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app.py

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+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
+import cv2
+import torch
+import os
+import time
+import glob
+
+from diffusers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    HeunDiscreteScheduler,
+    KDPM2DiscreteScheduler,
+    KDPM2AncestralDiscreteScheduler,
+    LMSDiscreteScheduler,
+    UniPCMultistepScheduler,
+)
+
+# 一時ファイルの管理設定
+TEMP_DIR = "temp_images"
+FILE_RETENTION_PERIOD = 3600  # 1時間
+os.makedirs(TEMP_DIR, exist_ok=True)
+
+def cleanup_old_files():
+    """古い一時ファイルを削除する"""
+    current_time = time.time()
+    pattern = os.path.join(TEMP_DIR, "output_*.png")
+    
+    for file_path in glob.glob(pattern):
+        try:
+            file_modified_time = os.path.getmtime(file_path)
+            if current_time - file_modified_time > FILE_RETENTION_PERIOD:
+                os.remove(file_path)
+        except Exception as e:
+            print(f"Error while cleaning up file {file_path}: {e}")
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+pipe = StableDiffusionXLPipeline.from_single_file(
+    "https://huggingface.co/bluepen5805/illustrious_pencil-XL/illustrious_pencil-XL-v2.0.0.safetensors",
+    use_safetensors=True,
+    torch_dtype=torch.float16,
+)
+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, sampler_name):
+    # 古い一時ファイルの削除
+    cleanup_old_files()
+
+    # サンプラーの設定
+    if sampler_name == "DDIM":
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "DPMSolverMultistep":
+        pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "Euler":
+        pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "EulerAncestral":
+        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "Heun":
+        pipe.scheduler = HeunDiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "KDPM2":
+        pipe.scheduler = KDPM2DiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "KDPM2Ancestral":
+        pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "LMS":
+        pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
+    elif sampler_name == "UniPC":
+        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+    else:
+        pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+    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]
+
+    # RGBモードで保存
+    if output_image.mode != 'RGB':
+        output_image = output_image.convert('RGB')
+    
+    # 一時ファイルとして保存
+    timestamp = int(time.time())
+    temp_filename = os.path.join(TEMP_DIR, f"output_{timestamp}.png")
+    output_image.save(temp_filename)
+    
+    return temp_filename
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("""
+         Text-to-Image Demo
+        using [illustrious_pencil-XL](https://huggingface.co/bluepen5805/illustrious_pencil-XL)
+        """)
+
+        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,
+            type="filepath",  # filepathに変更
+            elem_id="output_image"
+        )
+        
+        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,
+            )
+
+            sampler_name = gr.Dropdown(
+                label="Sampler",
+                choices=["DDIM", "DPMSolverMultistep", "Euler", "EulerAncestral", "Heun", "KDPM2", "KDPM2Ancestral", "LMS", "UniPC"],
+                value="EulerAncestral",
+            )
+
+            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,
+                )
+
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+
+            with gr.Row():
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=20.0,
+                    step=0.1,
+                    value=4,
+                )
+
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=28,
+                    step=1,
+                    value=28,
+                )
+
+    run_button.click(
+        fn=infer,
+        inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, sampler_name],
+        outputs=[result]
+    )
+
+# 起動時に古いファイルを削除
+cleanup_old_files()
+
+demo.queue().launch()

requirements.txt

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+accelerate
+diffusers
+Pillow
+safetensors
+torch
+torchvision
+transformers
+xformers
+controlnet-aux==0.0.7
+gradio_imageslider==0.0.20