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https://github.com/invoke-ai/InvokeAI
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Fix token display when using -t
Add true weight used for subprompt
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f0b500fba8
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@ -38,14 +38,14 @@ def get_uc_and_c(prompt, model, log_tokens=False, skip_normalize=False):
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c = torch.zeros_like(uc)
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# normalize each "sub prompt" and add it
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for subprompt, weight in weighted_subprompts:
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log_tokenization(subprompt, model, log_tokens)
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log_tokenization(subprompt, model, log_tokens, weight)
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c = torch.add(
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c,
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model.get_learned_conditioning([subprompt]),
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alpha=weight,
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)
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else: # just standard 1 prompt
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log_tokenization(prompt, model, log_tokens)
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log_tokenization(prompt, model, log_tokens, 1)
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c = model.get_learned_conditioning([prompt])
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uc = model.get_learned_conditioning([unconditioned_words])
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return (uc, c)
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@ -86,7 +86,7 @@ def split_weighted_subprompts(text, skip_normalize=False)->list:
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# shows how the prompt is tokenized
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# usually tokens have '</w>' to indicate end-of-word,
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# but for readability it has been replaced with ' '
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def log_tokenization(text, model, log=False):
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def log_tokenization(text, model, log=False, weight=1):
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if not log:
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return
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tokens = model.cond_stage_model.tokenizer._tokenize(text)
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@ -103,8 +103,8 @@ def log_tokenization(text, model, log=False):
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usedTokens += 1
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else: # over max token length
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discarded = discarded + f"\x1b[0;3{s};40m{token}"
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print(f"\n>> Tokens ({usedTokens}):\n{tokenized}\x1b[0m")
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if discarded != "":
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print(
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f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
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)
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print(f"\n>> Tokens ({usedTokens}), Weight ({weight:.2f}):\n{tokenized}\x1b[0m")
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if discarded != "":
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print(
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f">> Tokens Discarded ({totalTokens-usedTokens}):\n{discarded}\x1b[0m"
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)
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