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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
model_id = 'SG161222/Realistic_Vision_V1.3'
prefix = 'RAW photo,'
scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
scheduler=scheduler)
pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
scheduler=scheduler)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
pipe_i2i = pipe_i2i.to("cuda")
def error_str(error, title="Error"):
return f"""#### {title}
{error}""" if error else ""
def _parse_args(prompt, generator):
parser = argparse.ArgumentParser(
description="making it work."
)
parser.add_argument(
"--no-half-vae", help="no half vae"
)
cmdline_args = parser.parse_args()
command = cmdline_args.command
conf_file = cmdline_args.conf_file
conf_args = Arguments(conf_file)
opt = conf_args.readArguments()
if cmdline_args.config_overrides:
for config_override in cmdline_args.config_overrides.split(";"):
config_override = config_override.strip()
if config_override:
var_val = config_override.split("=")
assert (
len(var_val) == 2
), f"Config override '{var_val}' does not have the form 'VAR=val'"
conf_args.add_opt(opt, var_val[0], var_val[1], force_override=True)
def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
prompt = f"{prefix} {prompt}" if auto_prefix else prompt
try:
if img is not None:
return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
else:
return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
except Exception as e:
return None, error_str(e)
def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
result = pipe(
prompt,
negative_prompt = neg_prompt,
num_inference_steps = int(steps),
guidance_scale = guidance,
width = width,
height = height,
generator = generator)
return result.images[0]
def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
ratio = min(height / img.height, width / img.width)
img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
result = pipe_i2i(
prompt,
negative_prompt = neg_prompt,
init_image = img,
num_inference_steps = int(steps),
strength = strength,
guidance_scale = guidance,
width = width,
height = height,
generator = generator)
return result.images[0]
def fake_safety_checker(images, **kwargs):
return result.images[0], [False] * len(images)
pipe.safety_checker = fake_safety_checker
css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-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:
gr.HTML(
f"""
<div class="main-div">
<div>
<h1>Realistic Vision V1.3</h1>
</div>
<p>
Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V1.3">Realistic Vision V1.3</a> Stable Diffusion model by "Eugene".<br><br>
Please use this prompt template to get the desired generation results:<br>
<h2><b>Prompt</b>:</h2>
<details>
RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<br><br>
<i>Example: RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
</i>
</details>
<h2><b>Negative Prompt</b>:</h2>
<details>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck
</details>
<br>
Have Fun & Enjoy
//THAFX<br>
{"" if prefix else ""}
</p>
Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🔥</b>"}.
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
with gr.Row():
prompt = gr.Textbox(label="Prompt", show_label=False,max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
image_out = gr.Image(height=512)
error_output = gr.Markdown()
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")
auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically (RAW photo,)", value=prefix, visible=prefix)
with gr.Row():
guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
steps = gr.Slider(label="Steps", value=25, 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)
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)
auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
outputs = [image_out, error_output]
prompt.submit(inference, inputs=inputs, outputs=outputs)
generate.click(inference, inputs=inputs, outputs=outputs)
demo.queue(concurrency_count=1)
demo.launch()
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