|
|
|
import os |
|
import random |
|
import uuid |
|
import gradio as gr |
|
import numpy as np |
|
from PIL import Image |
|
import spaces |
|
from typing import Tuple |
|
import torch |
|
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler |
|
|
|
|
|
DESCRIPTION = """# InterDiffusion-3.8 |
|
### [https://huggingface.co/cutycat2000x/InterDiffusion-3.8](https://huggingface.co/cutycat2000x/InterDiffusion-3.8)""" |
|
|
|
|
|
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 may not 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( |
|
"cutycat2000x/InterDiffusion-3.8", |
|
torch_dtype=torch.float16, |
|
use_safetensors=True, |
|
) |
|
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) |
|
pipe.load_lora_weights("cutycat2000x/LoRA", weight_name="lora.safetensors", adapter_name="adapt") |
|
pipe.set_adapters("adapt") |
|
pipe.to("cuda") |
|
|
|
|
|
|
|
|
|
|
|
style_list = [ |
|
{ |
|
"name": "(LoRA)", |
|
"prompt": "{prompt}", |
|
"negative_prompt": "", |
|
}, |
|
|
|
|
|
] |
|
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list} |
|
STYLE_NAMES = list(styles.keys()) |
|
DEFAULT_STYLE_NAME = "(LoRA)" |
|
|
|
def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]: |
|
p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME]) |
|
if not negative: |
|
negative = "" |
|
return p.replace("{prompt}", positive), n + negative |
|
|
|
@spaces.GPU(enable_queue=True) |
|
def generate( |
|
prompt: str, |
|
negative_prompt: str = "", |
|
style: str = DEFAULT_STYLE_NAME, |
|
use_negative_prompt: bool = False, |
|
num_inference_steps: int = 30, |
|
num_images_per_prompt: int = 2, |
|
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 = "" |
|
prompt, negative_prompt = apply_style(style, prompt, negative_prompt) |
|
|
|
images = pipe( |
|
prompt=prompt, |
|
negative_prompt=negative_prompt, |
|
width=width, |
|
height=height, |
|
guidance_scale=guidance_scale, |
|
num_inference_steps=num_inference_steps, |
|
num_images_per_prompt=num_images_per_prompt, |
|
cross_attention_kwargs={"scale": 0.65}, |
|
output_type="pil", |
|
).images |
|
image_paths = [save_image(img) for img in images] |
|
print(image_paths) |
|
return image_paths, seed |
|
|
|
examples = [ |
|
'a smiling girl with sparkles in her eyes, walking in a garden, in the morning --style anime', |
|
'firewatch landscape, Graphic Novel, Pastel Art, Poster, Golden Hour, Electric Colors, 4k, RGB, Geometric, Volumetric, Lumen Global Illumination, Ray Tracing Reflections, Twisted Rays, Glowing Edges, RTX --raw', |
|
'Samsung Galaxy S9', |
|
'cat, 4k, 8k, hyperrealistic, realistic, High-resolution, unreal engine 5, rtx, 16k, taken on a sony camera, Cinematic, dramatic lighting', |
|
'cinimatic closeup of burning skull', |
|
'frozen elsa', |
|
'A rainbow tree, anime style, tree in focus', |
|
'A cat holding a sign that reads "Hello World" in cursive text', |
|
'A birthday card for "Meow"' |
|
] |
|
|
|
css = ''' |
|
.gradio-container{max-width: 560px !important} |
|
h1{text-align:center} |
|
footer { |
|
visibility: hidden |
|
} |
|
''' |
|
|
|
with gr.Blocks(css=css, theme="xiaobaiyuan/theme_brief") 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") |
|
result = gr.Gallery(label="Result", columns=1, preview=True) |
|
with gr.Accordion("Advanced options", open=False): |
|
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False, visible=True) |
|
negative_prompt = gr.Text( |
|
label="Negative prompt", |
|
max_lines=1, |
|
placeholder="Enter a negative prompt", |
|
visible=True, |
|
) |
|
with gr.Row(): |
|
num_inference_steps = gr.Slider( |
|
label="Steps", |
|
minimum=10, |
|
maximum=60, |
|
step=1, |
|
value=30, |
|
) |
|
with gr.Row(): |
|
num_images_per_prompt = gr.Slider( |
|
label="Images", |
|
minimum=1, |
|
maximum=5, |
|
step=1, |
|
value=2, |
|
) |
|
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, |
|
) |
|
with gr.Row(visible=True): |
|
style_selection = gr.Radio( |
|
show_label=True, |
|
container=True, |
|
interactive=True, |
|
choices=STYLE_NAMES, |
|
value=DEFAULT_STYLE_NAME, |
|
label="Image Style", |
|
) |
|
|
|
|
|
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, |
|
style_selection, |
|
use_negative_prompt, |
|
num_inference_steps, |
|
num_images_per_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) |
