add more misc logging

api/onnx_web/chain/base.py

@@ -252,7 +252,8 @@ class ChainPipeline:
                     except Exception:
                         worker.retries = worker.retries - 1
                         logger.exception(
-                            "error while running stage pipeline, %s retries left", worker.retries
+                            "error while running stage pipeline, %s retries left",
+                            worker.retries,
                         )
                         server.cache.clear()
                         run_gc([worker.get_device()])

api/onnx_web/chain/persist_disk.py

@@ -28,6 +28,10 @@ class PersistDiskStage(BaseStage):
         stage_source: Optional[Image.Image] = None,
         **kwargs,
     ) -> List[Image.Image]:
+        logger.info(
+            "persisting images to disk: %s, %s", [s.size for s in sources], output
+        )
+
         for source, name in zip(sources, output):
             dest = save_image(server, name, source, params=params, size=size)
             logger.info("saved image to %s", dest)

api/onnx_web/chain/source_txt2img.py

@@ -47,7 +47,10 @@ class SourceTxt2ImgStage(BaseStage):
             params = params.with_args(prompt=slice_prompt(params.prompt, prompt_index))
 
         logger.info(
-            "generating image using txt2img, %s steps: %s", params.steps, params.prompt
+            "generating image using txt2img, %s steps of %s: %s",
+            params.steps,
+            params.model,
+            params.prompt,
         )
 
         if len(sources):
@@ -125,6 +128,7 @@ class SourceTxt2ImgStage(BaseStage):
 
         output = list(sources)
         output.extend(result.images)
+        logger.debug("produced %s outputs", len(output))
         return output
 
     def steps(

api/onnx_web/convert/diffusion/diffusion_xl.py

@@ -69,6 +69,10 @@ def convert_diffusion_diffusers_xl(
         else:
             pipeline.vae = AutoencoderKL.from_pretrained(vae_path)
 
+    if path.exists(temp_path):
+        logger.debug("torch model already exists for %s: %s", source, temp_path)
+    else:
+        logger.debug("exporting torch model for %s: %s", source, temp_path)
         pipeline.save_pretrained(temp_path)
 
     # directory -> onnx using optimum exporters

api/onnx_web/diffusers/load.py

@@ -264,9 +264,7 @@ def load_pipeline(
         if hasattr(pipe, vae):
             vae_model = getattr(pipe, vae)
             vae_model.set_tiled(tiled=params.tiled_vae)
-            vae_model.set_window_size(
-                params.vae_tile // 8, params.vae_overlap
-            )
+            vae_model.set_window_size(params.vae_tile // 8, params.vae_overlap)
 
     # update panorama params
     if params.is_panorama():

api/onnx_web/diffusers/pipelines/panorama_xl.py

@@ -330,7 +330,11 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                 )
                 add_region_embeds.append(
                     np.concatenate(
-                        (region_negative_pooled_prompt_embeds, region_pooled_prompt_embeds), axis=0
+                        (
+                            region_negative_pooled_prompt_embeds,
+                            region_pooled_prompt_embeds,
+                        ),
+                        axis=0,
                     )
                 )
 
@@ -440,7 +444,15 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
 
             for r in range(len(regions)):
                 top, left, bottom, right, mult, prompt = regions[r]
-                logger.debug("running region prompt: %s, %s, %s, %s, %s, %s", top, left, bottom, right, mult, prompt)
+                logger.debug(
+                    "running region prompt: %s, %s, %s, %s, %s, %s",
+                    top,
+                    left,
+                    bottom,
+                    right,
+                    mult,
+                    prompt,
+                )
 
                 # convert coordinates to latent space
                 h_start = top // 8
@@ -476,7 +488,9 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
 
                 # perform guidance
                 if do_classifier_free_guidance:
-                    region_noise_pred_uncond, region_noise_pred_text = np.split(region_noise_pred, 2)
+                    region_noise_pred_uncond, region_noise_pred_text = np.split(
+                        region_noise_pred, 2
+                    )
                     region_noise_pred = region_noise_pred_uncond + guidance_scale * (
                         region_noise_pred_text - region_noise_pred_uncond
                     )
@@ -501,7 +515,9 @@ class StableDiffusionXLPanoramaPipelineMixin(StableDiffusionXLImg2ImgPipelineMix
                     value[:, :, h_start:h_end, w_start:w_end] = latents_region_denoised
                     count[:, :, h_start:h_end, w_start:w_end] = 1
                 else:
-                    value[:, :, h_start:h_end, w_start:w_end] += latents_region_denoised * mult
+                    value[:, :, h_start:h_end, w_start:w_end] += (
+                        latents_region_denoised * mult
+                    )
                     count[:, :, h_start:h_end, w_start:w_end] += mult
 
             # take the MultiDiffusion step. Eq. 5 in MultiDiffusion paper: https://arxiv.org/abs/2302.08113

api/onnx_web/diffusers/utils.py

@@ -228,7 +228,7 @@ def parse_float_group(group: Tuple[str, str]) -> Tuple[str, float]:
 def get_tokens_from_prompt(
     prompt: str,
     pattern: Pattern,
-    parser = parse_float_group,
+    parser=parse_float_group,
 ) -> Tuple[str, List[Tuple[str, float]]]:
     """
     TODO: replace with Arpeggio