diff --git a/swarm/diffusion/diffusion_func.py b/swarm/diffusion/diffusion_func.py
index 184fcb3..8e6c01d 100644
--- a/swarm/diffusion/diffusion_func.py
+++ b/swarm/diffusion/diffusion_func.py
@@ -19,7 +19,7 @@ def diffusion_callback(device_identifier, model_name, **kwargs):
     lora = kwargs.pop("lora", None)
     cross_attention_scale = kwargs.pop("cross_attention_scale", 1.0)
 
-    # set output_type if already there or upscale is selected (we use the latent upscaler)
+    # set output_type if already there or upscale is/ selected (we use the latent upscaler)
     output_type = kwargs.pop("output_type", "latent" if upscale else None)
     if output_type is not None:
         kwargs["output_type"] = output_type
@@ -43,14 +43,22 @@ def diffusion_callback(device_identifier, model_name, **kwargs):
                 "preprocessed_input", [kwargs.get("control_image")]
             )
 
-    pipeline = pipeline_type.from_pretrained(
-        model_name,
-        revision=kwargs.pop("revision", "main"),
-        variant=kwargs.pop("variant", None),
-        torch_dtype=torch.float16,
-        controlnet=controlnet if "controlnet" in locals() else None,
-        use_safe_tensors=use_safe_tensors,
-    )
+        pipeline = pipeline_type.from_pretrained(
+            model_name,
+            revision=kwargs.pop("revision", "main"),
+            variant=kwargs.pop("variant", None),
+            torch_dtype=torch.float16,
+            controlnet=controlnet if "controlnet" in locals() else None,
+            use_safe_tensors=use_safe_tensors,
+        )
+    else:
+        pipeline = pipeline_type.from_pretrained(
+            model_name,
+            revision=kwargs.pop("revision", "main"),
+            variant=kwargs.pop("variant", None),
+            torch_dtype=torch.float16,
+            use_safe_tensors=use_safe_tensors,
+        )
 
     if textual_inversion is not None:
         try:
diff --git a/swarm/diffusion/kandinsky.py b/swarm/diffusion/kandinsky.py
deleted file mode 100644
index a8ff0e0..0000000
--- a/swarm/diffusion/kandinsky.py
+++ /dev/null
@@ -1,73 +0,0 @@
-from diffusers import DiffusionPipeline
-import torch
-from ..post_processors.upscale import upscale_image
-from ..post_processors.output_processor import OutputProcessor
-
-
-def kandinsky_callback(device_identifier, model_name, **kwargs):
-    pipeline_type = kwargs.pop("pipeline_type", DiffusionPipeline)
-    pipeline_prior_type = kwargs.pop("pipeline_prior_type", DiffusionPipeline)
-    model_name_prior = kwargs.pop(
-        "model_name_prior", "kandinsky-community/kandinsky-2-1-prior"
-    )
-    upscale = kwargs.pop("upscale", False)
-    num_images_per_prompt = kwargs.get("num_images_per_prompt", 1)
-
-    guidance_scale = kwargs.get(
-        "guidance_scale", 1.0
-    )  # both pipelines need this so don't pop it
-
-    output_processor = OutputProcessor(
-        kwargs.pop("outputs", ["primary"]),
-        kwargs.pop("content_type", "image/jpeg"),
-    )
-
-    pipe_prior = pipeline_prior_type.from_pretrained(
-        model_name_prior, torch_dtype=torch.float16
-    )
-    pipe_prior.to(device_identifier)
-
-    prompt = kwargs.pop("prompt", "")
-    negative_prompt = kwargs.pop("negative_prompt", "")
-
-    generator = kwargs["generator"]
-
-    if "image" in kwargs and "image2" in kwargs:
-        images_texts = [prompt, kwargs.pop("image"), kwargs.pop("image2")]
-        weights = [0.2, 0.3, 0.5]
-        image_embeds, negative_image_embeds = pipe_prior.interpolate(
-            images_texts, weights
-        )
-        prompt = ""
-    else:
-        image_embeds, negative_image_embeds = pipe_prior(
-            prompt, negative_prompt, guidance_scale=1.0, generator=generator
-        ).to_tuple()
-
-    pipe = pipeline_type.from_pretrained(model_name, torch_dtype=torch.float16)
-    pipe.to(device_identifier)
-
-    height = kwargs.pop("height", 768)
-    width = kwargs.pop("width", 768)
-    images = pipe(
-        prompt,
-        negative_prompt=negative_prompt,
-        image_embeds=image_embeds,
-        negative_image_embeds=negative_image_embeds,
-        height=height,
-        width=width,
-        **kwargs,
-    ).images
-
-    if upscale:
-        images = upscale_image(
-            images,
-            device_identifier,
-            kwargs.get("prompt", ""),
-            kwargs.get("negative_prompt", ""),
-            num_images_per_prompt,
-            kwargs["generator"],
-        )
-
-    output_processor.add_outputs(images)
-    return (output_processor.get_results(), {})
diff --git a/swarm/job_arguments.py b/swarm/job_arguments.py
index 4a1cbe9..f78c106 100644
--- a/swarm/job_arguments.py
+++ b/swarm/job_arguments.py
@@ -6,7 +6,6 @@
 from .audio.audioldm import txt2audio_diffusion_callback
 from .audio.bark import bark_diffusion_callback
 from .diffusion.diffusion_func_if import diffusion_if_callback
-from .diffusion.kandinsky import kandinsky_callback
 from .type_helpers import get_type
 from .pre_processors.controlnet import scale_to_size
 from .external_resources import get_image, get_control_image, max_size, download_images
@@ -37,9 +36,6 @@ async def format_args(job):
     if args["model_name"].startswith("DeepFloyd/"):
         return diffusion_if_callback, args
 
-    if args["model_name"].startswith("kandinsky-"):
-        return await format_kandinsky_args(args)
-
     return await format_stable_diffusion_args(args, workflow)