toolkit/sampler.py

import copy
import math

from diffusers import (
    DDPMScheduler,
    EulerAncestralDiscreteScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    DDIMScheduler,
    EulerDiscreteScheduler,
    HeunDiscreteScheduler,
    KDPM2DiscreteScheduler,
    KDPM2AncestralDiscreteScheduler,
    LCMScheduler,
    FlowMatchEulerDiscreteScheduler,
)
from toolkit.samplers.mean_flow_scheduler import MeanFlowScheduler

from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler

from k_diffusion.external import CompVisDenoiser

from toolkit.samplers.custom_lcm_scheduler import CustomLCMScheduler

# scheduler:
SCHEDULER_LINEAR_START = 0.00085
SCHEDULER_LINEAR_END = 0.0120
SCHEDULER_TIMESTEPS = 1000
SCHEDLER_SCHEDULE = "scaled_linear"

sd_config = {
    "_class_name": "EulerAncestralDiscreteScheduler",
    "_diffusers_version": "0.24.0.dev0",
    "beta_end": 0.012,
    "beta_schedule": "scaled_linear",
    "beta_start": 0.00085,
    "clip_sample": False,
    "interpolation_type": "linear",
    "num_train_timesteps": 1000,
    "prediction_type": "epsilon",
    "sample_max_value": 1.0,
    "set_alpha_to_one": False,
    # "skip_prk_steps": False,  # for training
    "skip_prk_steps": True,
    # "steps_offset": 1,
    "steps_offset": 0,
    # "timestep_spacing": "trailing", # for training
    "timestep_spacing": "leading",
    "trained_betas": None
}

pixart_config = {
  "_class_name": "DPMSolverMultistepScheduler",
  "_diffusers_version": "0.22.0.dev0",
  "algorithm_type": "dpmsolver++",
  "beta_end": 0.02,
  "beta_schedule": "linear",
  "beta_start": 0.0001,
  "dynamic_thresholding_ratio": 0.995,
  "euler_at_final": False,
  # "lambda_min_clipped": -Infinity,
  "lambda_min_clipped": -math.inf,
  "lower_order_final": True,
  "num_train_timesteps": 1000,
  "prediction_type": "epsilon",
  "sample_max_value": 1.0,
  "solver_order": 2,
  "solver_type": "midpoint",
  "steps_offset": 0,
  "thresholding": False,
  "timestep_spacing": "linspace",
  "trained_betas": None,
  "use_karras_sigmas": False,
  "use_lu_lambdas": False,
  "variance_type": None
}

flux_config = {
  "_class_name": "FlowMatchEulerDiscreteScheduler",
  "_diffusers_version": "0.30.0.dev0",
  "base_image_seq_len": 256,
  "base_shift": 0.5,
  "max_image_seq_len": 4096,
  "max_shift": 1.15,
  "num_train_timesteps": 1000,
  "shift": 3.0,
  "use_dynamic_shifting": True
}

sd_flow_config = {
  "_class_name": "FlowMatchEulerDiscreteScheduler",
  "_diffusers_version": "0.30.0.dev0",
  "base_image_seq_len": 256,
  "base_shift": 0.5,
  "max_image_seq_len": 4096,
  "max_shift": 1.15,
  "num_train_timesteps": 1000,
  "shift": 3.0,
  "use_dynamic_shifting": False
}

lumina2_config = {
  "_class_name": "FlowMatchEulerDiscreteScheduler",
  "_diffusers_version": "0.33.0.dev0",
  "base_image_seq_len": 256,
  "base_shift": 0.5,
  "invert_sigmas": False,
  "max_image_seq_len": 4096,
  "max_shift": 1.15,
  "num_train_timesteps": 1000,
  "shift": 6.0,
  "shift_terminal": None,
  "use_beta_sigmas": False,
  "use_dynamic_shifting": False,
  "use_exponential_sigmas": False,
  "use_karras_sigmas": False
}


def get_sampler(
        sampler: str,
        kwargs: dict = None,
        arch: str = "sd"
):
    sched_init_args = {}
    if kwargs is not None:
        sched_init_args.update(kwargs)

    config_to_use = copy.deepcopy(sd_config) if arch == "sd" else copy.deepcopy(pixart_config)

    if sampler.startswith("k_"):
        sched_init_args["use_karras_sigmas"] = True

    if sampler == "ddim":
        scheduler_cls = DDIMScheduler
    elif sampler == "ddpm":  # ddpm is not supported ?
        scheduler_cls = DDPMScheduler
    elif sampler == "pndm":
        scheduler_cls = PNDMScheduler
    elif sampler == "lms" or sampler == "k_lms":
        scheduler_cls = LMSDiscreteScheduler
    elif sampler == "euler" or sampler == "k_euler":
        scheduler_cls = EulerDiscreteScheduler
    elif sampler == "euler_a":
        scheduler_cls = EulerAncestralDiscreteScheduler
    elif sampler == "dpmsolver" or sampler == "dpmsolver++" or sampler == "k_dpmsolver" or sampler == "k_dpmsolver++":
        scheduler_cls = DPMSolverMultistepScheduler
        sched_init_args["algorithm_type"] = sampler.replace("k_", "")
    elif sampler == "dpmsingle":
        scheduler_cls = DPMSolverSinglestepScheduler
    elif sampler == "heun":
        scheduler_cls = HeunDiscreteScheduler
    elif sampler == "dpm_2":
        scheduler_cls = KDPM2DiscreteScheduler
    elif sampler == "dpm_2_a":
        scheduler_cls = KDPM2AncestralDiscreteScheduler
    elif sampler == "lcm":
        scheduler_cls = LCMScheduler
    elif sampler == "custom_lcm":
        scheduler_cls = CustomLCMScheduler
    elif sampler == "mean_flow":
        scheduler_cls = MeanFlowScheduler
    elif sampler == "flowmatch":
        scheduler_cls = CustomFlowMatchEulerDiscreteScheduler
        config_to_use = copy.deepcopy(flux_config)
        if arch == "sd":
            config_to_use = copy.deepcopy(sd_flow_config)
        elif arch == "flux":
            config_to_use = copy.deepcopy(flux_config)
        elif arch == "lumina2":
            config_to_use = copy.deepcopy(lumina2_config)
        else:
            print(f"Unknown architecture {arch}, using default flux config")
            # use flux by default
            config_to_use = copy.deepcopy(flux_config)
    else:
        raise ValueError(f"Sampler {sampler} not supported")


    config = copy.deepcopy(config_to_use)
    config.update(sched_init_args)

    scheduler = scheduler_cls.from_config(config)

    return scheduler


# testing
if __name__ == "__main__":
    from diffusers import DiffusionPipeline

    from diffusers import StableDiffusionKDiffusionPipeline
    import torch
    import os

    inference_steps = 25

    pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
    pipe = pipe.to("cuda")

    k_diffusion_model = CompVisDenoiser(model)

    pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
    pipe = pipe.to("cuda")

    prompt = "an astronaut riding a horse on mars"
    pipe.set_scheduler("sample_heun")
    generator = torch.Generator(device="cuda").manual_seed(seed)
    image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]

    image.save("./astronaut_heun_k_diffusion.png")