diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
index 0242a25..5d216f1 100644
--- a/CODE_OF_CONDUCT.md
+++ b/CODE_OF_CONDUCT.md
@@ -25,6 +25,9 @@ Version = 0.0.5_2025-22-12
 |       Creepy Vibes... | Unacceptable. Words and flirts CAN hurt. End coercion.      |
 |   Users vs Developers | Everyone involved, anywhere. Skill DIVERSITY, not division. |
 
+\*More behavior guidelines
+https://www.recurse.com/social-rules
+
 ## Constructive Criticism Guide:
 
 - Ask consent first. Don't forget to wait for the answer!
diff --git a/divisor/acestep/__init__.py b/divisor/acestep/__init__.py
index db02da5..e69de29 100644
--- a/divisor/acestep/__init__.py
+++ b/divisor/acestep/__init__.py
@@ -1,7 +0,0 @@
-"""
-ACE-Step: A Step Towards Music Generation Foundation Model
-
-https://github.com/ace-step/ACE-Step
-
-Apache 2.0 License
-"""
diff --git a/divisor/acestep/apg_guidance.py b/divisor/acestep/apg_guidance.py
index fbe9a4d..f272e23 100644
--- a/divisor/acestep/apg_guidance.py
+++ b/divisor/acestep/apg_guidance.py
@@ -1,3 +1,6 @@
+# SPDX-License-Identifier:Apache-2.0
+# code from https://github.com/ace-step/ACE-Step
+
 import torch
 
 
@@ -25,9 +28,7 @@ def project(
     v1 = torch.nn.functional.normalize(v1, dim=dims)
     v0_parallel = (v0 * v1).sum(dim=dims, keepdim=True) * v1
     v0_orthogonal = v0 - v0_parallel
-    return v0_parallel.to(dtype).to(device_type), v0_orthogonal.to(dtype).to(
-        device_type
-    )
+    return v0_parallel.to(dtype).to(device_type), v0_orthogonal.to(dtype).to(device_type)
 
 
 def apg_forward(
@@ -67,15 +68,10 @@ def cfg_double_condition_forward(
     guidance_scale_text,
     guidance_scale_lyric,
 ):
-    return (
-        (1 - guidance_scale_text) * uncond_output
-        + (guidance_scale_text - guidance_scale_lyric) * only_text_cond_output
-        + guidance_scale_lyric * cond_output
-    )
+    return (1 - guidance_scale_text) * uncond_output + (guidance_scale_text - guidance_scale_lyric) * only_text_cond_output + guidance_scale_lyric * cond_output
 
 
 def optimized_scale(positive_flat, negative_flat):
-
     # Calculate dot production
     dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
 
@@ -104,7 +100,5 @@ def cfg_zero_star(
     if (i <= zero_steps) and use_zero_init:
         noise_pred = noise_pred_with_cond * 0.0
     else:
-        noise_pred = noise_pred_uncond * alpha + guidance_scale * (
-            noise_pred_with_cond - noise_pred_uncond * alpha
-        )
+        noise_pred = noise_pred_uncond * alpha + guidance_scale * (noise_pred_with_cond - noise_pred_uncond * alpha)
     return noise_pred
diff --git a/divisor/acestep/cpu_offload.py b/divisor/acestep/cpu_offload.py
index 17f3f30..5c582e0 100644
--- a/divisor/acestep/cpu_offload.py
+++ b/divisor/acestep/cpu_offload.py
@@ -1,6 +1,10 @@
+# SPDX-License-Identifier:Apache-2.0
+# adapted from https://github.com/ace-step/ACE-Step
+
 import torch
 import functools
 from typing import Callable, TypeVar
+from divisor.registry import gfx_sync, empty_cache
 
 
 class CpuOffloader:
@@ -8,21 +12,21 @@ def __init__(self, model, device="cpu"):
         self.model = model
         self.original_device = device
         self.original_dtype = model.dtype
-    
+
     def __enter__(self):
-        if not hasattr(self.model,"torchao_quantized"):
+        if not hasattr(self.model, "torchao_quantized"):
             self.model.to(self.original_device, dtype=self.original_dtype)
         return self.model
-    
+
     def __exit__(self, *args):
-        if not hasattr(self.model,"torchao_quantized"):
+        if not hasattr(self.model, "torchao_quantized"):
             self.model.to("cpu")
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
+        gfx_sync
+        empty_cache
+
 
+T = TypeVar("T")
 
-T = TypeVar('T')
 
 def cpu_offload(model_attr: str):
     def decorator(func: Callable[..., T]) -> Callable[..., T]:
@@ -35,9 +39,10 @@ def wrapper(self, *args, **kwargs):
             device = self.device
             # Get the model from the class attribute
             model = getattr(self, model_attr)
-            
+
             with CpuOffloader(model, device):
                 return func(self, *args, **kwargs)
-                        
+
         return wrapper
+
     return decorator
diff --git a/divisor/acestep/gradio.py b/divisor/acestep/gradio.py
index c659840..784f649 100644
--- a/divisor/acestep/gradio.py
+++ b/divisor/acestep/gradio.py
@@ -1,10 +1,6 @@
-"""
-ACE-Step: A Step Towards Music Generation Foundation Model
+# SPDX-License-Identifier:Apache-2.0
+# adapted from https://github.com/ace-step/ACE-Step
 
-https://github.com/ace-step/ACE-Step
-
-Apache 2.0 License
-"""
 
 import os
 
diff --git a/divisor/acestep/pipeline_ace_step.py b/divisor/acestep/pipeline_ace_step.py
index ac17894..b5e397f 100644
--- a/divisor/acestep/pipeline_ace_step.py
+++ b/divisor/acestep/pipeline_ace_step.py
@@ -1,9 +1,8 @@
+# SPDX-License-Identifier:Apache-2.0
+# adapted from https://github.com/ace-step/ACE-Step
+
 """
 ACE-Step: A Step Towards Music Generation Foundation Model
-
-https://github.com/ace-step/ACE-Step
-
-Apache 2.0 License
 """
 
 import json
@@ -14,6 +13,8 @@
 import time
 from typing import Literal
 
+import torch
+import torchaudio
 from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3 import (
     retrieve_timesteps,
 )
@@ -21,9 +22,6 @@
 from diffusers.utils.torch_utils import randn_tensor
 from huggingface_hub import snapshot_download
 from nnll.console import nfo
-from nnll.init_gpu import clear_cache, device
-import torch
-import torchaudio
 from tqdm import tqdm
 from transformers import AutoTokenizer, UMT5EncoderModel
 
