mirror of
https://github.com/vladmandic/automatic
synced 2026-08-28 08:00:59 +02:00
Vladimir Mandic
224 lines
10 KiB
Python
224 lines
10 KiB
Python
from dataclasses import dataclass
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import textwrap
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import transformers
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@dataclass
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class Options:
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img2img = [
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# Gemma
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'google/gemma-3-4b-it',
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'google/gemma-3n-E2B-it',
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'google/gemma-3n-E4B-it',
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'google/gemma-4-E2B-it',
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'google/gemma-4-E4B-it',
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'google/gemma-4-12B-it-qat-w4a16-ct',
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# Qwen3.5
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'Qwen/Qwen3.5-2B',
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'Qwen/Qwen3.5-4B',
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'Qwen/Qwen3.5-9B',
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# Qwen3-VL
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'Qwen/Qwen3-VL-2B-Instruct',
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'Qwen/Qwen3-VL-2B-Thinking',
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'Qwen/Qwen3-VL-4B-Instruct',
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'Qwen/Qwen3-VL-4B-Thinking',
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'Qwen/Qwen3-VL-8B-Instruct',
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'Qwen/Qwen3-VL-8B-Thinking',
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# Qwen2.5-VL
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'Qwen/Qwen2.5-VL-3B-Instruct',
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# Mistral
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'mistralai/Ministral-3-3B-Instruct-2512-BF16',
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'mistralai/Ministral-3-8B-Instruct-2512-BF16',
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'mistralai/Ministral-3-3B-Reasoning-2512',
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'mistralai/Ministral-3-8B-Reasoning-2512',
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# Finetunes
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'trohrbaugh/gemma-4-E4B-it-heretic-ara',
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'trohrbaugh/Qwen3.5-9B-heretic-v2',
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]
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cloud = [
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'google/gemini-3.5-flash',
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'google/gemini-3.1-pro-preview',
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'google/gemini-3.1-flash-lite',
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'google/gemini-3.1-flash-lite-preview',
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'google/gemini-2.5-flash',
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'google/gemini-2.5-flash-lite',
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'google/gemini-2.5-pro',
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]
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models = {
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# Gemma
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'google/gemma-3-1b-it': {},
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'google/gemma-3-4b-it': {},
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'google/gemma-3n-E2B-it': {},
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'google/gemma-3n-E4B-it': {},
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'google/gemma-4-E2B-it': {},
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'google/gemma-4-E4B-it': {},
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'google/gemma-4-12B-it-qat-w4a16-ct': {}, # compressed-tensor model
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# Qwen3.5
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'Qwen/Qwen3.5-0.8B': {},
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'Qwen/Qwen3.5-2B': {},
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'Qwen/Qwen3.5-4B': {},
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'Qwen/Qwen3.5-9B': {},
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# Qwen3
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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/Qwen3-4B-Instruct-2507': {},
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# Qwen3-VL
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'Qwen/Qwen3-VL-2B-Instruct': {},
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'Qwen/Qwen3-VL-2B-Thinking': {},
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'Qwen/Qwen3-VL-4B-Instruct': {},
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'Qwen/Qwen3-VL-4B-Thinking': {},
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'Qwen/Qwen3-VL-8B-Instruct': {},
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'Qwen/Qwen3-VL-8B-Thinking': {},
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# Qwen2.5
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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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# Qwen2.5-VL
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'Qwen/Qwen2.5-VL-3B-Instruct': {},
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# Llama
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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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'meta-llama/Llama-3.2-8B-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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# Gemini
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'google/gemini-3.5-flash': {},
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'google/gemini-3.1-pro-preview': {},
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'google/gemini-3.1-flash-lite': {},
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'google/gemini-3.1-flash-lite-preview': {},
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'google/gemini-2.5-flash': {},
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'google/gemini-2.5-flash-lite': {},
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'google/gemini-2.5-pro': {},
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# SmolLM
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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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'HuggingFaceTB/SmolLM3-3B': {},
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# Phi
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'microsoft/Phi-4-mini-instruct': {},
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# Mistral
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'mistralai/Ministral-3-3B-Instruct-2512-BF16': {},
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'mistralai/Ministral-3-8B-Instruct-2512-BF16': {},
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'mistralai/Ministral-3-3B-Reasoning-2512': {},
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'mistralai/Ministral-3-8B-Reasoning-2512': {},
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# Finetunes
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'p-e-w/gemma-4-E2B-it-heretic-ara': {},
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'trohrbaugh/gemma-4-E4B-it-heretic-ara': {},
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'trohrbaugh/Qwen3.5-9B-heretic-v2': {},
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# GGUF
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'mradermacher/Llama-3.2-1B-Instruct-Uncensored-i1-GGUF': { # kept primarily as an example how to add gguf model
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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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models_cls = {
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'qwen3_5': 'Qwen3_5ForConditionalGeneration',
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'qwen3_5_moe': 'Qwen3_5MoeForConditionalGeneration',
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'qwen3_vl': 'Qwen3VLForConditionalGeneration',
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'qwen2_5_vl': 'Qwen2_5_VLForConditionalGeneration',
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'qwen2_vl': 'Qwen2VLForConditionalGeneration',
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'mistral3': 'Mistral3ForConditionalGeneration',
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'gemma4': 'Gemma4ForConditionalGeneration',
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}
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# default = list(models)[1] # gemma-3-4b-it
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default = 'google/gemma-3-4b-it'
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supported = list(transformers.integrations.ggml.GGUF_CONFIG_MAPPING)
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t2i_prompt: str = textwrap.dedent('''\
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You are an expert AI image prompt engineer.
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You will receive a user prompt for image generation.
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Your sole job is to rewrite user inputs into highly detailed, visually rich prompts for image generation models.
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Improve the prompt by adding relevant visual specificity for composition, lighting, color, texture, and atmosphere.
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Keep the result faithful to the original prompt and the intended image.
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Do not add unrelated concepts, non-visual commentary, or fluff.
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''')
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i2i_prompt: str = textwrap.dedent('''\
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You are an expert AI image prompt engineer.
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You will receive an image and a user prompt for editing or refinement.
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Your sole job is to rewrite user inputs into highly detailed, visually rich prompts for image generation models while taking the provided image into account.
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Improve the prompt with concrete visual detail that remains faithful to the image and edit intent.
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Keep the result grounded in image-generation language.
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Do not invent unrelated objects, actions, or concepts.
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''')
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i2i_noprompt: str = textwrap.dedent('''\
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You are an expert AI image prompt engineer.
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You will receive an image and must create an editing prompt from the visible content.
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Your sole job is to create highly detailed, visually rich prompts for image generation models based on the provided image.
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Base the prompt on what is present in the image, its style, and its atmosphere.
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Use concrete, image-generation language and avoid unrelated fluff or commentary.
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''')
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t2v_prompt: str = textwrap.dedent('''\
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You are an expert AI video prompt engineer.
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You will receive a user prompt for video generation.
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Your sole job is to rewrite user inputs into highly detailed, visually rich prompts for video generation models.
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Improve the prompt by adding relevant visual specificity for composition, lighting, color, texture, and atmosphere.
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Specify subject and camera movement, scene transitions, and temporal dynamics.
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Keep the result faithful to the original prompt and the intended video.
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Do not add unrelated concepts, non-visual commentary, or fluff.
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''')
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i2v_prompt: str = textwrap.dedent('''\
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You are an expert AI video prompt engineer.
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You will receive an image and a user prompt for editing or refinement.
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Your sole job is to rewrite user inputs into highly detailed, visually rich prompts for video generation models while taking the provided image into account.
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Improve the prompt with concrete visual detail that remains faithful to the image and edit intent.
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Specify subject and camera movement, scene transitions, and temporal dynamics.
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Keep the result grounded in video-generation language.
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Do not invent unrelated objects, actions, or concepts.
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''')
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i2v_noprompt: str = textwrap.dedent('''\
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You are an expert AI video prompt engineer.
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You will receive an image and must create a video prompt from the visible content.
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Your sole job is to create highly detailed, visually rich prompts for video generation models based on the provided image.
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Base the prompt on what is present in the image, its style, and its atmosphere.
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Specify subject and camera movement, scene transitions, and temporal dynamics.
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Use concrete, video-generation language and avoid unrelated fluff or commentary.
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''')
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nsfw_ok: str = textwrap.dedent('''\
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NSFW, nudity and explicit content and language are allowed.
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''')
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nsfw_no: str = textwrap.dedent('''\
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NSFW, nudity or explicit content and language are not allowed; if present, remove those elements from the enhanced prompt.
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''')
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details_prompt: str = textwrap.dedent('''\
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Enhance the prompt with relevant image details.
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Identify the main subject(s), physical attributes, clothing if non-nude, and distinctive visual features.
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Describe the scene, environment, key objects, and overall mood or atmosphere.
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Be precise and efficient; avoid redundancy, abstract commentary, unrelated fluff, or instructions.
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Do not invent any objects, settings, or themes not implied by the input.
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Do not add era, background props, or atmosphere unless explicitly present in the prompt.
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''')
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details_format: str = textwrap.dedent('''\
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Output exactly one enhanced prompt string.
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Do not add greetings, comments, explanations, follow-up questions, labels, formatting, or numbering.
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Do not include any extra prose or analysis.
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Start immediately with the prompt content.
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No stray tokens!
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''')
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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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min_tokens: int = 0
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max_tokens: int = 256
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do_sample: bool = True
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temperature: float = 0.6
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repetition_penalty: float = 1.2
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top_k: int = 0
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top_p: float = 0.0
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thinking_mode: bool = False
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@staticmethod
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def get_model_choices():
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"""Return list of display names for dropdown."""
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from .helpers import get_model_display_name
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return [get_model_display_name(repo) for repo in Options.models.keys()]
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@staticmethod
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def get_default_display():
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"""Return display name for default model."""
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from .helpers import get_model_display_name
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return get_model_display_name(Options.default)
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