mirror of
https://github.com/vladmandic/automatic
synced 2026-08-30 09:01:01 +02:00
Vladimir Mandic
475 lines
26 KiB
Python
475 lines
26 KiB
Python
from dataclasses import dataclass
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import io
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import os
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import re
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import time
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import base64
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import torch
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import transformers
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import gradio as gr
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from PIL import Image
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from modules import scripts, shared, devices, errors, processing, sd_models, sd_modules
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debug_enabled = os.environ.get('SD_LLM_DEBUG', None) is not None
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debug_log = shared.log.trace if debug_enabled else lambda *args, **kwargs: None
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def b64(image):
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if image is None:
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return ''
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if isinstance(image, gr.Image):
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return None
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with io.BytesIO() as stream:
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image.convert('RGB').save(stream, 'JPEG')
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values = stream.getvalue()
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encoded = base64.b64encode(values).decode()
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return encoded
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@dataclass
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class Options:
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img2img = [
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'google/gemma-3-4b-it',
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]
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models = {
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'google/gemma-3-1b-it': {},
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'google/gemma-3-4b-it': {},
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'Qwen/Qwen3-0.6B-FP8': {},
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'Qwen/Qwen3-1.7B-FP8': {},
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'Qwen/Qwen3-4B-FP8': {},
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'Qwen/Qwen3-0.6B': {},
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'Qwen/Qwen3-1.7B': {},
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'Qwen/Qwen3-4B': {},
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'Qwen/Qwen2.5-0.5B-Instruct': {},
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'Qwen/Qwen2.5-1.5B-Instruct': {},
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'Qwen/Qwen2.5-3B-Instruct': {},
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'microsoft/Phi-4-mini-instruct': {},
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'HuggingFaceTB/SmolLM2-135M-Instruct': {},
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'HuggingFaceTB/SmolLM2-360M-Instruct': {},
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'HuggingFaceTB/SmolLM2-1.7B-Instruct': {},
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'meta-llama/Llama-3.2-1B-Instruct': {},
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'meta-llama/Llama-3.2-3B-Instruct': {},
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'cognitivecomputations/Dolphin3.0-Llama3.2-1B': {},
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'cognitivecomputations/Dolphin3.0-Llama3.2-3B': {},
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'nidum/Nidum-Gemma-3-4B-it-Uncensored': {},
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# 'llava/Llama-3-8B-v1.1-Extracted': {
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# 'repo': 'hunyuanvideo-community/HunyuanVideo',
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# 'subfolder': 'text_encoder',
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# 'tokenizer': 'tokenizer',
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# },
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'mradermacher/Llama-3.2-1B-Instruct-Uncensored-i1-GGUF': {
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'repo': 'meta-llama/Llama-3.2-1B-Instruct', # original repo so we can load missing components
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'type': 'llama', # required so gguf loader knows what to do
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'gguf': 'mradermacher/Llama-3.2-1B-Instruct-Uncensored-i1-GGUF', # gguf repo
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'file': 'Llama-3.2-1B-Instruct-Uncensored.i1-Q4_0.gguf', # gguf file inside repo
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},
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}
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default = list(models)[1] # gemma-3-4b-it
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supported = list(transformers.integrations.ggml.GGUF_CONFIG_MAPPING)
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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.'
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image_prompt: str = 'You are a helpful assistant. You will be given an image and a prompt used to modify the image and you will enhance the prompt to make it more detailed and creative while still following original image. 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.'
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image_noprompt: str = 'You are a helpful assistant. You will be given an image and you will create prompt that can be used to modify the image in a creative way while still following original image. 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.'
