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import spaces |
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import os |
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import gradio as gr |
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import numpy as np |
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import random |
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import torch |
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from diffusers import StableDiffusion3Pipeline |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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device = "cuda" |
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dtype = torch.float16 |
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repo = "aipicasso/emi-3" |
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t2i = StableDiffusion3Pipeline.from_pretrained(repo, torch_dtype=torch.bfloat16, token=os.environ["TOKEN"]).to(device) |
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model = AutoModelForCausalLM.from_pretrained( |
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"microsoft/Phi-3-mini-4k-instruct", |
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device_map="cuda", |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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token=os.environ["TOKEN"] |
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) |
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct", token=os.environ["TOKEN"]) |
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upsampler = pipeline( |
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"text-generation", |
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model=model, |
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tokenizer=tokenizer, |
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) |
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generation_args = { |
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"max_new_tokens": 226, |
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"return_full_text": False, |
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"temperature": 0.7, |
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"do_sample": True, |
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"top_p": 0.95 |
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} |
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MAX_SEED = np.iinfo(np.int32).max |
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MAX_IMAGE_SIZE = 1344 |
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@spaces.GPU |
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def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)): |
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messages = [ |
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{"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。「目も髪もカラフルに染まっている少女がいて、虹のような背景に\"Emi 3\"と白い文字が書かれている」"}, |
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{"role": "assistant", "content": "anime style, 1girl, looking at viewer, serene expression, gentle smile, multicolored hair, rainbow gradient hair, wavy long hair, heterochromia, purple left eye, blue right eye, pastel color scheme, magical girl aesthetic, white text overlay \"Emi 3\", centered text, modern typography, ethereal lighting, soft glow, fantasy atmosphere, rainbow gradient background, dreamy atmosphere, sparkles, light particles, magical effects, depth of field, bokeh effect"}, |
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{"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。「漫画風の富士山」"}, |
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{"role": "assistant", "content": "manga style, monochrome, no human, Illustration of snow-capped Mount Fuji. Clean, sharp line art with wispy clouds floating in the sky and 2-3 pine trees in the foreground. Dawn sky tinted pink, with the mountain casting deep blue shadows. Emphasize depth and perspective to capture the mountain's majesty. A glimpse of Lake Hakone visible at the bottom. "}, |
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{"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}」" }, |
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] |
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output = upsampler(messages, **generation_args) |
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upsampled_prompt=output[0]['generated_text'] |
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print(upsampled_prompt) |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator().manual_seed(seed) |
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image = t2i( |
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prompt = upsampled_prompt, |
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negative_prompt = negative_prompt, |
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guidance_scale = guidance_scale, |
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num_inference_steps = num_inference_steps, |
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width = width, |
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height = height, |
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generator = generator |
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).images[0] |
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return image, seed, upsampled_prompt |
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examples = [ |
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"目も髪もカラフルに染まっている少女がいて、虹のような背景に\"Emi 3\"と白い文字が書かれている", |
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"炎の魔法使いの少女", |
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"雷の魔法使いの少女", |
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"漫画風の富士山", |
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] |
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css=""" |
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#col-container { |
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margin: 0 auto; |
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max-width: 580px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(f""" |
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# 日本語が入力できる Emi 3 |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="プロンプト", |
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show_label=False, |
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max_lines=1, |
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placeholder="作りたい画像の特徴を入力してください", |
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container=False, |
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) |
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run_button = gr.Button("実行", scale=0) |
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result = gr.Image(label="結果", show_label=False) |
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generated_prompt = gr.Textbox(label="生成に使ったプロンプト", show_label=False, interactive=False) |
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with gr.Accordion("詳細設定", open=False): |
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negative_prompt = gr.Text( |
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label="ネガティブプロンプト", |
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max_lines=1, |
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placeholder="画像から排除したい要素を入力してください", |
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) |
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seed = gr.Slider( |
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label="乱数のシード", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=0, |
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) |
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randomize_seed = gr.Checkbox(label="ランダム生成", value=True) |
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with gr.Row(): |
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width = gr.Slider( |
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label="横", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=64, |
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value=1024, |
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) |
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height = gr.Slider( |
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label="縦", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=64, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="プロンプトの忠実さ", |
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minimum=0.0, |
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maximum=10.0, |
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step=0.1, |
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value=4.5, |
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) |
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num_inference_steps = gr.Slider( |
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label="推論回数", |
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minimum=1, |
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maximum=50, |
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step=1, |
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value=30, |
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) |
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gr.Examples( |
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examples = examples, |
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inputs = [prompt] |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit, negative_prompt.submit], |
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fn = infer, |
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inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps], |
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outputs = [result, seed, generated_prompt] |
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) |
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demo.launch() |
