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https://github.com/invoke-ai/InvokeAI
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bring in prompt parser from fix-prompts branch
attention is parsed but ignored, blends old syntax doesn't work, conjunctions are parsed but ignored, the only part that's used here is the new .blend() syntax and cross-attention control using .swap()
This commit is contained in:
Damian at mba
parent
42883545f9
commit
c9d27634b4
@ -4,7 +4,7 @@ weighted subprompts.
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Useful function exports:
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get_uc_and_c() get the conditioned and unconditioned latent
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get_uc_and_c_and_ec() get the conditioned and unconditioned latent, and edited conditioning if we're doing cross-attention control
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split_weighted_subpromopts() split subprompts, normalize and weight them
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log_tokenization() print out colour-coded tokens and warn if truncated
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@ -39,8 +39,10 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
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pp = PromptParser()
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parsed_prompt: Union[FlattenedPrompt, Blend] = pp.parse(prompt_string_cleaned)
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parsed_negative_prompt: FlattenedPrompt = pp.parse(unconditioned_words)
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# we don't support conjunctions for now
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parsed_prompt: Union[FlattenedPrompt, Blend] = pp.parse(prompt_string_cleaned).prompts[0]
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parsed_negative_prompt: FlattenedPrompt = pp.parse(unconditioned_words).prompts[0]
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print("parsed prompt to", parsed_prompt)
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conditioning = None
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edited_conditioning = None
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@ -50,7 +52,7 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
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blend: Blend = parsed_prompt
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embeddings_to_blend = None
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for flattened_prompt in blend.prompts:
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this_embedding = make_embeddings_for_flattened_prompt(model, flattened_prompt)
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this_embedding = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
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embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
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(embeddings_to_blend, this_embedding))
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conditioning, _ = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
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@ -72,16 +74,16 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
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# regular fragment
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original_prompt.append(fragment)
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edited_prompt.append(fragment)
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original_embeddings, original_tokens = make_embeddings_for_flattened_prompt(model, original_prompt)
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edited_embeddings, edited_tokens = make_embeddings_for_flattened_prompt(model, edited_prompt)
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original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, original_prompt)
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edited_embeddings, edited_tokens = build_embeddings_and_tokens_for_flattened_prompt(model, edited_prompt)
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conditioning = original_embeddings
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edited_conditioning = edited_embeddings
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edit_opcodes = build_token_edit_opcodes(original_tokens, edited_tokens)
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else:
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conditioning, _ = make_embeddings_for_flattened_prompt(model, flattened_prompt)
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conditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt)
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unconditioning = make_embeddings_for_flattened_prompt(parsed_negative_prompt)
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unconditioning, _ = build_embeddings_and_tokens_for_flattened_prompt(model, parsed_negative_prompt)
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return (unconditioning, conditioning, edited_conditioning, edit_opcodes)
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@ -91,11 +93,11 @@ def build_token_edit_opcodes(original_tokens, edited_tokens):
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return SequenceMatcher(None, original_tokens, edited_tokens).get_opcodes()
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def make_embeddings_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt):
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def build_embeddings_and_tokens_for_flattened_prompt(model, flattened_prompt: FlattenedPrompt):
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if type(flattened_prompt) is not FlattenedPrompt:
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raise f"embeddings can only be made from FlattenedPrompts, got {type(flattened_prompt)} instead"
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fragments = [x[0] for x in flattened_prompt.children]
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embeddings, tokens = model.get_learned_conditioning([fragments], return_tokens=True)
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fragments = [x.text for x in flattened_prompt.children]
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embeddings, tokens = model.get_learned_conditioning([' '.join(fragments)], return_tokens=True)
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return embeddings, tokens
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@ -34,7 +34,7 @@ class Img2Img(Generator):
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t_enc = int(strength * steps)
