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https://github.com/invoke-ai/InvokeAI
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improved comments & added warning if value couldn't be parsed correctly
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@ -457,52 +457,47 @@ The vast majority of these arguments default to reasonable values.
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image = image[None].transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return 2.*image - 1.
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"""
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example: "an apple: a banana:0 a watermelon:0.5"
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grabs all text up to the first occurance of ':'
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then removes the text, repeating until no characters left.
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if ':' has no weight defined, defaults to 1.0
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the above example turns into 3 sub-prompts:
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"an apple" 1.0
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"a banana" 0.0
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"a watermelon" 0.5
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The weights are added and normalized
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The resulting image will be: apple 66% (1.0 / 1.5), banana 0%, watermelon 33% (0.5 / 1.5)
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"""
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grabs all text up to the first occurrence of ':'
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uses the grabbed text as a sub-prompt, and takes the value following ':' as weight
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if ':' has no value defined, defaults to 1.0
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repeats until no text remaining
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"""
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def split_weighted_subprompts(text):
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# very simple, uses : to separate sub-prompts
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# assumes number following : and space after number
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# if no number found, defaults to 1.0
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remaining = len(text)
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prompts = []
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weights = []
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while remaining > 0:
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# find :
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if ":" in text:
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idx = text.index(":") # first occurrance from start
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# snip sub prompt
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idx = text.index(":") # first occurrence from start
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# grab up to index as sub-prompt
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prompt = text[:idx]
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remaining -= idx
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# remove from main text
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text = text[idx+1:]
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# get number
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# find value for weight
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if " " in text:
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idx = text.index(" ") # first occurance
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idx = text.index(" ") # first occurence
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else: # no space, read to end
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idx = len(text)
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if idx != 0:
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weight = float(text[:idx])
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else: # no number to grab
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try:
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weight = float(text[:idx])
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except: # couldn't treat as float
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print(f"Warning: '{text[:idx]}' is not a value, are you missing a space?")
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weight = 1.0
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else: # no value found
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weight = 1.0
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# remove
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# remove from main text
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remaining -= idx
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text = text[idx+1:]
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# append the sub-prompt and its weight
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prompts.append(prompt)
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weights.append(weight)
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else:
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if len(text) > 0:
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# take what remains as weight 1
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else: # no : found
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if len(text) > 0: # there is still text though
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# take remainder as weight 1
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prompts.append(text)
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weights.append(1.0)
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remaining = 0
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