@@ -46,14 +44,15 @@
     FlowMatchHeunDiscreteScheduler,
 )
 from divisor.acestep.schedulers.scheduling_flow_match_pingpong import FlowMatchPingPongScheduler
+from divisor.registry import gfx_device, empty_cache
 
-if device.type == "cuda":
+if gfx_device.type == "cuda":
     torch.backends.cudnn.benchmark = False
     torch.set_float32_matmul_precision("high")
     torch.backends.cudnn.deterministic = True
     torch.backends.cuda.matmul.allow_tf32 = False
     os.environ["TOKENIZERS_PARALLELISM"] = "false"
-elif device.type == "mps":
+elif gfx_device.type == "mps":
     os.environ["DYLD_FALLBACK_LIBRARY_PATH"] = "/opt/homebrew/lib"
 
 SUPPORT_LANGUAGES = {
@@ -116,13 +115,13 @@ def __init__(
         self.lora_path = "none"
         self.lora_weight = 1
         self.dtype = torch.bfloat16 if dtype == "bfloat16" else torch.float32
-        if device.type == "mps":
+        if gfx_device.type == "mps":
             if self.dtype == torch.bfloat16:
                 self.dtype = torch.float16
 
         if "ACE_PIPELINE_DTYPE" in os.environ and len(os.environ["ACE_PIPELINE_DTYPE"]):
             self.dtype = getattr(torch, os.environ["ACE_PIPELINE_DTYPE"])
-        self.device = device
+        self.device: torch.device = gfx_device
         self.loaded = False
         self.torch_compile = torch_compile
         self.cpu_offload = cpu_offload
@@ -205,8 +204,8 @@ def load_checkpoint(self, checkpoint_dir=None, export_quantized_weights=False):
         if self.torch_compile:
             if export_quantized_weights:
                 from torch.ao.quantization import (
-                    quantize_,
                     Int4WeightOnlyConfig,
+                    quantize_,
                 )
 
                 group_size = 128
@@ -1475,7 +1474,7 @@ def __call__(
         )
 
         # Clean up memory after generation
-        clear_cache()
+        empty_cache
 
         end_time = time.time()
         latent2audio_time_cost = end_time - start_time
diff --git a/divisor/controller.py b/divisor/controller.py
index 14f70eb..0800c65 100644
--- a/divisor/controller.py
+++ b/divisor/controller.py
@@ -6,21 +6,21 @@
 Allows users to manually increment through timesteps one at a time.
 """
 
-from dataclasses import asdict
 import json
+from dataclasses import asdict
 from typing import Any, Callable, Optional
 
+import torch
 from nnll.console import nfo
 from nnll.hyperchain import HyperChain
-from nnll.init_gpu import device
 from nnll.random import RNGState
-import torch
 
 from divisor.interaction_context import InteractionContext
+from divisor.registry import gfx_device
 from divisor.state import MenuState, StepState
 
-rng = RNGState(device=device.type)
-variation_rng = RNGState(device=device.type)
+rng = RNGState(device=gfx_device.type)
+variation_rng = RNGState(device=gfx_device.type)
 
 
 def time_shift(
diff --git a/divisor/denoise_step.py b/divisor/denoise_step.py
index 02f1b6f..f3db555 100644
--- a/divisor/denoise_step.py
+++ b/divisor/denoise_step.py
@@ -6,7 +6,7 @@
 from typing import Any, Callable, Optional
 
 from einops import repeat
-from nnll.init_gpu import device
+from divisor.registry import gfx_device
 import torch
 from torch import Tensor
 
@@ -193,7 +193,7 @@ def get_prediction(
         except (TypeError, StopIteration, AttributeError):
             # Fallback: use sample dtype if we can't get model dtype (for Mock objects in tests)
             model_dtype = sample.dtype
-        use_autocast = device.type == "cuda"
+        use_autocast = gfx_device.type == "cuda"
 
         # Ensure sample is in correct dtype before any operations
         if not use_autocast:
diff --git a/divisor/dimoo/inference_mmu.py b/divisor/dimoo/inference_mmu.py
index e4257c2..a582892 100644
--- a/divisor/dimoo/inference_mmu.py
+++ b/divisor/dimoo/inference_mmu.py
@@ -10,15 +10,15 @@
 import sys
 import time
 
-from huggingface_hub import snapshot_download
-from nnll.init_gpu import device
 import torch
+from huggingface_hub import snapshot_download
 from transformers import AutoTokenizer
 
 from divisor.dimoo.config import SPECIAL_TOKENS
 from divisor.dimoo.prompt_utils import generate_text_prompt
 from divisor.dimoo.text_understanding_generator import generate_text_understanding
 from divisor.mmada.modeling_llada import LLaDAModelLM
+from divisor.registry import gfx_device
 
 sys.path.append(os.path.dirname(os.path.dirname(__file__)))
 
diff --git a/divisor/dimoo/text_understanding_generator.py b/divisor/dimoo/text_understanding_generator.py
index e101472..fec7f7c 100644
--- a/divisor/dimoo/text_understanding_generator.py
+++ b/divisor/dimoo/text_understanding_generator.py
@@ -7,12 +7,11 @@
 
 from typing import Optional
 
-from nnll.init_gpu import device
 import numpy as np
 import torch
 import torch.nn.functional as F
 
-from divisor.contents import get_dtype
+from divisor.registry import gfx_dtype, gfx_device
 from divisor.mmada.live_token import get_num_transfer_tokens
 from divisor.noise import add_gumbel_noise
 
@@ -46,7 +45,7 @@ def generate_text_understanding(
         code_start: Prediction text token satrt index
     """
     device = next(model.parameters()).device or device
-    precision = get_dtype(device)
+    precision = gfx_dtype
     x = prompt
 
     prompt_index = x != mask_id
diff --git a/divisor/flux1/loading.py b/divisor/flux1/loading.py
index ff33636..020e84d 100644
--- a/divisor/flux1/loading.py
+++ b/divisor/flux1/loading.py
@@ -6,26 +6,29 @@
 import os
 from pathlib import Path
 
+import torch
 from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
 from huggingface_hub import snapshot_download
 from nnll.console import nfo
-from nnll.init_gpu import device
 from safetensors.torch import load_file as load_sft
-import torch
 