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uc, c, ec, edit_opcodes = conditioning
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structured_conditioning = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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def make_image(x_T):
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# encode (scaled latent)
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@ -52,7 +52,7 @@ class Img2Img(Generator):
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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init_latent = self.init_latent,
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structured_conditioning = structured_conditioning
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extra_conditioning_info = extra_conditioning_info
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# changes how noising is performed in ksampler
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)
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@ -22,7 +22,7 @@ class Txt2Img(Generator):
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"""
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self.perlin = perlin
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uc, c, ec, edit_opcodes = conditioning
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structured_conditioning = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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@torch.no_grad()
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def make_image(x_T):
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@ -46,7 +46,7 @@ class Txt2Img(Generator):
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verbose = False,
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unconditional_guidance_scale = cfg_scale,
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unconditional_conditioning = uc,
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structured_conditioning = structured_conditioning,
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extra_conditioning_info = extra_conditioning_info,
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eta = ddim_eta,
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img_callback = step_callback,
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threshold = threshold,
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@ -24,7 +24,7 @@ class Txt2Img2Img(Generator):
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kwargs are 'width' and 'height'
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"""
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uc, c, ec, edit_opcodes = conditioning
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structured_conditioning = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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extra_conditioning_info = InvokeAIDiffuserComponent.StructuredConditioning(edited_conditioning=ec, edit_opcodes=edit_opcodes)
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@torch.no_grad()
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def make_image(x_T):
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@ -63,7 +63,7 @@ class Txt2Img2Img(Generator):
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unconditional_conditioning = uc,
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eta = ddim_eta,
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img_callback = step_callback,
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structured_conditioning = structured_conditioning
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extra_conditioning_info = extra_conditioning_info
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)
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print(
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@ -97,7 +97,7 @@ class Txt2Img2Img(Generator):
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img_callback = step_callback,
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unconditional_guidance_scale=cfg_scale,
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unconditional_conditioning=uc,
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structured_conditioning = structured_conditioning
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extra_conditioning_info = extra_conditioning_info
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)
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if self.free_gpu_mem:
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@ -7,7 +7,7 @@ class Prompt():
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def __init__(self, parts: list):
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for c in parts:
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if not issubclass(type(c), BaseFragment):
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if type(c) is not Attention and not issubclass(type(c), BaseFragment):
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raise PromptParser.ParsingException(f"Prompt cannot contain {type(c)}, only {BaseFragment.__subclasses__()} are allowed")
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self.children = parts
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def __repr__(self):
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@ -56,6 +56,17 @@ class Fragment(BaseFragment):
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and other.text == self.text \
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and other.weight == self.weight
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class Attention():
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def __init__(self, weight: float, children: list):
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self.weight = weight
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self.children = children
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#print(f"A: requested attention '{children}' to {weight}")
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def __repr__(self):
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return f"Attention:'{self.children}' @ {self.weight}"
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def __eq__(self, other):
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return type(other) is Attention and other.weight == self.weight and other.fragment == self.fragment
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class CrossAttentionControlledFragment(BaseFragment):
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pass
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@ -65,7 +76,7 @@ class CrossAttentionControlSubstitute(CrossAttentionControlledFragment):
