603 lines
20 KiB
Python
603 lines
20 KiB
Python
"""
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Image-to-Video Prompt Expander Node
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Uses vision model to describe image, then expands motion prompt
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"""
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import torch
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import numpy as np
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from PIL import Image
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import io
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import base64
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from typing import Tuple, Optional
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from .llm_backend import LLMBackend
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from .expansion_engine import PromptExpander
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from .utils import (
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save_prompts_to_file,
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parse_keywords,
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validate_positive_keywords
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)
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class ImageToVideoPromptExpander:
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"""
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Advanced Image-to-Video prompt expansion with vision model
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1. Analyzes the image with vision model
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2. Takes user's motion/action input
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3. Combines both into detailed video prompt
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"""
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def __init__(self):
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self.expander = PromptExpander()
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self.type = "image_to_video_expansion"
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self.output_dir = "output/video_prompts"
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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# Image input
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"image": ("IMAGE",),
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# Motion/Action input
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"motion_description": ("STRING", {
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"multiline": True,
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"default": "slowly turns head and smiles at camera",
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"placeholder": "Describe the motion/action you want"
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}),
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# Core settings
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"preset": ([
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"custom",
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"cinematic",
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"surreal",
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"action",
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"stylized",
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"noir",
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"random"
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], {
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"default": "cinematic"
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}),
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"expansion_tier": ([
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"auto",
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"basic",
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"enhanced",
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"advanced",
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"cinematic"
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], {
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"default": "enhanced"
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}),
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# Vision model settings
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"use_vision_model": ("BOOLEAN", {
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"default": True
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}),
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"vision_backend": ([
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"lm_studio",
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"ollama"
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], {
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"default": "lm_studio"
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}),
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"vision_model_name": ("STRING", {
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"default": "llama-3.2-vision",
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"multiline": False,
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"placeholder": "e.g., llama-3.2-vision, llava, minicpm-v"
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}),
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"vision_endpoint": ("STRING", {
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"default": "http://localhost:1234/v1",
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"multiline": False
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}),
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# Text expansion model (can be same or different)
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"expansion_backend": ([
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"lm_studio",
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"ollama"
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], {
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"default": "lm_studio"
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}),
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"expansion_model_name": ("STRING", {
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"default": "llama3",
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"multiline": False
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}),
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"expansion_endpoint": ("STRING", {
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"default": "http://localhost:1234/v1",
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"multiline": False
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}),
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"temperature": ("FLOAT", {
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"default": 0.7,
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"min": 0.1,
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"max": 2.0,
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"step": 0.1
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}),
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# Camera/Motion controls
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"camera_movement": ([
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"auto",
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"none",
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"static shot",
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"camera pushes in",
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"dolly in",
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"camera pulls back",
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"dolly out",
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"camera pans right",
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"camera pans left",
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"camera tilts up",
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"camera tilts down",
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"tracking shot",
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"arc shot",
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"handheld camera",
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"steadicam"
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], {
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"default": "auto"
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}),
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"shot_size": ([
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"auto",
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"none",
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"extreme close-up shot",
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"close-up shot",
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"medium close-up shot",
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"medium shot",
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"medium wide shot",
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"wide shot"
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], {
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"default": "auto"
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}),
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"motion_speed": ([
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"auto",
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"very slow",
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"slow",
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"normal",
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"fast",
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"very fast"
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], {
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"default": "auto"
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}),
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# Lighting/Time
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"lighting_type": ([
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"auto",
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"none",
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"soft lighting",
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"hard lighting",
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"edge lighting",
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"rim lighting",
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"natural lighting"
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], {
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"default": "auto"
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}),
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"time_of_day": ([
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"auto",
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"none",
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"maintain current",
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"sunrise",
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"daytime",
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"sunset",
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"night"
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], {
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"default": "maintain current"
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}),
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# Keywords
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"positive_keywords": ("STRING", {
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"default": "",
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"multiline": True,
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"placeholder": "LoRA triggers, style keywords"
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}),
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"negative_keywords": ("STRING", {
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"default": "",
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"multiline": True
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}),
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# Output
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"save_to_file": ("BOOLEAN", {
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"default": False
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}),
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"filename_base": ("STRING", {
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"default": "img2vid_prompt",
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"multiline": False
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})
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}
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}
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RETURN_TYPES = ("STRING", "STRING", "STRING", "STRING")
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RETURN_NAMES = ("positive_prompt", "negative_prompt", "image_description", "status")
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FUNCTION = "expand_img2vid_prompt"
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CATEGORY = "Eric Prompt Enhancers"
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OUTPUT_NODE = True
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def expand_img2vid_prompt(
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self,
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image: torch.Tensor,
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motion_description: str,
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preset: str,
