Initial commit

api_bk1.py

@@ -0,0 +1,59 @@
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import torch
+from diffusers import FluxPipeline
+import base64
+from io import BytesIO
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Load FLUX.1-schnell (Best for 16GB VRAM + Fast Generation)
+print("Initializing Flux-Schnell on RTX 5060 Ti (sm_120)...")
+try:
+    pipe = FluxPipeline.from_pretrained(
+        "black-forest-labs/FLUX.1-schnell", 
+        torch_dtype=torch.bfloat16
+    )
+    # Optimized for 16GB: Moves parts of the model to CPU when not in use
+    # pipe.enable_model_cpu_offload()
+    pipe.enable_sequential_cpu_offload() 
+    pipe.vae.enable_tiling()
+except Exception as e:
+    print(f"Error loading model: {e}")
+
+class ImageRequest(BaseModel):
+    prompt: str
+    model: str = "flux-schnell" # Matches the error fix from earlier
+
+@app.post("/v1/images/generations")
+async def generate(request: ImageRequest):
+    try:
+        # Flux-Schnell generates high quality in just 4 steps
+        result = pipe(
+            request.prompt,
+            guidance_scale=0.0,
+            num_inference_steps=4,
+            max_sequence_length=256,
+        ).images[0]
+        
+        # Convert to Base64 for the API response
+        buffered = BytesIO()
+        result.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        
+        return {"data": [{"b64_json": img_str}]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=5005)