Upload folder using huggingface_hub

README.md

@@ -0,0 +1,22 @@
+# SDXL Flash
+
+Process SDXL models with [SDXL Flash](https://huggingface.co/sd-community/sdxl-flash)
+
+## Request
+
+JSON Request
+
+```java
+{
+              inputs (:obj: `array` | [])
+              seed (:obj: `int`)
+              prompt (:obj: `str`)
+              negative_prompt (:obj: `str`)
+              num_images_per_prompt (:obj: `int`)
+              steps (:obj: `int`)
+              guidance_scale (:obj: `float`)
+              width (:obj: `int`)
+              height (:obj: `int`)
+		model (:obj: `str`, :default: `sd-community/sdxl-flash`) 
+}
+```

deploy.bat

@@ -0,0 +1,9 @@
+
+set CURRENT_DIR=%CD%
+set SCRIPT_DIR=%~dp0
+
+cd %SCRIPT_DIR%
+
+F:\Projects\UAI\UAIBrainServer\Code\Env\Scripts\huggingface-cli.exe upload API-SDXL-Flash . .
+
+cd %CURRENT_DIR%

handler.py

@@ -0,0 +1,71 @@
+import os
+from typing import Dict, List, Any
+import sys
+rootDir = os.path.abspath(os.path.dirname(__file__))
+sys.path.append(rootDir)
+from uaiDiffusers.common.imageRequest import ImageRequest
+from diffusers import StableDiffusionXLPipeline, DPMSolverSinglestepScheduler        
+import torch
+from uaiDiffusers.uaiDiffusers import ImagesToBase64
+
+import torch
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        # self.model= load_model(path)
+        self.pipe = None
+        self.modelName = ""
+        baseReq = ImageRequest()
+        baseReq.model = path
+        print(f"Loading model: {path}")
+        self.LoadModel(baseReq)
+        
+    def LoadModel(self, request):
+        base = "sd-community/sdxl-flash"
+        if request.model == "default":
+            request.model = base
+        else:
+            base = request.model
+        if self.pipe is None:
+            del self.pipe
+            torch.cuda.empty_cache()
+            self.pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16, variant="fp16").to("cuda")
+
+            # Ensure sampler uses "trailing" timesteps.
+            self.pipe.scheduler = DPMSolverSinglestepScheduler.from_config(self.pipe.scheduler.config, timestep_spacing="trailing")
+        
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+         data args:
+              input (:obj: `str` | `PIL.Image` | `np.array`)
+              seed (:obj: `int`)
+              prompt (:obj: `str`)
+              negative_prompt (:obj: `str`)
+              num_images_per_prompt (:obj: `int`)
+              steps (:obj: `int`)
+              guidance_scale (:obj: `float`)
+              width (:obj: `int`)
+              height (:obj: `int`)
+              
+              kwargs
+        Return:
+              A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # inputs = data.pop("parameters", data)
+        request = ImageRequest.FromDict(data)
+        response = self.__runProcess__(request)
+        return response
+
+    def __runProcess__(self, request: ImageRequest) -> List[Dict[str, Any]]:
+        """
+        Run SDXL Lightning pipeline
+        """
+
+        self.LoadModel(request)
+        
+        # Ensure using the same inference steps as the loaded model and CFG set to 0.
+        images = self.pipe(request.prompt, negative_prompt = request.negative_prompt, num_inference_steps=request.steps, guidance_scale=request.guidance_scale, num_images_per_prompt=request.num_images_per_prompt ).images
+
+        return {"media":[{"media":ImagesToBase64(img)} for img in images]}

requirements.txt

@@ -0,0 +1 @@
+uaiDiffusers