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#!/usr/bin/env python
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, StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
DESCRIPTION = """
# DALL•E 3 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 not work on CPU.</p>"
MAX_SEED = np.iinfo(np.int32).max
USE_TORCH_COMPILE = 0
ENABLE_CPU_OFFLOAD = 0
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
pipe = StableDiffusionPipeline.from_pretrained(
"fluently/Fluently-XL-v2",
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)
pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
pipe.set_adapters("dalle")
@spaces.GPU(enable_queue=True)
def generate(
prompt: str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
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_3_5(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=25,
num_images_per_prompt=1,
cross_attention_kwargs={"scale": 0.8},
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) 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=False)
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""",
placeholder="Enter a negative prompt",
visible=False,
)
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=2048,
step=8,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=512,
maximum=2048,
step=8,
value=1024,
)
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)