File size: 7,002 Bytes
3230030 0744fc5 3230030 dfbb680 3230030 4fdacdf 3230030 4fdacdf 3230030 0744fc5 3230030 0744fc5 3230030 4fdacdf 3230030 0744fc5 3230030 0744fc5 3230030 0744fc5 3230030 21fe39c 3230030 21fe39c 3230030 21fe39c 0744fc5 3230030 0744fc5 3230030 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 |
import random
import os
import uuid
from datetime import datetime
import gradio as gr
import numpy as np
import spaces
import torch
from diffusers import DiffusionPipeline
from PIL import Image
# Create permanent storage directory
SAVE_DIR = "saved_images" # Gradio will handle the persistence
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR, exist_ok=True)
# Load the default image
DEFAULT_IMAGE_PATH = "cover1.webp"
device = "cuda" if torch.cuda.is_available() else "cpu"
repo_id = "black-forest-labs/FLUX.1-dev"
adapter_id = "alvdansen/pola-photo-flux"
pipeline = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
pipeline.load_lora_weights(adapter_id)
pipeline = pipeline.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
def save_generated_image(image, prompt):
# Generate unique filename with timestamp
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
unique_id = str(uuid.uuid4())[:8]
filename = f"{timestamp}_{unique_id}.png"
filepath = os.path.join(SAVE_DIR, filename)
# Save the image
image.save(filepath)
# Save metadata
metadata_file = os.path.join(SAVE_DIR, "metadata.txt")
with open(metadata_file, "a", encoding="utf-8") as f:
f.write(f"{filename}|{prompt}|{timestamp}\n")
return filepath
def load_generated_images():
if not os.path.exists(SAVE_DIR):
return []
# Load all images from the directory
image_files = [os.path.join(SAVE_DIR, f) for f in os.listdir(SAVE_DIR)
if f.endswith(('.png', '.jpg', '.jpeg', '.webp'))]
# Sort by creation time (newest first)
image_files.sort(key=lambda x: os.path.getctime(x), reverse=True)
return image_files
def load_predefined_images():
# Return empty list since we're not using predefined images
return []
@spaces.GPU(duration=120)
def inference(
prompt: str,
seed: int,
randomize_seed: bool,
width: int,
height: int,
guidance_scale: float,
num_inference_steps: int,
lora_scale: float,
progress: gr.Progress = gr.Progress(track_tqdm=True),
):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
image = pipeline(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
joint_attention_kwargs={"scale": lora_scale},
).images[0]
# Save the generated image
filepath = save_generated_image(image, prompt)
# Return the image, seed, and updated gallery
return image, seed, load_generated_images()
examples = [
"polaroid style, a woman with long blonde hair wearing big round hippie sunglasses with a slight smile, white oversized fur coat, black dress, early evening in the city, polaroid style [trigger]"
]
css = """
footer {
visibility: hidden;
}
"""
with gr.Blocks(theme=gr.themes.Soft(), css=css, analytics_enabled=False) as demo:
gr.HTML('<div class="title"> Polaroid style Image Generation </div>')
with gr.Tabs() as tabs:
with gr.Tab("Generation"):
with gr.Column(elem_id="col-container"):
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)
# Modified to include the default image
result = gr.Image(
label="Result",
show_label=False,
value=DEFAULT_IMAGE_PATH # Set the default image
)
with gr.Accordion("Advanced Settings", open=False):
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=42,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
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=768,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=10.0,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=30,
)
lora_scale = gr.Slider(
label="LoRA scale",
minimum=0.0,
maximum=1.0,
step=0.1,
value=1.0,
)
gr.Examples(
examples=examples,
inputs=[prompt],
outputs=[result, seed],
)
with gr.Tab("Gallery"):
gallery_header = gr.Markdown("### Generated Images Gallery")
generated_gallery = gr.Gallery(
label="Generated Images",
columns=6,
show_label=False,
value=load_generated_images(),
elem_id="generated_gallery",
height="auto"
)
refresh_btn = gr.Button("🔄 Refresh Gallery")
# Event handlers
def refresh_gallery():
return load_generated_images()
refresh_btn.click(
fn=refresh_gallery,
inputs=None,
outputs=generated_gallery,
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=inference,
inputs=[
prompt,
seed,
randomize_seed,
width,
height,
guidance_scale,
num_inference_steps,
lora_scale,
],
outputs=[result, seed, generated_gallery],
)
demo.queue()
demo.launch() |