Spaces:
Sleeping
Sleeping
File size: 3,378 Bytes
7a1d414 a8704d0 7a1d414 9bc957e 7a1d414 9bc957e 7a1d414 9bc957e 7a1d414 a8704d0 7a1d414 9bc957e a8704d0 9bc957e 7a1d414 9bc957e 7a1d414 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
"""Configuration dataclasses for Stable Diffusion execution and history storage."""
from __future__ import annotations
from dataclasses import asdict, dataclass, field
from datetime import datetime
from typing import Any, Dict, List, Optional
@dataclass
class Txt2ImgConfig:
"""Configuration for text-to-image generation.
Attributes:
prompt: Positive prompt text.
negative_prompt: Negative prompt text.
steps: Number of diffusion steps.
guidance_scale: Classifier-free guidance scale.
width: Requested image width.
height: Requested image height.
seed: Optional random seed.
device: Target torch device ("cuda" or "cpu").
"""
prompt: str
negative_prompt: str = ""
steps: int = 30
guidance_scale: float = 7.5
width: int = 512
height: int = 512
seed: Optional[int] = None
device: str = "cuda"
@dataclass
class Img2ImgConfig:
"""Configuration for image-to-image generation.
Attributes:
prompt: Positive prompt text.
init_image_path: Optional file path to source image.
negative_prompt: Negative prompt text.
strength: Img2Img blend strength in (0, 1].
steps: Number of diffusion steps.
guidance_scale: CFG scale.
width: Requested image width.
height: Requested image height.
seed: Optional random seed.
device: Target device.
"""
prompt: str
init_image_path: Optional[str] = None
negative_prompt: str = ""
strength: float = 0.7
steps: int = 30
guidance_scale: float = 7.5
width: int = 512
height: int = 512
seed: Optional[int] = None
device: str = "cuda"
@dataclass
class GenerationMetadata:
"""Generic metadata for any generation mode.
Fields are optional depending on mode:
- Txt2Img: prompt, negative, steps, guidance
- Img2Img: prompt, negative, strength, steps, guidance
- Upscale: scale, original size, final size
"""
mode: str # "txt2img", "img2img", "upscale"
# Shared
elapsed_seconds: float = 0.0
timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat())
id: Optional[str] = None
thumbnail: Optional[str] = None
full_image: Optional[str] = None
model_id: Optional[str] = None
# Txt2Img / Img2Img
prompt: Optional[str] = None
negative_prompt: Optional[str] = None
steps: Optional[int] = None
guidance_scale: Optional[float] = None
seed: Optional[int] = None
lora_names: Optional[List[str]] = None
lora_alphas: Optional[List[float]] = None
# Img2Img only
strength: Optional[float] = None
# Upscale only
scale: Optional[float] = None
original_width: Optional[int] = None
original_height: Optional[int] = None
width: Optional[int] = None
height: Optional[int] = None
def to_dict(self) -> Dict[str, Any]:
"""Drop None values for clean JSON."""
return {k: v for k, v in asdict(self).items() if v is not None}
@dataclass
class HistorySummary:
"""Minimal entry used for UI history lists."""
id: str
prompt: str
mode: str
seed: Optional[int]
width: int
height: int
timestamp: str
thumbnail: str
def to_dict(self) -> Dict[str, Any]:
"""Return a serializable dict representation."""
return asdict(self)
|