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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 | """Image-to-image processor for LightDiffusion-Next.
This processor handles image-to-image generation and upscaling
using the Ultimate SD Upscale approach.
"""
import logging
from typing import TYPE_CHECKING, Any, Optional, Callable
import numpy as np
import torch
from PIL import Image
if TYPE_CHECKING:
from src.Core.PipelineContext import PipelineContext
from src.Core.AbstractModel import AbstractModel
class Img2Img:
"""Image-to-image generation and upscaling processor.
Uses Ultimate SD Upscale for high-quality image transformation
and super-resolution.
"""
# Default settings
DEFAULT_UPSCALE_BY = 2
DEFAULT_STEPS = 8
DEFAULT_CFG = 6
DEFAULT_DENOISE = 0.3
DEFAULT_SCHEDULER = "karras"
DEFAULT_TILE_WIDTH = 512
DEFAULT_TILE_HEIGHT = 512
DEFAULT_MASK_BLUR = 16
DEFAULT_TILE_PADDING = 32
DEFAULT_UPSCALER = "RealESRGAN_x4plus.pth"
@classmethod
def apply(
cls,
ctx: "PipelineContext",
model: "AbstractModel",
positive: Any,
negative: Any,
image_path: str = None,
image_tensor: torch.Tensor = None,
upscale_by: float = None,
denoise: float = None,
callback: Optional[Callable] = None,
) -> torch.Tensor:
"""Apply image-to-image transformation.
Args:
ctx: Pipeline context with configuration
model: The loaded model instance
positive: Positive conditioning
negative: Negative conditioning
image_path: Path to input image (used if image_tensor not provided)
image_tensor: Input image tensor [B, H, W, C] or [H, W, C]
upscale_by: Upscale factor (default: 2)
denoise: Denoising strength (default: 0.3)
callback: Optional callback for live previews
Returns:
Processed image tensor
"""
logger = logging.getLogger(__name__)
# Determine input source
if image_tensor is None:
source_path = image_path or ctx.features.img2img_image
if source_path is None:
raise ValueError("No input image provided for img2img")
# Load image from path
image_tensor = cls._load_image(source_path)
# Determine upscale factor from context dimensions if not provided
if upscale_by is None:
input_w = image_tensor.shape[2] # [B, H, W, C]
target_w = ctx.generation.width
if target_w and target_w != input_w:
upscale_by = target_w / input_w
logger.info(f"Img2Img: calculated upscale_by={upscale_by:.2f} from target width {target_w}")
else:
upscale_by = cls.DEFAULT_UPSCALE_BY
denoise = denoise or cls.DEFAULT_DENOISE
# Determine model flags
is_flux = getattr(model.capabilities, "is_flux", False)
is_flux2 = getattr(model.capabilities, "is_flux2", False)
# Adjust CFG for Flux models
img2img_cfg = cls.DEFAULT_CFG
if is_flux or is_flux2:
img2img_cfg = 1.0
try:
# Import required modules
from src.UltimateSDUpscale import UltimateSDUpscale, USDU_upscaler
# Load upscaler model
upscale_loader = USDU_upscaler.UpscaleModelLoader()
upscale_model = upscale_loader.load_model(cls.DEFAULT_UPSCALER)[0]
# Initialize Ultimate SD Upscale
upscaler = UltimateSDUpscale.UltimateSDUpscale()
# Get current seed from context
current_seed = ctx.seed
logger.info(f"Img2Img: processing with {upscale_by}x upscale, denoise={denoise}")
# Run upscaling
result = upscaler.upscale(
upscale_by=upscale_by,
seed=current_seed,
steps=cls.DEFAULT_STEPS,
cfg=img2img_cfg,
sampler_name=ctx.sampling.sampler,
scheduler=cls.DEFAULT_SCHEDULER,
denoise=denoise,
mode_type="Linear",
tile_width=cls.DEFAULT_TILE_WIDTH,
tile_height=cls.DEFAULT_TILE_HEIGHT,
mask_blur=cls.DEFAULT_MASK_BLUR,
tile_padding=cls.DEFAULT_TILE_PADDING,
seam_fix_mode="Half Tile",
seam_fix_denoise=0.2,
seam_fix_width=64,
seam_fix_mask_blur=16,
seam_fix_padding=32,
force_uniform_tiles="enable",
image=image_tensor,
model=model.model,
positive=positive,
negative=negative,
vae=model.vae,
upscale_model=upscale_model,
pipeline=True,
callback=callback or ctx.callback,
)
logger.info("Img2Img: completed successfully")
return result[0]
except Exception as e:
logger.exception(f"Img2Img failed: {e}")
# Return original image on failure
return image_tensor
@classmethod
def _load_image(cls, path: str) -> torch.Tensor:
"""Load an image from disk and convert to tensor.
