caohy666 commited on
Commit
a0ee85f
·
1 Parent(s): d643b3f

<fix> fix some bugs.

Browse files
Files changed (1) hide show
  1. app.py +7 -9
app.py CHANGED
@@ -47,7 +47,9 @@ The corresponding condition images will be automatically extracted.
47
 
48
 
49
  def init_basemodel():
50
- global transformer, scheduler, vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, image_processor
 
 
51
 
52
  # init models
53
  scheduler = diffusers.FlowMatchEulerDiscreteScheduler()
@@ -77,8 +79,12 @@ def init_basemodel():
77
  @spaces.GPU
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  def process_image_and_text(condition_image, target_prompt, condition_image_prompt, task, random_seed, inpainting, fill_x1, fill_x2, fill_y1, fill_y2):
79
  # set up the model
 
80
  if pipe is None or current_task != task:
 
 
81
  # init transformer
 
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  transformer = HunyuanVideoTransformer3DModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V',
83
  subfolder="transformer",
84
  inference_subject_driven=task in ['subject_driven'])
@@ -181,7 +187,6 @@ def process_image_and_text(condition_image, target_prompt, condition_image_promp
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  img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
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  edges = cv2.Canny(img_gray, 100, 200)
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  edges_tmp = Image.fromarray(edges).convert("RGB")
184
- edges_tmp.save(os.path.join(save_dir, f"edges.png"))
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  edges[edges == 0] = 128
186
  return Image.fromarray(edges).convert("RGB")
187
  c_img = get_canny_edge(c_img)
@@ -210,7 +215,6 @@ def process_image_and_text(condition_image, target_prompt, condition_image_promp
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  )
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  return depth_pipe(img)["depth"].convert("RGB").resize((512, 512))
212
  c_img = get_depth_map(c_img)
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- c_img.save(os.path.join(save_dir, f"depth.png"))
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  k = (255 - 128) / 255
215
  b = 128
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  c_img = c_img.point(lambda x: k * x + b)
@@ -230,7 +234,6 @@ def process_image_and_text(condition_image, target_prompt, condition_image_promp
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  Image.new("RGB", (512, 512), (255, 255, 255)),
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  mask
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  )
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- c_img.save(os.path.join(save_dir, f"mask.png"))
234
  c_img = Image.composite(
235
  c_img,
236
  Image.new("RGB", (512, 512), (128, 128, 128)),
@@ -238,9 +241,7 @@ def process_image_and_text(condition_image, target_prompt, condition_image_promp
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  )
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  elif task == "sr":
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  c_img = c_img.resize((int(512 / 4), int(512 / 4))).convert("RGB")
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- c_img.save(os.path.join(save_dir, f"low_resolution.png"))
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  c_img = c_img.resize((512, 512))
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- c_img.save(os.path.join(save_dir, f"low_to_high.png"))
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245
  gen_img = pipe(
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  image=c_img,
@@ -318,8 +319,5 @@ def create_app():
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319
 
320
  if __name__ == "__main__":
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- global pipe, current_task
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- pipe = None
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- current_task = None
324
  init_basemodel()
325
  create_app().launch(debug=True, ssr_mode=False)
 
47
 
48
 
49
  def init_basemodel():
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+ global transformer, scheduler, vae, text_encoder, text_encoder_2, tokenizer, tokenizer_2, image_processor, pipe, current_task
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+ pipe = None
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+ current_task = None
53
 
54
  # init models
55
  scheduler = diffusers.FlowMatchEulerDiscreteScheduler()
 
79
  @spaces.GPU
80
  def process_image_and_text(condition_image, target_prompt, condition_image_prompt, task, random_seed, inpainting, fill_x1, fill_x2, fill_y1, fill_y2):
81
  # set up the model
82
+ global pipe, current_task
83
  if pipe is None or current_task != task:
84
+ current_task = task
85
+
86
  # init transformer
87
+ global transformer
88
  transformer = HunyuanVideoTransformer3DModel.from_pretrained('hunyuanvideo-community/HunyuanVideo-I2V',
89
  subfolder="transformer",
90
  inference_subject_driven=task in ['subject_driven'])
 
187
  img_gray = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
188
  edges = cv2.Canny(img_gray, 100, 200)
189
  edges_tmp = Image.fromarray(edges).convert("RGB")
 
190
  edges[edges == 0] = 128
191
  return Image.fromarray(edges).convert("RGB")
192
  c_img = get_canny_edge(c_img)
 
215
  )
216
  return depth_pipe(img)["depth"].convert("RGB").resize((512, 512))
217
  c_img = get_depth_map(c_img)
 
218
  k = (255 - 128) / 255
219
  b = 128
220
  c_img = c_img.point(lambda x: k * x + b)
 
234
  Image.new("RGB", (512, 512), (255, 255, 255)),
235
  mask
236
  )
 
237
  c_img = Image.composite(
238
  c_img,
239
  Image.new("RGB", (512, 512), (128, 128, 128)),
 
241
  )
242
  elif task == "sr":
243
  c_img = c_img.resize((int(512 / 4), int(512 / 4))).convert("RGB")
 
244
  c_img = c_img.resize((512, 512))
 
245
 
246
  gen_img = pipe(
247
  image=c_img,
 
319
 
320
 
321
  if __name__ == "__main__":
 
 
 
322
  init_basemodel()
323
  create_app().launch(debug=True, ssr_mode=False)