File size: 7,563 Bytes
b6acc0a | 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 | import sys
from dataclasses import dataclass
from pathlib import Path
from typing import List, Optional, Union
import numpy as np
import torch
from diffusers import DDIMScheduler, DiffusionPipeline
from diffusers.utils import BaseOutput
from PIL import Image
_ROOT = Path(__file__).resolve().parent
if str(_ROOT) not in sys.path:
sys.path.insert(0, str(_ROOT))
# Register alias for cached custom-pipeline imports.
sys.modules["pipeline"] = sys.modules[__name__]
from modular_pipeline import load_components, resolve_model_root # noqa: E402
@dataclass
class CRSDiffPipelineOutput(BaseOutput):
images: List[Image.Image]
class CRSDiffPipeline(DiffusionPipeline):
def register_modules(self, **kwargs):
for name, module in kwargs.items():
if module is None or (
isinstance(module, (tuple, list)) and len(module) > 0 and module[0] is None
):
self.register_to_config(**{name: (None, None)})
setattr(self, name, module)
elif _is_component_list(module):
self.register_to_config(**{name: (module[0], module[1])})
setattr(self, name, module)
else:
from diffusers.pipelines.pipeline_loading_utils import _fetch_class_library_tuple
library, class_name = _fetch_class_library_tuple(module)
self.register_to_config(**{name: (library, class_name)})
setattr(self, name, module)
def __init__(
self,
crs_model=None,
scheduler=None,
scale_factor: float = 0.18215,
model_path: Optional[Union[str, Path]] = None,
_name_or_path: Optional[Union[str, Path]] = None,
):
super().__init__()
if _is_component_list(crs_model) or _is_component_list(scheduler):
model_root = (
resolve_model_root(model_path)
or resolve_model_root(_name_or_path)
or resolve_model_root(getattr(getattr(self, "config", None), "_name_or_path", None))
)
if model_root is None:
raise ValueError(
"CRS-Diff received config placeholders but could not resolve model path. "
"Pass `model_path` or load via DiffusionPipeline.from_pretrained(<path>, custom_pipeline=...)."
)
loaded = load_components(model_root)
crs_model = loaded["crs_model"]
scheduler = loaded["scheduler"]
scale_factor = loaded["scale_factor"]
self.register_modules(crs_model=crs_model, scheduler=scheduler)
self.vae_scale_factor = scale_factor
@property
def device(self) -> torch.device:
params = list(self.crs_model.parameters())
if params:
return params[0].device
return torch.device("cpu")
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, Path],
device: Optional[Union[str, torch.device]] = None,
subfolder: Optional[str] = None,
**kwargs,
) -> "CRSDiffPipeline":
path = resolve_model_root(pretrained_model_name_or_path)
if path is None:
raise ValueError(f"Could not resolve CRS-Diff model root from: {pretrained_model_name_or_path}")
subfolder = kwargs.pop("subfolder", subfolder)
if subfolder == "scheduler":
return DDIMScheduler.from_pretrained(path, subfolder="scheduler")
loaded = load_components(path)
pipe = cls(crs_model=loaded["crs_model"], scheduler=loaded["scheduler"], scale_factor=loaded["scale_factor"])
if device is not None:
pipe = pipe.to(device)
return pipe
def _to_tensor(self, x, device: torch.device, dtype=torch.float32) -> torch.Tensor:
if isinstance(x, np.ndarray):
x = torch.from_numpy(x)
if not isinstance(x, torch.Tensor):
raise TypeError("Expected torch.Tensor or np.ndarray for conditioning inputs.")
return x.to(device=device, dtype=dtype)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
local_control,
global_control,
metadata,
negative_prompt: Union[str, List[str]] = "",
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
eta: float = 0.0,
strength: float = 1.0,
global_strength: float = 1.0,
generator: Optional[torch.Generator] = None,
output_type: str = "pil",
) -> CRSDiffPipelineOutput:
device = self.device
local_control = self._to_tensor(local_control, device=device)
global_control = self._to_tensor(global_control, device=device)
metadata = self._to_tensor(metadata, device=device)
batch_size = local_control.shape[0]
if isinstance(prompt, str):
prompt = [prompt] * batch_size
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt] * batch_size
if metadata.dim() == 1:
metadata = metadata.unsqueeze(0).repeat(batch_size, 1)
cond = {
"local_control": [local_control],
"c_crossattn": [self.crs_model.get_learned_conditioning(prompt)],
"global_control": [global_control],
}
un_cond = {
"local_control": [local_control],
"c_crossattn": [self.crs_model.get_learned_conditioning(negative_prompt)],
"global_control": [torch.zeros_like(global_control)],
}
if hasattr(self.crs_model, "local_control_scales"):
self.crs_model.local_control_scales = [strength] * 13
_, _, h, w = local_control.shape
latents = torch.randn(
(batch_size, self.crs_model.channels, h // 8, w // 8),
generator=generator,
device=device,
)
latents = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(num_inference_steps, device=device)
for t in self.scheduler.timesteps:
ts = torch.full((batch_size,), int(t), device=device, dtype=torch.long)
if guidance_scale > 1.0:
noise_text = self.crs_model.apply_model(latents, ts, cond, metadata, global_strength)
noise_uncond = self.crs_model.apply_model(latents, ts, un_cond, metadata, global_strength)
noise_pred = noise_uncond + guidance_scale * (noise_text - noise_uncond)
else:
noise_pred = self.crs_model.apply_model(latents, ts, cond, metadata, global_strength)
latents = self.scheduler.step(
model_output=noise_pred,
timestep=t,
sample=latents,
eta=eta,
generator=generator,
return_dict=True,
).prev_sample
images = self.crs_model.decode_first_stage(latents)
images = images.clamp(-1, 1)
images = ((images + 1.0) / 2.0).permute(0, 2, 3, 1).cpu().numpy()
images = (images * 255.0).clip(0, 255).astype(np.uint8)
if output_type == "pil":
images = [Image.fromarray(img) for img in images]
elif output_type != "numpy":
raise ValueError("output_type must be 'pil' or 'numpy'")
return CRSDiffPipelineOutput(images=images)
def _is_component_list(v):
return (
isinstance(v, (list, tuple))
and len(v) == 2
and isinstance(v[0], str)
and isinstance(v[1], str)
)
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