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b701455 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 | """SD1.5 model adapter for LightDiffusion-Next.
Provides a clean interface to the SD1.5 model that inherits from
AbstractModel and wraps the existing infrastructure.
"""
import logging
from typing import TYPE_CHECKING, Any, Callable, Optional
import torch
from src.Core.AbstractModel import AbstractModel, ModelCapabilities
if TYPE_CHECKING:
from src.Core.Context import Context
class SD15Model(AbstractModel):
"""SD1.5 model implementation.
Wraps the existing SD15 model loading and inference code
with the clean AbstractModel interface.
"""
def __init__(self, model_path: str = None):
"""Initialize the SD15 model adapter.
Args:
model_path: Path to the model checkpoint (safetensors/pt)
"""
super().__init__(model_path)
self._clip_skip = -2
def _create_capabilities(self) -> ModelCapabilities:
"""Create capabilities for SD1.5 models."""
return ModelCapabilities(
min_resolution=256,
max_resolution=2048,
preferred_resolution=512,
requires_resolution_multiple=64,
supports_hires_fix=True,
supports_img2img=True,
supports_inpainting=True,
supports_controlnet=True,
supports_stable_fast=True,
supports_deepcache=True,
supports_tome=True,
uses_dual_clip=False,
requires_size_conditioning=False,
)
def load(self, model_path: str = None) -> "SD15Model":
"""Load the SD1.5 model from disk.
Args:
model_path: Optional override for the model path
Returns:
Self for method chaining
"""
logger = logging.getLogger(__name__)
path = model_path or self.model_path
if path is None:
# Use default checkpoint
path = "./include/checkpoints/DreamShaper_8_pruned.safetensors"
# Guard: Don't reload if already loaded with same path
if self._loaded and self.model_path == path:
logger.info(f"SD15Model: Already loaded {path}, skipping redundant load")
return self
self.model_path = path
try:
from src.FileManaging import Loader
loader = Loader.CheckpointLoaderSimple()
result = loader.load_checkpoint(ckpt_name=path)
self.model = result[0]
self.clip = result[1]
self.vae = result[2]
self._loaded = True
logger.info(f"SD15Model: loaded {path}")
except Exception as e:
logger.exception(f"SD15Model: failed to load {path}: {e}")
raise
return self
def get_model_object(self, name: str) -> Any:
"""Get an attribute from the underlying model."""
if self.model:
return self.model.get_model_object(name)
return None
def encode_prompt(
self,
prompt: str | list[str],
negative_prompt: str | list[str] = "",
clip_skip: int = None,
) -> tuple[Any, Any]:
"""Encode text prompts into conditioning tensors.
Args:
prompt: Positive prompt(s) to encode
negative_prompt: Negative prompt(s) to encode
clip_skip: Number of CLIP layers to skip (default: -2)
Returns:
Tuple of (positive_conditioning, negative_conditioning)
"""
if not self._loaded:
raise RuntimeError("Model must be loaded before encoding prompts")
clip_skip = clip_skip if clip_skip is not None else self._clip_skip
try:
from src.clip import Clip
# Apply CLIP skip
clip_layer = Clip.CLIPSetLastLayer()
processed_clip = clip_layer.set_last_layer(
stop_at_clip_layer=clip_skip,
clip=self.clip,
)[0]
# Encode prompts
encoder = Clip.CLIPTextEncode()
positive = encoder.encode(
text=prompt,
clip=processed_clip,
)[0]
negative = encoder.encode(
text=negative_prompt,
clip=processed_clip,
)[0]
return positive, negative
except Exception as e:
logging.getLogger(__name__).exception(f"Prompt encoding failed: {e}")
raise
def generate(
self,
ctx: "Context",
positive: Any,
negative: Any,
latent_image: Optional[Any] = None,
start_step: Optional[int] = None,
last_step: Optional[int] = None,
disable_noise: bool = False,
callback: Optional[Callable] = None,
) -> dict:
"""Generate latents using the sampler.
