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import gradio as gr | |
import spaces | |
import numpy as np | |
import random | |
from diffusers import DiffusionPipeline | |
import torch | |
from PIL import Image | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
model_repo_id = "stabilityai/stable-diffusion-3.5-large-turbo" | |
torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32 | |
pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype) | |
pipe = pipe.to(device) | |
pipe.load_lora_weights("prithivMLmods/SD3.5-Turbo-Realism-2.0-LoRA", weight_name="SD3.5-Turbo-Realism-2.0-LoRA.safetensors") | |
trigger_word = "Turbo Realism" | |
pipe.fuse_lora(lora_scale=1.0) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 1024 | |
# Define styles | |
style_list = [ | |
{ | |
"name": "3840 x 2160", | |
"prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "2560 x 1440", | |
"prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "HD+", | |
"prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", | |
"negative_prompt": "cartoonish, low resolution, blurry, simplistic, abstract, deformed, ugly", | |
}, | |
{ | |
"name": "Style Zero", | |
"prompt": "{prompt}", | |
"negative_prompt": "", | |
}, | |
] | |
STYLE_NAMES = [style["name"] for style in style_list] | |
DEFAULT_STYLE_NAME = STYLE_NAMES[0] | |
grid_sizes = { | |
"2x1": (2, 1), | |
"1x2": (1, 2), | |
"2x2": (2, 2), | |
"2x3": (2, 3), | |
"3x2": (3, 2), | |
"1x1": (1, 1) | |
} | |
def infer( | |
prompt, | |
negative_prompt="", | |
seed=42, | |
randomize_seed=False, | |
width=1024, | |
height=1024, | |
guidance_scale=7.5, | |
num_inference_steps=10, | |
style="Style Zero", | |
grid_size="1x1", | |
progress=gr.Progress(track_tqdm=True), | |
): | |
selected_style = next(s for s in style_list if s["name"] == style) | |
styled_prompt = selected_style["prompt"].format(prompt=prompt) | |
styled_negative_prompt = selected_style["negative_prompt"] | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
grid_size_x, grid_size_y = grid_sizes.get(grid_size, (1, 1)) | |
num_images = grid_size_x * grid_size_y | |
options = { | |
"prompt": styled_prompt, | |
"negative_prompt": styled_negative_prompt, | |
"guidance_scale": guidance_scale, | |
"num_inference_steps": num_inference_steps, | |
"width": width, | |
"height": height, | |
"generator": generator, | |
"num_images_per_prompt": num_images, | |
} | |
torch.cuda.empty_cache() # Clear GPU memory | |
result = pipe(**options) | |
grid_img = Image.new('RGB', (width * grid_size_x, height * grid_size_y)) | |
for i, img in enumerate(result.images[:num_images]): | |
grid_img.paste(img, (i % grid_size_x * width, i // grid_size_x * height)) | |
return grid_img, seed | |
examples = [ | |
"A tiny astronaut hatching from an egg on the moon, 4k, planet theme", | |
"An anime-style illustration of a delicious, golden-brown wiener schnitzel on a plate, served with fresh lemon slices, parsley --style raw5", | |
"Cold coffee in a cup bokeh --ar 85:128 --v 6.0 --style raw5, 4K, Photo-Realistic", | |
"A cat holding a sign that says hello world --ar 85:128 --v 6.0 --style raw" | |
] | |
css = ''' | |
.gradio-container{max-width: 585px !important} | |
h1{text-align:center} | |
footer { | |
visibility: hidden | |
} | |
''' | |
with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: | |
with gr.Column(elem_id="col-container"): | |
gr.Markdown("## GRID 6X🪨") | |
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, variant="primary") | |
result = gr.Image(label="Result", show_label=False) | |
with gr.Row(visible=True): | |
grid_size_selection = gr.Dropdown( | |
choices=["2x1", "1x2", "2x2", "2x3", "3x2", "1x1"], | |
value="1x1", | |
label="Grid Size" | |
) | |
with gr.Accordion("Advanced Settings", open=False): | |
negative_prompt = gr.Text( | |
label="Negative prompt", | |
max_lines=1, | |
placeholder="Enter a negative prompt", | |
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", | |
visible=False, | |
) | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
with gr.Row(): | |
width = gr.Slider( | |
label="Width", | |
minimum=512, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
height = gr.Slider( | |
label="Height", | |
minimum=512, | |
maximum=MAX_IMAGE_SIZE, | |
step=32, | |
value=1024, | |
) | |
with gr.Row(): | |
guidance_scale = gr.Slider( | |
label="Guidance scale", | |
minimum=0.0, | |
maximum=7.5, | |
step=0.1, | |
value=0.0, | |
) | |
num_inference_steps = gr.Slider( | |
label="Number of inference steps", | |
minimum=1, | |
maximum=50, | |
step=1, | |
value=8, | |
) | |
style_selection = gr.Radio( | |
show_label=True, | |
container=True, | |
interactive=True, | |
choices=STYLE_NAMES, | |
value=DEFAULT_STYLE_NAME, | |
label="Quality Style", | |
) | |
gr.Examples(examples=examples, | |
inputs=[prompt], | |
outputs=[result, seed], | |
fn=infer, | |
cache_examples=False) | |
gr.on( | |
triggers=[run_button.click, prompt.submit], | |
fn=infer, | |
inputs=[ | |
prompt, | |
negative_prompt, | |
seed, | |
randomize_seed, | |
width, | |
height, | |
guidance_scale, | |
num_inference_steps, | |
style_selection, | |
grid_size_selection, | |
], | |
outputs=[result, seed], | |
) | |
if __name__ == "__main__": | |
demo.launch() |