feat(api): attempt to calculate total steps for chain pipelines

api/onnx_web/chain/base.py

@@ -7,7 +7,7 @@
 
 from ..errors import RetryException
 from ..output import save_image
-from ..params import ImageParams, StageParams
+from ..params import ImageParams, Size, StageParams
 from ..server import ServerContext
 from ..utils import is_debug, run_gc
 from ..worker import ProgressCallback, WorkerContext
@@ -85,6 +85,14 @@ def stage(self, callback: BaseStage, params: StageParams, **kwargs):
         self.stages.append((callback, params, kwargs))
         return self
 
+    def steps(self, params: ImageParams, size: Size):
+        steps = 0
+        for callback, _params, _kwargs in self.stages:
+            steps += callback.steps(params, size)
+
+        return steps
+
+
     def __call__(
         self,
         worker: WorkerContext,

api/onnx_web/chain/blend_grid.py

@@ -48,3 +48,10 @@ def run(
             output.paste(sources[n], (x * size[0], y * size[1]))
 
         return [*sources, output]
+
+    def outputs(
+            self,
+            params: ImageParams,
+            sources: int,
+    ) -> int:
+        return sources + 1

api/onnx_web/chain/blend_img2img.py

@@ -103,3 +103,17 @@ def run(
             outputs.extend(result.images)
 
         return outputs
+
+    def steps(
+            self,
+            params: ImageParams,
+            *args,
+    ) -> int:
+        return params.steps # TODO: multiply by strength
+
+    def outputs(
+            self,
+            params: ImageParams,
+            sources: int,
+    ) -> int:
+        return sources + 1

api/onnx_web/chain/source_noise.py

@@ -42,3 +42,10 @@ def run(
             outputs.append(output)
 
         return outputs
+
+    def outputs(
+            self,
+            params: ImageParams,
+            sources: int,
+    ) -> int:
+        return sources + 1

api/onnx_web/chain/source_s3.py

@@ -49,3 +49,10 @@ def run(
                 logger.exception("error loading image from S3")
 
         return outputs
+
+    def outputs(
+            self,
+            params: ImageParams,
+            sources: int,
+    ) -> int:
+        return sources + 1 # TODO: len(source_keys)

api/onnx_web/chain/source_txt2img.py

@@ -126,3 +126,17 @@ def run(
         output = list(sources)
         output.extend(result.images)
         return output
+
+    def steps(
+        self,
+        params: ImageParams,
+        size: Size,
+    ) -> int:
+        return params.steps
+
+    def outputs(
+        self,
+        params: ImageParams,
+        sources: int,
+    ) -> int:
+        return sources + 1

api/onnx_web/chain/source_url.py

@@ -42,3 +42,10 @@ def run(
             outputs.append(output)
 
         return outputs
+
+    def outputs(
+            self,
+            params: ImageParams,
+            sources: int,
+    ) -> int:
+        return sources + 1

api/onnx_web/chain/stage.py

@@ -25,7 +25,14 @@ def run(
 
     def steps(
         self,
-        _params: ImageParams,
+        params: ImageParams,
         size: Size,
     ) -> int:
-        raise NotImplementedError()
+        return 1
+
+    def outputs(
+        self,
+        params: ImageParams,
+        sources: int,
+    ) -> int:
+        return sources

api/onnx_web/chain/tile.py

@@ -50,6 +50,7 @@ def needs_tile(
     source: Optional[Image.Image] = None,
 ) -> bool:
     tile = min(max_tile, stage_tile)
+    logger.debug("")
 
     if source is not None:
         return source.width > tile or source.height > tile

api/onnx_web/diffusers/load.py

@@ -376,15 +376,15 @@ def load_pipeline(
                     provider=device.ort_provider("vae"),
                     sess_options=device.sess_options(),
                 )
-                components["vae_decoder_session"]._model_path = vae_decoder
+                components["vae_decoder_session"]._model_path = vae_decoder # "#\\not a real path on any system"
 
                 logger.debug("loading VAE encoder from %s", vae_encoder)
                 components["vae_encoder_session"] = OnnxRuntimeModel.load_model(
                     vae_encoder,
                     provider=device.ort_provider("vae"),
                     sess_options=device.sess_options(),
                 )
-                components["vae_encoder_session"]._model_path = vae_encoder
+                components["vae_encoder_session"]._model_path = vae_encoder # "#\\not a real path on any system"
 
             else:
                 logger.debug("loading VAE decoder from %s", vae_decoder)
@@ -439,12 +439,14 @@ def load_pipeline(
 
         if "vae_decoder_session" in components:
             pipe.vae_decoder = ORTModelVaeDecoder(
-                components["vae_decoder_session"], vae_decoder
+                components["vae_decoder_session"],
+                pipe, # TODO: find the right class to provide here. ORTModel is missing the dict json method
             )
 
         if "vae_encoder_session" in components:
             pipe.vae_encoder = ORTModelVaeEncoder(
-                components["vae_encoder_session"], vae_encoder
+                components["vae_encoder_session"],
+                pipe, # TODO: find the right class to provide here. ORTModel is missing the dict json method
             )
 
         if not server.show_progress:

api/onnx_web/server/api.py

@@ -387,7 +387,7 @@ def chain(server: ServerContext, pool: DevicePoolExecutor):
     validate(data, schema)
 
     # get defaults from the regular parameters
-    device, _params, _size = pipeline_from_request(server, data=data)
+    device, base_params, base_size = pipeline_from_request(server, data=data)
     pipeline = ChainPipeline()
     for stage_data in data.get("stages", []):
         stage_class = CHAIN_STAGES[stage_data.get("type")]
@@ -450,22 +450,23 @@ def chain(server: ServerContext, pool: DevicePoolExecutor):
 
     logger.info("running chain pipeline with %s stages", len(pipeline.stages))
 
-    output = make_output_name(server, "chain", params, size, count=len(pipeline.stages))
+    output = make_output_name(server, "chain", base_params, base_size, count=len(pipeline.stages))
     job_name = output[0]
 
     # build and run chain pipeline
     pool.submit(
         job_name,
         pipeline,
         server,
-        params,
+        base_params,
         [],
         output=output,
-        size=size,
+        size=base_size,
         needs_device=device,
     )
 
-    return jsonify(json_params(output, params, size))
+    step_params = params.with_args(steps=pipeline.steps(base_params, base_size))
+    return jsonify(json_params(output, step_params, base_size))
 
 
 def blend(server: ServerContext, pool: DevicePoolExecutor):