add app_makeup

app.py

@@ -1,13 +1,13 @@
 import gradio as gr
 
 from app_base import create_demo as create_demo_face
-# from app_makeup import create_demo as create_demo_makeup
+from app_makeup import create_demo as create_demo_makeup
 
 with gr.Blocks(css="style.css") as demo:
     with gr.Tabs():
         with gr.Tab(label="Face"):
             create_demo_face()
-        # with gr.Tab(label="Makeup"):
-        #     create_demo_makeup()
+        with gr.Tab(label="Makeup"):
+            create_demo_makeup()
 
 demo.launch()

app_makeup.py

@@ -35,6 +35,7 @@ def create_demo() -> gr.Blocks:
         w2 = 1.0
 
         input_image = Image.open(input_image_path)
+        icc_profile = input_image.info.get("icc_profile")
 
         w2 = 1.0
         run_model = adapter_run
@@ -59,17 +60,17 @@ def create_demo() -> gr.Blocks:
         tmpPrefix = "/tmp/gradio/"
 
         extension = 'png'
-        # if enhanced_image.mode == 'RGBA':
-        #     extension = 'png'
-        # else:
-        #     extension = 'jpg'
+        if enhanced_image.mode == 'RGBA':
+            extension = 'png'
+        else:
+            extension = 'jpg'
 
         targetDir = f"{tmpPrefix}output/"
         if not os.path.exists(targetDir):
             os.makedirs(targetDir)
 
         enhanced_path = f"{targetDir}{uuid.uuid4()}.{extension}"
-        enhanced_image.save(enhanced_path, quality=100)
+        enhanced_image.save(enhanced_path, quality=100, icc_profile=icc_profile)
 
         return enhanced_path
 

inversion_run_adapter.py

@@ -1,4 +1,5 @@
 import torch
+import inversion_run_base
 
 from diffusers import (
     DDPMScheduler,
@@ -17,35 +18,12 @@ from config import get_config, get_num_steps_actual
 from functools import partial
 from compel import Compel, ReturnedEmbeddingsType
 
-class Object(object):
-    pass
-
-args = Object()
-args.images_paths = None
-args.images_folder = None
-args.force_use_cpu = False
-args.folder_name = 'test_measure_time'
-args.config_from_file = 'run_configs/noise_shift_guidance_1_5.yaml'
-args.save_intermediate_results = False
-args.batch_size = None
-args.skip_p_to_p = True
-args.only_p_to_p = False
-args.fp16 = False
-args.prompts_file = 'dataset_measure_time/dataset.json'
-args.images_in_prompts_file = None
-args.seed = 986
-args.time_measure_n = 1
-
-
-assert (
-    args.batch_size is None or args.save_intermediate_results is False
-), "save_intermediate_results is not implemented for batch_size > 1"
+
+args = inversion_run_base.args
 
 generator = None
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-BASE_MODEL = "stabilityai/sdxl-turbo"
-
 lineart_detector = LineartDetector.from_pretrained("lllyasviel/Annotators")
 lineart_detector = lineart_detector.to(device)
 
@@ -67,8 +45,8 @@ adapters = MultiAdapter(
 )
 adapters = adapters.to(torch.float16)
 
-pipeline = DiffusionPipeline.from_pretrained(
-    BASE_MODEL,
+pipeline = DiffusionPipeline.from_pipe(
+    inversion_run_base.pipeline,
     torch_dtype=torch.float16, 
     variant="fp16", 
     use_safetensors=True,
@@ -77,9 +55,8 @@ pipeline = DiffusionPipeline.from_pretrained(
 )
 pipeline = pipeline.to(device)
 
-pipeline.scheduler = DDPMScheduler.from_pretrained(
-    BASE_MODEL,
-    subfolder="scheduler",
+pipeline.scheduler = DDPMScheduler.from_config(
+    inversion_run_base.pipeline.scheduler.config,
 )
 
 config = get_config(args)