zhiweili commited on
Commit
5d1594c
1 Parent(s): 7892d1d

change to inpaint

Browse files
Files changed (3) hide show
  1. app.py +1 -1
  2. app_haircolor.py +9 -5
  3. app_haircolor_inpaint.py +12 -11
app.py CHANGED
@@ -1,6 +1,6 @@
1
  import gradio as gr
2
 
3
- from app_haircolor import create_demo as create_demo_haircolor
4
 
5
  with gr.Blocks(css="style.css") as demo:
6
  with gr.Tabs():
 
1
  import gradio as gr
2
 
3
+ from app_haircolor_inpaint import create_demo as create_demo_haircolor
4
 
5
  with gr.Blocks(css="style.css") as demo:
6
  with gr.Tabs():
app_haircolor.py CHANGED
@@ -38,10 +38,15 @@ pidinet_detector = pidinet_detector.to(DEVICE)
38
 
39
  canndy_detector = CannyDetector()
40
 
 
 
 
 
 
41
  adapters = MultiAdapter(
42
  [
43
  T2IAdapter.from_pretrained(
44
- "TencentARC/t2iadapter_sketch_sd15v2",
45
  torch_dtype=torch.float16,
46
  varient="fp16",
47
  ),
@@ -80,13 +85,12 @@ def image_to_image(
80
  run_task_time = 0
81
  time_cost_str = ''
82
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
83
- # lineart_image = lineart_detector(input_image, int(generate_size*0.375), generate_size)
84
- # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
85
  canny_image = canndy_detector(input_image, int(generate_size*0.375), generate_size)
86
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
87
- sketch_image = pidinet_detector(input_image, int(generate_size*0.5), generate_size)
88
 
89
- cond_image = [sketch_image, canny_image]
90
  cond_scale = [lineart_scale, canny_scale]
91
 
92
  generator = torch.Generator(device=DEVICE).manual_seed(seed)
 
38
 
39
  canndy_detector = CannyDetector()
40
 
41
+ midas_detector = MidasDetector.from_pretrained(
42
+ "valhalla/t2iadapter-aux-models", filename="dpt_large_384.pt", model_type="dpt_large"
43
+ )
44
+ midas_detector = midas_detector.to(DEVICE)
45
+
46
  adapters = MultiAdapter(
47
  [
48
  T2IAdapter.from_pretrained(
49
+ "TencentARC/t2i-adapter-lineart-sdxl-1.0",
50
  torch_dtype=torch.float16,
51
  varient="fp16",
52
  ),
 
85
  run_task_time = 0
86
  time_cost_str = ''
87
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
88
+ lineart_image = lineart_detector(input_image, int(generate_size*0.375), generate_size)
89
+ run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
90
  canny_image = canndy_detector(input_image, int(generate_size*0.375), generate_size)
91
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
 
92
 
93
+ cond_image = [lineart_image, canny_image]
94
  cond_scale = [lineart_scale, canny_scale]
95
 
96
  generator = torch.Generator(device=DEVICE).manual_seed(seed)
app_haircolor_inpaint.py CHANGED
@@ -9,6 +9,7 @@ from segment_utils import(
9
  restore_result,
10
  )
11
  from diffusers import (
 
12
  DiffusionPipeline,
13
  T2IAdapter,
14
  MultiAdapter,
@@ -49,12 +50,12 @@ adapters = MultiAdapter(
49
  )
50
  adapters = adapters.to(torch.float16)
51
 
52
- basepipeline = DiffusionPipeline.from_pretrained(
53
  BASE_MODEL,
54
  torch_dtype=torch.float16,
55
  use_safetensors=True,
56
- adapter=adapters,
57
- custom_pipeline="./pipelines/pipeline_sdxl_adapter_inpaint_custom.py",
58
  )
59
 
60
  basepipeline = basepipeline.to(DEVICE)
@@ -76,13 +77,13 @@ def image_to_image(
76
  run_task_time = 0
77
  time_cost_str = ''
78
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
79
- lineart_image = lineart_detector(input_image, int(generate_size*0.375), generate_size)
80
- run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
81
- canny_image = canndy_detector(input_image, int(generate_size*0.375), generate_size)
82
- run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
83
 
84
- cond_image = [lineart_image, canny_image]
85
- cond_scale = [lineart_scale, canny_scale]
86
 
87
  generator = torch.Generator(device=DEVICE).manual_seed(seed)
88
  generated_image = basepipeline(
@@ -95,8 +96,8 @@ def image_to_image(
95
  width=generate_size,
96
  guidance_scale=guidance_scale,
97
  num_inference_steps=num_steps,
98
- adapter_image=cond_image,
99
- adapter_conditioning_scale=cond_scale,
100
  ).images[0]
101
 
102
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
 
9
  restore_result,
10
  )
11
  from diffusers import (
12
+ StableDiffusionXLInpaintPipeline,
13
  DiffusionPipeline,
14
  T2IAdapter,
15
  MultiAdapter,
 
50
  )
51
  adapters = adapters.to(torch.float16)
52
 
53
+ basepipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
54
  BASE_MODEL,
55
  torch_dtype=torch.float16,
56
  use_safetensors=True,
57
+ # adapter=adapters,
58
+ # custom_pipeline="./pipelines/pipeline_sdxl_adapter_inpaint_custom.py",
59
  )
60
 
61
  basepipeline = basepipeline.to(DEVICE)
 
77
  run_task_time = 0
78
  time_cost_str = ''
79
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
80
+ # lineart_image = lineart_detector(input_image, int(generate_size*0.375), generate_size)
81
+ # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
82
+ # canny_image = canndy_detector(input_image, int(generate_size*0.375), generate_size)
83
+ # run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)
84
 
85
+ # cond_image = [lineart_image, canny_image]
86
+ # cond_scale = [lineart_scale, canny_scale]
87
 
88
  generator = torch.Generator(device=DEVICE).manual_seed(seed)
89
  generated_image = basepipeline(
 
96
  width=generate_size,
97
  guidance_scale=guidance_scale,
98
  num_inference_steps=num_steps,
99
+ # adapter_image=cond_image,
100
+ # adapter_conditioning_scale=cond_scale,
101
  ).images[0]
102
 
103
  run_task_time, time_cost_str = get_time_cost(run_task_time, time_cost_str)