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self.edited = edited
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def __repr__(self):
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return f"CrossAttentionControlSubstitute:('{self.original}'->'{self.edited}')"
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return f"CrossAttentionControlSubstitute:({self.original}->{self.edited})"
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def __eq__(self, other):
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return type(other) is CrossAttentionControlSubstitute \
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and other.original == self.original \
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@ -137,7 +148,7 @@ class PromptParser():
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self.root = self.build_parser_logic()
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def parse(self, prompt: str) -> [list]:
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def parse(self, prompt: str) -> Conjunction:
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'''
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:param prompt: The prompt string to parse
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:return: a tuple
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@ -181,13 +192,17 @@ class PromptParser():
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for x in node:
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results = flatten_internal(x, weight_scale, results, prefix+'pr')
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#print(prefix, " ParseResults expanded, results is now", results)
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elif issubclass(type(node), BaseFragment):
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results.append(node)
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#elif type(node) is Attention:
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# #if node.weight < 1:
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# # todo: inject a blend when flattening attention with weight <1"
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# for c in node.children:
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# results = flatten_internal(c, weight_scale*node.weight, results, prefix+' ')
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elif type(node) is Attention:
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# if node.weight < 1:
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# todo: inject a blend when flattening attention with weight <1"
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for c in node.children:
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results = flatten_internal(c, weight_scale * node.weight, results, prefix + ' ')
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elif type(node) is Fragment:
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results += [Fragment(node.text, node.weight*weight_scale)]
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elif type(node) is CrossAttentionControlSubstitute:
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original = flatten_internal(node.original, weight_scale, [], ' CAo ')
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edited = flatten_internal(node.edited, weight_scale, [], ' CAe ')
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results += [CrossAttentionControlSubstitute(original, edited)]
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elif type(node) is Blend:
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flattened_subprompts = []
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#print(" flattening blend with prompts", node.prompts, "weights", node.weights)
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@ -204,7 +219,7 @@ class PromptParser():
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#print(prefix + "after flattening Prompt, results is", results)
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else:
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raise PromptParser.ParsingException(f"unhandled node type {type(node)} when flattening {node}")
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#print(prefix + "-> after flattening", type(node), "results is", results)
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print(prefix + "-> after flattening", type(node), "results is", results)
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return results
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#print("flattening", root)
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@ -239,32 +254,83 @@ class PromptParser():
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else:
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raise PromptParser.ParsingException("Cannot make fragment from " + str(x))
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# attention control of the form +(phrase) / -(phrase) / <weight>(phrase)
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# phrase can be multiple words, can have multiple +/- signs to increase the effect or type a floating point or integer weight
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attention = pp.Forward()
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attention_head = (number | pp.Word('+') | pp.Word('-'))\
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.set_name("attention_head")\
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.set_debug(False)
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fragment_inside_attention = pp.CharsNotIn(SPACE_CHARS+'()')\
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.set_parse_action(make_fragment)\
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.set_name("fragment_inside_attention")\
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.set_debug(False)
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attention_with_parens = pp.Forward()
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attention_with_parens_body = pp.nested_expr(content=pp.delimited_list((attention_with_parens | fragment_inside_attention), delim=SPACE_CHARS))
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attention_with_parens << (attention_head + attention_with_parens_body)
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def make_attention(x):
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# print("making Attention from parsing with args", x0, x1)
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weight = 1
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# number(str)
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if type(x[0]) is float or type(x[0]) is int:
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weight = float(x[0])
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# +(str) or -(str) or +str or -str
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elif type(x[0]) is str:
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base = self.attention_plus_base if x[0][0] == '+' else self.attention_minus_base
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weight = pow(base, len(x[0]))
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# print("Making attention with children of type", [str(type(x)) for x in x1])
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return Attention(weight=weight, children=x[1])
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attention_with_parens.set_parse_action(make_attention)\
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.set_name("attention_with_parens")\
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.set_debug(False)
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# attention control of the form ++word --word (no parens)
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attention_without_parens = (
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(pp.Word('+') | pp.Word('-')) +
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pp.CharsNotIn(SPACE_CHARS+'()').set_parse_action(lambda x: [[make_fragment(x)]])
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)\
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.set_name("attention_without_parens")\
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.set_debug(False)
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attention_without_parens.set_parse_action(make_attention)
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attention << (attention_with_parens | attention_without_parens)\
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.set_name("attention")\
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.set_debug(False)
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# cross-attention control
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empty_string = ((lparen + rparen) |
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pp.Literal('""').suppress() |
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(lparen + pp.Literal('""').suppress() + rparen)
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).set_parse_action(lambda x: Fragment(""))
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original_words = (
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(lparen + pp.Literal('"').suppress() + pp.CharsNotIn('"') + pp.Literal('"').suppress() + rparen).set_name('term1').set_debug(False) |
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(pp.Literal('"').suppress() + pp.CharsNotIn('"') + pp.Literal('"').suppress()).set_name('term2').set_debug(False) |
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(lparen + pp.CharsNotIn(')') + rparen).set_name('term3').set_debug(False)
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(lparen + pp.Literal('"').suppress() + (pp.OneOrMore(attention) | pp.CharsNotIn('"').set_parse_action(make_fragment)) + pp.Literal('"').suppress() + rparen).set_name('term1').set_debug(False) |
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(pp.Literal('"').suppress() + (pp.OneOrMore(attention) | pp.CharsNotIn('"').set_parse_action(make_fragment)) + pp.Literal('"').suppress()).set_name('term2').set_debug(False) |
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(lparen + (pp.OneOrMore(attention) | pp.CharsNotIn(')').set_parse_action(make_fragment)) + rparen).set_name('term3').set_debug(False)
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).set_name('original_words')
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edited_words = (
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(pp.Literal('"').suppress() + pp.CharsNotIn('"') + pp.Literal('"').suppress()).set_name('termA').set_debug(False) |
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pp.CharsNotIn(')').set_name('termB').set_debug(False)
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(pp.Literal('"').suppress() + (pp.OneOrMore(attention) | pp.CharsNotIn('"').set_parse_action(make_fragment)) + pp.Literal('"').suppress()).set_name('termA').set_debug(False) |
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pp.Literal('""').suppress().set_parse_action(lambda x: Fragment("")) |
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(pp.OneOrMore(attention) | pp.CharsNotIn(')').set_parse_action(make_fragment)).set_name('termB').set_debug(True)
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).set_name('edited_words')
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cross_attention_substitute = original_words + \
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cross_attention_substitute = (empty_string | original_words) + \
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pp.Literal(".swap").suppress() + \
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lparen + edited_words + rparen
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(empty_string | (lparen + edited_words + rparen)
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)
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cross_attention_substitute.set_name('cross_attention_substitute')
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def make_cross_attention_substitute(x):
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#print("making cacs for", x)
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return CrossAttentionControlSubstitute(x[0], x[1])
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cacs = CrossAttentionControlSubstitute(x[0], x[1])
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#print("made", cacs)
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#return cacs
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return cacs
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cross_attention_substitute.set_parse_action(make_cross_attention_substitute)
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# simple fragments of text
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prompt_part << (cross_attention_substitute
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#| attention
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| attention
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| word
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)
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prompt_part.set_debug(False)
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@ -16,10 +16,10 @@ class DDIMSampler(Sampler):
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def prepare_to_sample(self, t_enc, **kwargs):
