import gradio as gr
import torch
from diffusers import AutoPipelineForText2Image
from huggingface_hub import hf_hub_download
import os
from utils import load_lora_weights, get_available_loras, generate_image
# Global model cache
pipeline = None
current_lora = None
def load_model():
"""Load the Magic-Wan-Image-V2 model"""
global pipeline
if pipeline is None:
print("Loading Magic-Wan-Image-V2 model...")
pipeline = AutoPipelineForText2Image.from_pretrained(
"wikeeyang/Magic-Wan-Image-V2",
torch_dtype=torch.float16,
variant="fp16"
)
if torch.cuda.is_available():
pipeline = pipeline.to("cuda")
print("Model loaded successfully!")
return pipeline
def generate(
prompt,
negative_prompt,
lora_name,
lora_scale,
width,
height,
num_inference_steps,
guidance_scale,
seed,
randomize_seed
):
"""Generate image from text prompt with optional LoRA"""
global pipeline, current_lora
try:
# Load model if not already loaded
pipe = load_model()
# Handle LoRA loading
if lora_name and lora_name != "None":
if current_lora != lora_name:
# Unload previous LoRA if exists
if current_lora:
pipe.unload_lora_weights()
# Load new LoRA
lora_path = get_available_loras().get(lora_name)
if lora_path:
load_lora_weights(pipe, lora_path)
current_lora = lora_name
print(f"Loaded LoRA: {lora_name}")
# Set LoRA scale
if hasattr(pipe, 'set_adapters'):
pipe.set_adapters(["default"], [lora_scale])
else:
# Unload LoRA if "None" selected
if current_lora:
pipe.unload_lora_weights()
current_lora = None
# Handle seed
if randomize_seed:
seed = torch.randint(0, 2**32 - 1, (1,)).item()
generator = torch.Generator(device=pipeline.device).manual_seed(seed) if seed != -1 else None
# Generate image
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt if negative_prompt else None,
width=width,
height=height,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=generator,
).images[0]
return image, str(seed)
except Exception as e:
raise gr.Error(f"Generation failed: {str(e)}")
# Get available LoRAs
available_loras = get_available_loras()
lora_choices = ["None"] + list(available_loras.keys())
# Create Gradio 6 app with modern theme
with gr.Blocks() as demo:
# Header with anycoder link
gr.HTML("""
🎨 Magic Wan Image V2 - Text to Image
Generate stunning images from text prompts with LoRA support
Built with anycoder 🚀
""")
with gr.Row():
# Left column - Controls
with gr.Column(scale=1):
gr.Markdown("### 📝 Prompt")
prompt = gr.Textbox(
label="Prompt",
placeholder="Describe the image you want to generate...",
lines=3,
value="A beautiful sunset over mountains, highly detailed, 8k"
)
negative_prompt = gr.Textbox(
label="Negative Prompt (Optional)",
placeholder="What to avoid in the image...",
lines=2,
value="blurry, low quality, distorted"
)
gr.Markdown("### 🎭 LoRA Style")
lora_dropdown = gr.Dropdown(
choices=lora_choices,
value="None",
label="Select LoRA Adapter",
info="Choose a style adapter or leave as None for base model"
)
lora_scale = gr.Slider(
minimum=0.0,
maximum=2.0,
value=1.0,
step=0.1,
label="LoRA Scale",
info="Strength of LoRA effect (0.0 = no effect)"
)
gr.Markdown("### ⚙️ Generation Settings")
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():
num_inference_steps = gr.Slider(
minimum=10,
maximum=100,
value=30,
step=1,
label="Inference Steps"
)
guidance_scale = gr.Slider(
minimum=1.0,
maximum=20.0,
value=7.5,
step=0.5,
label="Guidance Scale"
)
gr.Markdown("### 🎲 Seed Settings")
with gr.Row():
seed = gr.Number(
value=-1,
label="Seed (-1 for random)",
precision=0
)
randomize_seed = gr.Checkbox(
value=True,
label="Randomize Seed"
)
generate_btn = gr.Button(
"🚀 Generate Image",
variant="primary",
size="lg"
)
# Right column - Output
with gr.Column(scale=1):
gr.Markdown("### 🖼️ Generated Image")
output_image = gr.Image(
label="Generated Image",
type="pil",
height=512
)
seed_output = gr.Textbox(
label="Used Seed",
interactive=False
)
gr.Markdown("### 💡 Tips")
gr.Markdown("""
- **Prompt**: Be descriptive and specific
- **LoRA**: Try different style adapters for unique looks
- **Steps**: More steps = better quality but slower
- **Guidance**: Higher = more prompt adherence
- **Seed**: Use same seed for reproducible results
""")
# Examples section
gr.Markdown("### 📚 Examples")
examples = gr.Examples(
examples=[
[
"A cyberpunk city at night, neon lights, rain, highly detailed",
"blurry, low quality",
"None",
1.0,
512,
512,
30,
7.5,
-1,
True
],
[
"Portrait of a fantasy elf warrior, intricate armor, forest background",
"deformed, ugly, bad anatomy",
"None",
1.0,
512,
768,
30,
7.5,
-1,
True
],
[
"Magical library with floating books, mystical atmosphere, warm lighting",
"dark, scary",
"None",
1.0,
768,
512,
30,
7.5,
-1,
True
],
[
"Steampunk airship flying through clouds, detailed mechanical parts",
"modern, electronic",
"None",
1.0,
512,
512,
30,
7.5,
-1,
True
],
],
inputs=[
prompt,
negative_prompt,
lora_dropdown,
lora_scale,
width,
height,
num_inference_steps,
guidance_scale,
seed,
randomize_seed
],
label="Click an example to load settings"
)
# Connect generate button
generate_btn.click(
fn=generate,
inputs=[
prompt,
negative_prompt,
lora_dropdown,
lora_scale,
width,
height,
num_inference_steps,
guidance_scale,
seed,
randomize_seed
],
outputs=[output_image, seed_output],
api_visibility="public"
)
# Footer
gr.HTML("""
""")
# Launch with Gradio 6 syntax - theme goes in launch(), not Blocks!
if __name__ == "__main__":
demo.launch(
theme=gr.themes.Soft(
primary_hue="indigo",
secondary_hue="purple",
neutral_hue="slate",
font=gr.themes.GoogleFont("Inter"),
text_size="md",
spacing_size="md",
radius_size="md"
).set(
button_primary_background_fill="*primary_600",
button_primary_background_fill_hover="*primary_700",
block_title_text_weight="600",
),
footer_links=[
{"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"},
{"label": "Model Card", "url": "https://huggingface.co/wikeeyang/Magic-Wan-Image-V2"},
"api"
],
allow_flagging="never"
)