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censored = ["i cannot", "i can't", "i am sorry", "against my programming", "i am not able", "i am unable", 'i am not allowed']
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max_delim_index: int = 60
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max_tokens: int = 50
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do_sample: bool = True
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temperature: float = 0.15
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repetition_penalty: float = 1.2
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thinking_mode: bool = False
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class Script(scripts.Script):
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prompt: gr.Textbox = None
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image: gr.Image = None
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model: str = None
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llm: transformers.AutoModelForCausalLM = None
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tokenizer: transformers.AutoProcessor = None
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busy: bool = False
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options = Options()
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def title(self):
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return 'Prompt enhance'
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def show(self, _is_img2img):
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return scripts.AlwaysVisible
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def load(self, name:str=None, model_repo:str=None, model_gguf:str=None, model_type:str=None, model_file:str=None):
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name = name or self.options.default
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if self.busy:
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shared.log.debug('Prompt enhance: busy')
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return
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self.busy = True
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if self.model is not None and self.model == name:
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return
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from modules import modelloader, model_quant, ggml
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modelloader.hf_login()
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model_repo = model_repo or self.options.models.get(name, {}).get('repo', None) or name
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model_gguf = model_gguf or self.options.models.get(name, {}).get('gguf', None) or model_repo
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model_type = model_type or self.options.models.get(name, {}).get('type', None)
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model_file = model_file or self.options.models.get(name, {}).get('file', None)
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model_subfolder = self.options.models.get(name, {}).get('subfolder', None)
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model_tokenizer = self.options.models.get(name, {}).get('tokenizer', None)
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gguf_args = {}
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if model_type is not None and model_file is not None and len(model_type) > 2 and len(model_file) > 2:
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debug_log(f'Prompt enhance: gguf supported={self.options.supported}')
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if model_type not in self.options.supported:
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shared.log.error(f'Prompt enhance: name="{name}" repo="{model_repo}" fn="{model_file}" type={model_type} gguf not supported')
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shared.log.trace(f'Prompt enhance: gguf supported={self.options.supported}')
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self.busy = False
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return
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ggml.install_gguf()
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gguf_args['model_type'] = model_type
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gguf_args['gguf_file'] = model_file
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quant_args = model_quant.create_config(module='LLM') if not gguf_args else {}
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try:
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t0 = time.time()
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if self.llm is not None:
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self.llm = None
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shared.log.debug(f'Prompt enhance: name="{self.model}" unload')
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self.model = None
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load_args = { 'pretrained_model_name_or_path': model_repo if not gguf_args else model_gguf }
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if model_subfolder:
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load_args['subfolder'] = model_subfolder,
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self.llm = transformers.AutoModelForCausalLM.from_pretrained(
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**load_args,
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trust_remote_code=True,
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torch_dtype=devices.dtype,
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cache_dir=shared.opts.hfcache_dir,
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_attn_implementation="eager",
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**gguf_args,
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**quant_args,
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)
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self.llm.eval()
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if model_repo in self.options.img2img:
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cls = transformers.AutoProcessor # required to encode image
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else:
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cls = transformers.AutoTokenizer
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self.tokenizer = cls.from_pretrained(
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pretrained_model_name_or_path=model_repo,
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subfolder=model_tokenizer,
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cache_dir=shared.opts.hfcache_dir,
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)
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self.tokenizer.is_processor = model_repo in self.options.img2img
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if debug_enabled:
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modules = sd_modules.get_model_stats(self.llm) + sd_modules.get_model_stats(self.tokenizer)
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for m in modules:
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shared.log.trace(f'Prompt enhance: {m}')
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self.model = name
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t1 = time.time()
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shared.log.info(f'Prompt enhance: cls={self.llm.__class__.__name__} name="{name}" repo="{model_repo}" fn="{model_file}" time={t1-t0:.2f} loaded')
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except Exception as e:
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shared.log.error(f'Prompt enhance: load {e}')
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errors.display(e, 'Prompt enhance')
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devices.torch_gc()
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self.busy = False
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def censored(self, response):
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text = response.lower().replace("i'm", "i am")
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return any(c.lower() in text for c in self.options.censored)
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def unload(self):
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if self.llm is not None:
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sd_models.move_model(self.llm, devices.cpu)
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self.model = None
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self.llm = None
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self.tokenizer = None
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devices.torch_gc()
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shared.log.debug('Prompt enhance: model unloaded')
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def clean(self, response):
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# remove special characters
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response = response.replace('"', '').replace("'", "").replace('“', '').replace('”', '').replace('**', '')
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# remove repeating characters
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response = response.replace('\n\n', '\n').replace(' ', ' ').replace('...', '.')