-from divisor.flux1.autoencoder import AutoEncoder as AutoEncoder1, AutoEncoderParams
+from divisor.flux1.autoencoder import AutoEncoder as AutoEncoder1
+from divisor.flux1.autoencoder import AutoEncoderParams
 from divisor.flux1.model import Flux, FluxLoraWrapper
 from divisor.flux1.text_embedder import HFEmbedder
 from divisor.flux2.autoencoder import (
     AutoEncoder as AutoEncoder2,
+)
+from divisor.flux2.autoencoder import (
     AutoEncoderParams as AutoEncoder2Params,
 )
 from divisor.flux2.model import Flux2, Flux2Params
 from divisor.flux2.text_encoder import Mistral3SmallEmbedder
-from divisor.spec import ModelSpec, CompatibilitySpec, optionally_expand_state_dict
+from divisor.mmada.modeling_mmada import MMadaConfig as MMaDAParams
+from divisor.mmada.modeling_mmada import MMadaModelLM as MMaDAModelLM
+from divisor.registry import gfx_device, gfx_dtype
+from divisor.spec import ModelSpec, optionally_expand_state_dict
 from divisor.xflux1.model import XFlux, XFluxParams
-from divisor.contents import get_dtype
-from divisor.mmada.modeling_mmada import MMadaConfig as MMaDAParams, MMadaModelLM as MMaDAModelLM
 
 
 def print_load_warning(missing: list[str], unexpected: list[str]) -> None:
@@ -112,7 +115,7 @@ def load_lora_weights(
     model: FluxLoraWrapper,
     lora_repo_id: str,
     lora_filename: str,
-    device: str | torch.device = device,
+    device: str | torch.device = gfx_device,
     verbose: bool = True,
 ) -> None:
     """Load LoRA weights into a FluxLoraWrapper model.\n
@@ -131,7 +134,7 @@ def load_lora_weights(
 
 def load_flow_model(
     model_spec: ModelSpec,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
     verbose: bool = True,
     lora_repo_id: str | None = None,
     lora_filename: str | None = None,
@@ -173,7 +176,7 @@ def load_flow_model(
 
 def load_ae(
     model_spec: ModelSpec,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
 ) -> AutoEncoder1 | AutoEncoder2 | AutoencoderTiny:
     """Load the autoencoder model.\n
     :param mir_id: Model ID (e.g., "model.vae.flux1-dev" or "model.taesd.flux1-dev")
@@ -203,7 +206,7 @@ def load_ae(
 
 def load_mmada_model(
     model_spec: ModelSpec,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
 ) -> MMaDAModelLM:
     """Load a MMaDA model\n
     :param model_spec: ModelSpec object containing model details
@@ -213,7 +216,7 @@ def load_mmada_model(
     :returns: Loaded MMaDA model
     :raises: TypeError if model_spec.params is not a MMaDAParams
     """
-    precision = get_dtype(device)
+    precision = gfx_dtype
     if isinstance(model_spec.params, MMaDAParams):
         model_spec.params.llm_model_path = model_spec.repo_id
         model = MMaDAModelLM.from_pretrained(model_spec.repo_id, dtype=precision)  # type: ignore
@@ -224,14 +227,14 @@ def load_mmada_model(
     raise TypeError(f"MMaDA params not found for: {model_spec.repo_id} with params type {type(model_spec.params).__name__}")
 
 
-def load_t5(device: str | torch.device = device, max_length: int = 512) -> HFEmbedder:
+def load_t5(device: str | torch.device = gfx_device, max_length: int = 512) -> HFEmbedder:
     # max length 64, 128, 256 and 512 should work (if your sequence is short enough)
     return HFEmbedder("google/t5-v1_1-xxl", max_length=max_length, dtype=torch.bfloat16).to(device)
 
 
-def load_clip(device: str | torch.device = device) -> HFEmbedder:
+def load_clip(device: str | torch.device = gfx_device) -> HFEmbedder:
     return HFEmbedder("openai/clip-vit-large-patch14", max_length=77, dtype=torch.bfloat16).to(device)
 
 
-def load_mistral_small_embedder(device: str | torch.device = device) -> Mistral3SmallEmbedder:
+def load_mistral_small_embedder(device: str | torch.device = gfx_device) -> Mistral3SmallEmbedder:
     return Mistral3SmallEmbedder().to(device)
diff --git a/divisor/flux1/prompt.py b/divisor/flux1/prompt.py
index faf3396..f14f6ce 100644
--- a/divisor/flux1/prompt.py
+++ b/divisor/flux1/prompt.py
@@ -6,7 +6,7 @@
 
 from fire import Fire
 from nnll.console import nfo
-from nnll.init_gpu import clear_cache, device
+from divisor.registry import gfx_device, empty_cache
 import torch
 
 from divisor.controller import rng
@@ -94,7 +94,7 @@ def main(
     seed: int = rng.next_seed(),
     prompt: str = "",
     quantization: bool = False,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
     num_steps: int = 50,
     loop: bool = False,
     offload: bool = False,
@@ -186,7 +186,7 @@ def main(
 
         if offload:
             ae = ae.cpu()
-            clear_cache()
+            empty_cache
 
             t5, clip = t5.to(device), clip.to(device)
         x_3d = prepare_4d_noise_for_3d_model(
@@ -211,7 +211,7 @@ def main(
         # offload TEs to CPU, load model to gpu
         if offload:
             t5, clip = t5.cpu(), clip.cpu()
-            clear_cache()
+            empty_cache
             if is_compiled:
                 raise RuntimeError("Can't move compiled models. Compile on target device, or disable compilation when offloading.")
             model = model.to(device)  # type: ignore[attr-defined]
diff --git a/divisor/flux1/sampling.py b/divisor/flux1/sampling.py
index 486f8e0..85a3006 100644
--- a/divisor/flux1/sampling.py
+++ b/divisor/flux1/sampling.py
@@ -6,12 +6,9 @@
 import time
 from typing import Callable, Optional
 
+import torch
 from einops import rearrange, repeat
 from nnll.console import nfo
-from nnll.constants import ExtensionType
-from nnll.init_gpu import sync_torch
-from nnll.save_generation import name_save_file_as, save_with_hyperchain
-import torch
 
 from torch import Tensor
 
@@ -26,9 +23,11 @@
 from divisor.flux1.model import Flux
 from divisor.flux1.text_embedder import HFEmbedder
 from divisor.interaction_context import InteractionContext
+from divisor.registry import gfx_sync
+from divisor.save import SaveFile
 from divisor.state import (
-    InferenceState,
     ImageEmbeddingState,
+    InferenceState,
     TextEmbeddingState,
 )
 
@@ -135,7 +134,7 @@ def denoise(
     img_cond = settings.img_cond
     img_cond_seq = settings.img_cond_seq
     img_cond_seq_ids = settings.img_cond_seq_ids
-    from nnll.init_gpu import device as default_device
+    from divisor.registry import gfx_device as default_device
 
     denoise_device = settings.device if settings.device is not None else default_device
     initial_layer_dropout = settings.initial_layer_dropout
@@ -234,7 +233,6 @@ def denoise(
 