Args:
ctx: Pipeline context with generation parameters
positive: Positive conditioning
negative: Negative conditioning
latent_image: Optional existing latent to continue from
start_step: Optional step to start sampling from
last_step: Optional step to stop sampling at
Returns:
Dictionary with 'samples' key containing generated latents
"""
if not self._loaded:
raise RuntimeError("Model must be loaded before generating")
try:
from src.sample import sampling
from src.Utilities import Latent
from src.hidiffusion import msw_msa_attention
# Use provided latent or create empty one
if latent_image is not None:
latent = latent_image
else:
# Create empty latent
latent_gen = Latent.EmptyLatentImage()
latent = latent_gen.generate(
width=ctx.generation.width,
height=ctx.generation.height,
batch_size=ctx.generation.batch,
)[0]
# Add seeds for deterministic noise
latent["seeds"] = ctx.seeds[:ctx.generation.batch] if ctx.seeds else [ctx.seed]
# Apply HiDiffusion optimization only for very high resolutions
if ctx.generation.width > 2048 or ctx.generation.height > 2048:
try:
# Clone model before patching
patch_model = self.model.clone()
hidiff = msw_msa_attention.ApplyMSWMSAAttentionSimple()
optimized_model = hidiff.go(model_type="sd15", model=patch_model)[0]
except Exception:
optimized_model = self.model
else:
optimized_model = self.model
# Run sampling
ksampler = sampling.KSampler()
result = ksampler.sample(
seed=ctx.seed,
steps=ctx.sampling.steps,
cfg=ctx.sampling.cfg,
sampler_name=ctx.sampling.sampler,
scheduler=ctx.sampling.scheduler,
denoise=ctx.sampling.denoise,
pipeline=True,
model=optimized_model,
positive=positive,
negative=negative,
latent_image=latent,
start_step=start_step,
last_step=last_step,
disable_noise=disable_noise,
callback=callback or ctx.callback,
enable_multiscale=ctx.sampling.enable_multiscale,
multiscale_factor=ctx.sampling.multiscale_factor,
multiscale_fullres_start=ctx.sampling.multiscale_fullres_start,
multiscale_fullres_end=ctx.sampling.multiscale_fullres_end,
multiscale_intermittent_fullres=ctx.sampling.multiscale_intermittent_fullres,
cfg_free_enabled=ctx.sampling.cfg_free_enabled,
cfg_free_start_percent=ctx.sampling.cfg_free_start_percent,
batched_cfg=ctx.sampling.batched_cfg,
dynamic_cfg_rescaling=ctx.sampling.dynamic_cfg_rescaling,
dynamic_cfg_method=ctx.sampling.dynamic_cfg_method,
dynamic_cfg_percentile=ctx.sampling.dynamic_cfg_percentile,
dynamic_cfg_target_scale=ctx.sampling.dynamic_cfg_target_scale,
adaptive_noise_enabled=ctx.sampling.adaptive_noise_enabled,
adaptive_noise_method=ctx.sampling.adaptive_noise_method,
)
return result[0]
except Exception as e:
logging.getLogger(__name__).exception(f"Generation failed: {e}")
raise
def decode(self, latents: torch.Tensor) -> torch.Tensor:
"""Decode latents to pixel space.
Args:
latents: Latent tensor or dict with 'samples' key
Returns:
Decoded image tensor in [0, 1] range
"""
if not self._loaded:
raise RuntimeError("Model must be loaded before decoding")
try:
from src.AutoEncoders import VariationalAE
decoder = VariationalAE.VAEDecode()
# Handle both raw tensor and dict input
if isinstance(latents, dict):
samples = latents
else:
samples = {"samples": latents}
result = decoder.decode(
samples=samples,
vae=self.vae,
)
return result[0]
except Exception as e:
logging.getLogger(__name__).exception(f"Decoding failed: {e}")
raise
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