load many models

handler.py

@@ -28,10 +28,11 @@ CONTROLNET_MAPPING = {
 
 
 SD_ID_MAPPING = {
+    "default":  "Lykon/dreamshaper-8",
     "dreamshaper":  "stablediffusionapi/dreamshaper-xl",
     "juggernaut":  "stablediffusionapi/juggernaut-xl-v8",
-    "realistic-vision":"SG161222/Realistic_Vision_V1.4",
-    "rev-animated":"s6yx/ReV_Animated"
+    "realistic":"SG161222/Realistic_Vision_V1.4",
+    "rev":"s6yx/ReV_Animated"
 }
 
 class EndpointHandler():
@@ -39,14 +40,34 @@ class EndpointHandler():
         self.control_type = "depth"
         self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],torch_dtype=dtype).to(device)
 
-        self.stable_diffusion_id = "Lykon/dreamshaper-8"
+        self.stable_diffusion_id_0 = "Lykon/dreamshaper-8"
+        self.dreamshaper = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id_0,
+                                                                      controlnet=self.controlnet, 
+                                                                      torch_dtype=dtype,
+                                                                      safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
 
-        print(f"Using stable diffusion model: {self.stable_diffusion_id}")
-        
-        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id,
+        self.stable_diffusion_id_1 = "stablediffusionapi/dreamshaper-xl"
+        self.juggernaut = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id_1,
+                                                                      controlnet=self.controlnet, 
+                                                                      torch_dtype=dtype,
+                                                                      safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
+        self.stable_diffusion_id_2 = "stablediffusionapi/juggernaut-xl-v8"
+        self.realistic = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id_2,
+                                                                      controlnet=self.controlnet, 
+                                                                      torch_dtype=dtype,
+                                                                      safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
+        self.stable_diffusion_id_3 = "SG161222/Realistic_Vision_V1.4"
+        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id_3,
                                                                       controlnet=self.controlnet, 
                                                                       torch_dtype=dtype,
                                                                       safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
+        self.stable_diffusion_id_4 = "s6yx/ReV_Animated"
+        self.rev = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id_4,
+                                                                      controlnet=self.controlnet, 
+                                                                      torch_dtype=dtype,
+                                                                      safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
+
+
 
         # Define Generator with seed
         self.generator = torch.Generator(device=device.type).manual_seed(3)
@@ -59,7 +80,7 @@ class EndpointHandler():
         
 
         # hyperparamters
-        #sd_model = data.pop("sd_model", None)
+        sd_model = data.pop("sd_model", "default")
         prompt = data.pop("inputs", None)
         negative_prompt = data.pop("negative_prompt", None)
         image_depth_map = data.pop("image_depth_map", None)
@@ -69,13 +90,19 @@ class EndpointHandler():
         width = data.pop("width", None)
         controlnet_conditioning_scale = data.pop("controlnet_conditioning_scale", 1.0)
 
-        # Check if neither prompt nor image is provided
+
+        if sd_model is None or not hasattr(self, sd_model):
+            return {"error": "Modelo SD no especificado o no válido"}
+    
         if prompt is None:
             return {"error": "Please provide a prompt"}
         
         if(image_depth_map is None):
             return {"error": "Please provide a image_depth_map"}
                 
+                
+        pipe = getattr(self, sd_model)
+
         # process image
         image = self.decode_base64_image(image_depth_map)
 

playload.json

@@ -1,4 +1,4 @@
 {
   "inputs": "",
-  "image_depth_map": 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"
 }