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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()
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