Args:
path: Path to the image file
Returns:
Image tensor in [B, H, W, C] format, normalized to [0, 1]
"""
img = Image.open(path)
img_array = np.array(img)
img_tensor = torch.from_numpy(img_array).float().to("cpu") / 255.0
# Add batch dimension
if img_tensor.dim() == 3:
img_tensor = img_tensor.unsqueeze(0)
return img_tensor
@classmethod
def simple_img2img(
cls,
ctx: "PipelineContext",
model: "AbstractModel",
positive: Any,
negative: Any,
image_tensor: torch.Tensor,
denoise: float = 0.75,
last_step: Optional[int] = None,
callback: Optional[Callable] = None,
) -> dict:
"""Simple image-to-image without upscaling.
Encodes the input image to latents and runs diffusion with
the specified denoising strength.
Args:
ctx: Pipeline context
model: The loaded model
positive: Positive conditioning
negative: Negative conditioning
image_tensor: Input image tensor
denoise: Denoising strength (0.0 = no change, 1.0 = full generation)
last_step: Optional step to stop at (for refiner handoff)
Returns:
Dictionary with 'samples' key containing generated latents
"""
logger = logging.getLogger(__name__)
try:
from src.AutoEncoders import VariationalAE
from src.sample import sampling
from src.hidiffusion import msw_msa_attention
# Determine model flags
is_flux = getattr(model.capabilities, "is_flux", False)
is_flux2 = getattr(model.capabilities, "is_flux2", False)
# Encode image to latents (pass flux flag for correct encoding)
vae_encode = VariationalAE.VAEEncode()
latents = vae_encode.encode(
vae=model.vae,
pixels=image_tensor,
flux=is_flux or is_flux2,
)[0]
# Apply HiDiffusion optimizer (not for Flux)
if not is_flux:
try:
hidiff = msw_msa_attention.ApplyMSWMSAAttentionSimple()
optimized_model = hidiff.go(model_type="auto", model=model.model)[0]
except Exception:
optimized_model = model.model
else:
optimized_model = model.model
# Run sampling with denoise < 1.0
ksampler = sampling.KSampler()
result = ksampler.sample(
seed=ctx.seed,
steps=ctx.sampling.steps,
cfg=ctx.sampling.cfg if not is_flux else 1.0,
sampler_name=ctx.sampling.sampler,
scheduler=ctx.sampling.scheduler,
denoise=denoise,
model=optimized_model,
positive=positive,
negative=negative,
latent_image=latents,
pipeline=True,
flux=is_flux,
flux2=is_flux2,
enable_multiscale=False if is_flux else ctx.sampling.enable_multiscale,
cfg_free_enabled=ctx.sampling.cfg_free_enabled,
cfg_free_start_percent=ctx.sampling.cfg_free_start_percent,
last_step=last_step,
callback=callback or ctx.callback, # Enable live previews during sampling
)
return result[0]
except Exception as e:
logger.exception(f"Simple img2img failed: {e}")
raise
@classmethod
def is_enabled(cls, ctx: "PipelineContext") -> bool:
"""Check if Img2Img mode is enabled.
Args:
ctx: Pipeline context
Returns:
True if img2img mode is enabled
"""
return ctx.features.img2img
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