Merge branch 'claude/confident-bhaskara'

This commit is contained in:
JasonHoku
2026-02-15 12:31:02 -08:00
2 changed files with 14 additions and 6 deletions
+5 -2
View File
@@ -76,8 +76,11 @@ def batch_encode_with_cache(clip_model, prompts, cond_cache, prompt_type="positi
original_layer = clip_model.cond_stage_model.clip_layer
clip_model.cond_stage_model.set_clip_options({"layer": clip_skip})
cond, pooled = clip_model.encode_from_tokens(tokens, return_pooled=True)
conditioning = [[cond, {"pooled_output": pooled}]]
# Use return_dict=True to preserve all extra conditioning keys
# (e.g. t5xxl_ids/t5xxl_weights for Anima, attention_mask for Lumina, etc.)
pooled_dict = clip_model.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
cond = pooled_dict.pop("cond")
conditioning = [[cond, pooled_dict]]
results[prompt] = conditioning
cond_cache.set(prompt, conditioning, prompt_type)
+9 -4
View File
@@ -616,8 +616,11 @@ def run_generation_loop(
patched_clip.cond_stage_model.set_clip_options({"layer": clip_skip})
tokens = patched_clip.tokenize(prompt)
cond, pooled = patched_clip.encode_from_tokens(tokens, return_pooled=True)
conditioning_cache["positive"][prompt] = [[cond, {"pooled_output": pooled}]]
# Use return_dict=True to preserve all extra conditioning keys
# (e.g. t5xxl_ids/t5xxl_weights for Anima, attention_mask for Lumina, etc.)
pooled_dict = patched_clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
cond = pooled_dict.pop("cond")
conditioning_cache["positive"][prompt] = [[cond, pooled_dict]]
if original_layer is not None:
patched_clip.cond_stage_model.set_clip_options({"layer": original_layer})
@@ -635,8 +638,10 @@ def run_generation_loop(
patched_clip.cond_stage_model.set_clip_options({"layer": clip_skip})
tokens = patched_clip.tokenize(prompt)
cond, pooled = patched_clip.encode_from_tokens(tokens, return_pooled=True)
conditioning_cache["negative"][prompt] = [[cond, {"pooled_output": pooled}]]
# Use return_dict=True to preserve all extra conditioning keys
pooled_dict = patched_clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
cond = pooled_dict.pop("cond")
conditioning_cache["negative"][prompt] = [[cond, pooled_dict]]
if original_layer is not None:
patched_clip.cond_stage_model.set_clip_options({"layer": original_layer})