lint(api): consolidate upscale/correction logic

api/onnx_web/__init__.py

@@ -1,3 +1,10 @@
+from .diffusion import (
+  get_latents_from_seed,
+  load_pipeline,
+  run_img2img_pipeline,
+  run_inpaint_pipeline,
+  run_txt2img_pipeline,
+)
 from .image import (
   expand_image,
   mask_filter_gaussian_multiply,
@@ -10,15 +17,9 @@ from .image import (
   noise_source_normal,
   noise_source_uniform,
 )
-from .pipeline import (
-  get_latents_from_seed,
-  load_pipeline,
-  run_img2img_pipeline,
-  run_inpaint_pipeline,
-  run_txt2img_pipeline,
-)
 from .upscale import (
   make_resrgan,
+  run_upscale_pipeline,
   upscale_gfpgan,
   upscale_resrgan,
   UpscaleParams,
@@ -26,7 +27,9 @@ from .upscale import (
 from .utils import (
   get_and_clamp_float,
   get_and_clamp_int,
+  get_from_list,
   get_from_map,
+  get_not_empty,
   safer_join,
   BaseParams,
   Border,

api/onnx_web/diffusion.py

@@ -61,7 +61,7 @@ def load_pipeline(pipeline: DiffusionPipeline, model: str, provider: str, schedu
         gc.collect()
         torch.cuda.empty_cache()
 
-        print('loading different pipeline')
+        print('loading new pipeline')
         pipe = pipeline.from_pretrained(
             model,
             provider=provider,
@@ -77,7 +77,7 @@ def load_pipeline(pipeline: DiffusionPipeline, model: str, provider: str, schedu
         last_pipeline_scheduler = scheduler
 
     if last_pipeline_scheduler != scheduler:
-        print('changing pipeline scheduler')
+        print('loading new scheduler')
         scheduler = scheduler.from_pretrained(
             model, subfolder='scheduler')
 
@@ -117,9 +117,7 @@ def run_txt2img_pipeline(
         num_inference_steps=params.steps,
     )
     image = result.images[0]
-
-    if upscale.faces or upscale.scale > 1:
-        image = upscale_resrgan(ctx, upscale, image)
+    image = run_upscale_pipeline(ctx, upscale, image)
 
     dest = safer_join(ctx.output_path, output)
     image.save(dest)
@@ -153,9 +151,7 @@ def run_img2img_pipeline(
         strength=strength,
     )
     image = result.images[0]
-
-    if upscale.faces or upscale.scale > 1:
-        image = upscale_resrgan(ctx, upscale, image)
+    image = run_upscale_pipeline(ctx, upscale, image)
 
     dest = safer_join(ctx.output_path, output)
     image.save(dest)
@@ -219,8 +215,7 @@ def run_inpaint_pipeline(
     else:
         print('output image size does not match source, skipping post-blend')
 
-    if upscale.faces or upscale.scale > 1:
-        image = upscale_resrgan(ctx, upscale, image)
+    image = run_upscale_pipeline(ctx, upscale, image)
 
     dest = safer_join(ctx.output_path, output)
     image.save(dest)

api/onnx_web/serve.py

@@ -1,5 +1,4 @@
 from diffusers import (
-    # schedulers
     DDIMScheduler,
     DDPMScheduler,
     DPMSolverMultistepScheduler,
@@ -23,6 +22,12 @@ from onnxruntime import get_available_providers
 from os import makedirs, path, scandir
 from typing import Tuple
 
+from .diffusion import (
+    run_img2img_pipeline,
+    run_inpaint_pipeline,
+    run_txt2img_pipeline,
+    run_upscale_pipeline,
+)
 from .image import (
     # mask filters
     mask_filter_gaussian_multiply,
@@ -36,12 +41,6 @@ from .image import (
     noise_source_normal,
     noise_source_uniform,
 )
-from .pipeline import (
-    run_img2img_pipeline,
-    run_inpaint_pipeline,
-    run_txt2img_pipeline,
-    run_upscale_pipeline,
-)
 from .upscale import (
     UpscaleParams,
 )

api/onnx_web/upscale.py

@@ -188,3 +188,15 @@ def upscale_gfpgan(ctx: ServerContext, params: UpscaleParams, image, upsampler=N
         image, has_aligned=False, only_center_face=False, paste_back=True, weight=params.face_strength)
 
     return output
+
+
+def run_upscale_pipeline(ctx: ServerContext, params: UpscaleParams, image: Image) -> Image:
+    print('running upscale pipeline')
+
+    if params.scale > 1:
+        image = upscale_resrgan(ctx, params, image)
+
+    if params.faces:
+        image = upscale_gfpgan(ctx, params, image)
+
+    return image