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| import torch | |
| import gradio as gr | |
| from diffusers import UniDiffuserPipeline | |
| from diffusers.utils import load_image | |
| from accelerate import Accelerator | |
| accelerator = Accelerator(cpu=True) | |
| pipe = accelerator.prepare(UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.bfloat16)) | |
| pipe = pipe.to("cpu") | |
| apol=[] | |
| def plex(image_url,stips): | |
| init_image = load_image(image_url).resize((512, 512)) | |
| sample = pipe(image=init_image, num_inference_steps=stips, guidance_scale=8.0) | |
| i2t_text = sample.text[0] | |
| sample = pipe(prompt=i2t_text, num_inference_steps=stips, guidance_scale=8.0) | |
| for i, imge in enumerate(sample["images"]): | |
| apol.append(imge) | |
| return apol | |
| iface = gr.Interface(fn=plex, inputs=[gr.Image(label="img",type="filepath"), gr.Slider(label="num inference steps", minimum=1, step=1, maximum=5, value=5)], outputs=gr.Gallery(label="out", columns=2)) | |
| iface.queue(max_size=1,api_open=False) | |
| iface.launch(max_threads=1) |