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dylanebert
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Running on Zero

dylanebert HF staff commited on
Commit
23d923b
1 Parent(s): 71f77fc

replace examples, support manual seed

Browse files
app.py CHANGED
@@ -1,11 +1,12 @@
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  import os
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  import shlex
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  import subprocess
 
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  import gradio as gr
 
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  import spaces
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  import torch
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  from diffusers import DiffusionPipeline
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- from gradio_client import Client, file
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  subprocess.run(
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  shlex.split(
@@ -34,7 +35,10 @@ splat_pipeline = DiffusionPipeline.from_pretrained(
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  @spaces.GPU
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- def run(input_image):
 
 
 
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  input_image = input_image.astype("float32") / 255.0
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  images = image_pipeline(
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  "", input_image, guidance_scale=5, num_inference_steps=30, elevation=0
@@ -78,27 +82,28 @@ with block:
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  with gr.Row(variant="panel"):
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  with gr.Column(scale=1):
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  input_image = gr.Image(label="image", type="numpy")
 
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  button_gen = gr.Button("Generate")
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  with gr.Column(scale=1):
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  output_splat = gr.Model3D(label="3D Gaussians")
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  button_gen.click(
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- fn=run, inputs=[input_image], outputs=[output_splat]
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  )
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  gr.Examples(
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  examples=[
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- "data_test/frog_sweater.jpg",
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- "data_test/bird.jpg",
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- "data_test/boy.jpg",
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- "data_test/cat_statue.jpg",
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- "data_test/dragontoy.jpg",
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- "data_test/gso_rabbit.jpg",
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  ],
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  inputs=[input_image],
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  outputs=[output_splat],
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- fn=lambda x: run(input_image=x),
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  cache_examples=True,
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  label="Image-to-3D Examples",
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  )
 
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  import os
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  import shlex
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  import subprocess
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+
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  import gradio as gr
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+ import numpy as np
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  import spaces
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  import torch
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  from diffusers import DiffusionPipeline
 
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  subprocess.run(
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  shlex.split(
 
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  @spaces.GPU
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+ def run(input_image, seed):
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+ np.random.seed(seed)
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+ torch.manual_seed(seed)
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+ torch.cuda.manual_seed(seed)
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  input_image = input_image.astype("float32") / 255.0
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  images = image_pipeline(
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  "", input_image, guidance_scale=5, num_inference_steps=30, elevation=0
 
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  with gr.Row(variant="panel"):
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  with gr.Column(scale=1):
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  input_image = gr.Image(label="image", type="numpy")
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+ seed_input = gr.Number(label="seed", value=42)
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  button_gen = gr.Button("Generate")
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  with gr.Column(scale=1):
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  output_splat = gr.Model3D(label="3D Gaussians")
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  button_gen.click(
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+ fn=run, inputs=[input_image, seed_input], outputs=[output_splat]
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  )
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  gr.Examples(
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  examples=[
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/a_cat_statue.jpg",
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/a_baby_penguin.jpg",
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/A_cartoon_house_with_red_roof.jpg",
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/a_hat.jpg",
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/an_antique_chest.jpg",
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+ "https://huggingface.co/datasets/dylanebert/iso3d/resolve/main/jpg@512/metal.jpg",
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  ],
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  inputs=[input_image],
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  outputs=[output_splat],
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+ fn=lambda x: run(input_image=x, seed=42),
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  cache_examples=True,
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  label="Image-to-3D Examples",
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  )
data_test/bird.jpg DELETED
Binary file (29.2 kB)
 
data_test/boy.jpg DELETED
Binary file (11.8 kB)
 
data_test/cat_statue.jpg DELETED
Binary file (10.8 kB)
 
data_test/dragontoy.jpg DELETED
Binary file (16.6 kB)
 
data_test/frog_sweater.jpg DELETED
Binary file (36.3 kB)
 
data_test/gso_rabbit.jpg DELETED
Binary file (15.9 kB)