BeautifulPrompt：PAI 推出自研 Prompt 美化器，赋能 AIGC 一键出美图

Instruction: Summarize this image description in 10 words or less and ignore words like archdaily, wallpaper, highly detailed, 8k, [r/earthporn]. Check English. Ignore modifiers 'by xxx', 'with xxx' or 'in xxx'. Ignore adjective.Input: a beautiful very detailed illustration of abandoned urbex unfinished building city nature industrial architecture architecture building spaceport by caspar david friedrich, scumm bar meadow nature synthwave, archdaily, wallpaper, highly detailed, trending on artstation.Output: of abandoned urban building in nature.
Input: portrait painting of a lost boy by cedric peyravernay and greg ruthkowski, in the style of dishonored concept art, concept design, trending on artstation \nOutput:

Instruction: create a detailed and creative description of the 'input'. Your response should include specific details about the colors, textures, and overall composition of the painting, as well as any unique features or elements that make it stand out.Please provide a clear and concise response that captures the essence of the painting while also encouraging creativity and originality in your description. You may consider describing the setting or environment depicted in the painting.Input: Digital painting of a girl with candy hat.

reward = a * score_model(prompt) + b * consistency_model(raw_prompt, prompt)

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd')model = AutoModelForCausalLM.from_pretrained('alibaba-pai/pai-bloom-1b1-text2prompt-sd').eval().cuda()
raw_prompt = '1 girl'input = f'Instruction: Give a simple description of the image to generate a drawing prompt.\nInput: {raw_prompt}\nOutput:'input_ids = tokenizer.encode(input, return_tensors='pt').cuda()
outputs = model.generate(    input_ids,    max_length=384,    do_sample=True,    temperature=1.0,    top_k=50,    top_p=0.95,    repetition_penalty=1.2,    num_return_sequences=5)
prompts = tokenizer.batch_decode(outputs[:, input_ids.size(1):], skip_special_tokens=True)prompts = [p.strip() for p in prompts]print(prompts)