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super().prepare_to_sample(t_enc, **kwargs)
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structured_conditioning = kwargs.get('structured_conditioning', None)
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extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
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if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
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else:
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self.invokeai_diffuser.remove_cross_attention_control()
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@ -34,10 +34,10 @@ class CFGDenoiser(nn.Module):
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def prepare_to_sample(self, t_enc, **kwargs):
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structured_conditioning = kwargs.get('structured_conditioning', None)
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extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
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if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
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else:
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self.invokeai_diffuser.remove_cross_attention_control()
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@ -164,7 +164,7 @@ class KSampler(Sampler):
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log_every_t=100,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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structured_conditioning=None,
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extra_conditioning_info=None,
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threshold = 0,
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perlin = 0,
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# this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
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@ -197,7 +197,7 @@ class KSampler(Sampler):
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x = torch.randn([batch_size, *shape], device=self.device) * sigmas[0]
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model_wrap_cfg = CFGDenoiser(self.model, threshold=threshold, warmup=max(0.8*S,S-10))
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model_wrap_cfg.prepare_to_sample(S, structured_conditioning=structured_conditioning)
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model_wrap_cfg.prepare_to_sample(S, extra_conditioning_info=extra_conditioning_info)
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extra_args = {
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'cond': conditioning,
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'uncond': unconditional_conditioning,
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@ -224,7 +224,7 @@ class KSampler(Sampler):
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index,
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unconditional_guidance_scale=1.0,
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unconditional_conditioning=None,
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structured_conditioning=None,
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extra_conditioning_info=None,
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**kwargs,
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):
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if self.model_wrap is None:
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@ -250,7 +250,7 @@ class KSampler(Sampler):
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# so the actual formula for indexing into sigmas:
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# sigma_index = (steps-index)
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s_index = t_enc - index - 1
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self.model_wrap.prepare_to_sample(s_index, structured_conditioning=structured_conditioning)
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self.model_wrap.prepare_to_sample(s_index, extra_conditioning_info=extra_conditioning_info)
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img = K.sampling.__dict__[f'_{self.schedule}'](
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self.model_wrap,
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img,
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@ -20,10 +20,10 @@ class PLMSSampler(Sampler):
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def prepare_to_sample(self, t_enc, **kwargs):
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super().prepare_to_sample(t_enc, **kwargs)
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structured_conditioning = kwargs.get('structured_conditioning', None)
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extra_conditioning_info = kwargs.get('extra_conditioning_info', None)
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if structured_conditioning is not None and structured_conditioning.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(structured_conditioning)
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if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
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self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info)
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else:
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self.invokeai_diffuser.remove_cross_attention_control()
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@ -439,6 +439,13 @@ class FrozenCLIPEmbedder(AbstractEncoder):
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param.requires_grad = False
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def forward(self, text, **kwargs):
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should_return_tokens = False
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if 'return_tokens' in kwargs:
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should_return_tokens = kwargs.get('return_tokens', False)
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# self.transformer doesn't like having extra kwargs
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kwargs.pop('return_tokens')
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|
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batch_encoding = self.tokenizer(
|
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text,
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truncation=True,
|
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@ -451,7 +458,7 @@ class FrozenCLIPEmbedder(AbstractEncoder):
