Create app3.py

app3.py

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+import gradio as gr
+import torch
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if device == "cuda" else torch.float32
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+
+# 預設可選模型
+available_models = [
+    "digiplay/AM-mix1",
+    "digiplay/pan04",
+    "digiplay/2K"
+]
+
+def load_model(selected_model_id, custom_model_id):
+    model_id = custom_model_id.strip() if custom_model_id.strip() else selected_model_id
+    try:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
+        return pipe, model_id, f"✅ Model '{model_id}' loaded successfully!"
+    except Exception as e:
+        return None, "", f"❌ Failed to load model: {e}"
+
+def generate_image(pipe, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    if pipe is None:
+        raise ValueError("No model loaded. Please load a model first.")
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    generator = torch.Generator().manual_seed(seed)
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+    ).images[0]
+
+    return image, seed
+
+with gr.Blocks(css="#container { max-width: 700px; margin: auto; }") as demo:
+    gr.Markdown("## Text-to-Image Generator with Model Selector")
+
+    pipe_state = gr.State(None)
+    model_id_state = gr.State("")
+
+    with gr.Column(elem_id="container"):
+        gr.Markdown("### 1. Choose or Enter Model")
+        with gr.Row():
+            selected_model = gr.Dropdown(label="Choose a model", choices=available_models, value=available_models[0])
+            custom_model = gr.Textbox(label="Or enter custom model ID", placeholder="e.g. runwayml/stable-diffusion-v1-5")
+
+        load_button = gr.Button("Load Model")
+        load_status = gr.Textbox(label="Model Load Status", interactive=False)
+
+        load_button.click(
+            fn=load_model,
+            inputs=[selected_model, custom_model],
+            outputs=[pipe_state, model_id_state, load_status]
+        )
+
+        gr.Markdown("### 2. Generate Image")
+        prompt = gr.Textbox(label="Prompt", placeholder="e.g. A futuristic city at night")
+        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="(optional)", value="", visible=True)
+        with gr.Row():
+            width = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=512, label="Width")
+            height = gr.Slider(256, MAX_IMAGE_SIZE, step=32, value=512, label="Height")
+        with gr.Row():
+            guidance_scale = gr.Slider(0.0, 10.0, step=0.1, value=7.5, label="Guidance Scale")
+            num_inference_steps = gr.Slider(1, 50, step=1, value=25, label="Inference Steps")
+        with gr.Row():
+            seed = gr.Slider(0, MAX_SEED, step=1, value=0, label="Seed")
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+
+        generate_button = gr.Button("Generate Image")
+        output_image = gr.Image(label="Result")
+        final_seed = gr.Number(label="Used Seed", precision=0)
+
+        generate_button.click(
+            fn=generate_image,
+            inputs=[pipe_state, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+            outputs=[output_image, final_seed]
+        )
+
+if __name__ == "__main__":
+    demo.launch()