Add new endpoints for product diffusion API and SDXL-LoRA inference

product_diffusion_api/endpoints.py

@@ -0,0 +1,23 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+
+
+
+app = FastAPI(openapi_url='/api/v1/product-diffusion/openapi.json',docs_url='/api/v1/product_diffusion/docs')
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins = ["*"],
+    allow_methods = ["*"],
+    allow_headers=["*"],
+    allow_credentials = True
+    
+)
+
+app.include_router(sdxl_text_to_image.router, prefix='/api/v1/product-diffusion')
+app.include_router()
+
+
+@app.get('/')
+async def root():
+    return {'message: Product Diffusion API'}

scripts/sdxl_lora_inference.py

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+from wandb.integration.diffusers import autolog
+from diffusers import DiffusionPipeline
+import torch
+from config import PROJECT_NAME
+autolog(init=dict(project=PROJECT_NAME))
+
+class SDXLLoraInference:
+    """
+    Class for running inference using the SDXL-LoRA model to generate stunning product photographs.
+    
+    Args:
+        num_inference_steps (int): The number of inference steps to perform.
+        guidance_scale (float): The scale factor for guidance during inference.
+    """
+    def __init__(self, num_inference_steps: int, guidance_scale: float) -> None:
+        self.model_path = "VikramSingh178/sdxl-lora-finetune-product-caption"
+        self.pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
+        self.pipe.to("cuda")
+        self.pipe.load_lora_weights(self.model_path)
+        self.num_inference_steps = num_inference_steps
+        self.guidance_scale = guidance_scale
+
+    def run_inference(self, prompt):
+        """
+        Runs inference using the SDXL-LoRA model to generate a stunning product photograph.
+        
+        Args:
+            prompt: The input prompt for generating the product photograph.
+        
+        Returns:
+            images: The generated product photograph(s).
+        """
+        
+        prompt = prompt
+        images = self.pipe(prompt, num_inference_steps=self.num_inference_steps, guidance_scale=self.guidance_scale).images
+        return images
+
+inference = SDXLLoraInference(num_inference_steps=100, guidance_scale=2.5)
+inference.run_inference(prompt= "A stunning 4k Shot of a Balenciaga X Anime Hoodie with a person wearing it in a party" )

scripts/wandb/debug-internal.log

@@ -1 +1 @@
-run-20240429_145519-r0eclldx/logs/debug-internal.log
+run-20240430_104236-lcgqwfyr/logs/debug-internal.log

scripts/wandb/debug.log

@@ -1 +1 @@
-run-20240429_145519-r0eclldx/logs/debug.log
+run-20240430_104236-lcgqwfyr/logs/debug.log

scripts/wandb/latest-run

@@ -1 +1 @@
-run-20240429_145519-r0eclldx
+run-20240430_104236-lcgqwfyr