Update app.py

app.py

@@ -1,130 +1,36 @@
-import spaces
-import gradio as gr
-import numpy as np
-import PIL.Image
-from PIL import Image
-import random
-from diffusers import ControlNetModel, StableDiffusionXLPipeline, AutoencoderKL
-from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
-import cv2
 import torch
+from diffusers import StableDiffusionPipeline
+import gradio as gr
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# ตรวจสอบว่าใช้ GPU ได้หรือไม่
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "votepurchase/NSFW-gen-v2",
-    torch_dtype=torch.float16,
+# โหลดโมเดล Stable Diffusion จาก Hugging Face
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    revision="fp16" if device == "cuda" else "main",
+    use_auth_token=True  # ต้อง login ผ่าน huggingface-cli ก่อน
 )
 
-pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1216
-
-    
-@spaces.GPU
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    output_image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator
-    ).images[0]
-
-    return output_image
-
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-
-    with gr.Column(elem_id="col-container"):
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0)
-
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,#832,
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,#1216,
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=20.0,
-                    step=0.1,
-                    value=7,
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=28,
-                    step=1,
-                    value=28,
-                )
-
-    run_button.click(#lambda x: None, inputs=None, outputs=result).then(
-        fn=infer,
-        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs=[result]
-    )
-
-demo.queue().launch()
+# ส่งโมเดลไปยังอุปกรณ์ที่ใช้
+pipe = pipe.to(device)
+
+# ถ้าใช้ CPU แนะนำเปิด attention slicing เพื่อลดการใช้ RAM
+if device == "cpu":
+    pipe.enable_attention_slicing()
+
+# ฟังก์ชันสำหรับแปลงข้อความเป็นภาพ
+def text_to_image(prompt):
+    with torch.autocast(device) if device == "cuda" else torch.inference_mode():
+        image = pipe(prompt).images[0]
+    return image
+
+# Gradio Interface
+gr.Interface(
+    fn=text_to_image,
+    inputs=gr.Textbox(label="📝 Text Prompt"),
+    outputs=gr.Image(label="🎨 Generated Image"),
+    title="🖼 Text-to-Image with Stable Diffusion",
+    description="แอปนี้สามารถรันบน CPU หรือ GPU ได้ตามเครื่องที่มี โดยใช้โมเดล Stable Diffusion จาก Hugging Face"
+).launch()