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Update app.py
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app.py
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import torch
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from diffusers import UniDiffuserPipeline
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from diffusers.utils import load_image
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pipe = UniDiffuserPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
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pipe.to(device)
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# 1. Image-to-text generation
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image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unidiffuser/unidiffuser_example_image.jpg"
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init_image = load_image(image_url).resize((512, 512))
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sample = pipe(image=init_image, num_inference_steps=
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i2t_text = sample.text[0]
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print(i2t_text)
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import torch
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import gradio as gr
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from diffusers import UniDiffuserPipeline
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from diffusers.utils import load_image
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from accelerate import Accelerator
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Accelerator = Accelerator(cpu=True)
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pipe = accelerator.prepare(UniDiffuserPipeline.from_pretrained("thu-ml/unidiffuser-v1", torch_dtype=torch.bfloat16))
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pipe = accelerator.prepare(pipe.to("cpu"))
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def plex(image_url,stips)
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init_image = load_image(image_url).resize((512, 512))
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sample = pipe(image=init_image, num_inference_steps=stips, guidance_scale=8.0)
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i2t_text = sample.text[0]
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sample = pipe(prompt=i2t_text, num_inference_steps=stips, guidance_scale=8.0)
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for i, imge in enumerate(sample["images"]):
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apol.append(imge)
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return apol
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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))
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iface.queue(max_size=1)
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iface.launch(max_threads=1)
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