     # Interactive loop
     while not controller.is_complete:
-        file_path_named = name_save_file_as(ExtensionType.WEBP)
         state = controller.current_state
 
         # Check if prompt changed and recompute embeddings if needed
@@ -281,7 +279,7 @@ def denoise(
             # Unpack requires float32, but we'll convert back to correct dtype after
             intermediate = unpack(intermediate.float(), state.height, state.width)
 
-            sync_torch(denoise_device)
+            gfx_sync
             t1 = time.perf_counter()
 
             nfo(f"Step time: {t1 - t0:.1f}s")
@@ -313,12 +311,10 @@ def denoise(
                     intermediate_image = ae.decode(intermediate)
                 if state.seed is not None:
                     controller.store_state_in_chain(current_seed=state.seed)
-                save_with_hyperchain(
-                    file_path_named,
-                    intermediate_image,
-                    controller.hyperchain,
-                    ExtensionType.WEBP,
-                )
+                with SaveFile() as saver:
+                    saver.intermediate_image = intermediate_image  # set up image
+                    saver.hyperchain = (controller.hyperchain,)  # set up hyperchain
+                    saver.with_hyperchain()
 
     return controller.current_sample
 
diff --git a/divisor/flux2/prompt.py b/divisor/flux2/prompt.py
index da08457..89e1142 100644
--- a/divisor/flux2/prompt.py
+++ b/divisor/flux2/prompt.py
@@ -4,13 +4,11 @@
 
 from dataclasses import replace
 
-from PIL import Image
+import torch
 from fire import Fire
 from nnll.console import nfo
-from nnll.init_gpu import clear_cache, device
-import torch
+from PIL import Image
 
-from divisor.contents import get_dtype
 from divisor.controller import rng
 from divisor.flux1.loading import load_ae, load_flow_model, load_mistral_small_embedder
 from divisor.flux1.prompt import parse_prompt
@@ -22,7 +20,8 @@
     get_schedule,
 )
 from divisor.noise import get_noise
-from divisor.spec import flux_configs, get_model_spec, ModelSpec
+from divisor.registry import gfx_device, gfx_dtype, empty_cache
+from divisor.spec import ModelSpec, flux_configs, get_model_spec
 from divisor.state import InferenceState, MenuState
 
 
@@ -35,7 +34,7 @@ def main(
     seed: int = rng.next_seed(),
     prompt: str = "",
     quantization: bool = False,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
     num_steps: int = 50,
     upsample_prompt: bool = False,
     loop: bool = False,
@@ -57,7 +56,7 @@ def main(
     :param guidance: guidance value used for guidance distillation
     """
 
-    precision = get_dtype(device)
+    precision = gfx_dtype
     prompt_parts = prompt.split("|")
     if len(prompt_parts) == 1:
         prompt = prompt_parts[0]
@@ -168,7 +167,7 @@ def main(
                 else:
                     mistral = None
                     del mistral
-                clear_cache()
+                empty_cache
                 if is_compiled:
                     raise RuntimeError("Cannot use offload=True with compile=True. Compile after model is on device, or disable compilation when offloading.")
                 model = model.to(device)  # type: ignore[attr-defined]
diff --git a/divisor/flux2/sampling.py b/divisor/flux2/sampling.py
index b9490bf..46f0b65 100644
--- a/divisor/flux2/sampling.py
+++ b/divisor/flux2/sampling.py
@@ -9,9 +9,6 @@
 from PIL import Image
 from einops import rearrange
 from nnll.console import nfo
-from nnll.constants import ExtensionType
-from nnll.init_gpu import sync_torch
-from nnll.save_generation import name_save_file_as, save_with_hyperchain
 import torch
 from torch import Tensor
 import torchvision
@@ -24,8 +21,8 @@
     create_get_prediction,
     create_recompute_text_embeddings,
 )
-from divisor.contents import get_dtype
-from nnll.init_gpu import device
+from divisor.registry import gfx_device, gfx_dtype, gfx_sync
+from divisor.save import SaveFile
 from divisor.flux2.model import Flux2
 from divisor.state import (
     InferenceState,
@@ -75,7 +72,7 @@ def scatter_ids(x: Tensor, x_ids: Tensor) -> list[Tensor]:
 
 
 def encode_image_refs(ae, img_ctx: list[Image.Image]):
-    precision = get_dtype(device)
+    precision = gfx_dtype
     scale = 10
 
     if len(img_ctx) > 1:
@@ -94,7 +91,7 @@ def encode_image_refs(ae, img_ctx: list[Image.Image]):
 
     # Encode each reference image
     encoded_refs = []
-    torch_device = torch.device("cuda") if torch.cuda.is_available() else torch.device("mps")
+    torch_device = gfx_device
     for img in img_ctx_prep:
         encoded = ae.encode(img[None].to(torch_device))[0]
         encoded_refs.append(encoded)
@@ -347,7 +344,7 @@ def denoise_interactive(
     timesteps = settings.timesteps
     img_cond_seq = settings.img_cond_seq
     img_cond_seq_ids = settings.img_cond_seq_ids
-    from nnll.init_gpu import device as default_device
+    from divisor.registry import gfx_device as default_device
 
     denoise_device = settings.device if settings.device is not None else default_device
     initial_layer_dropout = settings.initial_layer_dropout
@@ -444,7 +441,6 @@ def denoise_interactive(
 
     # Interactive loop
     while not controller.is_complete:
-        file_path_named = name_save_file_as(ExtensionType.WEBP)
         state = controller.current_state
 
         # Check if prompt changed and recompute embeddings if needed
@@ -498,7 +494,7 @@ def denoise_interactive(
                 if intermediate.dim() == 5:
                     intermediate = intermediate[:, :, 0, :, :]  # Take first time slice
 
-            sync_torch(denoise_device)
+            gfx_sync
             t1 = time.perf_counter()
 
             nfo(f"Step time: {t1 - t0:.1f}s")
@@ -526,12 +522,10 @@ def denoise_interactive(
                     intermediate_image = ae.decode(intermediate)
                 if state.seed is not None:
                     controller.store_state_in_chain(current_seed=state.seed)
-                save_with_hyperchain(
-                    file_path_named,
-                    intermediate_image,
-                    controller.hyperchain,
-                    ExtensionType.WEBP,
-                )
+                with SaveFile() as saver:
+                    saver.intermediate_image = intermediate_image  # set up image
+                    saver.hyperchain = (controller.hyperchain,)  # set up hyperchain
+                    saver.with_hyperchain()
 
     return controller.current_sample
 
diff --git a/divisor/flux2/text_encoder.py b/divisor/flux2/text_encoder.py
index edb492e..6771052 100644
--- a/divisor/flux2/text_encoder.py
+++ b/divisor/flux2/text_encoder.py
@@ -6,12 +6,12 @@
 from PIL import Image
 from einops import rearrange
 from nnll.console import nfo
-from nnll.init_gpu import device
+from divisor.registry import gfx_dtype
 import torch
 import torch.nn as nn
 from transformers import AutoProcessor, Mistral3ForConditionalGeneration
 
