| 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):
|
| SD_2_1 = "stable-diffusion-v2-1"
|
| SD_2_1_768 = "stable-diffusion-v2-1-768"
|
| 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.SD_2_1: SamplingSpec(
|
| height=512,
|
| width=512,
|
| channels=4,
|
| factor=8,
|
| is_legacy=True,
|
| config="sd_2_1.yaml",
|
| ckpt="v2-1_512-ema-pruned.safetensors",
|
| is_guided=True,
|
| ),
|
| ModelArchitecture.SD_2_1_768: SamplingSpec(
|
| height=768,
|
| width=768,
|
| channels=4,
|
| factor=8,
|
| is_legacy=True,
|
| config="sd_2_1_768.yaml",
|
| ckpt="v2-1_768-ema-pruned.safetensors",
|
| is_guided=True,
|
| ),
|
| 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}!")
|
|
|
