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from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
import gradio as gr
import torch
from PIL import Image
model_name = '$model_name'
prefix = '$prefix'
scheduler = DPMSolverMultistepScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
num_train_timesteps=1000,
trained_betas=None,
predict_epsilon=True,
thresholding=False,
algorithm_type="dpmsolver++",
solver_type="midpoint",
lower_order_final=True,
)
last_mode = "txt2img"
def get_pipe(img2img=False):
if img2img:
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else 'auto',
scheduler=scheduler)
else:
pipe = StableDiffusionPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else 'auto',
scheduler=scheduler)
if torch.cuda.is_available():
pipe = pipe.to("cuda")
return pipe
pipe = get_pipe()
device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
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=True):
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):
global last_mode
global pipe
if last_mode != "txt2img":
pipe = get_pipe()
last_mode = "txt2img"
prompt = prompt
result = pipe(
prompt,
negative_prompt = neg_prompt,
num_inference_steps = int(steps),
guidance_scale = guidance,
width = width,
height = height,
generator = generator)
return replace_nsfw_images(result)
def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
global last_mode
global pipe
if last_mode != "img2img":
pipe = get_pipe(img2img=True)
last_mode = "img2img"
prompt = 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,
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 = """.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>$model_name</h1>
</div>
<p>
Demo for the <a href="https://huggingface.co/$model_name">$model_name</a> Stable Diffusion model.<br>
Add the following tokens to your prompts for the effect: <b>$prefix</b>.
</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=55):
with gr.Group():
with gr.Row(variant='box'):
prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
generate = gr.Button(value="Generate")
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 ($prefix)", value=True)
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)
gr.HTML("""
<div style="border-top: 1px solid #303030;">
<br>
<p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
</div>
""")
demo.queue(concurrency_count=1)
demo.launch()
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