File size: 6,429 Bytes
63488fd 8609e3f 63488fd 8609e3f 63488fd 8609e3f 63488fd 8609e3f 63488fd fe9ee70 63488fd 78b17b2 61c0c53 78b17b2 f2dfa3d 61c0c53 8609e3f f2dfa3d 78b17b2 8609e3f 63488fd fe9ee70 63488fd 61c0c53 63488fd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
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 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]
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>
Please use this prompt template to get the desired generation results:<br>
<h2><b>Prompt</b>:</h2>
RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<br>
<i>Example: <details> 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 </details> </i>
<br>
<h2><b>Negative Prompt</b>:</h2>
(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
<br>
<b>Settings</b><br>
-Steps : 25 ,CFG Scale : 7
Have Fun & Enjoy
//THAFX<br>
{"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" 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()
|