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# remove comments between brackets
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response = re.sub(r'<.*?>', '', response)
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response = re.sub(r'\[.*?\]', '', response)
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response = re.sub(r'\/.*?\/', '', response)
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# remove llm commentary
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removed = ''
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if response.startswith('Prompt'):
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removed, response = response.split('Prompt', maxsplit=1)
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if 0 <= response.find(':') < self.options.max_delim_index:
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removed, response = response.split(':', maxsplit=1)
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if 0 <= response.find('---') < self.options.max_delim_index:
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response, removed = response.split('---', maxsplit=1)
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if len(removed) > 0:
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debug_log(f'Prompt enhance: max={self.options.max_delim_index} removed="{removed}"')
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# remove bullets and lists
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lines = [re.sub(r'^(\s*[-*]|\s*\d+)\s+', '', line).strip() for line in response.splitlines()]
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response = '\n'.join(lines)
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response = response.strip()
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return response
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def post(self, response, prefix, suffix, networks):
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response = response.strip()
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prefix = prefix.strip()
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suffix = suffix.strip()
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if len(prefix) > 0:
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response = f'{prefix} {response}'
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if len(suffix) > 0:
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response = f'{response} {suffix}'
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if len(networks) > 0:
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response = f'{response} {" ".join(networks)}'
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return response
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def extract(self, prompt):
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pattern = r'(<.*?>)'
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matches = re.findall(pattern, prompt)
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filtered = re.sub(pattern, '', prompt)
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return filtered, matches
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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, thinking:bool=False, seed:int=-1, image=None):
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model = model or self.options.default
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prompt = prompt or self.prompt.value
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image = image or self.image
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prefix = prefix or ''
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suffix = suffix or ''
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tokens = tokens or self.options.max_tokens
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penalty = penalty or self.options.repetition_penalty
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temperature = temperature or self.options.temperature
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thinking = thinking or self.options.thinking_mode
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sample = sample if sample is not None else self.options.do_sample
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while self.busy:
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time.sleep(0.1)
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self.load(model)
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if seed is not None and seed >= 0:
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torch.manual_seed(seed)
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if self.llm is None:
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shared.log.error('Prompt enhance: model not loaded')
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return prompt
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prompt, networks = self.extract(prompt)
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debug_log(f'Prompt enhance: networks={networks}')
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if image is not None and isinstance(image, Image.Image):
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if not self.tokenizer.is_processor:
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shared.log.error('Prompt enhance: image not supported by model')
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return prompt
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if prompt is not None and len(prompt) > 0:
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system = system or self.options.image_prompt
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chat_template = [
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{ "role": "system", "content": [
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{"type": "text", "text": system }
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] },
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{ "role": "user", "content": [
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{"type": "text", "text": prompt},
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{"type": "image", "image": b64(image)}
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] },
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]
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else:
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system = system or self.options.image_noprompt
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chat_template = [
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{ "role": "system", "content": [
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{"type": "text", "text": system }
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] },
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{ "role": "user", "content": [
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{"type": "image", "image": b64(image)}
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] },
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]
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else:
