diff --git a/api/transformerlab/plugins/image_diffusion/diffusion_worker.py b/api/transformerlab/plugins/image_diffusion/diffusion_worker.py
index bfa7701b1..0241d7586 100644
--- a/api/transformerlab/plugins/image_diffusion/diffusion_worker.py
+++ b/api/transformerlab/plugins/image_diffusion/diffusion_worker.py
@@ -28,6 +28,8 @@
     EulerAncestralDiscreteScheduler,
     DPMSolverMultistepScheduler,
     ControlNetModel,
+    DDIMScheduler,
+    DDPMScheduler,
     StableDiffusionControlNetPAGPipeline,
     StableDiffusionXLControlNetPAGPipeline,
     FluxControlNetPipeline,
@@ -39,6 +41,21 @@
     StableDiffusionXLControlNetImg2ImgPipeline,
     StableDiffusionXLControlNetUnionImg2ImgPipeline,
     StableDiffusionXLControlNetPAGImg2ImgPipeline,
+    StableDiffusionPipeline,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionInpaintPipeline,
+    StableDiffusionXLImg2ImgPipeline,
+    StableDiffusionXLInpaintPipeline,
+    StableDiffusionXLInstructPix2PixPipeline,
+    StableDiffusionXLKDiffusionPipeline,
+    StableDiffusion3Pipeline,
+    LatentConsistencyModelPipeline,
+    LatentConsistencyModelImg2ImgPipeline,
+    StableDiffusionControlNetXSPipeline,
+    StableDiffusionXLControlNetXSPipeline,
+    LEditsPPPipelineStableDiffusion,
+    LEditsPPPipelineStableDiffusionXL,
+    PIAPipeline,
     FluxControlNetImg2ImgPipeline,
     StableDiffusionControlNetInpaintPipeline,
     StableDiffusionXLControlNetInpaintPipeline,
@@ -68,6 +85,26 @@
     "LMSDiscreteScheduler": LMSDiscreteScheduler,
     "EulerAncestralDiscreteScheduler": EulerAncestralDiscreteScheduler,
     "DPMSolverMultistepScheduler": DPMSolverMultistepScheduler,
+    "DDIMScheduler": DDIMScheduler,
+    "DDPMScheduler": DDPMScheduler,
+}
+
+SINGLE_FILE_MAP = {
+    "StableDiffusionPipeline": StableDiffusionPipeline,
+    "StableDiffusionImg2ImgPipeline": StableDiffusionImg2ImgPipeline,
+    "StableDiffusionInpaintPipeline": StableDiffusionInpaintPipeline,
+    "StableDiffusionXLImg2ImgPipeline": StableDiffusionXLImg2ImgPipeline,
+    "StableDiffusionXLInpaintPipeline": StableDiffusionXLInpaintPipeline,
+    "StableDiffusionXLInstructPix2PixPipeline": StableDiffusionXLInstructPix2PixPipeline,
+    "StableDiffusionXLKDiffusionPipeline": StableDiffusionXLKDiffusionPipeline,
+    "StableDiffusion3Pipeline": StableDiffusion3Pipeline,
+    "LatentConsistencyModelPipeline": LatentConsistencyModelPipeline,
+    "LatentConsistencyModelImg2ImgPipeline": LatentConsistencyModelImg2ImgPipeline,
+    "StableDiffusionControlNetXSPipeline": StableDiffusionControlNetXSPipeline,
+    "StableDiffusionXLControlNetXSPipeline": StableDiffusionXLControlNetXSPipeline,
+    "LEditsPPPipelineStableDiffusion": LEditsPPPipelineStableDiffusion,
+    "LEditsPPPipelineStableDiffusionXL": LEditsPPPipelineStableDiffusionXL,
+    "PIAPipeline": PIAPipeline,
 }
 
 
@@ -96,6 +133,13 @@ def latents_to_rgb(latents):
     return Image.fromarray(image_array)
 
 
+def is_single_file_model(model_path):
+    """Check if the model is a single-file format (.safetensors or .ckpt)"""
+    if isinstance(model_path, str):
+        return model_path.endswith((".safetensors", ".ckpt", ".pt"))
+    return False
+
+
 def create_decode_callback(output_dir):
     """Create a callback function to decode and save latents at each step"""
 
@@ -374,7 +418,6 @@ def load_pipeline_with_sharding(
 
         # Handle 'auto' device safely by falling back to cuda:0 for controlnet
         safe_device = "cuda:0" if device == "auto" else device
-
         controlnet = controlnet_class.from_pretrained(
             controlnet_id,
             torch_dtype=torch.float16 if safe_device != "cpu" else torch.float32,
@@ -598,6 +641,28 @@ def load_pipeline_with_device_map(
             torch.cuda.empty_cache()
 
     # Load appropriate pipeline
+    is_single_file = is_single_file_model(model_path)
+    if is_single_file:
+        if is_inpainting:
+            architecture = "StableDiffusionInpaintPipeline"
+        elif is_img2img:
+            architecture = "StableDiffusionImg2ImgPipeline"
+        else:
+            architecture = "StableDiffusionPipeline"
+        single_pipeline_class = SINGLE_FILE_MAP.get(architecture)
+        if not single_pipeline_class:
+            raise ValueError(f"Model architecture '{architecture}' not supported for single-file models")
+
+        if single_pipeline_class:
+            pipe = single_pipeline_class.from_single_file(
+                model_path,
+                torch_dtype=torch.float16 if device != "cpu" else torch.float32,
+            )
+            if device != "auto":
+                pipe = pipe.to(device)
+            return pipe
+        else:
+            print(f"Warning: No single-file pipeline class found for architecture '{architecture}'")
     if is_controlnet:
         CONTROLNET_PIPELINE_MAP = {
             "StableDiffusionPipeline": StableDiffusionControlNetPipeline,