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Clean up the comments

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BigStationW 2025-05-07 00:41:01 +02:00 committed by GitHub
parent 9e738b989e
commit 62529c4221
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1 changed files with 6 additions and 25 deletions
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comfy/sd.py
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@ -88,7 +88,7 @@ class CLIP:
if no_init: if no_init:
return return
self.clip_type_enum = clip_type_enum # MODIFIED: Store the original CLIPType self.clip_type_enum = clip_type_enum
params = target.params.copy() params = target.params.copy()
clip = target.clip clip = target.clip
@ -134,7 +134,7 @@ class CLIP:
n.tokenizer_options = self.tokenizer_options.copy() n.tokenizer_options = self.tokenizer_options.copy()
n.use_clip_schedule = self.use_clip_schedule n.use_clip_schedule = self.use_clip_schedule
n.apply_hooks_to_conds = self.apply_hooks_to_conds n.apply_hooks_to_conds = self.apply_hooks_to_conds
n.clip_type_enum = self.clip_type_enum # MODIFIED: Clone the stored CLIPType n.clip_type_enum = self.clip_type_enum
return n return n
def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
@ -198,17 +198,14 @@ class CLIP:
o = self.cond_stage_model.encode_token_weights(tokens) o = self.cond_stage_model.encode_token_weights(tokens)
cond, pooled = o[:2] cond, pooled = o[:2]
# --- MODIFICATION FOR SCHEDULED PATH (CONSISTENCY) ---
# Populate initial pooled_dict including o[2] if present, then filter
pooled_dict = {"pooled_output": pooled} pooled_dict = {"pooled_output": pooled}
if len(o) > 2 and isinstance(o[2], dict): # Check if o[2] is a dict if len(o) > 2 and isinstance(o[2], dict):
pooled_dict.update(o[2]) pooled_dict.update(o[2])
if hasattr(self, 'clip_type_enum') and self.clip_type_enum == CLIPType.CHROMA: if hasattr(self, 'clip_type_enum') and self.clip_type_enum == CLIPType.CHROMA:
if 'attention_mask' in pooled_dict: if 'attention_mask' in pooled_dict:
logging.debug(f"CLIP type {self.clip_type_enum.name} (scheduled path): Removing 'attention_mask' from conditioning output.") logging.debug(f"CLIP type {self.clip_type_enum.name} (scheduled path): Removing 'attention_mask' from conditioning output.")
pooled_dict.pop('attention_mask', None) pooled_dict.pop('attention_mask', None)
# --- END MODIFICATION FOR SCHEDULED PATH ---
pooled_dict["clip_start_percent"] = t_range[0] pooled_dict["clip_start_percent"] = t_range[0]
pooled_dict["clip_end_percent"] = t_range[1] pooled_dict["clip_end_percent"] = t_range[1]
@ -235,17 +232,15 @@ class CLIP:
cond, pooled = o[:2] cond, pooled = o[:2]
if return_dict: if return_dict:
out = {"cond": cond, "pooled_output": pooled} out = {"cond": cond, "pooled_output": pooled}
if len(o) > 2 and isinstance(o[2], dict): # Check if o[2] is a dict if len(o) > 2 and isinstance(o[2], dict):
for k in o[2]: for k in o[2]:
out[k] = o[2][k] out[k] = o[2][k]
self.add_hooks_to_dict(out) self.add_hooks_to_dict(out)
# ---- START MODIFICATION for non-scheduled path ----
if hasattr(self, 'clip_type_enum') and self.clip_type_enum == CLIPType.CHROMA: if hasattr(self, 'clip_type_enum') and self.clip_type_enum == CLIPType.CHROMA:
if 'attention_mask' in out: if 'attention_mask' in out:
logging.debug(f"CLIP type {self.clip_type_enum.name} (non-scheduled path): Removing 'attention_mask' from conditioning output.") logging.debug(f"CLIP type {self.clip_type_enum.name} (non-scheduled path): Removing 'attention_mask' from conditioning output.")
out.pop('attention_mask', None) out.pop('attention_mask', None)
# ---- END MODIFICATION for non-scheduled path ----
return out return out
if return_pooled: if return_pooled:
@ -837,12 +832,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
elif clip_type == CLIPType.LTXV: elif clip_type == CLIPType.LTXV:
clip_target.clip = comfy.text_encoders.lt.ltxv_te(**common_t5_args) clip_target.clip = comfy.text_encoders.lt.ltxv_te(**common_t5_args)
clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
# ---- START MODIFICATION for T5_XXL model selection ---- elif clip_type == CLIPType.PIXART:
elif clip_type == CLIPType.PIXART: # CHROMA removed from this OR condition
# PIXART keeps its specific text encoder.
clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**common_t5_args) clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**common_t5_args)
clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer
# ---- END MODIFICATION for T5_XXL model selection ----
elif clip_type == CLIPType.WAN: elif clip_type == CLIPType.WAN:
clip_target.clip = comfy.text_encoders.wan.te(**common_t5_args) clip_target.clip = comfy.text_encoders.wan.te(**common_t5_args)
clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
@ -852,12 +844,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None) clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else: else:
# This 'else' now covers:
# - MOCHI (T5XXL)
# - CHROMA (T5XXL) - because it's not caught by the PIXART elif anymore
# - STABLE_DIFFUSION (T5XXL) - if it falls here by default
# - Any other unhandled CLIPType with T5XXL
# All these will use comfy.text_encoders.genmo.mochi_te
if clip_type == CLIPType.CHROMA: if clip_type == CLIPType.CHROMA:
logging.debug(f"TEModel.T5_XXL with CLIPType.CHROMA: Using Mochi-like TE (comfy.text_encoders.genmo.mochi_te) for tensor generation.") logging.debug(f"TEModel.T5_XXL with CLIPType.CHROMA: Using Mochi-like TE (comfy.text_encoders.genmo.mochi_te) for tensor generation.")
else: else:
@ -882,17 +868,13 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None) clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else: else:
# clip_l default
# This branch is taken for TEModel.CLIP_L or if te_model is None/unrecognized.
# If clip_type is CHROMA here (e.g. Chroma with a CLIP-L model),
# sd1_clip.SD1ClipModel will be used, and its attention_mask will be removed by the CLIP class logic.
if clip_type == CLIPType.SD3: if clip_type == CLIPType.SD3:
clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False) clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
elif clip_type == CLIPType.HIDREAM: elif clip_type == CLIPType.HIDREAM:
clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None) clip_target.clip = comfy.text_encoders.hidream.hidream_clip(clip_l=True, clip_g=False, t5=False, llama=False, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None)
clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
else: # Default for CLIP_L like models (includes STABLE_DIFFUSION, and CHROMA if CLIP_L) else:
clip_target.clip = sd1_clip.SD1ClipModel clip_target.clip = sd1_clip.SD1ClipModel
clip_target.tokenizer = sd1_clip.SD1Tokenizer clip_target.tokenizer = sd1_clip.SD1Tokenizer
elif len(clip_data) == 2: elif len(clip_data) == 2:
@ -940,7 +922,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
parameters += comfy.utils.calculate_parameters(c) parameters += comfy.utils.calculate_parameters(c)
tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options) tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
# MODIFIED: Pass the original clip_type (enum) to the CLIP constructor
clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options, clip_type_enum=clip_type) clip = CLIP(clip_target, embedding_directory=embedding_directory, parameters=parameters, tokenizer_data=tokenizer_data, model_options=model_options, clip_type_enum=clip_type)
for c in clip_data: for c in clip_data: