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df83e9f | 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 | import gradio as gr
import torch
from diffusers import StableDiffusionPipeline
from peft import PeftModel
import os
from PIL import Image
import random
class LoRAWebInterface:
def __init__(self, base_model="runwayml/stable-diffusion-v1-5", lora_path="models/lora_model"):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.lora_path = lora_path
print("Loading models...")
# Load base pipeline
self.pipeline = StableDiffusionPipeline.from_pretrained(
base_model,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
safety_checker=None,
requires_safety_checker=False
)
# Load LoRA weights if they exist
if os.path.exists(lora_path):
print(f"Loading LoRA model from {lora_path}")
try:
self.pipeline.unet = PeftModel.from_pretrained(self.pipeline.unet, lora_path)
self.lora_loaded = True
except Exception as e:
print(f"Error loading LoRA: {e}")
self.lora_loaded = False
else:
print("No LoRA model found, using base model")
self.lora_loaded = False
self.pipeline.to(self.device)
# Enable memory efficient attention
try:
self.pipeline.enable_xformers_memory_efficient_attention()
except:
pass
print("Model loaded successfully!")
def generate_image(self, prompt, negative_prompt, num_steps, guidance_scale,
width, height, seed, use_random_seed):
"""Generate image with given parameters"""
if use_random_seed:
seed = random.randint(0, 999999)
if seed is not None and seed >= 0:
torch.manual_seed(int(seed))
try:
with torch.autocast(self.device.type):
image = self.pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=int(num_steps),
guidance_scale=guidance_scale,
width=int(width),
height=int(height)
).images[0]
return image, f"✅ Generated successfully! Seed: {seed}"
except Exception as e:
error_msg = f"❌ Error generating image: {str(e)}"
print(error_msg)
# Return a blank image on error
blank_image = Image.new('RGB', (512, 512), color='white')
return blank_image, error_msg
def create_interface(self):
"""Create Gradio interface"""
with gr.Blocks(title="LoRA Image Generator", theme=gr.themes.Soft()) as interface:
gr.Markdown("# 🎨 LoRA Image Generator")
gr.Markdown(f"**Model Status:** {'✅ LoRA model loaded' if self.lora_loaded else '⚠️ Using base model only'}")
with gr.Row():
with gr.Column(scale=1):
# Input controls
prompt = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
value="a beautiful artistic composition",
lines=3
)
negative_prompt = gr.Textbox(
label="Negative Prompt (Optional)",
placeholder="Things you don't want in the image...",
value="blurry, low quality, distorted",
lines=2
)
with gr.Row():
num_steps = gr.Slider(
minimum=10,
maximum=100,
value=50,
step=5,
label="Inference Steps"
)
guidance_scale = gr.Slider(
minimum=1.0,
maximum=20.0,
value=7.5,
step=0.5,
label="Guidance Scale"
)
with gr.Row():
width = gr.Slider(
minimum=256,
maximum=1024,
value=512,
step=64,
label="Width"
)
height = gr.Slider(
minimum=256,
maximum=1024,
value=512,
step=64,
label="Height"
)
with gr.Row():
seed = gr.Number(
label="Seed (-1 for random)",
value=-1,
precision=0
)
use_random_seed = gr.Checkbox(
label="Use Random Seed",
value=True
)
generate_btn = gr.Button("🎨 Generate Image", variant="primary")
with gr.Column(scale=1):
# Output
output_image = gr.Image(
label="Generated Image",
type="pil",
height=512
)
status_text = gr.Textbox(
label="Status",
interactive=False,
lines=2
)
# Example prompts
gr.Markdown("## 💡 Example Prompts")
example_prompts = [
"a serene landscape in artistic style",
"abstract flowing patterns with vibrant colors",
"geometric composition with soft lighting",
"organic forms inspired by nature",
"minimalist design with elegant curves"
]
examples = gr.Examples(
examples=[[prompt] for prompt in example_prompts],
inputs=[prompt],
label="Click an example to try:"
)
# Event handlers
generate_btn.click(
fn=self.generate_image,
inputs=[prompt, negative_prompt, num_steps, guidance_scale,
width, height, seed, use_random_seed],
outputs=[output_image, status_text]
)
# Auto-disable seed input when random is selected
use_random_seed.change(
fn=lambda x: gr.update(interactive=not x),
inputs=[use_random_seed],
outputs=[seed]
)
return interface
def launch(self, share=False, server_port=7860):
"""Launch the interface"""
interface = self.create_interface()
interface.launch(
share=share,
server_port=server_port,
inbrowser=True
)
def main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--lora_path", default="models/lora_model", help="Path to LoRA model")
parser.add_argument("--share", action="store_true", help="Create public link")
parser.add_argument("--port", type=int, default=7860, help="Server port")
args = parser.parse_args()
# Create and launch interface
interface = LoRAWebInterface(lora_path=args.lora_path)
interface.launch(share=args.share, server_port=args.port)
if __name__ == "__main__":
main() |