diff --git a/skynet/cli.py b/skynet/cli.py
index 4a5850e..58297ca 100755
--- a/skynet/cli.py
+++ b/skynet/cli.py
@@ -1,16 +1,8 @@
 import json
 import logging
-import random
-
-from functools import partial
 
 import click
 
-from leap.protocol import (
-    Name,
-    Asset,
-)
-
 from .config import (
     load_skynet_toml,
     set_hf_vars,
@@ -49,7 +41,7 @@ def txt2img(*args, **kwargs):
 
     config = load_skynet_toml()
     set_hf_vars(config.dgpu.hf_token, config.dgpu.hf_home)
-    utils.txt2img(hf_token, **kwargs)
+    utils.txt2img(config.dgpu.hf_token, **kwargs)
 
 
 @click.command()
@@ -74,7 +66,7 @@ def img2img(model, prompt, input, output, strength, guidance, steps, seed):
     config = load_skynet_toml()
     set_hf_vars(config.dgpu.hf_token, config.dgpu.hf_home)
     utils.img2img(
-        hf_token,
+        config.dgpu.hf_token,
         model=model,
         prompt=prompt,
         img_path=input,
@@ -102,7 +94,7 @@ def inpaint(model, prompt, input, mask, output, strength, guidance, steps, seed)
     config = load_skynet_toml()
     set_hf_vars(config.dgpu.hf_token, config.dgpu.hf_home)
     utils.inpaint(
-        hf_token,
+        config.dgpu.hf_token,
         model=model,
         prompt=prompt,
         img_path=input,
diff --git a/skynet/dgpu/__init__.py b/skynet/dgpu/__init__.py
index a22655d..cd460ae 100755
--- a/skynet/dgpu/__init__.py
+++ b/skynet/dgpu/__init__.py
@@ -5,7 +5,7 @@ import trio
 import urwid
 
 from skynet.config import Config
-from skynet.dgpu.tui import init_tui
+from skynet.dgpu.tui import init_tui, WorkerMonitor
 from skynet.dgpu.daemon import dgpu_serve_forever
 from skynet.dgpu.network import NetConnector, maybe_open_contract_state_mngr
 
@@ -15,7 +15,7 @@ async def open_worker(config: Config):
     # suppress logs from httpx (logs url + status after every query)
     logging.getLogger("httpx").setLevel(logging.WARNING)
 
-    tui = None
+    tui: WorkerMonitor | None = None
     if config.tui:
         tui = init_tui(config)
 
diff --git a/skynet/dgpu/compute.py b/skynet/dgpu/compute.py
index c309d4b..82e68e2 100755
--- a/skynet/dgpu/compute.py
+++ b/skynet/dgpu/compute.py
@@ -7,6 +7,7 @@ import gc
 import logging
 
 from hashlib import sha256
+from typing import Callable, Generator
 from contextlib import contextmanager as cm
 
 import trio
@@ -20,7 +21,14 @@ from skynet.dgpu.errors import (
     DGPUInferenceCancelled,
 )
 
-from skynet.dgpu.utils import crop_image, convert_from_cv2_to_image, convert_from_image_to_cv2, convert_from_img_to_bytes, init_upscaler, pipeline_for
+from skynet.dgpu.utils import (
+    Pipeline,
+    crop_image,
+    convert_from_cv2_to_image,
+    convert_from_image_to_cv2,
+    convert_from_img_to_bytes,
+    pipeline_for
+)
 
 
 def prepare_params_for_diffuse(
@@ -68,17 +76,21 @@ def prepare_params_for_diffuse(
 
 _model_name: str = ''
 _model_mode: str = ''
-_model = None
+_model: Pipeline | None = None
 
 @cm
-def maybe_load_model(name: str, mode: ModelMode):
+def maybe_load_model(name: str, mode: ModelMode) -> Generator[Pipeline, None, None]:
     if mode == ModelMode.DIFFUSE:
         mode = ModelMode.TXT2IMG
 
     global _model_name, _model_mode, _model
     config = load_skynet_toml().dgpu
 
-    if _model_name != name or _model_mode != mode:
+    if (
+        _model_name != name
+        or
+        _model_mode != mode
+    ):
         # unload model
         _model = None
         gc.collect()
@@ -94,24 +106,26 @@ def maybe_load_model(name: str, mode: ModelMode):
         _model_mode = mode
 
         if torch.cuda.is_available():
-            logging.debug('memory summary:')
-            logging.debug('\n' + torch.cuda.memory_summary())
+            logging.debug(
+                'memory summary:\n'
+                f'{torch.cuda.memory_summary()}'
+            )
 
     yield _model
 
 
 def compute_one(
-    model,
+    model: Pipeline,
     request_id: int,
     method: ModelMode,
     params: BodyV0Params,
     inputs: list[bytes] = [],
-    should_cancel = None
+    should_cancel: Callable[[int, ...], dict] = None
 ):
     total_steps = params.step
     def inference_step_wakeup(*args, **kwargs):
         '''This is a callback function that gets invoked every inference step,
-        we need to raise an exception here if we need to cancel work
+        we must raise DGPUInferenceCancelled here if we need to cancel work
         '''
         step = args[0]
         # compat with callback_on_step_end
@@ -122,6 +136,9 @@ def compute_one(
 
         should_raise = False
         if should_cancel:
+            '''Pump main thread event loop, evaluate if we should keep working
+            on this request, based on latest network info like competitors...
+            '''
             should_raise = trio.from_thread.run(should_cancel, request_id)
 
         if should_raise:
@@ -137,60 +154,56 @@ def compute_one(
     output_type = params.output_type
     output = None
     output_hash = None
-    try:
-        name = params.model
+    name = params.model
 