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expansion_tier: str,
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use_vision_model: bool,
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vision_backend: str,
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vision_model_name: str,
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vision_endpoint: str,
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expansion_backend: str,
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expansion_model_name: str,
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expansion_endpoint: str,
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temperature: float,
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camera_movement: str,
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shot_size: str,
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motion_speed: str,
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lighting_type: str,
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time_of_day: str,
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positive_keywords: str,
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negative_keywords: str,
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save_to_file: bool,
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filename_base: str
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) -> Tuple[str, str, str, str]:
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"""
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Main processing: Analyze image → Combine with motion → Expand
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"""
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try:
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# STEP 1: Get image description
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if use_vision_model:
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image_desc_result = self._analyze_image(
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image,
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vision_backend,
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vision_model_name,
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vision_endpoint,
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temperature
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)
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if not image_desc_result["success"]:
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return (
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motion_description,
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"",
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f"ERROR: {image_desc_result['error']}",
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f"❌ Vision model failed"
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)
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image_description = image_desc_result["description"]
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else:
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image_description = "[Image description skipped - using motion only]"
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# STEP 2: Build combined prompt
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combined_input = self._build_combined_prompt(
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image_description,
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motion_description,
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motion_speed
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)
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# STEP 3: Gather aesthetic controls
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aesthetic_controls = {}
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if camera_movement not in ["auto", "none"]:
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aesthetic_controls["camera_movement"] = camera_movement
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if shot_size not in ["auto", "none"]:
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aesthetic_controls["shot_size"] = shot_size
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if lighting_type not in ["auto", "none"]:
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aesthetic_controls["lighting_type"] = lighting_type
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if time_of_day not in ["auto", "none", "maintain current"]:
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aesthetic_controls["time_of_day"] = time_of_day
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# STEP 4: Expand with expansion engine
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pos_kw_list = parse_keywords(positive_keywords)
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neg_kw_list = parse_keywords(negative_keywords)
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system_prompt, user_prompt, breakdown_dict = self.expander.expand_prompt(
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basic_prompt=combined_input,
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preset=preset,
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tier=expansion_tier,
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mode="image-to-video",
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positive_keywords=pos_kw_list,
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variation_seed=None,
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aesthetic_controls=aesthetic_controls
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)
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# STEP 5: Call expansion LLM
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expansion_llm = LLMBackend(
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backend_type=expansion_backend,
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endpoint=expansion_endpoint,
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model_name=expansion_model_name,
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temperature=temperature
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)
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response = expansion_llm.send_prompt(
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system_prompt=system_prompt,
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user_prompt=user_prompt,
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max_tokens=2000
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)
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if not response["success"]:
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return (
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combined_input,
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"",
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image_description,
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f"❌ Expansion failed: {response['error']}"
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)
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# STEP 6: Parse and validate
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parsed = self.expander.parse_llm_response(response["response"])
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enhanced_prompt = parsed["prompt"]
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if not enhanced_prompt or len(enhanced_prompt) < 20:
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enhanced_prompt = combined_input
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# Add required keywords
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if pos_kw_list:
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keywords_present, missing = validate_positive_keywords(pos_kw_list, enhanced_prompt)
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if missing:
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enhanced_prompt += f" {', '.join(missing)}"
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# STEP 7: Generate negative prompt
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negative_prompt = self.expander.generate_negative_prompt(
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preset=preset,
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custom_negatives=neg_kw_list,
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mode="image-to-video"
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)
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# STEP 8: Save if requested
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if save_to_file:
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metadata = {
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"type": "image-to-video",
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"preset": preset,
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"tier": expansion_tier,
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"vision_model": vision_model_name if use_vision_model else "none",
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"expansion_model": expansion_model_name,
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"image_description": image_description,
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"motion_input": motion_description
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}
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breakdown_text = self._format_breakdown(
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image_description,
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motion_description,
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combined_input,
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aesthetic_controls,
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breakdown_dict
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)
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save_result = save_prompts_to_file(
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positive_prompt=enhanced_prompt,
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negative_prompt=negative_prompt,
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breakdown=breakdown_text,
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metadata=metadata,
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filename_base=filename_base,
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output_dir=self.output_dir
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)
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file_status = f"💾 Saved" if save_result["success"] else "⚠️ Save failed"
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else:
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file_status = "Not saved"
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status = f"✅ Image-to-Video prompt | Vision: {use_vision_model} | {file_status}"
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return (
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enhanced_prompt,
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negative_prompt,
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image_description,
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status
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)
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except Exception as e:
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import traceback
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error_detail = traceback.format_exc()
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print(f"ERROR in image-to-video expansion: {error_detail}")
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return (
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motion_description,
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"",
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f"ERROR: {str(e)}",
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f"❌ {str(e)}"
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)
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def _analyze_image(
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self,
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image: torch.Tensor,
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backend: str,
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model_name: str,
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endpoint: str,
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temperature: float
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) -> dict:
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"""Use vision model to analyze the image"""
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try:
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# Convert tensor to PIL Image
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# ComfyUI image format: [batch, height, width, channels]
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img_np = image[0].cpu().numpy() # Take first image from batch
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img_np = (img_np * 255).astype(np.uint8)
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pil_image = Image.fromarray(img_np)
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# Convert to base64
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buffered = io.BytesIO()
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pil_image.save(buffered, format="PNG")
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img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
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# Build vision prompt
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vision_system_prompt = """You are an expert at describing images in detail for video generation.