-from divisor.contents import get_dtype
+
 from divisor.flux2.sampling import cap_pixels, concatenate_images
 from divisor.flux2.system_messages import (
     PROMPT_IMAGE_INTEGRITY,
@@ -26,7 +26,7 @@
 OUTPUT_LAYERS = [10, 20, 30]
 MAX_LENGTH = 512
 UPSAMPLING_MAX_IMAGE_SIZE = 768**2
-precision: torch.dtype = get_dtype(device)
+precision: torch.dtype = gfx_dtype
 
 
 class Mistral3SmallEmbedder(nn.Module):
diff --git a/divisor/mmada/app.py b/divisor/mmada/app.py
index 764ce6d..3882b49 100644
--- a/divisor/mmada/app.py
+++ b/divisor/mmada/app.py
@@ -2,11 +2,11 @@
 # Adapted from https://github.com/Gen-Verse/MMaDA
 
 from nnll.console import nfo
-from nnll.init_gpu import device
+from divisor.registry import gfx_device, gfx_dtype
 import torch
 import torch.nn.functional as F
 
-from divisor.contents import get_dtype
+
 from divisor.flux1.loading import load_mmada_model
 from divisor.mmada.live_token import (
     get_highlighted_text_tuples,
@@ -41,7 +41,7 @@ def generate_viz_wrapper_lm(
     remasking_strategy,
     thinking_mode_lm,
 ):
-    precision = get_dtype(device)
+    precision = gfx_dtype
     model_spec: ModelSpec = get_model_spec(mir_id, mmada_configs)
     if not isinstance(model_spec.params, MMaDAParams) or not isinstance(model_spec.init, InitialParamsMMaDA):
         raise TypeError(
@@ -52,7 +52,7 @@ def generate_viz_wrapper_lm(
     else:
         mask_id = model_spec.init.mask_id
         max_position_embeddings = model_spec.init.max_position_embeddings
-        model = load_mmada_model(model_spec, device=device)
+        model = load_mmada_model(model_spec, device=gfx_device)
         hf = HFEmbedder(model_spec.repo_id, max_length=max_position_embeddings)
     if thinking_mode_lm:
         prompt_text = THINKING_MODE_LM_PROMPT + prompt_text
@@ -62,7 +62,7 @@ def generate_viz_wrapper_lm(
     prompt_len = input_ids.shape[1]
     raw_prompt_attention_mask = None
 
-    x = torch.full((batch_size, prompt_len + gen_length), mask_id, dtype=torch.long, device=device)
+    x = torch.full((batch_size, prompt_len + gen_length), mask_id, dtype=torch.long, device=gfx_device)
     x[:, :prompt_len] = input_ids.clone()
     nfo(f"Starting generation: Prompt ({prompt_len} tokens) + Initial Masks")
     # yield get_highlighted_text_tuples(x, input_ids, prompt_len, TOKENIZER, MASK_ID, raw_prompt_attention_mask), "Starting generation: Prompt + Initial Masks"
diff --git a/divisor/mmada/gradio.py b/divisor/mmada/gradio.py
index 177efa7..5e1b663 100644
--- a/divisor/mmada/gradio.py
+++ b/divisor/mmada/gradio.py
@@ -3,7 +3,7 @@
 
 import gradio as gr
 from nnll.console import nfo
-from nnll.init_gpu import device
+from divisor.registry import gfx_device
 
 from divisor.flux1.loading import load_mmada_model
 from divisor.mmada import app
@@ -113,7 +113,7 @@ def initialize_model():
         default_model_id = model_choices[0]
         try:
             model_spec = get_model_spec(default_model_id, mmada_configs)
-            load_mmada_model(model_spec, device=device)
+            load_mmada_model(model_spec, device=gfx_device)
             status = f"Model '{default_model_id}' loaded successfully."
         except Exception as e:
             status = f"Error loading model '{default_model_id}': {str(e)}"
@@ -144,7 +144,7 @@ def clear_outputs():
 
 
 def main():
-    nfo(f"Starting Gradio App. Attempting to use device: {device.type}")
+    nfo(f"Starting Gradio App. Attempting to use device: {gfx_device.type}")
     demo.launch(share=True)
 
 
diff --git a/divisor/mmada/modeling_llada.py b/divisor/mmada/modeling_llada.py
index cb7225a..0a6c538 100644
--- a/divisor/mmada/modeling_llada.py
+++ b/divisor/mmada/modeling_llada.py
@@ -31,7 +31,7 @@
 from transformers.cache_utils import Cache
 from contextlib import nullcontext
 
-from nnll.init_gpu import device
+from divisor.registry import gfx_device
 
 if sys.version_info.minor > 8:
     from collections.abc import MutableMapping
@@ -195,7 +195,7 @@ def _non_meta_init_device(config: ModelConfig) -> torch.device:
     if config.init_device is not None and config.init_device != "meta":
         return torch.device(config.init_device)
     else:
-        return device
+        return gfx_device
 
 
 class Dropout(nn.Dropout):
@@ -262,7 +262,7 @@ def reset_parameters(self):
             torch.nn.init.zeros_(self.bias)  # type: ignore
 
 
-def get_autocast_context(device: torch.device = device) -> torch.autocast | nullcontext:
+def get_autocast_context(device: torch.device = gfx_device) -> torch.autocast | nullcontext:
     if device.type == "cuda" or device.type == "cpu":
         context = torch.autocast(device_type=device.type, enabled=False)
     else:
@@ -617,7 +617,7 @@ def _cast_attn_bias(cls, bias: torch.Tensor, input_dtype: torch.dtype) -> torch.
         # `is_autocast_cpu_enabled()` for CPU autocast.
         # See https://github.com/pytorch/pytorch/issues/110966.
         if torch.is_autocast_enabled():
-            target_dtype = torch.get_autocast_dtype(device.type)
+            target_dtype = torch.get_autocast_dtype(gfx_device.type)
         if bias.dtype != target_dtype:
             bias = bias.to(target_dtype)
             ensure_finite_(bias, check_neg_inf=True, check_pos_inf=False)
@@ -1018,7 +1018,7 @@ def __init__(self, config: ModelConfig, init_params: bool = True):
         if not (0 < self.config.block_group_size <= self.config.n_layers and self.config.n_layers % self.config.block_group_size == 0):
             raise Exception("n layers must be divisible by block group size")
 