-        match method:
-            case (
-                ModelMode.DIFFUSE |
-                ModelMode.TXT2IMG |
-                ModelMode.IMG2IMG |
-                ModelMode.INPAINT
-            ):
-                arguments = prepare_params_for_diffuse(
-                    params, method, inputs)
-                prompt, guidance, step, seed, extra_params = arguments
+    match method:
+        case (
+            ModelMode.DIFFUSE |
+            ModelMode.TXT2IMG |
+            ModelMode.IMG2IMG |
+            ModelMode.INPAINT
+        ):
+            arguments = prepare_params_for_diffuse(
+                params, method, inputs)
+            prompt, guidance, step, seed, extra_params = arguments
 
-                if 'flux' in name.lower():
-                    extra_params['callback_on_step_end'] = inference_step_wakeup
+            if 'flux' in name.lower():
+                extra_params['callback_on_step_end'] = inference_step_wakeup
 
-                else:
-                    extra_params['callback'] = inference_step_wakeup
-                    extra_params['callback_steps'] = 1
+            else:
+                extra_params['callback'] = inference_step_wakeup
+                extra_params['callback_steps'] = 1
 
-                output = model(
-                    prompt,
-                    guidance_scale=guidance,
-                    num_inference_steps=step,
-                    generator=seed,
-                    **extra_params
-                ).images[0]
+            output = model(
+                prompt,
+                guidance_scale=guidance,
+                num_inference_steps=step,
+                generator=seed,
+                **extra_params
+            ).images[0]
 
-                output_binary = b''
-                match output_type:
-                    case 'png':
-                        output_binary = convert_from_img_to_bytes(output)
+            output_binary = b''
+            match output_type:
+                case 'png':
+                    output_binary = convert_from_img_to_bytes(output)
 
-                    case _:
-                        raise DGPUComputeError(f'Unsupported output type: {output_type}')
+                case _:
+                    raise DGPUComputeError(f'Unsupported output type: {output_type}')
 
-                output_hash = sha256(output_binary).hexdigest()
+            output_hash = sha256(output_binary).hexdigest()
 
-            case 'upscale':
-                input_img = inputs[0].convert('RGB')
-                up_img, _ = model.enhance(
-                    convert_from_image_to_cv2(input_img), outscale=4)
+        case 'upscale':
+            input_img = inputs[0].convert('RGB')
+            up_img, _ = model.enhance(
+                convert_from_image_to_cv2(input_img), outscale=4)
 
-                output = convert_from_cv2_to_image(up_img)
+            output = convert_from_cv2_to_image(up_img)
 
-                output_binary = convert_from_img_to_bytes(output)
-                output_hash = sha256(output_binary).hexdigest()
+            output_binary = convert_from_img_to_bytes(output)
+            output_hash = sha256(output_binary).hexdigest()
 
-            case _:
-                raise DGPUComputeError('Unsupported compute method')
-
-    except BaseException as err:
-        raise DGPUComputeError(str(err)) from err
+        case _:
+            raise DGPUComputeError('Unsupported compute method')
 
     maybe_update_tui(lambda tui: tui.set_status(''))
 
diff --git a/skynet/dgpu/daemon.py b/skynet/dgpu/daemon.py
index dd7022b..8016e64 100755
--- a/skynet/dgpu/daemon.py
+++ b/skynet/dgpu/daemon.py
@@ -132,6 +132,11 @@ async def maybe_serve_one(
             output_hash = None
             match config.backend:
                 case 'sync-on-thread':
+                    '''Block this task until inference completes, pass
+                    state_mngr.should_cancel_work predicate as the inference_step_wakeup cb
+                    used by torch each step of the inference, it will use a
+                    trio.from_thread to unblock the main thread and pump the event loop
+                    '''
                     output_hash, output = await trio.to_thread.run_sync(
                         partial(
                             compute_one,
diff --git a/skynet/dgpu/tui.py b/skynet/dgpu/tui.py
index e1c572a..ce8db5c 100644
--- a/skynet/dgpu/tui.py
+++ b/skynet/dgpu/tui.py
@@ -141,10 +141,6 @@ class WorkerMonitor:
 
         self.progress_bar.current = current
 
-        pct = 0
-        if self.progress_bar.done != 0:
-            pct = int((self.progress_bar.current / self.progress_bar.done) * 100)
-
     def update_requests(self, new_requests):
         """
         Replace the data in the existing ListBox with new request widgets.
diff --git a/skynet/dgpu/utils.py b/skynet/dgpu/utils.py
index 6a17c93..daf955f 100755
--- a/skynet/dgpu/utils.py
+++ b/skynet/dgpu/utils.py
@@ -75,6 +75,9 @@ class DummyPB:
     def update(self):
         ...
 
+
+type Pipeline = DiffusionPipeline | RealESRGANer
+
 @torch.compiler.disable
 @contextmanager
 def dummy_progress_bar(*args, **kwargs):
@@ -90,7 +93,7 @@ def pipeline_for(
     mode: str,
     mem_fraction: float = 1.0,
     cache_dir: str | None = None
-) -> DiffusionPipeline:
+) -> Pipeline:
     diffusers.utils.logging.disable_progress_bar()
 
     logging.info(f'pipeline_for {model} {mode}')
diff --git a/skynet/types.py b/skynet/types.py
index 45450aa..44e1bce 100644
--- a/skynet/types.py
+++ b/skynet/types.py
@@ -13,7 +13,7 @@ class ModelMode(StrEnum):
 
 class ModelDesc(Struct):
     short: str  # short unique name
-    mem: float  # recomended mem
+    mem: float  # recomended mem in gb
     attrs: dict  # additional mode specific attrs
     tags: list[ModelMode]