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Describe this image comprehensively:
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- Main subject(s): appearance, clothing, pose, expression
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- Setting/environment: location, objects, background
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- Lighting: quality, direction, mood
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- Color palette: dominant colors, saturation
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- Composition: arrangement of elements
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- Atmosphere/mood: overall feeling
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Be specific and detailed. Focus on visual elements that matter for video generation.
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Output ONLY the description, no labels or meta-commentary."""
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vision_user_prompt = "Describe this image in detail for video generation purposes."
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# Call vision model
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llm = LLMBackend(
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backend_type=backend,
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endpoint=endpoint,
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model_name=model_name,
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temperature=temperature
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)
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# For vision models, we need to send the image
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if backend == "lm_studio":
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response = self._call_vision_lm_studio(
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llm,
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vision_system_prompt,
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vision_user_prompt,
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img_base64
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)
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elif backend == "ollama":
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response = self._call_vision_ollama(
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llm,
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vision_system_prompt,
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vision_user_prompt,
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img_base64
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)
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else:
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return {"success": False, "error": "Unknown backend"}
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if not response["success"]:
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return {"success": False, "error": response["error"]}
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description = response["response"].strip()
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return {
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"success": True,
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"description": description
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}
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except Exception as e:
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return {
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"success": False,
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"error": f"Image analysis failed: {str(e)}"
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}
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def _call_vision_lm_studio(self, llm, system_prompt: str, user_prompt: str, img_base64: str) -> dict:
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"""Call LM Studio with vision (OpenAI-compatible format)"""
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import requests
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try:
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url = f"{llm.endpoint}/chat/completions"
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payload = {
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"model": llm.model_name,
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"messages": [
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{"role": "system", "content": system_prompt},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": user_prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/png;base64,{img_base64}"
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}
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}
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]
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}
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],
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"temperature": llm.temperature,
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"max_tokens": 1000
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}
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response = requests.post(url, json=payload, timeout=120)
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response.raise_for_status()
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data = response.json()
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content = data['choices'][0]['message']['content']
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return {"success": True, "response": content, "error": None}
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except Exception as e:
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return {"success": False, "response": "", "error": str(e)}
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def _call_vision_ollama(self, llm, system_prompt: str, user_prompt: str, img_base64: str) -> dict:
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"""Call Ollama with vision"""
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import requests
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try:
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url = f"{llm.endpoint}/api/generate"
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full_prompt = f"{system_prompt}\n\n{user_prompt}"
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payload = {
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"model": llm.model_name,
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"prompt": full_prompt,
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"images": [img_base64],
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"stream": False,
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"options": {
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"temperature": llm.temperature,
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"num_predict": 1000
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}
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}
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response = requests.post(url, json=payload, timeout=120)
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response.raise_for_status()
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data = response.json()
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content = data.get('response', '')
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return {"success": True, "response": content, "error": None}
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except Exception as e:
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return {"success": False, "response": "", "error": str(e)}
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def _build_combined_prompt(
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self,
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image_description: str,
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motion_description: str,
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motion_speed: str
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) -> str:
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"""Combine image description and motion into expansion-ready prompt"""
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parts = []
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# Add image description
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if image_description and not image_description.startswith("ERROR") and not image_description.startswith("[Image"):
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parts.append(f"Scene: {image_description}")
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# Add motion with speed modifier
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motion = motion_description
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if motion_speed not in ["auto", "normal"]:
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motion = f"{motion_speed}, {motion}"
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parts.append(f"Motion: {motion}")
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return " | ".join(parts)
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def _format_breakdown(
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self,
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image_description: str,
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motion_input: str,
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combined_prompt: str,
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aesthetic_controls: dict,
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breakdown_dict: dict
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) -> str:
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"""Format breakdown for file output"""
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lines = [
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"=" * 60,
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"IMAGE-TO-VIDEO PROMPT EXPANSION",
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"=" * 60,
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"\nIMAGE ANALYSIS:",
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image_description,
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"\nMOTION INPUT:",
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motion_input,
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"\nCOMBINED PROMPT:",
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combined_prompt,
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f"\nExpansion Tier: {breakdown_dict.get('detected_tier', 'N/A')}",
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f"Preset: {breakdown_dict.get('applied_preset', 'N/A')}"
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]
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if aesthetic_controls:
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lines.append("\nAesthetic Controls:")
|
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for key, value in aesthetic_controls.items():
|
|
lines.append(f" - {key.replace('_', ' ').title()}: {value}")
|
|
|
|
lines.append("\n" + "=" * 60)
|
|
|
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return "\n".join(lines)
|