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import os
import random
import uuid
import gradio as gr
import numpy as np
from PIL import Image
import spaces
import torch
from diffusers import StableDiffusionPipeline, EulerAncestralDiscreteScheduler
DESCRIPTION = """
# DALL XL v2
"""
def save_image(img):
unique_name = str(uuid.uuid4()) + ".png"
img.save(unique_name)
return unique_name
def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
if randomize_seed:
seed = random.randint(0, MAX_SEED)
return seed
MAX_SEED = np.iinfo(np.int32).max
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
USE_TORCH_COMPILE = 0
ENABLE_CPU_OFFLOAD = 0
if torch.cuda.is_available():
pipe = StableDiffusionXLPipeline.from_pretrained(
"fluently/Fluently-XL-v2",
torch_dtype=torch.float16,
use_safetensors=True,
)
else:
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float32,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
def generate(
prompt: str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 0,
width: int = 512,
height: int = 512,
guidance_scale: float = 3,
randomize_seed: bool = False,
progress=gr.Progress(track_tqdm=True),
):
seed = int(randomize_seed_fn(seed, randomize_seed))
if not use_negative_prompt:
negative_prompt = "" # type: ignore
images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=25,
num_images_per_prompt=1,
output_type="pil",
).images
image_paths = [save_image(img) for img in images]
print(image_paths)
return image_paths, seed
examples = [
"neon holography crystal cat",
"a cat eating a piece of cheese",
"an astronaut riding a horse in space",
"a cartoon of a boy playing with a tiger",
"a cute robot artist painting on an easel, concept art",
"a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
]
css = '''
.gradio-container{max-width: 560px !important}
h1{text-align:center}
footer {
visibility: hidden
}
'''
with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=False,
)
with gr.Group():
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
with gr.Accordion("Advanced options", open=False):
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
negative_prompt = gr.Text(
label="Negative prompt",
lines=4,
max_lines=6,
value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
placeholder="Enter a negative prompt",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
visible=True
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row(visible=True):
width = gr.Slider(
label="Width",
minimum=512,
maximum=1024,
step=8,
value=512,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=1024,
step=8,
value=512,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=20.0,
step=0.1,
value=6,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=[result, seed],
fn=generate,
cache_examples=False,
)
use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=use_negative_prompt,
outputs=negative_prompt,
api_name=False,
)
gr.on(
triggers=[
prompt.submit,
negative_prompt.submit,
run_button.click,
],
fn=generate,
inputs=[
prompt,
negative_prompt,
use_negative_prompt,
seed,
width,
height,
guidance_scale,
randomize_seed,
],
outputs=[result, seed],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch(show_api=False, debug=False) |