-        if self.config.init_device == "cuda" or device.type == "cuda":
+        if self.config.init_device == "cuda" or gfx_device.type == "cuda":
             torch.backends.cuda.enable_flash_sdp(True)
             torch.backends.cuda.enable_mem_efficient_sdp(False)  # this is super slow so make sure torch won't use it
 
diff --git a/divisor/mmada/modeling_mmada.py b/divisor/mmada/modeling_mmada.py
index a7e9036..ced77f8 100644
--- a/divisor/mmada/modeling_mmada.py
+++ b/divisor/mmada/modeling_mmada.py
@@ -3,22 +3,21 @@
 
 from __future__ import annotations
 
-from PIL import Image
-from nnll.init_gpu import device
 import numpy as np
 import torch
 import torch.nn.functional as F
 from nnll.console import nfo
+from PIL import Image
 from transformers import PretrainedConfig
 from transformers.models.auto import AutoConfig, AutoModel, AutoModelForCausalLM
 
-from divisor.contents import get_dtype
 from divisor.mmada.live_token import get_num_transfer_tokens
 from divisor.mmada.modeling_llada import LLaDAModelLM
 from divisor.mmada.sampling import cosine_schedule, mask_by_random_topk
 from divisor.noise import add_gumbel_noise
+from divisor.registry import gfx_device, gfx_dtype
 
-if device.type == "cuda":
+if gfx_device.type == "cuda":
     import torch.backends.cuda
 
 
@@ -382,7 +381,7 @@ def mmu_generate(
 
         # print(f"num_blocks: {num_blocks}, steps: {steps}")
         # num_transfer_tokens = get_num_transfer_tokens(prompt_index, steps)
-        precision = get_dtype(device)
+        precision = gfx_dtype
         for num_block in range(num_blocks):
             block_mask_index = x[:, idx.shape[1] + num_block * block_length : idx.shape[1] + (num_block + 1) * block_length :] == mask_id
             num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
@@ -487,7 +486,7 @@ def mmu_generate_fast(
         assert steps % num_blocks == 0
         steps = steps // num_blocks
 
-        precision = get_dtype(device)
+        precision = gfx_dtype
         for num_block in range(num_blocks):
             block_mask_index = x[:, idx.shape[1] + num_block * block_length : idx.shape[1] + (num_block + 1) * block_length :] == mask_id
             num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
diff --git a/divisor/mmada/sampling.py b/divisor/mmada/sampling.py
index 127c62d..6e1ff95 100644
--- a/divisor/mmada/sampling.py
+++ b/divisor/mmada/sampling.py
@@ -5,7 +5,7 @@
 from functools import partial
 import math
 
-from nnll.init_gpu import device
+from divisor.registry import gfx_device
 import torch
 import torch.nn.functional as F
 from torchvision import transforms
diff --git a/divisor/mmada/text_embedder.py b/divisor/mmada/text_embedder.py
index b56846e..1abe95a 100644
--- a/divisor/mmada/text_embedder.py
+++ b/divisor/mmada/text_embedder.py
@@ -2,7 +2,7 @@
 # Adapted from https://github.com/Gen-Verse/MMaDA
 # Adapted from https://github.com/black-forest-labs/flux
 
-from nnll.init_gpu import device
+from divisor.registry import gfx_device
 import torch
 from transformers import AutoTokenizer
 
diff --git a/divisor/noise.py b/divisor/noise.py
index e0af9ad..9a0c417 100644
--- a/divisor/noise.py
+++ b/divisor/noise.py
@@ -6,12 +6,11 @@
 import math
 from typing import Any, Optional
 
-from nnll.init_gpu import device
 import torch
 from torch import Tensor
 
-from divisor.contents import get_dtype
 from divisor.controller import rng
+from divisor.registry import gfx_dtype
 
 
 def get_noise(
@@ -118,7 +117,7 @@ def add_gumbel_noise(logits, temperature):
     if abs(temperature) < 1e-9:  # Effectively zero temperature
         return logits
 
-    max_device_precision = get_dtype(device, max_precision=True)
+    max_device_precision = gfx_dtype(full_precision=True)
     logits = logits.to(max_device_precision)
     noise = torch.rand_like(logits, dtype=max_device_precision)
     # Standard Gumbel noise: -log(-log(U)), U ~ Uniform(0,1) Add small epsilon for numerical stability inside logs
@@ -131,7 +130,7 @@ def add_unstable_gumbel_noise(logits, temperature):
     """The Gumbel max is a method for sampling categorical distributions.\n
     arXiv:2409.02908 low-precision Gumbel Max improves MDM perplexity score but reduces generation quality.\n
     Thus, we use float64... unless mps, in which case we must use float32"""
-    precision = get_dtype(device)
+    precision = gfx_dtype
     if temperature == 0:
         return logits
     logits = logits.to(precision)
diff --git a/divisor/contents.py b/divisor/registry.py
similarity index 64%
rename from divisor/contents.py
rename to divisor/registry.py
index fdd468e..ccbec42 100644
--- a/divisor/contents.py
+++ b/divisor/registry.py
@@ -2,8 +2,20 @@
 # <!-- // /*  d a r k s h a p e s */ -->
 
 from typing import Any
-import torch
-from nnll.init_gpu import device
+
+from nnll.init_gpu import Gfx
+
+
+def _init_gfx() -> Gfx:
+    """Initialise (and cache) a single :class:`~nnll.init_gpu.Gfx` instance."""
+    return Gfx(full_precision=False)
+
+
+gfx: Gfx = _init_gfx()
+gfx_device = gfx.device
+gfx_dtype = gfx.dtype
+gfx_sync = gfx.sync
+empty_cache = gfx.empty_cache
 
 
 def get_lora_rank(checkpoint):
@@ -12,19 +24,6 @@ def get_lora_rank(checkpoint):
             return checkpoint[k].shape[0]
 
