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https://github.com/jags111/efficiency-nodes-comfyui.git
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copilot/fi
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copilot/fi
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ff375071c0 | ||
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6e8cb9be98 |
22
.gitignore
vendored
22
.gitignore
vendored
@@ -1,22 +0,0 @@
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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# Virtual environments
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venv/
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ENV/
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env/
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# IDEs
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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# OS
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.DS_Store
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Thumbs.db
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@@ -53,7 +53,6 @@ from .py import city96_latent_upscaler
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from .py import ttl_nn_latent_upscaler
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from .py import bnk_tiled_samplers
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from .py import bnk_adv_encode
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from .py.bnk_adv_encode import normalize_prompt_text
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sys.path.remove(my_dir)
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from comfy import samplers
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@@ -72,9 +71,6 @@ SCHEDULERS = samplers.KSampler.SCHEDULERS + ["AYS SD1", "AYS SDXL", "AYS SVD", "
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def encode_prompts(positive_prompt, negative_prompt, token_normalization, weight_interpretation, clip, clip_skip,
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refiner_clip, refiner_clip_skip, ascore, is_sdxl, empty_latent_width, empty_latent_height,
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return_type="both"):
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# Ensure prompts are valid strings to prevent tokenization errors
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positive_prompt = normalize_prompt_text(positive_prompt)
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negative_prompt = normalize_prompt_text(negative_prompt)
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positive_encoded = negative_encoded = refiner_positive_encoded = refiner_negative_encoded = None
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@@ -117,7 +117,10 @@ class SeedControl {
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this.updateButtonLabel();
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}
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this.seedWidget.value = this.serializedCtx.seedUsed;
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// Don't update the widget value to maintain -1 in the UI when seed was special
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if (!this.serializedCtx.wasSpecial) {
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this.seedWidget.value = this.serializedCtx.seedUsed;
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}
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if (this.serializedCtx.wasSpecial) {
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this.lastSeed = this.serializedCtx.seedUsed;
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@@ -133,12 +136,8 @@ class SeedControl {
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return; // Exit the function immediately
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}
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if (this.serializedCtx.wasSpecial) {
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this.seedWidget.value = -1;
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}
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// Check if seed has changed to a non -1 value, and if so, update lastSeed
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if (this.seedWidget.value !== -1) {
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// Update lastSeed if user manually changed the seed to a specific value
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if (!this.serializedCtx.wasSpecial && this.seedWidget.value !== -1) {
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this.lastSeed = this.seedWidget.value;
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}
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@@ -6,34 +6,6 @@ from math import gcd
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from comfy import model_management
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from comfy.sdxl_clip import SDXLClipModel, SDXLRefinerClipModel, SDXLClipG
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def normalize_prompt_text(text):
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"""
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Normalize prompt text to prevent tokenization errors.
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Converts None, empty strings, or whitespace-only strings to a single space.
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Ensures the input is a string type by converting non-string values.
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This function is designed to handle edge cases gracefully without crashing,
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which is important for ComfyUI workflows where users might have empty prompts.
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Parameters:
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text: The input prompt text to normalize. Can be of any type, though
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string, None, or convertible types are expected.
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Returns:
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str: A normalized string that is safe to pass to the tokenizer.
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Returns " " (single space) for None, empty, or whitespace-only inputs.
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Returns the original text unchanged if it's a valid non-empty string.
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Returns str(text) for non-string types.
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"""
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if text is None:
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return " "
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if not isinstance(text, str):
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# Convert non-string types to string
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text = str(text)
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if not text.strip():
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return " "
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return text
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def _grouper(n, iterable):
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it = iter(iterable)
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while True:
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@@ -265,8 +237,6 @@ def prepareXL(embs_l, embs_g, pooled, clip_balance):
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return embs_g, pooled
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def advanced_encode(clip, text, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
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# Ensure text is a valid string to prevent tokenization errors
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text = normalize_prompt_text(text)
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tokenized = clip.tokenize(text, return_word_ids=True)
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if isinstance(clip.cond_stage_model, (SDXLClipModel, SDXLRefinerClipModel, SDXLClipG)):
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embs_l = None
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@@ -295,9 +265,6 @@ def advanced_encode(clip, text, token_normalization, weight_interpretation, w_ma
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lambda x: (clip.encode_from_tokens({'l': x}), None),
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w_max=w_max)
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def advanced_encode_XL(clip, text1, text2, token_normalization, weight_interpretation, w_max=1.0, clip_balance=.5, apply_to_pooled=True):
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# Ensure texts are valid strings to prevent tokenization errors
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text1 = normalize_prompt_text(text1)
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text2 = normalize_prompt_text(text2)
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tokenized1 = clip.tokenize(text1, return_word_ids=True)
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tokenized2 = clip.tokenize(text2, return_word_ids=True)
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