rynmurdock commited on
Commit
b96b83f
1 Parent(s): 2df7643

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +6 -6
app.py CHANGED
@@ -57,14 +57,14 @@ def next_image():
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  prompt = calibrate_prompts.pop(0)
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  print(prompt)
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- image = replicate.run(
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  "rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
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  input={"prompt": prompt,}
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  )
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- response = requests.get(image['file1'])
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  image = Image.open(BytesIO(response.content))
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- embs.append(torch.tensor(urlopen(image['file2']).read().decode('utf-8').split(', ')))
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  return image
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  else:
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  print('######### Roaming #########')
@@ -97,15 +97,15 @@ def next_image():
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  prompt= 'an image' if glob_idx % 2 == 0 else rng_prompt
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  print(prompt)
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- image = replicate.run(
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  "rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
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  input={"prompt": prompt, 'im_emb': pickle.dumps(im_emb)}
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  )
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- response = requests.get(image['file1'])
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  image = Image.open(BytesIO(response.content))
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- im_emb = torch.tensor(urlopen(image['file2']).read().decode('utf-8').split(', '))
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  embs.append(im_emb)
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  torch.save(lin_class.coef_, f'./{start_time}.pt')
 
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  prompt = calibrate_prompts.pop(0)
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  print(prompt)
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+ output = replicate.run(
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  "rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
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  input={"prompt": prompt,}
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  )
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+ response = requests.get(output['file1'])
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  image = Image.open(BytesIO(response.content))
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+ embs.append(torch.tensor(urlopen(output['file2']).read().decode('utf-8').split(', ')))
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  return image
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  else:
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  print('######### Roaming #########')
 
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  prompt= 'an image' if glob_idx % 2 == 0 else rng_prompt
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  print(prompt)
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+ output = replicate.run(
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  "rynmurdock/zahir:49ebb1916c4baae35884ebfa16b092cf45d086c1913b53f62bb07d575cdbe683",
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  input={"prompt": prompt, 'im_emb': pickle.dumps(im_emb)}
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  )
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+ response = requests.get(output['file1'])
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  image = Image.open(BytesIO(response.content))
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+ im_emb = torch.tensor(urlopen(output['file2']).read().decode('utf-8').split(', '))
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  embs.append(im_emb)
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  torch.save(lin_class.coef_, f'./{start_time}.pt')