 
-def get_dtype(device: torch.device = device, max_precision: bool = False) -> torch.dtype:
-    """Assign dtype based on accelerator availablilty\n
-    :param device: Device to get the dtype for
-    :param max_precision: If True, return the maximum precision dtype for the given device
-    :returns: Dtype for the given device"""
-    dtype_by_device = {
-        "cuda": torch.bfloat16 if not max_precision else torch.float64,
-        "mps": torch.bfloat16 if not max_precision else torch.float32,
-        "cpu": torch.float32,
-    }
-    return dtype_by_device[device.type]
-
-
 def populate_model_choices(configs: dict[str, Any]) -> list[str]:
     """Generate model choices from all entries in configs\n
     :returns: List of model choices"""
@@ -54,3 +53,21 @@ def build_available_models(configs: dict[str, Any]) -> dict[str, str]:
                 model_args[key] = model
 
     return model_args
+
+
+if __name__ == "__main__":
+    import typing as _t
+
+    def _debug_dump() -> dict[str, _t.Any]:
+        """
+        Return a dictionary with the most important registry values.
+        Useful when debugging import‑order issues.
+        """
+        return {
+            "gfx": gfx,
+            "device": gfx_device,
+            "device_repr": repr(gfx_device),
+            "device.type": gfx_device.type,
+        }
+
+    print(_debug_dump())
diff --git a/divisor/save.py b/divisor/save.py
new file mode 100644
index 0000000..55802e9
--- /dev/null
+++ b/divisor/save.py
@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: MPL-2.0 AND LicenseRef-Commons-Clause-License-Condition-1.0
+# <!-- // /*  d a r k s h a p e s */ -->
+
+from pathlib import Path
+from nnll.save_generation import name_save_file_as, save_output
+from nnll.constants import ExtensionType
+from nnll.hyperchain import HyperChain
+from PIL import Image
+
+
+class SaveFile:
+    intermediate_image: Image
+    hyperchain: HyperChain
+    extension: ExtensionType = ExtensionType.WEBP
+    save_folder_path: Path = Path(__file__).resolve().parent.parent / ".output"
+
+    def __enter__(self) -> "SaveFile":
+        """Return instanced self"""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb) -> None:
+        """Perform cleanup regardless of success or exception"""
+        self._cleanup()
+
+    def _cleanup(self) -> None:
+        """Release resources held by the instance and close methods (PIL.Image)"""
+        if hasattr(self.intermediate_image, "close"):
+            try:
+                self.intermediate_image.close()
+            except Exception:
+                pass  # ignore errors during cleanup
+
+        self.intermediate_image = None  # type: ignore[assignment]
+        self.hyperchain = None  # type: ignore[assignment]
+
+    def with_hyperchain(self) -> None:
+        file_path_named = name_save_file_as(
+            extension=self.extension,
+            save_folder_path=self.save_folder_path,
+        )
+        save_output(file_path_named, self.intermediate_image, str(self.hyperchain))
diff --git a/divisor/spec.py b/divisor/spec.py
index 3e401e8..93f15ab 100644
--- a/divisor/spec.py
+++ b/divisor/spec.py
@@ -10,7 +10,7 @@
 from nnll.console import nfo
 import torch
 
-from divisor.contents import build_available_models
+from divisor.registry import build_available_models
 from divisor.flux1.autoencoder import AutoEncoderParams as AutoEncoder1Params
 from divisor.flux1.model import FluxLoraWrapper, FluxParams
 from divisor.flux2.autoencoder import AutoEncoderParams as AutoEncoder2Params
diff --git a/divisor/xflux1/prompt.py b/divisor/xflux1/prompt.py
index 20114ab..6620a77 100644
--- a/divisor/xflux1/prompt.py
+++ b/divisor/xflux1/prompt.py
@@ -4,16 +4,16 @@
 
 from dataclasses import replace
 
+import torch
 from fire import Fire
 from nnll.console import nfo
-from nnll.init_gpu import clear_cache, device
-import torch
 
 from divisor.controller import rng
 from divisor.flux1.loading import load_ae, load_clip, load_flow_model, load_t5
 from divisor.flux1.prompt import parse_prompt
 from divisor.flux1.sampling import get_schedule, prepare
 from divisor.noise import get_noise
+from divisor.registry import empty_cache, gfx_device
 from divisor.spec import InitialParamsFlux, ModelSpec, flux_configs, get_model_spec
 from divisor.state import MenuState
 from divisor.xflux1.sampling import denoise
@@ -29,7 +29,7 @@ def main(
     seed: int = rng.next_seed(),
     prompt: str = "",
     quantization: bool = False,
-    device: torch.device = device,
+    device: torch.device = gfx_device,
     num_steps: int = 50,
     loop: bool = False,
     offload: bool = False,
@@ -125,7 +125,7 @@ def main(
         # prepare input
         if offload:
             ae = ae.cpu()
-            clear_cache()
+            empty_cache
 
             t5, clip = t5.to(device), clip.to(device)
         inp = prepare(t5, clip, x, prompt=state.prompt)
@@ -140,7 +140,7 @@ def main(
         # offload TEs to CPU, load model to gpu
         if offload:
             t5, clip = t5.cpu(), clip.cpu()
-            clear_cache()
+            empty_cache
             # Move model to device
             if is_compiled:
                 # Can't move compiled models, so recompile after moving
diff --git a/divisor/xflux1/sampling.py b/divisor/xflux1/sampling.py
index 7307150..64ba8cf 100644
--- a/divisor/xflux1/sampling.py
+++ b/divisor/xflux1/sampling.py
@@ -4,11 +4,8 @@
 import time
 from typing import Callable
 
-from nnll.console import nfo
-from nnll.constants import ExtensionType
-from nnll.init_gpu import device, sync_torch
-from nnll.save_generation import name_save_file_as, save_with_hyperchain
 import torch
+from nnll.console import nfo
 from torch import Tensor
 
 from divisor.cli_menu import route_choices
@@ -20,10 +17,12 @@
 )
 from divisor.flux1.sampling import prepare, unpack
 from divisor.interaction_context import InteractionContext
+from divisor.registry import gfx_device, gfx_sync
+from divisor.save import SaveFile
 from divisor.state import (
-    StepStateXFlux1,
-    InferenceState,
     ImageEmbeddingState,
+    InferenceState,
+    StepStateXFlux1,
     TextEmbeddingState,
 )
 from divisor.xflux1.model import XFlux
@@ -80,7 +79,7 @@ def get_prediction(
             model_dtype = next(pred_set.model_ref[0].parameters()).dtype
         except (TypeError, StopIteration, AttributeError):
             model_dtype = sample.dtype
-        use_autocast = device.type == "cuda"
+        use_autocast = gfx_device.type == "cuda"
 
         if not use_autocast:
             sample = sample.to(dtype=model_dtype)
@@ -298,7 +297,6 @@ def denoise(
 