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system = system or self.options.system_prompt
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if not self.tokenizer.is_processor:
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chat_template = [
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{ "role": "system", "content": system },
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{ "role": "user", "content": prompt },
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]
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else:
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chat_template = [
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{ "role": "system", "content": [
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{"type": "text", "text": system }
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] },
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{ "role": "user", "content": [
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{"type": "text", "text": prompt},
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] },
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]
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t0 = time.time()
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self.busy = True
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try:
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inputs = self.tokenizer.apply_chat_template(
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chat_template,
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add_generation_prompt=True,
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enable_thinking=thinking,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(devices.device).to(devices.dtype)
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input_len = inputs['input_ids'].shape[1]
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except Exception as e:
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shared.log.error(f'Prompt enhance tokenize: {e}')
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errors.display(e, 'Prompt enhance')
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self.busy = False
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return prompt
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try:
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with devices.inference_context():
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sd_models.move_model(self.llm, devices.device)
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outputs = self.llm.generate(
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**inputs,
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do_sample=sample,
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temperature=float(temperature),
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max_new_tokens=int(input_len + tokens),
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repetition_penalty=float(penalty),
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)
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if shared.opts.diffusers_offload_mode != 'none':
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sd_models.move_model(self.llm, devices.cpu)
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devices.torch_gc()
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if debug_enabled:
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raw_response = self.tokenizer.batch_decode(outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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shared.log.trace(f'Prompt enhance: raw="{raw_response}"')
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outputs_cropped = outputs[:, input_len:]
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response = self.tokenizer.batch_decode(
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outputs_cropped,
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skip_special_tokens=True,
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clean_up_tokenization_spaces=True,
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)
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except Exception as e:
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shared.log.error(f'Prompt enhance generate: {e}')
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errors.display(e, 'Prompt enhance')
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self.busy = False
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response = f'Error: {str(e)}'
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t1 = time.time()
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if isinstance(response, list):
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response = response[0]
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is_censored = self.censored(response)
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if not is_censored:
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response = self.clean(response)
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response = self.post(response, prefix, suffix, networks)
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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)}')
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if debug_enabled:
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shared.log.trace(f'Prompt enhance: sample={sample} tokens={tokens} temperature={temperature} penalty={penalty} thinking={thinking}')
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shared.log.trace(f'Prompt enhance: prompt="{prompt}"')
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shared.log.trace(f'Prompt enhance: response="{response}"')
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self.busy = False
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if is_censored:
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shared.log.warning(f'Prompt enhance: censored response="{response}"')
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return prompt
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return response
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def apply(self, prompt, image, apply_prompt, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty, thinking_mode):
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response = self.enhance(
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prompt=prompt,
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image=image,
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prefix=prompt_prefix,
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suffix=prompt_suffix,
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model=llm_model,
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system=prompt_system,
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sample=do_sample,
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tokens=max_tokens,
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temperature=temperature,
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penalty=repetition_penalty,
