File size: 10,468 Bytes
e009c22
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
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("""
    <div style="text-align: center; margin-bottom: 20px;">
        <h1>🎨 Magic Wan Image V2 - Text to Image</h1>
        <p>Generate stunning images from text prompts with LoRA support</p>
        <p style="font-size: 14px;">
            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="color: #FF9D00; text-decoration: none;">
                <strong>Built with anycoder</strong> πŸš€
            </a>
        </p>
    </div>
    """)
    
    with gr.Row():
        # Left column - Controls
        with gr.Column(scale=1):
            gr.Markdown("### πŸ“ 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("### βš™οΈ 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(
                "πŸš€ Generate Image",
                variant="primary",
                size="lg"
            )
        
        # Right column - Output
        with gr.Column(scale=1):
            gr.Markdown("### πŸ–ΌοΈ Generated Image")
            output_image = gr.Image(
                label="Generated Image",
                type="pil",
                height=512
            )
            
            seed_output = gr.Textbox(
                label="Used Seed",
                interactive=False
            )
            
            gr.Markdown("### πŸ’‘ 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("### πŸ“š 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("""
    <div style="text-align: center; margin-top: 30px; padding: 20px; border-top: 1px solid #e0e0e0;">
        <p style="color: #666; font-size: 12px;">
            Model: <a href="https://huggingface.co/wikeeyang/Magic-Wan-Image-V2" target="_blank">Magic-Wan-Image-V2</a> | 
            Powered by Gradio 6
        </p>
    </div>
    """)

# 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"
    )