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import os |
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import torch |
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import gradio as gr |
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import spaces |
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import random |
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import numpy as np |
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from diffusers.utils import logging |
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from PIL import Image |
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from diffusers import OvisImagePipeline |
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logging.set_verbosity_error() |
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MAX_SEED = np.iinfo(np.int32).max |
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device = "cuda" |
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_dtype = torch.bfloat16 |
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hf_token = os.getenv("HF_TOKEN") |
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pipe = OvisImagePipeline.from_pretrained( |
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"AIDC-AI/Ovis-Image-7B", |
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token=hf_token, |
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torch_dtype=torch.bfloat16 |
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) |
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pipe.to("cuda") |
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@spaces.GPU(duration=75) |
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def generate(prompt, img_height=1024, img_width=1024, seed=42, steps=50, guidance_scale=5.0): |
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print(f'inference with prompt : {prompt}, size: {img_height}x{img_width}, seed : {seed}, step : {steps}, cfg : {guidance_scale}') |
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image = pipe( |
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prompt, |
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negative_prompt="", |
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height=img_height, |
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width=img_width, |
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num_inference_steps=steps, |
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true_cfg_scale=guidance_scale, |
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).images[0] |
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return image |
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examples = [ |
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"Solar punk vehicle in a bustling city", |
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"An anthropomorphic cat riding a Harley Davidson in Arizona with sunglasses and a leather jacket", |
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"An elderly woman poses for a high fashion photoshoot in colorful, patterned clothes with a cyberpunk 2077 vibe", |
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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: 520px; |
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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"""# Ovis-Image |
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[[code](https://github.com/AIDC-AI/Ovis-Image)] [[model](https://huggingface.co/AIDC-AI/Ovis-Image-7B)] |
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""") |
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with gr.Row(): |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt here", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Image(label="Result", show_label=False) |
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with gr.Accordion("Advanced Settings", open=False): |
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with gr.Row(): |
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img_height = gr.Slider( |
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label="Image Height", |
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minimum=256, |
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maximum=2048, |
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step=32, |
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value=1024, |
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) |
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img_width = gr.Slider( |
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label="Image Width", |
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minimum=256, |
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maximum=2048, |
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step=32, |
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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="Guidance Scale", |
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minimum=1, |
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maximum=14, |
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step=0.1, |
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value=5.0, |
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) |
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num_inference_steps = gr.Slider( |
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label="Number of inference steps", |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=50, |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=42, |
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) |
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gr.Examples( |
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examples = examples, |
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fn = generate, |
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inputs = [prompt], |
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outputs = [result], |
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cache_examples="lazy" |
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) |
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gr.on( |
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triggers=[run_button.click, prompt.submit], |
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fn = generate, |
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inputs = [prompt, img_height, img_width, seed, num_inference_steps, guidance_scale], |
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outputs = [result] |
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) |
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if __name__ == '__main__': |
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demo.launch() |
