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 StableDiffusion3Pipeline, DPMSolverMultistepScheduler, AutoencoderKL, StableDiffusion3Img2ImgPipeline
from huggingface_hub import snapshot_download
# Ctrl+F for "spaces" to use with ZeroGPU
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
model_path = snapshot_download(
repo_id="stabilityai/stable-diffusion-3-medium",
revision="refs/pr/26",
repo_type="model",
ignore_patterns=["*.md", "*..gitattributes"],
local_dir="stable-diffusion-3-medium",
token=huggingface_token, # type a new token-id.
)
DESCRIPTION = """# Stable Diffusion 3"""
if not torch.cuda.is_available():
DESCRIPTION += "\nRunning on CPU 🥶 This demo may not work on CPU.
"
MAX_SEED = np.iinfo(np.int32).max
CACHE_EXAMPLES = False
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
USE_TORCH_COMPILE = False
ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
def load_pipeline(pipeline_type):
if pipeline_type == "text2img":
return StableDiffusion3Pipeline.from_pretrained(model_path, torch_dtype=torch.float16)
elif pipeline_type == "img2img":
return StableDiffusion3Img2ImgPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
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
# @spaces.GPU
def generate(
prompt:str,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 0,
width: int = 1024,
height: int = 1024,
guidance_scale: float = 7,
randomize_seed: bool = False,
num_inference_steps=30,
NUM_IMAGES_PER_PROMPT=1,
use_resolution_binning: bool = True,
progress=gr.Progress(track_tqdm=True),
):
pipe = load_pipeline("text2img")
pipe.to(device)
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
output_type="battery",
).images
return output
# @spaces.GPU
def img2img_generate(
prompt:str,
init_image: gr.Image,
negative_prompt: str = "",
use_negative_prompt: bool = False,
seed: int = 0,
guidance_scale: float = 7,
randomize_seed: bool = False,
num_inference_steps=30,
strength: float = 0.8,
NUM_IMAGES_PER_PROMPT=1,
use_resolution_binning: bool = True,
progress=gr.Progress(track_tqdm=True),
):
pipe = load_pipeline("img2img")
pipe.to(device)
seed = int(randomize_seed_fn(seed, randomize_seed))
generator = torch.Generator().manual_seed(seed)
if not use_negative_prompt:
negative_prompt = None # type: ignore
init_image = init_image.resize((768, 768))
output = pipe(
prompt=prompt,
image=init_image,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
generator=generator,
strength=strength,
num_images_per_prompt=NUM_IMAGES_PER_PROMPT,
output_type="battery",
).images
return output
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: 1000px !important}
h1{text-align:center}
'''
with gr.Blocks(css=css, theme="Nymbo/Nymbo_Theme") as demo:
with gr.Row():
with gr.Column():
gr.HTML(
"""
Stable Diffusion 3
"""
)
gr.HTML(
"""
"""
)
with gr.Tabs():
with gr.TabItem("Text to Image"):
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", elem_id="gallery", show_label=False)
with gr.Accordion("Advanced options", open=False):
with gr.Row():
use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
value = "deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation",
visible=True,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
steps = gr.Slider(
label="Steps",
minimum=0,
maximum=60,
step=1,
value=25,
)
number_image = gr.Slider(
label="Number of Images",
minimum=1,
maximum=4,
step=1,
value=1,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row(visible=True):
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=10,
step=0.1,
value=7.0,
)
gr.Examples(
examples=examples,
inputs=prompt,
outputs=[result],
fn=generate,
cache_examples=CACHE_EXAMPLES,
)
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,
steps,
number_image,
],
outputs=[result],
api_name="run",
)
with gr.TabItem("Image to Image"):
with gr.Group():
with gr.Row(equal_height=True):
with gr.Column(scale=1):
img2img_prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
init_image = gr.Image(label="Input Image", type="pil")
with gr.Row():
img2img_run_button = gr.Button("Generate", variant="primary")
with gr.Column(scale=1):
img2img_output = gr.Gallery(label="Result", elem_id="gallery")
with gr.Accordion("Advanced options", open=False):
with gr.Row():
img2img_use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
img2img_negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
value="deformed, distorted, disfigured, poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, mutated hands and fingers, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, NSFW",
visible=True,
)
img2img_seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
img2img_steps = gr.Slider(
label="Steps",
minimum=0,
maximum=60,
step=1,
value=25,
)
img2img_number_image = gr.Slider(
label="Number of Images",
minimum=1,
maximum=4,
step=1,
value=1,
)
img2img_randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
img2img_guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=0.1,
maximum=10,
step=0.1,
value=7.0,
)
strength = gr.Slider(label="Img2Img Strength", minimum=0.0, maximum=1.0, step=0.01, value=0.8)
img2img_use_negative_prompt.change(
fn=lambda x: gr.update(visible=x),
inputs=img2img_use_negative_prompt,
outputs=img2img_negative_prompt,
api_name=False,
)
gr.on(
triggers=[
img2img_prompt.submit,
img2img_negative_prompt.submit,
img2img_run_button.click,
],
fn=img2img_generate,
inputs=[
img2img_prompt,
init_image,
img2img_negative_prompt,
img2img_use_negative_prompt,
img2img_seed,
img2img_guidance_scale,
img2img_randomize_seed,
img2img_steps,
strength,
img2img_number_image,
],
outputs=[img2img_output],
api_name="img2img_run",
)
if __name__ == "__main__":
demo.queue().launch()