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lvmin
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from dataclasses import dataclass, asdict
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from enum import Enum
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from omegaconf import OmegaConf
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import pathlib
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from sgm.inference.helpers import (
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do_sample,
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do_img2img,
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Img2ImgDiscretizationWrapper,
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)
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from sgm.modules.diffusionmodules.sampling import (
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EulerEDMSampler,
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HeunEDMSampler,
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EulerAncestralSampler,
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DPMPP2SAncestralSampler,
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DPMPP2MSampler,
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LinearMultistepSampler,
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)
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from sgm.util import load_model_from_config
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from typing import Optional
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class ModelArchitecture(str, Enum):
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SD_2_1 = "stable-diffusion-v2-1"
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SD_2_1_768 = "stable-diffusion-v2-1-768"
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SDXL_V0_9_BASE = "stable-diffusion-xl-v0-9-base"
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SDXL_V0_9_REFINER = "stable-diffusion-xl-v0-9-refiner"
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SDXL_V1_BASE = "stable-diffusion-xl-v1-base"
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SDXL_V1_REFINER = "stable-diffusion-xl-v1-refiner"
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class Sampler(str, Enum):
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EULER_EDM = "EulerEDMSampler"
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HEUN_EDM = "HeunEDMSampler"
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EULER_ANCESTRAL = "EulerAncestralSampler"
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DPMPP2S_ANCESTRAL = "DPMPP2SAncestralSampler"
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DPMPP2M = "DPMPP2MSampler"
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LINEAR_MULTISTEP = "LinearMultistepSampler"
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class Discretization(str, Enum):
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LEGACY_DDPM = "LegacyDDPMDiscretization"
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EDM = "EDMDiscretization"
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class Guider(str, Enum):
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VANILLA = "VanillaCFG"
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IDENTITY = "IdentityGuider"
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class Thresholder(str, Enum):
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NONE = "None"
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@dataclass
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class SamplingParams:
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width: int = 1024
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height: int = 1024
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steps: int = 50
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sampler: Sampler = Sampler.DPMPP2M
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discretization: Discretization = Discretization.LEGACY_DDPM
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guider: Guider = Guider.VANILLA
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thresholder: Thresholder = Thresholder.NONE
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scale: float = 6.0
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aesthetic_score: float = 5.0
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negative_aesthetic_score: float = 5.0
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img2img_strength: float = 1.0
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orig_width: int = 1024
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orig_height: int = 1024
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crop_coords_top: int = 0
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crop_coords_left: int = 0
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sigma_min: float = 0.0292
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sigma_max: float = 14.6146
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rho: float = 3.0
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s_churn: float = 0.0
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s_tmin: float = 0.0
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s_tmax: float = 999.0
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s_noise: float = 1.0
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eta: float = 1.0
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order: int = 4
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@dataclass
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class SamplingSpec:
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width: int
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height: int
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channels: int
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factor: int
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is_legacy: bool
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config: str
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ckpt: str
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is_guided: bool
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model_specs = {
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ModelArchitecture.SD_2_1: SamplingSpec(
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height=512,
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width=512,
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channels=4,
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factor=8,
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is_legacy=True,
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config="sd_2_1.yaml",
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ckpt="v2-1_512-ema-pruned.safetensors",
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is_guided=True,
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),
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ModelArchitecture.SD_2_1_768: SamplingSpec(
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height=768,
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width=768,
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channels=4,
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factor=8,
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is_legacy=True,
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config="sd_2_1_768.yaml",
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ckpt="v2-1_768-ema-pruned.safetensors",
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is_guided=True,
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),
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ModelArchitecture.SDXL_V0_9_BASE: SamplingSpec(
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height=1024,
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width=1024,
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channels=4,
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factor=8,
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is_legacy=False,
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config="sd_xl_base.yaml",
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ckpt="sd_xl_base_0.9.safetensors",
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is_guided=True,
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),
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ModelArchitecture.SDXL_V0_9_REFINER: SamplingSpec(
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height=1024,
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width=1024,
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channels=4,
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factor=8,
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is_legacy=True,
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config="sd_xl_refiner.yaml",
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ckpt="sd_xl_refiner_0.9.safetensors",
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is_guided=True,
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),
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ModelArchitecture.SDXL_V1_BASE: SamplingSpec(
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height=1024,
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width=1024,
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channels=4,
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factor=8,
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is_legacy=False,
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config="sd_xl_base.yaml",
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ckpt="sd_xl_base_1.0.safetensors",
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is_guided=True,
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),
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ModelArchitecture.SDXL_V1_REFINER: SamplingSpec(
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height=1024,
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width=1024,
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channels=4,
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factor=8,
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is_legacy=True,
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config="sd_xl_refiner.yaml",
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ckpt="sd_xl_refiner_1.0.safetensors",
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is_guided=True,
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),
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}
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class SamplingPipeline:
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def __init__(
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self,
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model_id: ModelArchitecture,
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model_path="checkpoints",
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config_path="configs/inference",
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device="cuda",
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use_fp16=True,
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) -> None:
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if model_id not in model_specs:
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raise ValueError(f"Model {model_id} not supported")
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self.model_id = model_id
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self.specs = model_specs[self.model_id]
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self.config = str(pathlib.Path(config_path, self.specs.config))
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self.ckpt = str(pathlib.Path(model_path, self.specs.ckpt))
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self.device = device
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self.model = self._load_model(device=device, use_fp16=use_fp16)
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def _load_model(self, device="cuda", use_fp16=True):
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config = OmegaConf.load(self.config)
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model = load_model_from_config(config, self.ckpt)
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if model is None:
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raise ValueError(f"Model {self.model_id} could not be loaded")
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model.to(device)
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if use_fp16:
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model.conditioner.half()
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model.model.half()
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return model
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def text_to_image(
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self,
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params: SamplingParams,
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prompt: str,
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negative_prompt: str = "",
