Update app.py

app.py

@@ -1,154 +1,80 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from diffusers import StableDiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+model_name = "andite/anything-v4.0"
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16 if device=="cuda" else torch.float32)
 pipe = pipe.to(device)
 
+if device == "cuda":
+    pipe.enable_xformers_memory_efficient_attention()
+
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
+MAX_IMAGE_SIZE = 512
+
+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)
-
+        
+    generator = torch.Generator(device).manual_seed(seed)
+    
     image = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
+        negative_prompt=negative_prompt if negative_prompt else None,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,
         height=height,
-        generator=generator,
+        generator=generator
     ).images[0]
-
-    return image, seed
-
+    
+    return image
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A fantasy portrait of a warrior woman, detailed and dramatic lighting",
+    "A cute anime character with blue hair and big eyes"
 ]
 
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 1024px;
 }
 """
 
+power_device = "GPU" if device=="cuda" else "CPU"
+
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(f"""
+        # Text-to-Image Generation (Stable Diffusion 1.5 / Anything)
+        Running on {power_device}.
+        """)
 
         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, variant="primary")
-
-        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",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            prompt = gr.Textbox(label="Prompt / 프롬프트", show_label=False, max_lines=1, placeholder="Enter your prompt / 프롬프트를 입력하세요")
+            run_button = gr.Button("Run / 실행", scale=0)
 
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+        result = gr.Image(label="Result / 결과", show_label=False)
 
+        with gr.Accordion("Advanced Settings / 고급 설정", open=False):
+            negative_prompt = gr.Textbox(label="Negative prompt / 네거티브 프롬프트", max_lines=1, placeholder="Enter a negative prompt / 네거티브 프롬프트를 입력하세요", visible=False)
+            seed = gr.Slider(label="Seed / 시드", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="Randomize seed / 시드 랜덤화", value=True)
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
+                width = gr.Slider(label="Width / 너비", minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=512)
+                height = gr.Slider(label="Height / 높이", minimum=256, maximum=MAX_IMAGE_SIZE, step=64, value=512)
+            with gr.Row():
+                guidance_scale = gr.Slider(label="Guidance scale / 가이던스 스케일", minimum=0.0, maximum=15.0, step=0.1, value=7.5)
+                num_inference_steps = gr.Slider(label="Number of inference steps / 추론 단계 수", minimum=1, maximum=50, step=1, value=20)
 
         gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+
+    run_button.click(fn=infer, inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], outputs=[result])
+
+demo.queue().launch()