Update api.py

api.py

@@ -79,15 +79,15 @@ def generate_random_sequence():
     return f"{random_numbers}_{random_words}"
 
 # Load the default pipeline once globally for efficiency
-flux_pipe = FluxPipeline.from_pretrained("trongg/Flux-Dev2Pro_nsfw_fluxtastic-v3", torch_dtype=torch.bfloat16)
+flux_pipe = FluxPipeline.from_pretrained("pranavajay/flow", torch_dtype=torch.bfloat16)
 flux_pipe.enable_model_cpu_offload()
 logging.info("FluxPipeline loaded successfully.")
 
-img_pipe = FluxImg2ImgPipeline.from_pretrained("trongg/Flux-Dev2Pro_nsfw_fluxtastic-v3", torch_dtype=torch.bfloat16)
+img_pipe = FluxImg2ImgPipeline.from_pretrained("pranavajay/flow", torch_dtype=torch.bfloat16)
 img_pipe.enable_model_cpu_offload()
 logging.info("FluxImg2ImgPipeline loaded successfully.")
 
-inpainting_pipe = FluxInpaintPipeline.from_pretrained("trongg/Flux-Dev2Pro_nsfw_fluxtastic-v3", torch_dtype=torch.bfloat16)
+inpainting_pipe = FluxImg2ImgPipeline.from_pretrained("pranavajay/flow", torch_dtype=torch.bfloat16)
 inpainting_pipe.enable_model_cpu_offload()
 logging.info("FluxInpaintPipeline loaded successfully.")
 
@@ -157,7 +157,7 @@ async def set_controlnet_adapter(adapter: str, is_inpainting: bool = False):
     controlnet = FluxControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.bfloat16)
     pipeline_cls = FluxControlNetPipeline if not is_inpainting else FluxControlNetInpaintPipeline
     flux_controlnet_pipe = pipeline_cls.from_pretrained(
-        "trongg/Flux-Dev2Pro_nsfw_fluxtastic-v3", controlnet=controlnet, torch_dtype=torch.bfloat16
+        "pranavajay/flow", controlnet=controlnet, torch_dtype=torch.bfloat16
     )
     flux_controlnet_pipe.to("cuda")
     logging.info(f"ControlNet adapter '{adapter}' loaded successfully.")