anzorq's picture
set concurrency_count to 1
9cc9b11
raw
history blame
11.8 kB
from diffusers import AutoencoderKL, UNet2DConditionModel
from diffusers import StableDiffusionPipeline
from diffusers import StableDiffusionImg2ImgPipeline
import gradio as gr
import torch
from PIL import Image
import utils
is_colab = utils.is_google_colab()
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("Custom model", "", ""),
Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
# Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
# Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
# Model("Modern Disney", "nitrosocke/modern-disney-diffusion", "modern disney style "),
# Model("Classic Disney", "nitrosocke/classic-anim-diffusion", ""),
# Model("Waifu", "hakurei/waifu-diffusion", ""),
# Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""),
# Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""),
# Model("Robo Diffusion", "nousr/robo-diffusion", ""),
# Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
# Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy")
]
last_mode = "txt2img"
current_model = models[1]
current_model_path = current_model.path
# pipe = None
if is_colab:
pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16)
else: # download all models
vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae", torch_dtype=torch.float16)
for model in models[1:]:
try:
unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet", torch_dtype=torch.float16)
model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16)
model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, torch_dtype=torch.float16)
except:
models.remove(model)
pipe = models[1].pipe_t2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
def custom_model_changed(path):
models[0].path = path
global current_model
current_model = models[0]
def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
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
if img is not None:
return img_to_img(model_path, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator)
else:
return txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator)
def txt_to_img(model_path, prompt, neg_prompt, guidance, steps, width, height, generator=None):
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 == models[0]:
pipe = StableDiffusionPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
else:
# pipe = pipe.to("cpu")
pipe.to("cpu")
pipe = current_model.pipe_t2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
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, generator=None):
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 == models[0]:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model_path, torch_dtype=torch.float16)
else:
# pipe = pipe.to("cpu")
pipe.to("cpu")
pipe = current_model.pipe_i2i
if torch.cuda.is_available():
pipe = pipe.to("cuda")
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,
init_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):
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 = """
<style>
.finetuned-diffusion-div {
text-align: center;
max-width: 700px;
margin: 0 auto;
}
.finetuned-diffusion-div div {
display: inline-flex;
align-items: center;
gap: 0.8rem;
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%;
}
.finetuned-diffusion-div p a {
text-decoration: underline;
}
.tabs {
margin-top: 0px;
margin-bottom: 0px;
}
#gallery {
min-height: 20rem;
}
</style>
"""
with gr.Blocks(css=css) as demo:
gr.HTML(
f"""
<div class="finetuned-diffusion-div">
<div>
<h1>Finetuned Diffusion</h1>
</div>
<p>
Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
<a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spiderverse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokemon</a>, <a href="https://huggingface.co/yuk/fuyuko-waifu-diffusion">Fuyuko Waifu</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony</a>, <a href="https://huggingface.co/sd-dreambooth-library/herge-style">Hergé (Tintin)</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a> + any other custom Diffusers 🧨 SD model hosted on HuggingFace 🤗.
</p>
<p>Don't want to wait in queue? <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", visible=False, interactive=True)
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
image_out = gr.Image(height=512)
# gallery = gr.Gallery(
# label="Generated images", show_label=False, elem_id="gallery"
# ).style(grid=[1], height="auto")
with gr.Column(scale=45):
with gr.Tab("Options"):
with gr.Group():
neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
# 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=50, minimum=2, maximum=100, 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)
seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
with gr.Tab("Image to image"):
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)
model_name.change(lambda x: gr.update(visible = x == models[0].name), inputs=model_name, outputs=custom_model_path)
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)
inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
prompt.submit(inference, inputs=inputs, outputs=image_out)
generate.click(inference, inputs=inputs, outputs=image_out)
# ex = gr.Examples([
# [models[1].name, "jason bateman disassembling the demon core", 7.5, 50],
# [models[4].name, "portrait of dwayne johnson", 7.0, 75],
# [models[5].name, "portrait of a beautiful alyx vance half life", 10, 50],
# [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45],
# [models[5].name, "fantasy portrait painting, digital art", 4.0, 30],
# ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=False)#not is_colab and torch.cuda.is_available())
# ex.dataset.headers = [""]
gr.Markdown('''
Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br>
Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe)
![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion)
''')
if not is_colab:
demo.queue(concurrency_count=1)
demo.launch(debug=is_colab, share=is_colab)