adirik commited on
Commit
bb7ed14
β€’
1 Parent(s): 19cb3e9

merge CLIPSeg demo

Browse files
Files changed (2) hide show
  1. __pycache__/share_btn.cpython-38.pyc +0 -0
  2. app.py +8 -4
__pycache__/share_btn.cpython-38.pyc ADDED
Binary file (6.99 kB). View file
 
app.py CHANGED
@@ -1,6 +1,7 @@
1
  import os
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  import torch
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  import gradio as gr
 
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  from PIL import Image
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  import matplotlib.pyplot as plt
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  from diffusers import DiffusionPipeline
@@ -38,7 +39,7 @@ def read_content(file_path):
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  return content
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40
 
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- def predict(dict, reference, scale, seed, step):
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  width, height = dict["image"].size
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  if width < height:
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  factor = width / 512.0
@@ -52,6 +53,8 @@ def predict(dict, reference, scale, seed, step):
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  init_image = dict["image"].convert("RGB").resize((width, height))
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  mask = dict["mask"].convert("RGB").resize((width, height))
 
 
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  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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  output = pipe(
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  image=init_image,
@@ -119,8 +122,9 @@ with image_blocks as demo:
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  with gr.Box():
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  with gr.Row():
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  with gr.Column():
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- image = gr.Image(source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Source Image")
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- reference = gr.Image(source='upload', elem_id="image_upload", type="pil", label="Reference Image")
 
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  with gr.Column():
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  image_out = gr.Image(label="Output", elem_id="output-img").style(height=400)
@@ -146,7 +150,7 @@ with image_blocks as demo:
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  with gr.Column():
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  gr.Examples(ref_list, inputs=[reference],label="Examples - Reference Image",examples_per_page=12)
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- btn.click(fn=predict, inputs=[image, reference, guidance, seed, steps], outputs=[image_out, community_icon, loading_icon, share_button])
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  share_button.click(None, [], [], _js=share_js)
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  gr.HTML(
 
1
  import os
2
  import torch
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  import gradio as gr
4
+ import numpy as np
5
  from PIL import Image
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  import matplotlib.pyplot as plt
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  from diffusers import DiffusionPipeline
 
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  return content
40
 
41
 
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+ def predict(dict, text_query, reference, scale, seed, step):
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  width, height = dict["image"].size
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  if width < height:
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  factor = width / 512.0
 
53
 
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  init_image = dict["image"].convert("RGB").resize((width, height))
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  mask = dict["mask"].convert("RGB").resize((width, height))
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+ print(np.array(mask))
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+ print(text_query)
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  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
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  output = pipe(
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  image=init_image,
 
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  with gr.Box():
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  with gr.Row():
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  with gr.Column():
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+ image = gr.Image(source="upload", tool="sketch", elem_id="image_upload", type="pil", label="Source Image")
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+ text = gr.Textbox(lines=1, placeholder="Clothing item you want to replace...")
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+ reference = gr.Image(source="upload", elem_id="image_upload", type="pil", label="Reference Image")
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  with gr.Column():
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  image_out = gr.Image(label="Output", elem_id="output-img").style(height=400)
 
150
  with gr.Column():
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  gr.Examples(ref_list, inputs=[reference],label="Examples - Reference Image",examples_per_page=12)
152
 
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+ btn.click(fn=predict, inputs=[image, text, reference, guidance, seed, steps], outputs=[image_out, community_icon, loading_icon, share_button])
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  share_button.click(None, [], [], _js=share_js)
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  gr.HTML(