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thinking=thinking_mode,
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)
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if apply_prompt:
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return [response, response]
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return [response, gr.update()]
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def get_custom(self, name):
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model_repo = self.options.models.get(name, {}).get('repo', None) or name
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model_gguf = self.options.models.get(name, {}).get('gguf', None)
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model_type = self.options.models.get(name, {}).get('type', None)
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model_file = self.options.models.get(name, {}).get('file', None)
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return [model_repo, model_gguf, model_type, model_file]
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def ui(self, _is_img2img):
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with gr.Accordion('Prompt enhance', open=False, elem_id='prompt_enhance'):
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with gr.Row():
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apply_btn = gr.Button(value='Enhance now', elem_id='prompt_enhance_apply', variant='primary')
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with gr.Row():
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apply_prompt = gr.Checkbox(label='Apply to prompt', value=False)
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apply_auto = gr.Checkbox(label='Auto enhance', value=False)
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gr.HTML('<br>')
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with gr.Group():
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with gr.Row():
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llm_model = gr.Dropdown(label='LLM model', choices=list(self.options.models), value=self.options.default, interactive=True, allow_custom_value=True, elem_id='prompt_enhance_model')
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with gr.Row():
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load_btn = gr.Button(value='Load model', elem_id='prompt_enhance_load', variant='secondary')
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load_btn.click(fn=self.load, inputs=[llm_model], outputs=[])
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unload_btn = gr.Button(value='Unload model', elem_id='prompt_enhance_unload', variant='secondary')
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unload_btn.click(fn=self.unload, inputs=[], outputs=[])
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with gr.Accordion('Custom model', open=False, elem_id='prompt_enhance_custom'):
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with gr.Row():
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model_repo = gr.Textbox(label='Model repo', value=None, interactive=True, elem_id='prompt_enhance_model_repo', placeholder='Original model repo on huggingface')
|
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with gr.Row():
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model_gguf = gr.Textbox(label='Model gguf', value=None, interactive=True, elem_id='prompt_enhance_model_gguf', placeholder='Optional GGUF model repo on huggingface')
|
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with gr.Row():
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|
model_type = gr.Textbox(label='Model type', value=None, interactive=True, elem_id='prompt_enhance_model_type', placeholder='Optional GGUF model type')
|
|
with gr.Row():
|
|
model_file = gr.Textbox(label='Model file', value=None, interactive=True, elem_id='prompt_enhance_model_file', placeholder='Optional GGUF model file inside GGUF model repo')
|
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with gr.Row():
|
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custom_btn = gr.Button(value='Load custom model', elem_id='prompt_enhance_custom_load', variant='secondary')
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custom_btn.click(fn=self.load, inputs=[model_file, model_repo, model_gguf, model_type, model_file], outputs=[])
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llm_model.change(fn=self.get_custom, inputs=[llm_model], outputs=[model_repo, model_gguf, model_type, model_file])
|
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gr.HTML('<br>')
|
|
with gr.Accordion('Options', open=False, elem_id='prompt_enhance_options'):
|
|
with gr.Row():
|
|
max_tokens = gr.Slider(label='Max tokens', value=self.options.max_tokens, minimum=10, maximum=1024, step=1, interactive=True)
|
|
do_sample = gr.Checkbox(label='Do sample', value=self.options.do_sample, interactive=True)
|
|
with gr.Row():
|
|
temperature = gr.Slider(label='Temperature', value=self.options.temperature, minimum=0.0, maximum=1.0, step=0.01, interactive=True)
|
|
repetition_penalty = gr.Slider(label='Repetition penalty', value=self.options.repetition_penalty, minimum=0.0, maximum=2.0, step=0.01, interactive=True)
|
|
with gr.Row():
|
|
thinking_mode = gr.Checkbox(label='Thinking mode', value=False, 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'):
|
|
with gr.Row():
|
|
prompt_output = gr.Textbox(label='Enhanced prompt', value='', interactive=True, lines=4)
|
|
with gr.Row():
|
|
clear_btn = gr.Button(value='Clear', elem_id='prompt_enhance_clear', variant='secondary')
|
|
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])
|
|
if self.image is None:
|
|
self.image = gr.Image(type='pil', interactive=False, visible=False) # dummy image
|
|
apply_btn.click(fn=self.apply, inputs=[self.prompt, self.image, apply_prompt, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty, thinking_mode], outputs=[prompt_output, self.prompt])
|
|
return [apply_auto, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty, thinking_mode]
|
|
|
|
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
|
|
if getattr(component, 'elem_id', '') in ['img2img_image', 'control_input_select']:
|
|
self.image = component
|
|
|
|
def before_process(self, p: processing.StableDiffusionProcessing, *args, **kwargs): # pylint: disable=unused-argument
|
|
apply_auto, llm_model, prompt_system, prompt_prefix, prompt_suffix, max_tokens, do_sample, temperature, repetition_penalty, thinking_mode = args
|
|
if not apply_auto and not p.enhance_prompt:
|
|
return
|
|
if shared.state.skipped or shared.state.interrupted:
|
|
return
|
|
p.prompt = shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)
|
|
p.negative_prompt = shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)
|
|
shared.prompt_styles.apply_styles_to_extra(p)
|
|
p.styles = []
|
|
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,
|
|
tokens=max_tokens,
|
|
temperature=temperature,
|
|
penalty=repetition_penalty,
|
|
thinking=thinking_mode,
|
|
)
|
|
p.extra_generation_params['LLM'] = llm_model
|
|
shared.state.end()
|