feat: improve I2I pipeline num_inference_steps behavoir

This commit ensures that the `strength` is set correctly when the
sdxl-turbo model is used and cleans up the `num_inference_steps`
implementation.

runner/app/pipelines/image_to_image.py

@@ -5,14 +5,6 @@
 
 import PIL
 import torch
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import (
-    SafetyChecker,
-    get_model_dir,
-    get_torch_device,
-    is_lightning_model,
-    is_turbo_model,
-)
 from diffusers import (
     AutoPipelineForImage2Image,
     EulerAncestralDiscreteScheduler,
@@ -25,6 +17,15 @@
 from PIL import ImageFile
 from safetensors.torch import load_file
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import (
+    SafetyChecker,
+    get_model_dir,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+)
+
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 logger = logging.getLogger(__name__)
@@ -171,6 +172,7 @@ def __call__(
         self, prompt: str, image: PIL.Image, **kwargs
     ) -> Tuple[List[PIL.Image], List[Optional[bool]]]:
         seed = kwargs.pop("seed", None)
+        num_inference_steps = kwargs.get("num_inference_steps", None)
         safety_check = kwargs.pop("safety_check", True)
 
         if seed is not None:
@@ -183,7 +185,7 @@ def __call__(
                     torch.Generator(get_torch_device()).manual_seed(s) for s in seed
                 ]
 
-        if "num_inference_steps" in kwargs and kwargs["num_inference_steps"] < 1:
+        if num_inference_steps is None or num_inference_steps < 1:
             del kwargs["num_inference_steps"]
 
         if (
@@ -194,15 +196,12 @@ def __call__(
             # it should be set to 0
             kwargs["guidance_scale"] = 0.0
 
-            # num_inference_steps * strength should be >= 1 because
-            # the pipeline will be run for int(num_inference_steps * strength) steps
-            kwargs["strength"] = kwargs.get("strength", 0.5)
-            if (
-                kwargs.get("num_inference_steps")
-                and kwargs["strength"] * kwargs["num_inference_steps"] < 1
-            ):
+            # Ensure num_inference_steps * strength >= 1 for minimum pipeline
+            # execution steps.
+            if "num_inference_steps" in kwargs:
                 kwargs["strength"] = max(
-                    1.0 / kwargs["num_inference_steps"], kwargs["strength"]
+                    1.0 / kwargs.get("num_inference_steps", 1),
+                    kwargs.get("strength", 0.5),
                 )
         elif ModelName.SDXL_LIGHTNING.value in self.model_id:
             # SDXL-Lightning models should have guidance_scale = 0 and use
@@ -218,8 +217,6 @@ def __call__(
             else:
                 # Default to 2step
                 kwargs["num_inference_steps"] = 2
-        elif ModelName.INSTRUCT_PIX2PIX.value in self.model_id:
-            kwargs["num_inference_steps"] = kwargs.get("num_inference_steps", 10)
 
         output = self.ldm(prompt, image=image, **kwargs)
 

runner/app/routes/image_to_image.py

@@ -3,14 +3,15 @@
 import random
 from typing import Annotated
 
-from app.dependencies import get_pipeline
-from app.pipelines.base import Pipeline
-from app.routes.util import HTTPError, ImageResponse, http_error, image_to_data_url
 from fastapi import APIRouter, Depends, File, Form, UploadFile, status
 from fastapi.responses import JSONResponse
 from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 from PIL import Image, ImageFile
 
+from app.dependencies import get_pipeline
+from app.pipelines.base import Pipeline
+from app.routes.util import HTTPError, ImageResponse, http_error, image_to_data_url
+
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 router = APIRouter()
@@ -44,7 +45,9 @@ async def image_to_image(
     negative_prompt: Annotated[str, Form()] = "",
     safety_check: Annotated[bool, Form()] = True,
     seed: Annotated[int, Form()] = None,
-    num_inference_steps: Annotated[int, Form()] = 25,  # TODO: Make optional.
+    num_inference_steps: Annotated[
+        int, Form()
+    ] = 100,  # NOTE: Hardcoded due to varying pipeline values.
     num_images_per_prompt: Annotated[int, Form()] = 1,
     pipeline: Pipeline = Depends(get_pipeline),
     token: HTTPAuthorizationCredentials = Depends(HTTPBearer(auto_error=False)),