from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
model_id = 'SG161222/Realistic_Vision_V5.0_noVAE'
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 style="color:orange;">📷 Realistic Vision V5.0 📸</h1>
</div>
<p>
Demo for <a href="https://huggingface.co/SG161222/Realistic_Vision_V5.0_noVAE">Realistic Vision V5.0</a>
Stable Diffusion model by <a href="https://huggingface.co/SG161222/"><abbr title="SG1611222">Eugene</abbr></a>. {"" if prefix else ""}
Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU ⚡</b>"}.
</p>
<p>Please use the prompt template below to get an example of the desired generation results:
</p>
<b>Prompt</b>:
<details><code>
* subject *, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<br>
<br>
<q><i>
Example: a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, <br>
(high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
</i></q>
</code></details>
<br>
<b>Negative Prompt</b>:
<details><code>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, <br>
low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, <br>
dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, <br>
extra legs, fused fingers, too many fingers, long neck
</code></details>
<br>
Have Fun & Enjoy âš¡ <a href="https://www.thafx.com"><abbr title="Website">//THAFX</abbr></a>
<br>
</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=5, maximum=15)
steps = gr.Slider(label="Steps", value=20, 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()