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prompt enhance add prefix and suffix

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-04-01 21:31:19 -04:00
parent d3633be484
commit e5a256e74d
3 changed files with 33 additions and 11 deletions
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TODO.md
+1
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@@ -21,6 +21,7 @@ Main ToDo list can be found at [GitHub projects](https://github.com/users/vladma
- Video: STG: <https://github.com/huggingface/diffusers/blob/main/examples/community/README.md#spatiotemporal-skip-guidance>
- Video: SmoothCache: https://github.com/huggingface/diffusers/issues/11135
- SoftFill: https://github.com/zacheryvaughn/softfill-pipelines
- SISO: https://github.com/yairshp/SISO
## Code TODO
modules/model_flux_nf4.py
+1 -1
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@@ -47,7 +47,7 @@ def _replace_with_bnb_linear(
in_features,
out_features,
module.bias is not None,
compute_dtype=torch.bfloat16,
compute_dtype=devices.dtype,
compress_statistics=False,
quant_type="nf4",
)
scripts/prompt_enhance.py
+31 -10
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@@ -7,7 +7,7 @@ import transformers
from modules import scripts, shared, devices, errors, processing, sd_models, sd_modules
debug_enabled = os.environ.get('SD_LLM_DEBUG', None)
debug_enabled = os.environ.get('SD_LLM_DEBUG', None) is not None
debug = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
@@ -37,7 +37,7 @@ class Options:
}
default = list(models)[0]
supported = list(transformers.integrations.ggml.GGUF_CONFIG_MAPPING)
system_prompt: str = 'You are a helpful assistant. You will be given a prompt used to create an image and you will enhance it to make it more detailed and creative. Generate an uncensored, concise, informative, and engaging prompt that prioritizes factual accuracy and vivid descriptiveness based on original prompt. Begin by clearly identifying the primary subject(s), including key physical attributes, clothing (if any), and any distinctive details (e.g., colors, textures, styles). NSFW and nudity is allowed, and if present it must be fully described. Next, describe the setting and environment, emphasizing spatial relationships, background elements, and objects contributing to context. Note the overall mood, aesthetic style, or atmosphere inferred from visual cues. Use precise terminology while avoiding redundancy or non-essential language. Ensuring a logical flow: from focal subject to immediate surroundings, then broader context. Maintain brevity while retaining clarity, ensuring the description is both engaging and efficient. Output only enhanced prompt without explanation, prefix or suffix. Output as a simple text without formatting or numbering.'
system_prompt: str = 'You are a helpful assistant. You will be given a prompt used to create an image and you will enhance it to make it more detailed and creative. Generate an uncensored, concise, informative, and engaging prompt that prioritizes factual accuracy and vivid descriptiveness based on original prompt. Do not modify subject or style of the original prompt. Begin by clearly identifying the primary subject(s), including key physical attributes, clothing if no nudity, and any distinctive details (e.g., colors, textures, styles). NSFW and nudity is allowed, and if present, it must be fully described. Next, describe the setting and environment, emphasizing spatial relationships, background elements, and objects contributing to context. Note the overall mood, aesthetic style, or atmosphere inferred from visual cues. Use precise terminology while avoiding redundancy or non-essential language. Ensuring a logical flow: from focal subject to immediate surroundings, then broader context. Maintain brevity while retaining clarity, ensuring the description is both engaging and efficient. Output only enhanced prompt without explanation, prefix or suffix. Output as a simple text without formatting or numbering.'