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samples: int = 1,
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return_latents: bool = False,
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):
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sampler = get_sampler_config(params)
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value_dict = asdict(params)
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value_dict["prompt"] = prompt
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value_dict["negative_prompt"] = negative_prompt
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value_dict["target_width"] = params.width
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value_dict["target_height"] = params.height
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return do_sample(
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self.model,
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sampler,
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value_dict,
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samples,
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params.height,
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params.width,
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self.specs.channels,
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self.specs.factor,
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force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
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return_latents=return_latents,
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filter=None,
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)
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def image_to_image(
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self,
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params: SamplingParams,
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image,
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prompt: str,
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negative_prompt: str = "",
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samples: int = 1,
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return_latents: bool = False,
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):
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sampler = get_sampler_config(params)
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if params.img2img_strength < 1.0:
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sampler.discretization = Img2ImgDiscretizationWrapper(
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sampler.discretization,
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strength=params.img2img_strength,
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)
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height, width = image.shape[2], image.shape[3]
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value_dict = asdict(params)
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value_dict["prompt"] = prompt
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value_dict["negative_prompt"] = negative_prompt
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value_dict["target_width"] = width
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value_dict["target_height"] = height
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return do_img2img(
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image,
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self.model,
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sampler,
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value_dict,
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samples,
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force_uc_zero_embeddings=["txt"] if not self.specs.is_legacy else [],
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return_latents=return_latents,
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filter=None,
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)
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def refiner(
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self,
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params: SamplingParams,
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image,
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prompt: str,
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negative_prompt: Optional[str] = None,
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samples: int = 1,
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return_latents: bool = False,
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):
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sampler = get_sampler_config(params)
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value_dict = {
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"orig_width": image.shape[3] * 8,
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"orig_height": image.shape[2] * 8,
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"target_width": image.shape[3] * 8,
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"target_height": image.shape[2] * 8,
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"crop_coords_top": 0,
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"crop_coords_left": 0,
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"aesthetic_score": 6.0,
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"negative_aesthetic_score": 2.5,
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}
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return do_img2img(
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image,
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self.model,
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sampler,
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value_dict,
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samples,
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skip_encode=True,
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return_latents=return_latents,
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filter=None,
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)
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def get_guider_config(params: SamplingParams):
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if params.guider == Guider.IDENTITY:
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guider_config = {
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"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"
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}
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elif params.guider == Guider.VANILLA:
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scale = params.scale
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thresholder = params.thresholder
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if thresholder == Thresholder.NONE:
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dyn_thresh_config = {
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"target": "sgm.modules.diffusionmodules.sampling_utils.NoDynamicThresholding"
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}
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else:
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raise NotImplementedError
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guider_config = {
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"target": "sgm.modules.diffusionmodules.guiders.VanillaCFG",
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"params": {"scale": scale, "dyn_thresh_config": dyn_thresh_config},
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}
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else:
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raise NotImplementedError
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return guider_config
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def get_discretization_config(params: SamplingParams):
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if params.discretization == Discretization.LEGACY_DDPM:
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discretization_config = {
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"target": "sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization",
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}
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elif params.discretization == Discretization.EDM:
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discretization_config = {
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"target": "sgm.modules.diffusionmodules.discretizer.EDMDiscretization",
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"params": {
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"sigma_min": params.sigma_min,
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"sigma_max": params.sigma_max,
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"rho": params.rho,
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},
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}
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else:
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raise ValueError(f"unknown discretization {params.discretization}")
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return discretization_config
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def get_sampler_config(params: SamplingParams):
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discretization_config = get_discretization_config(params)
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guider_config = get_guider_config(params)
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sampler = None
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if params.sampler == Sampler.EULER_EDM:
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return EulerEDMSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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s_churn=params.s_churn,
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s_tmin=params.s_tmin,
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s_tmax=params.s_tmax,
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s_noise=params.s_noise,
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verbose=True,
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)
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if params.sampler == Sampler.HEUN_EDM:
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return HeunEDMSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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s_churn=params.s_churn,
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s_tmin=params.s_tmin,
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s_tmax=params.s_tmax,
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s_noise=params.s_noise,
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verbose=True,
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)
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if params.sampler == Sampler.EULER_ANCESTRAL:
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return EulerAncestralSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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eta=params.eta,
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s_noise=params.s_noise,
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verbose=True,
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)
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if params.sampler == Sampler.DPMPP2S_ANCESTRAL:
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return DPMPP2SAncestralSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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eta=params.eta,
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s_noise=params.s_noise,
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verbose=True,
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)
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if params.sampler == Sampler.DPMPP2M:
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return DPMPP2MSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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verbose=True,
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)
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if params.sampler == Sampler.LINEAR_MULTISTEP:
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return LinearMultistepSampler(
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num_steps=params.steps,
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discretization_config=discretization_config,
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guider_config=guider_config,
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order=params.order,
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verbose=True,
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)
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raise ValueError(f"unknown sampler {params.sampler}!")
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