update

install.sh

@@ -4,4 +4,4 @@
 pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --extra-index-url https://download.pytorch.org/whl/cu121
 
 # 第二步：安装其他依赖
-pip install --no-cache-dir -r requirements.txt
+pip install --no-cache-dir -r requirements.txt --no-deps

models/model.py

@@ -115,7 +115,6 @@ class StyleGenerator(Qwen2ForCausalLM):
         logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
-        # breakpoint()
         if labels is not None:
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
         coefficient = get_suppression_coefficient(code_freq, code_freq_threshold, k).to(logits.device)

models/pipe.py

@@ -30,7 +30,7 @@ import numpy as np
 from PIL import Image
 from .utils import retrieve_raw_timesteps
 from .lakonlab.pipelines.piflow_loader import PiFlowLoaderMixin
-# breakpoint()
+
 from .lakonlab.models.diffusions.piflow_policies.dx import DXPolicy
 from .lakonlab.models.diffusions.piflow_policies.gmflow import GMFlowPolicy
 
@@ -389,7 +389,7 @@ class CoTylePipeline(QwenImageEditPipeline):
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latents.shape[0]).to(latents.dtype)
                 with self.transformer.cache_context("cond"):
-                    breakpoint()
+
                     noise_pred = self.transformer(
                         hidden_states=latent_model_input.to(dtype=self.transformer.dtype),
                         timestep=timestep / 1000,

src/lakonlab

@@ -0,0 +1 @@
+Subproject commit b1ef16e5e305251bccdfeac2a0e3d0ef339b974a