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tokens = batch_encoding['input_ids'].to(self.device)
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z = self.transformer(input_ids=tokens, **kwargs)
|
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|
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if kwargs.get('return_tokens', False):
|
||||
if should_return_tokens:
|
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return z, tokens
|
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else:
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return z
|
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|
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@ -1,6 +1,7 @@
|
||||
import unittest
|
||||
|
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from ldm.invoke.prompt_parser import PromptParser, Blend, Conjunction, FlattenedPrompt, CrossAttentionControlSubstitute
|
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from ldm.invoke.prompt_parser import PromptParser, Blend, Conjunction, FlattenedPrompt, CrossAttentionControlSubstitute, \
|
||||
Fragment
|
||||
|
||||
|
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def parse_prompt(prompt_string):
|
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@ -135,7 +136,7 @@ class PromptParserTestCase(unittest.TestCase):
|
||||
|
||||
def test_cross_attention_control(self):
|
||||
fire_flames_to_trees = Conjunction([FlattenedPrompt([('fire', 1.0), \
|
||||
CrossAttentionControlSubstitute('flames', 'trees')])])
|
||||
CrossAttentionControlSubstitute([Fragment('flames', 1)], [Fragment('trees', 1)])])])
|
||||
self.assertEqual(fire_flames_to_trees, parse_prompt('fire "flames".swap(trees)'))
|
||||
self.assertEqual(fire_flames_to_trees, parse_prompt('fire (flames).swap(trees)'))
|
||||
self.assertEqual(fire_flames_to_trees, parse_prompt('fire ("flames").swap(trees)'))
|
||||
@ -144,13 +145,13 @@ class PromptParserTestCase(unittest.TestCase):
|
||||
self.assertEqual(fire_flames_to_trees, parse_prompt('fire ("flames").swap("trees")'))
|
||||
|
||||
fire_flames_to_trees_and_houses = Conjunction([FlattenedPrompt([('fire', 1.0), \
|
||||
CrossAttentionControlSubstitute('flames', 'trees and houses')])])
|
||||
CrossAttentionControlSubstitute([Fragment('flames', 1)], [Fragment('trees and houses', 1)])])])
|
||||
self.assertEqual(fire_flames_to_trees_and_houses, parse_prompt('fire ("flames").swap("trees and houses")'))
|
||||
self.assertEqual(fire_flames_to_trees_and_houses, parse_prompt('fire (flames).swap("trees and houses")'))
|
||||
self.assertEqual(fire_flames_to_trees_and_houses, parse_prompt('fire "flames".swap("trees and houses")'))
|
||||
|
||||
trees_and_houses_to_flames = Conjunction([FlattenedPrompt([('fire', 1.0), \
|
||||
CrossAttentionControlSubstitute('trees and houses', 'flames')])])
|
||||
CrossAttentionControlSubstitute([Fragment('trees and houses', 1)], [Fragment('flames',1)])])])
|
||||
self.assertEqual(trees_and_houses_to_flames, parse_prompt('fire ("trees and houses").swap("flames")'))
|
||||
self.assertEqual(trees_and_houses_to_flames, parse_prompt('fire (trees and houses).swap("flames")'))
|
||||
self.assertEqual(trees_and_houses_to_flames, parse_prompt('fire "trees and houses".swap("flames")'))
|
||||
@ -159,14 +160,46 @@ class PromptParserTestCase(unittest.TestCase):
|
||||
self.assertEqual(trees_and_houses_to_flames, parse_prompt('fire "trees and houses".swap(flames)'))
|
||||
|
||||
flames_to_trees_fire = Conjunction([FlattenedPrompt([
|
||||
CrossAttentionControlSubstitute('flames', 'trees'),
|
||||
CrossAttentionControlSubstitute([Fragment('flames',1)], [Fragment('trees',1)]),
|
||||
(', fire', 1.0)])])
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"flames".swap(trees), fire'))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('(flames).swap(trees), fire '))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('("flames").swap(trees), fire '))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"flames".swap("trees"), fire'))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('(flames).swap("trees"), fire'))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('("flames").swap("trees"), fire'))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"flames".swap(trees), fire'))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('(flames).swap(trees), fire '))
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('("flames").swap(trees), fire '))
|
||||
|
||||
|
||||
self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
|
||||
CrossAttentionControlSubstitute([Fragment('',1)], [Fragment('in winter',1)])])]),
|
||||
parse_prompt('a forest landscape "".swap("in winter")'))
|
||||
self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
|
||||
CrossAttentionControlSubstitute([Fragment(' ',1)], [Fragment('in winter',1)])])]),
|
||||
parse_prompt('a forest landscape " ".swap("in winter")'))
|
||||
|
||||
self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
|
||||
CrossAttentionControlSubstitute([Fragment('in winter',1)], [Fragment('',1)])])]),
|
||||
parse_prompt('a forest landscape "in winter".swap("")'))
|
||||
self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
|
||||
CrossAttentionControlSubstitute([Fragment('in winter',1)], [Fragment('',1)])])]),
|
||||
parse_prompt('a forest landscape "in winter".swap()'))
|
||||
self.assertEqual(Conjunction([FlattenedPrompt([Fragment('a forest landscape', 1),
|
||||
CrossAttentionControlSubstitute([Fragment('in winter',1)], [Fragment(' ',1)])])]),
|
||||
parse_prompt('a forest landscape "in winter".swap(" ")'))
|
||||
|
||||
def test_cross_attention_control_with_attention(self):
|
||||
flames_to_trees_fire = Conjunction([FlattenedPrompt([
|
||||
CrossAttentionControlSubstitute([Fragment('flames',0.5)], [Fragment('trees',0.7)]),
|
||||
Fragment(',', 1), Fragment('fire', 2.0)])])
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"0.5(flames)".swap("0.7(trees)"), 2.0(fire)'))
|
||||
flames_to_trees_fire = Conjunction([FlattenedPrompt([
|
||||
CrossAttentionControlSubstitute([Fragment('fire',0.5), Fragment('flames',0.25)], [Fragment('trees',0.7)]),
|
||||
Fragment(',', 1), Fragment('fire', 2.0)])])
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"0.5(fire 0.5(flames))".swap("0.7(trees)"), 2.0(fire)'))
|
||||
flames_to_trees_fire = Conjunction([FlattenedPrompt([
|
||||
CrossAttentionControlSubstitute([Fragment('fire',0.5), Fragment('flames',0.25)], [Fragment('trees',0.7), Fragment('houses', 1)]),
|
||||
Fragment(',', 1), Fragment('fire', 2.0)])])
|
||||
self.assertEqual(flames_to_trees_fire, parse_prompt('"0.5(fire 0.5(flames))".swap("0.7(trees) houses"), 2.0(fire)'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
Loading…
Reference in New Issue
Block a user