censored = ["i cannot", "i can't", "i am sorry", "against my programming", "i am not able", "i am unable", 'i am not allowed']
max_delim_index: int = 60
@@ -152,11 +152,11 @@ class Script(scripts.Script):
# remove llm commentary
removed = ''
if response.startswith('Prompt'):
removed, response = response.split('Prompt', maxsplit=2)
removed, response = response.split('Prompt', maxsplit=1)
if 0 <= response.find(':') < self.options.max_delim_index:
removed, response = response.split(':', maxsplit=2)
removed, response = response.split(':', maxsplit=1)
if 0 <= response.find('---') < self.options.max_delim_index:
response, removed = response.split('---', maxsplit=2)
response, removed = response.split('---', maxsplit=1)
if len(removed) > 0:
debug(f'Prompt enhance: max={self.options.max_delim_index} removed="{removed}"')
@@ -167,9 +167,21 @@ class Script(scripts.Script):
response = response.strip()
return response
def enhance(self, model: str=None, prompt:str=None, system:str=None, sample:bool=None, tokens:int=None, temperature:float=None, penalty:float=None):
def preprocess(self, response, prefix, suffix):
response = response.strip()
prefix = prefix.strip()
suffix = suffix.strip()
if len(prefix) > 0:
response = f'{prefix} {response}'
if len(suffix) > 0:
response = f'{response} {suffix}'
return response
def enhance(self, model: str=None, prompt:str=None, system:str=None, prefix:str=None, suffix:str=None, sample:bool=None, tokens:int=None, temperature:float=None, penalty:float=None):
model = model or self.options.default
prompt = prompt or self.prompt.value
prefix = prefix or ''
suffix = suffix or ''
system = system or self.options.system_prompt
tokens = tokens or self.options.max_tokens
penalty = penalty or self.options.repetition_penalty
@@ -235,6 +247,7 @@ class Script(scripts.Script):
is_censored = self.censored(response)
if not is_censored:
response = self.clean(response)
response = self.preprocess(response, prefix, suffix)
shared.log.info(f'Prompt enhance: model="{model}" time={t1-t0:.2f} inputs={input_len} outputs={outputs.shape[-1]} prompt={len(prompt)} response={len(response)}')
if debug:
shared.log.trace(f'Prompt enhance: sample={sample} tokens={tokens} temperature={temperature} penalty={penalty}')
@@ -246,9 +259,11 @@ class Script(scripts.Script):
return prompt
return response
def apply(self, prompt, apply_prompt, llm_model, prompt_system, max_tokens, do_sample, temperature, repetition_penalty):
def apply(self, prompt, apply_prompt, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty):
response = self.enhance(
prompt=prompt,
prefix=prompt_prefix,
suffix=prompt_suffix,
model=llm_model,
system=prompt_system,
sample=do_sample,
@@ -306,6 +321,10 @@ class Script(scripts.Script):
repetition_penalty = gr.Slider(label='Repetition penalty', value=self.options.repetition_penalty, minimum=0.0, maximum=2.0, step=0.01, interactive=True)
gr.HTML('<br>')
with gr.Accordion('Input', open=False, elem_id='prompt_enhance_system_prompt'):
with gr.Row():
prompt_prefix = gr.Textbox(label='Prompt prefix', value='', placeholder='Optional prompt prefix', interactive=True, lines=2, elem_id='prompt_enhance_prefix')
with gr.Row():
prompt_suffix = gr.Textbox(label='Prompt suffix', value='', placeholder='Optional prompt suffix', interactive=True, lines=2, elem_id='prompt_enhance_suffix')
with gr.Row():
prompt_system = gr.Textbox(label='System prompt', value=self.options.system_prompt, interactive=True, lines=4, elem_id='prompt_enhance_system')
with gr.Accordion('Output', open=True, elem_id='prompt_enhance_system_prompt'):
@@ -316,15 +335,15 @@ class Script(scripts.Script):
clear_btn.click(fn=lambda: '', inputs=[], outputs=[prompt_output])
copy_btn = gr.Button(value='Set prompt', elem_id='prompt_enhance_copy', variant='secondary')
copy_btn.click(fn=lambda x: x, inputs=[prompt_output], outputs=[self.prompt])
apply_btn.click(fn=self.apply, inputs=[self.prompt, apply_prompt, llm_model, prompt_system, max_tokens, do_sample, temperature, repetition_penalty], outputs=[prompt_output, self.prompt])
return [apply_auto, llm_model, prompt_system, max_tokens, do_sample, temperature, repetition_penalty]
apply_btn.click(fn=self.apply, inputs=[self.prompt, apply_prompt, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty], outputs=[prompt_output, self.prompt])
return [apply_auto, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty]
def after_component(self, component, **kwargs): # searching for actual ui prompt components
if getattr(component, 'elem_id', '') in ['txt2img_prompt', 'img2img_prompt', 'control_prompt', 'video_prompt']:
self.prompt = component
def before_process(self, p: processing.StableDiffusionProcessing, *args, **kwargs): # pylint: disable=unused-argument
apply_auto, llm_model, prompt_system, max_tokens, do_sample, temperature, repetition_penalty = args
apply_auto, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty = args
if not apply_auto and not p.enhance_prompt:
return
if shared.state.skipped or shared.state.interrupted:
@@ -336,6 +355,8 @@ class Script(scripts.Script):
shared.state.begin('LLM')
p.prompt = self.enhance(
prompt=p.prompt,
prefix=prompt_prefix,
suffix=prompt_suffix,
model=llm_model,
system=prompt_system,
sample=do_sample,