator):
print(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=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
else:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
current_model_path,
torch_dtype=torch.float16,
scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
)
# pipe = pipe.to("cpu")
# pipe = current_model.pipe_i2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
pipe.enable_xformers_memory_efficient_attention()
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)
return replace_nsfw_images(result)
def replace_nsfw_images(results):
#Only return 1 image for community sharing
if is_colab:
return results.images[0]
for i in range(len(results.images)):
if results.nsfw_content_detected[i]:
results.images[i] = Image.open("nsfw_placeholder.jpg")
return results.images[0]
css = """
#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
.animate-spin {
animation: spin 1s linear infinite;
}
@keyframes spin {
from {
transform: rotate(0deg);
}
to {
transform: rotate(360deg);
}
}
#share-btn-container {
display: flex; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
}
#share-btn {
all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;right:0;
}
#share-btn * {
all: unset;
}
#share-btn-container div:nth-child(-n+2){
width: auto !important;
min-height: 0px !important;
}
#share-btn-container .wrap {
display: none !important;
}
#gallery{min-height:20rem}
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
"""
Another online version without queue: 
"""
)
gr.Markdown('''
👇 Buy me a coffee if you like ♥ this project!
[](https://www.buymeacoffee.com/dgspitzer)
''')
with gr.Row():
with gr.Column():
model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
#prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2, placeholder="Enter your prompt~", elem_id="input-prompt").style(container=False)
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2, placeholder="Enter your prompt~", elem_id="input-prompt")
neg_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter what you don't want to generate", elem_id="input-neg-prompt")
width_input = gr.Slider(label="Width", value=576, maximum=768, minimum=384, step=64)
height_input = gr.Slider(label="Height", value=576, maximum=768, minimum=384, step=64)
#n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
guidance = gr.Slider(label="Guidance scale", value=7, maximum=10)
steps = gr.Slider(label="Steps", value=20, maximum=50, minimum=2)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
run = gr.Button(value="Run")
gr.Markdown(f"Running on: {device}")
# with gr.Column(scale=45):
# with gr.Tab("Text to Image"):
# with gr.Group():
# gr.Markdown(f"Current in text to Image Mode, ")
# #seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
# with gr.Tab("Image to Image"):
# with gr.Group():
# gr.Markdown(f"Image to Image Mode")
# 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.Column():
image_out = gr.Image(height=512, type="filepath", elem_id="output-img")
error_output = gr.Markdown()
#gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
with gr.Column(elem_id="col-container"):
with gr.Group(elem_id="share-btn-container"):
community_icon = gr.HTML(community_icon_html, visible=False)
loading_icon = gr.HTML(loading_icon_html, visible=False)
share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
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)
#outputs = [gallery, error_output]
run.click(inference, inputs=[model_name, prompt, neg_prompt, guidance, steps, width_input, height_input, seed], outputs=[image_out, error_output, share_button, community_icon, loading_icon])
share_button.click(None, [], [], _js=share_js)
gr.Examples([
[models[0].name, "perfect face portrait of beautiful smile girl, clean face, wears hoody, half body, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 512, 704],
[models[0].name, "portrait of a beautiful fancy gorgeous anime girl, intricate details", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 448, 640],
[models[0].name, "a beautiful perfect face girl, Anime fine details portrait of school girl in front of modern tokyo city landscape on the background deep bokeh, anime masterpiece by studio ghibli, 8k, sharp high quality anime, artstation", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 704, 704],
[models[0].name, "city landscape with fancy car, racing on the road, gopro, intricate details, 4k, cyberpunk", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 704, 704],
[models[0].name, "portrait of liu yifei girl, soldier working in a cyberpunk city, cleavage, intricate, 8k, highly detailed, digital painting, intense, sharp focus", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 704, 704],
[models[0].name, "portrait of a muscular beard male in dgs illustration style, half-body, holding robot arms, strong chest", "out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 512, 640],
[models[1].name, "Portrait of guanyu walking in ancient battlefield, close up shot", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "Portrait of a man holding very cute panda plushie, Guanyu", "", 7, 30, 576, 576],
[models[1].name, "taking selfie on ancient battlefield of China, guanyu, gopro, sharp focus, old scratched photo, three kingdoms warriors everywhere, masterpiece", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "superman, portrait of fancy superhero guanyu, golden spiderman, mech, robot, high tech, shining core, intricate details, 4k", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "batman, portrait of fancy superhero guanyu, golden spiderman, mech, robot, high tech, shining core, intricate details, 4k", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a huge gundam mech fighting against guanyu", "blurry, out of focus", 7, 30, 576, 576],
[models[1].name, "a cute nendoroid figure toy guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a cute funko figure toy guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a lego guanyu", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a gorgeous wuxia girl standing in the palace", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a handsome wuxia man", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
[models[1].name, "a wuxia girl diving underwater, surrounded by a shark", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 20, 576, 576],
[models[1].name, "a cute nendoroid wuxia figure toy", "blurry, out of focus, scary, creepy, evil, disfigured, missing limbs, ugly, gross, missing fingers", 7, 30, 576, 576],
], [model_name, prompt, neg_prompt, guidance, steps, width_input, height_input], outputs=image_out, fn=inference_example, cache_examples=torch.cuda.is_available())
gr.Markdown('''
Models and Space by [@DGSpitzer](https://www.youtube.com/channel/UCzzsYBF4qwtMwJaPJZ5SuPg)❤️ [@大谷的游戏创作小屋](https://space.bilibili.com/176003)
[](https://twitter.com/DGSpitzer)
''')
if not is_colab:
demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab) 