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363 lines (327 loc) · 11.3 KB
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import pathlib
from dataclasses import asdict, dataclass
from enum import Enum
from typing import Optional
from omegaconf import OmegaConf
from sgm.inference.helpers import (Img2ImgDiscretizationWrapper, do_img2img,
do_sample)
from sgm.modules.diffusionmodules.sampling import (DPMPP2MSampler,
DPMPP2SAncestralSampler,
EulerAncestralSampler,
EulerEDMSampler,
HeunEDMSampler,
LinearMultistepSampler)
from sgm.util import load_model_from_config
class ModelArchitecture(str, Enum):
SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
class Sampler(str, Enum):
EULER_EDM = "EulerEDMSampler"
HEUN_EDM = "HeunEDMSampler"
EULER_ANCESTRAL = "EulerAncestralSampler"
DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
DPMPP2M = "DPMPP2MSampler"
LINEAR_MULTISTEP = "LinearMultistepSampler"
class Discretization(str, Enum):
LEGACY_DDPM = "LegacyDDPMDiscretization"
EDM = "EDMDiscretization"
class Guider(str, Enum):
VANILLA = "VanillaCFG"
IDENTITY = "IdentityGuider"
class Thresholder(str, Enum):
NONE = "None"
@dataclass
class SamplingParams:
width: int = 1024
height: int = 1024
steps: int = 50
sampler: Sampler = Sampler.DPMPP2M
discretization: Discretization = Discretization.LEGACY_DDPM
guider: Guider = Guider.VANILLA
thresholder: Thresholder = Thresholder.NONE
scale: float = 6.0
aesthetic_score: float = 5.0
negative_aesthetic_score: float = 5.0
img2img_strength: float = 1.0
orig_width: int = 1024
orig_height: int = 1024
crop_coords_top: int = 0
crop_coords_left: int = 0
sigma_min: float = 0.0292
sigma_max: float = 14.6146
rho: float = 3.0
s_churn: float = 0.0
s_tmin: float = 0.0
s_tmax: float = 999.0
s_noise: float = 1.0
eta: float = 1.0
order: int = 4
@dataclass
class SamplingSpec:
width: int
height: int
channels: int
factor: int
is_legacy: bool
config: str
ckpt: str
is_guided: bool
model_specs = {
ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=False,
config="sd_xl_base.yaml",
ckpt="sd_xl_base_0.9.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=True,
config="sd_xl_refiner.yaml",
ckpt="sd_xl_refiner_0.9.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=False,
config="sd_xl_base.yaml",
ckpt="sd_xl_base_1.0.safetensors",
is_guided=True,
),
ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
height=1024,
width=1024,
channels=4,
factor=8,
is_legacy=True,
config="sd_xl_refiner.yaml",
ckpt="sd_xl_refiner_1.0.safetensors",
is_guided=True,
),
}
class SamplingPipeline:
def __init__(
self,
model_id: ModelArchitecture,
model_path="checkpoints",
config_path="configs/inference",
device="cuda",
use_fp16=True,
) -> None:
if model_id not in model_specs:
raise ValueError(f"Model {model_id} not supported")
self.model_id = model_id
self.specs = model_specs[self.model_id]
self.config = str(pathlib.Path(config_path, self.specs.config))
self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
self.device = device
self.model = self._load_model(device=device, use_fp16=use_fp16)
def _load_model(self, device="cuda", use_fp16=True):
config = OmegaConf.load(self.config)
model = load_model_from_config(config, self.ckpt)
if model is None:
raise ValueError(f"Model {self.model_id} could not be loaded")
model.to(device)
if use_fp16:
model.conditioner.half()
model.model.half()
return model
def text_to_image(
self,
params: SamplingParams,
prompt: str,
negative_prompt: str = "",
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
value_dict = asdict(params)
value_dict["prompt"] = prompt
value_dict["negative_prompt"] = negative_prompt
value_dict["target_width"] = params.width
value_dict["target_height"] = params.height
return do_sample(
self.model,
sampler,
value_dict,
samples,
params.height,
params.width,
self.specs.channels,
self.specs.factor,
force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
return_latents=return_latents,
filter=None,
)
def image_to_image(
self,
params: SamplingParams,
image,
prompt: str,
negative_prompt: str = "",
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
if params.img2img_strength < 1.0:
sampler.discretization = Img2ImgDiscretizationWrapper(
sampler.discretization,
strength=params.img2img_strength,
)
height, width = image.shape[2], image.shape[3]
value_dict = asdict(params)
value_dict["prompt"] = prompt
value_dict["negative_prompt"] = negative_prompt
value_dict["target_width"] = width
value_dict["target_height"] = height
return do_img2img(
image,
self.model,
sampler,
value_dict,
samples,
force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
return_latents=return_latents,
filter=None,
)
def refiner(
self,
params: SamplingParams,
image,
prompt: str,
negative_prompt: Optional[str] = None,
samples: int = 1,
return_latents: bool = False,
):
sampler = get_sampler_config(params)
value_dict = {
"orig_width": image.shape[3] * 8,
"orig_height": image.shape[2] * 8,
"target_width": image.shape[3] * 8,
"target_height": image.shape[2] * 8,
"prompt": prompt,
"negative_prompt": negative_prompt,
"crop_coords_top": 0,
"crop_coords_left": 0,
"aesthetic_score": 6.0,
"negative_aesthetic_score": 2.5,
}
return do_img2img(
image,
self.model,
sampler,
value_dict,
samples,
skip_encode=True,
return_latents=return_latents,
filter=None,
)
def get_guider_config(params: SamplingParams):
if params.guider == Guider.IDENTITY:
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
}
elif params.guider == Guider.VANILLA:
scale = params.scale
thresholder = params.thresholder
if thresholder == Thresholder.NONE:
dyn_thresh_config = {
"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
}
else:
raise NotImplementedError
guider_config = {
"target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
"params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
}
else:
raise NotImplementedError
return guider_config
def get_discretization_config(params: SamplingParams):
if params.discretization == Discretization.LEGACY_DDPM:
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
}
elif params.discretization == Discretization.EDM:
discretization_config = {
"target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
"params": {
"sigma_min": params.sigma_min,
"sigma_max": params.sigma_max,
"rho": params.rho,
},
}
else:
raise ValueError(f"unknown discretization {params.discretization}")
return discretization_config
def get_sampler_config(params: SamplingParams):
discretization_config = get_discretization_config(params)
guider_config = get_guider_config(params)
sampler = None
if params.sampler == Sampler.EULER_EDM:
return EulerEDMSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=params.s_churn,
s_tmin=params.s_tmin,
s_tmax=params.s_tmax,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.HEUN_EDM:
return HeunEDMSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
s_churn=params.s_churn,
s_tmin=params.s_tmin,
s_tmax=params.s_tmax,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.EULER_ANCESTRAL:
return EulerAncestralSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=params.eta,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
return DPMPP2SAncestralSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
eta=params.eta,
s_noise=params.s_noise,
verbose=True,
)
if params.sampler == Sampler.DPMPP2M:
return DPMPP2MSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
verbose=True,
)
if params.sampler == Sampler.LINEAR_MULTISTEP:
return LinearMultistepSampler(
num_steps=params.steps,
discretization_config=discretization_config,
guider_config=guider_config,
order=params.order,
verbose=True,
)
raise ValueError(f"unknown sampler {params.sampler}!")
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