모델 업로드

app3.py

@@ -1,3 +1,44 @@
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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import re  # ← 문장 분리용
+
+# 1. 디바이스 설정
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 2. 한국어 GPT-2 모델과 토크나이저 로드
+tokenizer = AutoTokenizer.from_pretrained("skt/kogpt2-base-v2")
+model = AutoModelForCausalLM.from_pretrained("skt/kogpt2-base-v2").to(device)
+
+# 3. 한국어 소설 생성 함수 (4문장만 출력)
+def generate_korean_story(prompt, max_length=300, num_sentences=4):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
+
+    outputs = model.generate(
+        input_ids,
+        max_length=max_length,
+        min_length=100,
+        do_sample=True,
+        temperature=0.9,
+        top_k=50,
+        top_p=0.95,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=3,
+        eos_token_id=tokenizer.eos_token_id
+    )
+
+    full_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # 문장 단위로 자르기 (정규표현식으로 마침표/물음표/느낌표 기준)
+    sentences = re.split(r'(?<=[.?!])\s+', full_text.strip())
+
+    # 앞에서 4문장만 선택 후 합치기
+    story = " ".join(sentences[:num_sentences])
+    return story
+
+# 4. 실행
+if __name__ == "__main__":
+    user_prompt = input("📜 소설의 시작 문장을 입력하세요 (한국어): ")
+    result = generate_korean_story(user_prompt, max_length=500, num_sentences=4)
+    
+    print("\n📖 생성된 한국어 소설 (4문장):\n")
+    print(result)

model/animagine_xl.py

@@ -0,0 +1,21 @@
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+import torch
+
+def generate_animagine_xl(prompt: str):
+    model_id = "Linaqruf/animagine-xl"
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+        variant="fp16"
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    pipe = pipe.to("cuda")
+    image = pipe(prompt=prompt, width=1024, height=1024).images[0]
+    image.save("output_animagine_xl.png")
+    print("✅ 저장 완료: output_animagine_xl.png")
+    return image
+
+if __name__ == "__main__":
+    prompt = "그녀를 바라보는 한 남자의 야망"
+    generate_animagine_xl(prompt)

model/animesai.py

@@ -0,0 +1,23 @@
+from diffusers import StableDiffusionXLPipeline, AutoencoderKL
+import torch
+
+def generate_animesai(prompt: str):
+    model_id = "enhanceaiteam/AnimeSAI"
+    vae = AutoencoderKL.from_pretrained(
+        "madebyollin/sdxl-vae-fp16-fix",
+        torch_dtype=torch.float16
+    ).to("cuda")
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        model_id,
+        vae=vae,
+        torch_dtype=torch.float16,
+        use_safetensors=True
+    ).to("cuda")
+    image = pipe(prompt=prompt, width=1024, height=1024, guidance_scale=7).images[0]
+    image.save("output_animesai.png")
+    print("✅ 저장 완료: output_animesai.png")
+    return image
+
+if __name__ == "__main__":
+    prompt = "그녀를 바라보는 한 남자의 야망"
+    generate_animesai(prompt)

model/generate_sdxl_with_refiner.py

@@ -0,0 +1,39 @@
+from diffusers import StableDiffusionXLPipeline, StableDiffusionXLImg2ImgPipeline
+import torch
+from PIL import Image
+
+def generate_sdxl_with_refiner(prompt: str):
+    # 1단계: Base 모델로 초기 이미지 생성
+    base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+    base_pipe = StableDiffusionXLPipeline.from_pretrained(
+        base_model_id,
+        torch_dtype=torch.float16,
+        variant="fp16",
+        use_safetensors=True
+    ).to("cuda")
+
+    base_image = base_pipe(prompt=prompt, num_inference_steps=30).images[0]
+
+    # 2단계: Refiner 모델로 이미지 개선
+    refiner_model_id = "stabilityai/stable-diffusion-xl-refiner-1.0"
+    refiner_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+        refiner_model_id,
+        torch_dtype=torch.float16,
+        variant="fp16",
+        use_safetensors=True
+    ).to("cuda")
+
+    # Refiner는 PIL 이미지를 입력받아 후처리
+    refined_image = refiner_pipe(
+        prompt=prompt,
+        image=base_image,
+        strength=0.3  # 얼마나 많이 보정할지 (0~1)
+    ).images[0]
+
+    refined_image.save("output_sdxl_refined.png")
+    return refined_image
+
+if __name__ == "__main__":
+    prompt = '그녀를 바라보는 한 남자의 야망'
+    img = generate_sdxl_with_refiner(prompt)
+    img.show()

model/ghibli.py

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+from diffusers import StableDiffusionPipeline
+import torch
+
+def generate_ghibli(prompt: str):
+    model_id = "nitrosocke/Ghibli-Diffusion"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=True
+    ).to("cuda")
+    image = pipe(prompt=prompt).images[0]
+    image.save("output_ghibli.png")
+    print("✅ 저장 완료: output_ghibli.png")
+    return image
+
+if __name__ == "__main__":
+    prompt = "그녀를 바라보는 한 남자의 야망"
+    generate_ghibli(prompt)

model/realistic.py

@@ -0,0 +1,21 @@
+from diffusers import StableDiffusionPipeline
+import torch
+
+def generate_realistic(prompt: str):
+    model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=False,
+        # variant="fp16"
+    ).to("cuda")
+    
+    image = pipe(prompt=prompt).images[0]
+    return image
+
+if __name__ == "__main__":
+    prompt = '그녀를 바라보는 한 남자의 야망'
+    image = generate_realistic(prompt)
+    image.save("output_realistic.png")
+    print("✅ 저장 완료: output_realistic.png")
+    image.show()

model/sd_turbo.py

@@ -0,0 +1,21 @@
+from diffusers import StableDiffusionPipeline
+import torch
+
+def generate_sd_turbo(prompt: str):
+    model_id = "stabilityai/sd-turbo"
+
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=True
+    ).to("cuda")
+
+    image = pipe(prompt=prompt, guidance_scale=0.0).images[0]
+    image.save("output_sd_turbo.png")
+    return image
+
+if __name__ == "__main__":
+    prompt = "그녀를 바라보는 한 남자의 야망"
+    img = generate_sd_turbo(prompt)
+    print("✅ 저장 완료: output_sd_turbo.png")
+    img.show()

model/waifu.py

@@ -0,0 +1,21 @@
+from diffusers import StableDiffusionPipeline
+import torch
+
+def generate_waifu(prompt: str):
+    model_id = "hakurei/waifu-diffusion"
+    pipe = StableDiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        use_safetensors=False,
+        revision="fp16"
+    ).to("cuda")
+
+    image = pipe(prompt=prompt).images[0]
+    return image
+
+if __name__ == "__main__":
+    prompt = '그녀를 바라보는 한 남자의 야망'
+    image = generate_waifu(prompt)
+    image.save("output_waifu.png")
+    print("✅ 저장 완료: output_waifu.png")
+    image.show()