     # Interactive loop
     while not controller.is_complete:
-        file_path_named = name_save_file_as(ExtensionType.WEBP)
         state = controller.current_state
 
         # Update timestep index for CFG check
@@ -348,9 +346,9 @@ def denoise(
             # Unpack requires float32, but we'll convert back to correct dtype after
             intermediate = unpack(intermediate.float(), state.height, state.width)
 
-            from nnll.init_gpu import device as default_device
+            from divisor.registry import gfx_device as default_device
 
-            sync_torch(default_device)
+            gfx_sync
             t1 = time.perf_counter()
 
             nfo(f"Step time: {t1 - t0:.1f}s")
@@ -382,11 +380,9 @@ def denoise(
                     intermediate_image = ae.decode(intermediate)
                 if state.seed is not None:
                     controller.store_state_in_chain(current_seed=state.seed)
-                save_with_hyperchain(
-                    file_path_named,
-                    intermediate_image,
-                    controller.hyperchain,
-                    ExtensionType.WEBP,
-                )
+                with SaveFile() as saver:
+                    saver.intermediate_image = intermediate_image  # set up image
+                    saver.hyperchain = (controller.hyperchain,)  # set up hyperchain
+                    saver.with_hyperchain()
 
     return controller.current_sample
diff --git a/ideas.md b/ideas.md
deleted file mode 100644
index f19be62..0000000
--- a/ideas.md
+++ /dev/null
@@ -1 +0,0 @@
-preprint soon
diff --git a/pyproject.toml b/pyproject.toml
index 08de58c..99be669 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,14 +9,20 @@ description = "fragment / denoise / process"
 readme = "README.md"
 requires-python = ">=3.13"
 dependencies = [
+    "ffmpeg>=1.4",
     "fire>=0.7.1",
     "gradio>=5.23.1",
     "hangul-romanize>=0.1.0",
+    "librosa>=0.11.0",
     "nnll[full]",
+    "num2words>=0.5.14",
     "py3langid>=0.3.0",
     "pypinyin>=0.55.0",
+    "spacy>=3.8.11",
     "torchcodec>=0.9.1",
     "transformers",
+    "pdoc>=16.0.0",
+    "opencc>=1.1.9",
 ]
 [project.scripts]
 dvzr = "divisor.app:main"
@@ -25,8 +31,9 @@ divisor = "divisor.app:main"
 [dependency-groups]
 dev = [
   "nnll[dev]",
-  "pdoc>=16.0.0",
-  "ty>=0.0.5",
+  "pyright>=1.1.407",
+  "pytest>=9.0.2",
+  "ruff>=0.14.10",
 ]
 [project.optional-dependencies]
 cuda = [
diff --git a/tests/test_commands_sampling_integration.py b/tests/test_commands_sampling_integration.py
index 61dc11a..6e2d816 100644
--- a/tests/test_commands_sampling_integration.py
+++ b/tests/test_commands_sampling_integration.py
@@ -390,9 +390,8 @@ def test_denoise_initializes_controller_with_state_values(self):
 
             # Mock other dependencies
             with (
-                patch("divisor.flux1.sampling.name_save_file_as"),
-                patch("divisor.flux1.sampling.save_with_hyperchain"),
-                patch("divisor.flux1.sampling.sync_torch"),
+                patch("divisor.flux1.sampling.SaveFile"),
+                patch("divisor.flux1.sampling.gfx_sync"),
                 patch("divisor.flux1.sampling.nfo"),
             ):
                 settings = InferenceState(
@@ -492,9 +491,8 @@ def mock_step():
             MockController.return_value = mock_controller_instance
 
             with (
-                patch("divisor.flux1.sampling.name_save_file_as"),
-                patch("divisor.flux1.sampling.save_with_hyperchain"),
-                patch("divisor.flux1.sampling.sync_torch"),
+                patch("divisor.flux1.sampling.SaveFile"),
+                patch("divisor.flux1.sampling.gfx_sync"),
                 patch("divisor.flux1.sampling.nfo"),
                 patch("divisor.flux1.sampling.route_choices", return_value=state),
             ):
@@ -615,9 +613,8 @@ def test_route_choices_integration_with_denoise_loop(self):
                 mock_process.return_value = state
 
                 with (
-                    patch("divisor.flux1.sampling.name_save_file_as"),
-                    patch("divisor.flux1.sampling.save_with_hyperchain"),
-                    patch("divisor.flux1.sampling.sync_torch"),
+                    patch("divisor.flux1.sampling.SaveFile"),
+                    patch("divisor.flux1.sampling.gfx_sync"),
                     patch("divisor.flux1.sampling.nfo"),
                 ):
                     settings = InferenceState(
diff --git a/uv.lock b/uv.lock
index df74687..7763959 100644
--- a/uv.lock
+++ b/uv.lock
@@ -38,11 +38,11 @@ wheels = [
 
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 name = "aiofiles"
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+version = "24.1.0"
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-dependencies = [
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-    { name = "av", marker = "sys_platform == 'darwin'" },
-    { name = "cryptography", marker = "sys_platform == 'darwin'" },
-    { name = "google-crc32c", marker = "sys_platform == 'darwin'" },
-    { name = "pyee", marker = "sys_platform == 'darwin'" },
-    { name = "pylibsrtp", marker = "sys_platform == 'darwin'" },
-    { name = "pyopenssl", marker = "sys_platform == 'darwin'" },
-]
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 version = "1.4.0"
@@ -141,12 +110,6 @@ wheels = [
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-
 [[package]]
 name = "anyio"
 version = "4.12.0"
@@ -161,23 +124,23 @@ wheels = [
 
 [[package]]
 name = "apache-tvm-ffi"
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+version = "0.1.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -666,12 +668,18 @@ name = "divisor"
 version = "0.1.4"
 source = { editable = "." }
 dependencies = [
+    { name = "ffmpeg" },
     { name = "fire" },
     { name = "gradio" },
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+    { name = "librosa" },
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+    { name = "num2words" },
+    { name = "opencc" },
+    { name = "pdoc" },
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+    { name = "spacy" },
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 [package.dev-dependencies]
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-    { name = "pdoc" },
-    { name = "ty" },
+    { name = "pyright" },
+    { name = "pytest" },
+    { name = "ruff" },
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 [package.metadata]
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+    { name = "librosa", specifier = ">=0.11.0" },
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+    { name = "num2words", specifier = ">=0.5.14" },
+    { name = "opencc", specifier = ">=1.1.9" },
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+    { name = "spacy", specifier = ">=3.8.11" },
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+    { name = "pytest", specifier = ">=9.0.2" },
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