Spaces:
Sleeping
Sleeping
app.py
CHANGED
@@ -161,15 +161,15 @@ def sample_ddim(n_sample, n=20):
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intermediate = np.stack(intermediate)
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return samples, intermediate
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def greet(
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samples, intermediate = sample_ddim(32, n=
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#response = transform2(transform(np.moveaxis(samples.detach().cpu().numpy(),1,3)[-1]))
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#response2 = transform2(transform(np.moveaxis(samples.detach().cpu().numpy(),1,3)[1]))
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#response = im.fromarray(intermediate[24][0][1]).convert("RGB")
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response = intermediate.shape;
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response2 = transform2(transform(np.moveaxis(intermediate,2,4)[0][-1]))
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response3 = transform2(transform(np.moveaxis(intermediate,2,4)[
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response4 = transform2(transform(np.moveaxis(intermediate,2,4)[
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response5 = transform2(transform(np.moveaxis(intermediate,2,4)[-1][-1]))
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return response, response2, response3, response4, response5
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@@ -181,6 +181,6 @@ transform2 = transforms.ToPILImage()
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Co-Retailing Business")], outputs=[gr.outputs.Image(type="pil", width=64, label="Output Image"), gr.outputs.Image(type="pil", width=64, label="Output Image2"), gr.outputs.Image(type="pil", width=64, label="Output Image3"), gr.outputs.Image(type="pil", width=64, label="Output Image4")])
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iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Co-Retailing Business")], outputs=[gr.Textbox()])
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iface.launch()
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intermediate = np.stack(intermediate)
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return samples, intermediate
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+
def greet(steps):
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samples, intermediate = sample_ddim(32, n=steps)
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#response = transform2(transform(np.moveaxis(samples.detach().cpu().numpy(),1,3)[-1]))
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#response2 = transform2(transform(np.moveaxis(samples.detach().cpu().numpy(),1,3)[1]))
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#response = im.fromarray(intermediate[24][0][1]).convert("RGB")
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response = intermediate.shape;
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response2 = transform2(transform(np.moveaxis(intermediate,2,4)[0][-1]))
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response3 = transform2(transform(np.moveaxis(intermediate,2,4)[steps/2][-1]))
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response4 = transform2(transform(np.moveaxis(intermediate,2,4)[steps/4][-1]))
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response5 = transform2(transform(np.moveaxis(intermediate,2,4)[-1][-1]))
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return response, response2, response3, response4, response5
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Co-Retailing Business")], outputs=[gr.outputs.Image(type="pil", width=64, label="Output Image"), gr.outputs.Image(type="pil", width=64, label="Output Image2"), gr.outputs.Image(type="pil", width=64, label="Output Image3"), gr.outputs.Image(type="pil", width=64, label="Output Image4")])
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iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Steps", value=100)], outputs=[gr.Textbox(label="Info"), gr.Image(type="pil", width=64, label="Output Image"), gr.Image(type="pil", width=64, label="Output Image2"), gr.Image(type="pil", width=64, label="Output Image3"), gr.Image(type="pil", width=64, label="Output Image4")])
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#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Co-Retailing Business")], outputs=[gr.Textbox()])
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iface.launch()
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