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| import gradio as gr | |
| from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler | |
| import torch | |
| from PIL import Image | |
| model_id = "alibaba-pai/pai-diffusion-artist-xlarge-zh" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id) | |
| pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) | |
| pipe = pipe.to("cpu") | |
| def infer_text2img(prompt, guide, steps): | |
| image = pipe([prompt], guidance_scale=guide, num_inference_steps=steps).images[0] | |
| return image | |
| with gr.Blocks() as demo: | |
| examples = [ | |
| ["草地上的帐篷，背景是山脉"], | |
| ["卧室里有一张床和一张桌子"], | |
| ["雾蒙蒙的日出在湖面上"], | |
| ] | |
| with gr.Row(): | |
| with gr.Column(scale=1, ): | |
| image_out = gr.Image(label = '输出(output)') | |
| with gr.Column(scale=1, ): | |
| prompt = gr.Textbox(label = '提示词(prompt)') | |
| submit_btn = gr.Button("生成图像(Generate)") | |
| with gr.Row(scale=0.5 ): | |
| guide = gr.Slider(2, 15, value = 7, label = '文本引导强度(guidance scale)') | |
| steps = gr.Slider(10, 50, value = 20, step = 1, label = '迭代次数(inference steps)') | |
| ex = gr.Examples(examples, fn=infer_text2img, inputs=[prompt, guide, steps], outputs=image_out) | |
| submit_btn.click(fn = infer_text2img, inputs = [prompt, guide, steps], outputs = image_out) | |
| demo.queue(concurrency_count=1, max_size=8).launch() | |