Create app.py

app.py

@@ -0,0 +1,175 @@
+# Thanks: https://huggingface.co/spaces/stabilityai/stable-diffusion-3-medium
+import spaces
+import os
+import gradio as gr
+import numpy as np
+import random
+import torch
+from diffusers import StableDiffusion3Pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+device = "cuda"
+dtype = torch.float16
+
+repo = "stabilityai/stable-diffusion-3.5-large"
+t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.bfloat16, token=os.environ["TOKEN"]).to(device)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-4k-instruct", 
+    device_map="cuda", 
+    torch_dtype=torch.bfloat16, 
+    trust_remote_code=True, 
+    token=os.environ["TOKEN"]
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", token=os.environ["TOKEN"])
+upsampler = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+generation_args = {
+    "max_new_tokens": 226,
+    "return_full_text": False,
+    "temperature": 0.7,
+    "do_sample": True,
+    "top_p": 0.95
+}
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1344
+
+@spaces.GPU
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
+    messages = [
+        {"role": "user", "content": "次のプロンプトを想像を膨らませて英語に翻訳してください。「クールなアニメ風の女の子」"},
+        {"role": "assistant", "content": "An anime style illustration of a cool-looking teenage girl with an edgy, confident expression. She has piercing eyes, a slight smirk, and colorful hair that flows in the wind. "},
+        {"role": "user", "content": "次のプロンプトを想像を膨らませて英語に翻訳してください。「実写風の女子高生」"},
+        {"role": "assistant", "content": "A photorealistic image of a female high school student standing on a city street. She is wearing a traditional Japanese school uniform, consisting of a navy blue blazer, a white blouse, and a knee-length plaid skirt. "},
+        {"role": "user", "content": f"次のプロンプトを想像を膨らませて英語に翻訳してください。「{prompt}」" },
+    ]
+    output = upsampler(messages, **generation_args)
+    upsampled_prompt=output[0]['generated_text']
+    print(upsampled_prompt)
+    
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    image = t2i(
+        prompt = upsampled_prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
+    ).images[0] 
+    
+    return image, seed, upsampled_prompt
+
+examples = [
+    "美味しい肉",
+    "馬に乗った宇宙飛行士",
+    "アニメ風の美少女",
+    "女子高生の写真",
+    "寿司でできた家に入っているコーギー",
+    "バナナとアボカドが戦っている様子"
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 580px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # 日本語が入力できる SD3.5 Large
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="プロンプト",
+                show_label=False,
+                max_lines=1,
+                placeholder="作りたい画像の特徴を入力してください",
+                container=False,
+            )
+            
+            run_button = gr.Button("実行", scale=0)
+        
+        result = gr.Image(label="結果", show_label=False)
+        generated_prompt = gr.Textbox(label="生成に使ったプロンプト", show_label=False, interactive=False)
+        
+        with gr.Accordion("詳細設定", open=False):
+            
+            negative_prompt = gr.Text(
+                label="ネガティブプロンプト",
+                max_lines=1,
+                placeholder="画像から排除したい要素を入力してください",
+            )
+            
+            seed = gr.Slider(
+                label="乱数のシード",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="ランダム生成", value=True)
+            
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="横",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=64,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="縦",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=64,
+                    value=1024,
+                )
+            
+            with gr.Row():
+                
+                guidance_scale = gr.Slider(
+                    label="プロンプトの忠実さ",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=3.5,
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="推論回数",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=28,
+                )
+        
+        gr.Examples(
+            examples = examples,
+            inputs = [prompt]
+        )
+    gr.on(
+        triggers=[run_button.click, prompt.submit, negative_prompt.submit],
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result, seed, generated_prompt]
+    )
+
+demo.launch()