fix(api): pass additional params to new stages

api/onnx_web/chain/blend_img2img.py

@@ -64,7 +64,6 @@ def blend_img2img(
             image=source,
             negative_prompt=params.negative_prompt,
             num_inference_steps=params.steps,
-            strength=strength,
             callback=callback,
             **pipe_params,
         )
@@ -81,7 +80,6 @@ def blend_img2img(
             image=source,
             negative_prompt=params.negative_prompt,
             num_inference_steps=params.steps,
-            strength=strength,
             callback=callback,
             **pipe_params,
         )

api/onnx_web/chain/upscale_highres.py

@@ -1,13 +1,11 @@
 from logging import getLogger
 from typing import Any, Optional
 
-import numpy as np
-import torch
 from PIL import Image
 
-from ..diffusers.load import load_pipeline
+from ..chain.base import ChainPipeline
+from ..chain.img2img import blend_img2img
 from ..diffusers.upscale import append_upscale_correction
-from ..diffusers.utils import parse_prompt
 from ..params import HighresParams, ImageParams, StageParams, UpscaleParams
 from ..server import ServerContext
 from ..worker import WorkerContext
@@ -30,25 +28,12 @@ def upscale_highres(
     callback: Optional[ProgressCallback] = None,
     **kwargs,
 ) -> Image.Image:
-    image = stage_source or source
+    source = stage_source or source
 
     if highres.scale <= 1:
-        return image
+        return source
-
-    # load img2img pipeline once
-    pipe_type = params.get_valid_pipeline("img2img")
-    logger.debug("using %s pipeline for highres", pipe_type)
-
-    _prompt_pairs, loras, inversions = parse_prompt(params)
-    highres_pipe = pipeline or load_pipeline(
-        server,
-        params,
-        pipe_type,
-        job.get_device(),
-        inversions=inversions,
-        loras=loras,
-    )
 
+    chain = ChainPipeline()
     scaled_size = (source.width * highres.scale, source.height * highres.scale)
 
     # TODO: upscaling within the same stage prevents tiling from happening and causes OOM
@@ -60,7 +45,7 @@ def upscale_highres(
         source = source.resize(scaled_size, resample=Image.Resampling.LANCZOS)
     else:
         logger.debug("using upscaling pipeline for highres")
-        upscale = append_upscale_correction(
+        append_upscale_correction(
             StageParams(),
             params,
             upscale=upscale.with_args(
@@ -68,41 +53,24 @@ def upscale_highres(
                 scale=highres.scale,
                 outscale=highres.scale,
             ),
-        )
+            chain=chain,
-        source = upscale(
-            job,
-            server,
-            source,
-            callback=callback,
         )
 
-    if pipe_type == "lpw":
+    chain.append(
-        rng = torch.manual_seed(params.seed)
+        (
-        result = highres_pipe.img2img(
+            blend_img2img,
-            source,
+            StageParams(),
-            params.prompt,
+            {
-            generator=rng,
+                "overlap": params.overlap,
-            guidance_scale=params.cfg,
+                "strength": highres.strength,
-            negative_prompt=params.negative_prompt,
+            },
-            num_images_per_prompt=1,
-            num_inference_steps=highres.steps,
-            strength=highres.strength,
-            eta=params.eta,
-            callback=callback,
         )
-        return result.images[0]
+    )
-    else:
+
-        rng = np.random.RandomState(params.seed)
+    return chain(
-        result = highres_pipe(
+        job,
-            params.prompt,
+        server,
-            source,
+        params,
-            generator=rng,
+        source,
-            guidance_scale=params.cfg,
+        callback=callback,
-            negative_prompt=params.negative_prompt,
+    )
-            num_images_per_prompt=1,
-            num_inference_steps=highres.steps,
-            strength=highres.strength,
-            eta=params.eta,
-            callback=callback,
-        )
-        return result.images[0]

api/onnx_web/diffusers/run.py

@@ -63,16 +63,17 @@ def run_txt2img_pipeline(
         )
 
     # apply highres
-    chain.append(
+    for _i in range(highres.iterations):
-        (
+        chain.append(
-            upscale_highres,
+            (
-            stage,
+                upscale_highres,
-            {
+                stage,
-                "highres": highres,
+                {
-                "upscale": upscale,
+                    "highres": highres,
-            },
+                    "upscale": upscale,
+                },
+            )
         )
-    )
 
     # apply upscaling and correction, after highres
     append_upscale_correction(

api/onnx_web/diffusers/upscale.py

@@ -1,16 +1,13 @@
 from logging import getLogger
 from typing import List, Optional, Tuple
 
-from ..chain import (
+from ..chain import ChainPipeline, PipelineStage
-    ChainPipeline,
+from ..chain.correct_codeformer import correct_codeformer
-    PipelineStage,
+from ..chain.correct_gfpgan import correct_gfpgan
-    correct_codeformer,
+from ..chain.upscale_bsrgan import upscale_bsrgan
-    correct_gfpgan,
+from ..chain.upscale_resrgan import upscale_resrgan
-    upscale_bsrgan,
+from ..chain.upscale_stable_diffusion import upscale_stable_diffusion
-    upscale_resrgan,
+from ..chain.upscale_swinir import upscale_swinir
-    upscale_stable_diffusion,
-    upscale_swinir,
-)
 from ..params import ImageParams, SizeChart, StageParams, UpscaleParams
 
 logger = getLogger(__name__)
@@ -65,6 +62,9 @@ def append_upscale_correction(
         for stage, params in pre_stages:
             chain.append((stage, params))
 
+    upscale_opts = {
+        "upscale": upscale,
+    }
     upscale_stage = None
     if upscale.scale > 1:
         if "bsrgan" in upscale.upscale_model:
@@ -72,23 +72,23 @@ def append_upscale_correction(
                 tile_size=stage.tile_size,
                 outscale=upscale.outscale,
             )
-            upscale_stage = (upscale_bsrgan, bsrgan_params, None)
+            upscale_stage = (upscale_bsrgan, bsrgan_params, upscale_opts)
         elif "esrgan" in upscale.upscale_model:
             esrgan_params = StageParams(
                 tile_size=stage.tile_size,
                 outscale=upscale.outscale,
             )
-            upscale_stage = (upscale_resrgan, esrgan_params, None)
+            upscale_stage = (upscale_resrgan, esrgan_params, upscale_opts)
         elif "stable-diffusion" in upscale.upscale_model:
             mini_tile = min(SizeChart.mini, stage.tile_size)
             sd_params = StageParams(tile_size=mini_tile, outscale=upscale.outscale)
-            upscale_stage = (upscale_stable_diffusion, sd_params, None)
+            upscale_stage = (upscale_stable_diffusion, sd_params, upscale_opts)
         elif "swinir" in upscale.upscale_model:
             swinir_params = StageParams(
                 tile_size=stage.tile_size,
                 outscale=upscale.outscale,
             )
-            upscale_stage = (upscale_swinir, swinir_params, None)
+            upscale_stage = (upscale_swinir, swinir_params, upscale_opts)
         else:
             logger.warn("unknown upscaling model: %s", upscale.upscale_model)
 

api/onnx_web/params.py

@@ -87,14 +87,14 @@ class Size:
 
     def tojson(self) -> Dict[str, int]:
         return {
-            "height": self.height,
             "width": self.width,
+            "height": self.height,
         }
 
     def with_args(self, **kwargs):
         return Size(
-            kwargs.get("height", self.height),
             kwargs.get("width", self.width),
+            kwargs.get("height", self.height),
         )