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| import os | |
| import torch | |
| import gradio as gr | |
| import spaces | |
| import random | |
| import numpy as np | |
| from diffusers.utils import logging | |
| from PIL import Image | |
| from diffusers import OvisImagePipeline | |
| logging.set_verbosity_error() | |
| MAX_SEED = np.iinfo(np.int32).max | |
| device = "cpu" | |
| _dtype = torch.bfloat16 | |
| hf_token = os.getenv("HF_TOKEN") | |
| pipe = OvisImagePipeline.from_pretrained( | |
| "AIDC-AI/Ovis-Image-7B", | |
| token=hf_token, | |
| torch_dtype=torch.bfloat16 | |
| ) | |
| pipe.to(device) | |
| # @spaces.GPU(duration=75) | |
| def generate(prompt, img_height=256, img_width=256, seed=42, steps=13): | |
| print(f'inference with prompt : {prompt}, size: {img_height}x{img_width}, seed : {seed}, step : {steps}') | |
| generator = torch.Generator().manual_seed(seed) | |
| image = pipe( | |
| prompt, | |
| negative_prompt="", | |
| height=img_height, | |
| width=img_width, | |
| num_inference_steps=steps, | |
| generator=generator, | |
| ).images[0] | |
| return image | |
| examples = [ | |
| "A trendy, playful 3D render of the text \"Smart Digital Networks\" designed as glossy, inflated balloon letters. The typography is bubbly and rounded. The material is a shiny, reflective plastic in vibrant Orange. The text is floating in a bright, clean white studio space with soft pastel lighting. There are subtle reflections of the studio softboxes on the curves of the balloons. High-fashion retail aesthetic, pop art style, C4D render, cute and energetic.", | |
| "一张写实风格的现代化教室场景摄影图。画面焦点集中在前方墙壁正中央的一块洁白明亮的白板上，白板上用清晰、工整的黑色马克笔手写体写着多行文字：最上方是巨大的英文标题“OVIS-IMAGE”，紧接着下方依次分行写着中文说明：“7B文生图模型”、“无需专有模型即可实现双语渲染”、“可在消费级显卡部署”。白板下方的铝合金笔槽内摆放着几支直立或横躺的彩色白板笔（蓝、红、黄、绿）。前景是两位背对镜头的学生（呈虚化状态，营造景深感），坐在木质课桌前，右侧课桌上还放着一块折叠的棕色毛巾。左侧隐约可见部分绿色黑板，顶部有一盏明亮的长条日光灯提供照明。整体光线柔和自然，构图对称，对焦精准在白板文字上，营造出真实的教育和技术演示氛围。", | |
| "微距摄影，一只鲜艳的红色瓢虫停留在翠绿的叶子上，特写镜头。叶子表面分布着晶莹剔透的水珠（晨露），瓢虫红色的甲壳光滑如镜，反射着柔和的自然光，能够清晰看到瓢虫腿部和触角的细微纹理。背景是梦幻的绿色虚化（散景），突显主体，极度逼真，8k分辨率，高清晰度，电影级质感。", | |
| ] | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| footer { | |
| visibility: hidden; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(f"""# Text Guided Image Editing | |
| Text Guided Image Editing via Ovis-Image is specifically optimized for high-quality text rendering under stringent computational constraints using CPU. NOTE: CPU processing is slow... 6/7 min generation time. | |
| """) | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your guidance here", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Generate", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| with gr.Accordion("Advanced Settings", open=False): | |
| with gr.Row(): | |
| img_height = gr.Slider( | |
| label="Image Height", | |
| minimum=256, | |
| maximum=2048, | |
| step=32, | |
| value=256, | |
| ) | |
| img_width = gr.Slider( | |
| label="Image Width", | |
| minimum=256, | |
| maximum=2048, | |
| step=32, | |
| value=256, | |
| ) | |
| with gr.Row(): | |
| num_inference_steps = gr.Slider( | |
| label="Guidence Iterations", | |
| minimum=1, | |
| maximum=67, | |
| step=1, | |
| value=13, | |
| ) | |
| seed = gr.Slider( | |
| label="Seed", | |
| minimum=0, | |
| maximum=MAX_SEED, | |
| step=1, | |
| value=67, | |
| ) | |
| gr.Examples( | |
| examples = examples, | |
| fn = generate, | |
| inputs = [prompt], | |
| outputs = [result], | |
| cache_examples=True | |
| ) | |
| gr.on( | |
| triggers=[run_button.click, prompt.submit], | |
| fn = generate, | |
| inputs = [prompt, img_height, img_width, seed, num_inference_steps], | |
| outputs = [result] | |
| ) | |
| if __name__ == '__main__': | |
| demo.launch() |