m

.gitignore

@@ -0,0 +1,2 @@
+.othervenv
+.myvenv

babe.py

@@ -1,24 +1,18 @@
-""" from main import get_nude
+import concurrent.futures
+from main import get_nude
 from PIL import Image
 
-x = get_nude(Image.open("girl.png"))
+x = get_nude(Image.open("girl.png"), replicate_api_key="r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",with_big_tits=True)
 x[0].save("babe.png")
 
-x = get_nude(Image.open("girl.png"), loras="breastsizeslideroffset.safetensors", lora_weights="1")
-x[0].save("babe2.png") """
-
-import concurrent.futures
-from main import get_nude
-from PIL import Image
+x2 = get_nude(Image.open("girl.png"), replicate_api_key="r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",with_big_tits=True)
+x2[0].save("babe2.png")
 
-def process_image_1():
-    x = get_nude(Image.open("girl.png"))
-    x[0].save("babe.png")
+x3 = get_nude(Image.open("girl.png"), replicate_api_key="r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",with_small_tits=True)
+x3[0].save("babe3.png")
 
-def process_image_2():
-    x = get_nude(Image.open("girl.png"), loras="breastsizeslideroffset.safetensors", lora_weights="1")
-    x[0].save("babe2.png")
+x4 = get_nude(Image.open("girl.png"), replicate_api_key="r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk")
+x4[0].save("babe4.png")
 
-with concurrent.futures.ThreadPoolExecutor() as executor:
-    executor.submit(process_image_1)
-    executor.submit(process_image_2)
+x5 = get_nude(Image.open("girl.png"), replicate_api_key="r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",with_big_tits=True)
+x5[0].save("babe5.png")

handler.py

@@ -62,18 +62,16 @@ class EndpointHandler():
         original_image_res = requests.get(data.get("original_link"))
         original_pil = Image.open(BytesIO(original_image_res.content))
 
-        loras = []
-        if (data.get("loras")):
-            loras = data.get("loras", "").split(",")
+        replicate_api_key = data.get("replicate_api_key", "")
 
-        lora_weights = []
-        if (data.get("lora_weights")):
-            lora_weights = data.get("lora_weights", "").split(",")
+        print("got repli", replicate_api_key)
 
-        replicate_api_key = data.get("replicate_api_key", "")
+        with_small_tits = data.get("with_small_tits", False)
+
+        with_big_tits = data.get("with_big_tits", False)
 
         nude_pils = get_nude_function(cfg_scale=data.get("cfg_scale"), generate_max_size=data.get("generate_max_size"), original_max_size=data.get(
-            "original_max_size"), original_pil=original_pil, positive_prompt=data.get("positive_prompt"), steps=data.get("steps"), loras=loras, lora_weights=lora_weights, replicate_api_key=replicate_api_key)
+            "original_max_size"), original_pil=original_pil, positive_prompt=data.get("positive_prompt"), steps=data.get("steps"), replicate_api_key=replicate_api_key, with_small_tits=with_small_tits, with_big_tits=with_big_tits)
 
         filenames = []
 

main.py

@@ -181,30 +181,20 @@ base_pipe.enable_model_cpu_offload()
 base_pipe.safety_checker = None
 base_pipe.enable_xformers_memory_efficient_attention()
 
-pipe_with_all_loras = _.clone_deep(base_pipe)
+pipe_with_tit_slider = _.clone_deep(base_pipe)
 
-for lora in os.listdir(os.path.join(os.environ.get('path', "."), "models")):
-    pipe_with_all_loras.load_lora_weights(os.path.join(os.environ.get(
-        'path', "."), "models", lora), weight_name=lora, adapter_name=lora.split(".")[0])
+pipe_with_tit_slider.load_lora_weights(os.path.join(os.environ.get(
+        'path', "."), "models", "breastsizeslideroffset.safetensors"), weight_name="breastsizeslideroffset.safetensors", adapter_name="breastsizeslideroffset")
 
+pipe_with_small_tits = _.clone_deep(pipe_with_tit_slider)
+pipe_with_small_tits.set_adapters("breastsizeslideroffset", adapter_weights=[-0.5])
 
-def get_pipe(loras, lora_weights):
-    if len(
-            loras) == 0:
-        return base_pipe
+pipe_with_medium_tits = _.clone_deep(base_pipe)
 
-    pipe = _.clone_deep(pipe_with_all_loras)
+pipe_with_big_tits = _.clone_deep(pipe_with_tit_slider)
+pipe_with_big_tits.set_adapters("breastsizeslideroffset", adapter_weights=[1])
 
-    lora_names = _.map_(loras, lambda x: x.split(".")[0])
-    lora_weights = _.map_(lora_weights, lambda x: float(x))
-
-    if len(lora_weights) > 0 and len(lora_weights) == len(lora_names):
-        pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-
-    return pipe
-
-
-def get_nude(original_pil, original_max_size=2000, generate_max_size=768, positive_prompt="nude girl, pussy, tits", negative_prompt="ugly", steps=20, cfg_scale=7, loras=[], lora_weights=[], replicate_api_key=""):
+def get_nude(original_pil, original_max_size=2000, generate_max_size=768, positive_prompt="nude girl, pussy, tits", negative_prompt="ugly", steps=20, cfg_scale=7, loras=[], lora_weights=[], replicate_api_key="", with_small_tits=False, with_big_tits=False):
     try:
         exif_data = original_pil._getexif()
         orientation_tag = 274  # The Exif tag for orientation
@@ -299,7 +289,13 @@ def get_nude(original_pil, original_max_size=2000, generate_max_size=768, positi
     dwpose_pil_resized = dwpose_pil.resize(
         (int(expanded_mask_image_width), int(expanded_mask_image_height)))
 
-    pipe = get_pipe(loras, lora_weights)
+    pipe = _.clone_deep(base_pipe)
+
+    if with_small_tits:
+        pipe = _.clone_deep(pipe_with_small_tits)
+
+    if with_big_tits:
+        pipe = _.clone_deep(pipe_with_big_tits)
 
     end_result_images = pipe(
         positive_prompt,

test_handler.py

@@ -23,23 +23,28 @@ handler = EndpointHandler(".")
 
 def process_request_1():
     result = handler(data={
+        "replicate_api_key": "r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",
         "original_link": "https://i.ibb.co/w0nJvJQ/Td9sw-XMr-Rh-Xp-YPn0-B65q.png",
-        "prompt": "nude girl, pussy, tits"
+        "with_small_tits": True
     })
     print("1", result)
 
 def process_request_2():
     result = handler(data={
+        "replicate_api_key": "r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",
         "original_link": "https://i.ibb.co/TM7tdCG/image-5.png",
-        "loras": "breastsizeslideroffset.safetensors",
-        "lora_weights": "1"
+        "with_big_tits": True
     })
     print("2", result)
 
+def process_request_3():
+    result = handler(data={
+        "replicate_api_key": "r8_GTeyENFqfOXFAI0COiGlB2RkhqEzqS64XBuIk",
+        "original_link": "https://i.ibb.co/TM7tdCG/image-5.png",
+    })
+    print("3", result)
+
 with concurrent.futures.ThreadPoolExecutor() as executor:
     executor.submit(process_request_1)
     executor.submit(process_request_2)
-    executor.submit(process_request_2)
-    executor.submit(process_request_1)
-    executor.submit(process_request_1)
-    executor.submit(process_request_2)
+    executor.submit(process_request_3)