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Remove k-sampler requirement and remove it form the cold. not used anymore anyway

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This commit is contained in:
Jaret Burkett
2026-07-27 14:58:31 -06:00
parent b677cdb026
commit 7e7053fc9a
4 changed files with 2 additions and 314 deletions
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1
requirements_base.txt
View File

@@ -17,7 +17,6 @@ albumentations==1.4.15
albucore==0.0.16
pydantic
omegaconf
k-diffusion
open_clip_torch
timm==1.0.22
prodigyopt

277
toolkit/pipelines.py
View File

@@ -1,5 +1,3 @@
import importlib
import inspect
from typing import Union, List, Optional, Dict, Any, Tuple, Callable
import numpy as np
@@ -8,12 +6,9 @@ from diffusers import StableDiffusionXLPipeline, StableDiffusionPipeline, LMSDis
from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
# from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_k_diffusion import ModelWrapper
from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import rescale_noise_cfg
from diffusers.utils import is_torch_xla_available
from k_diffusion.external import CompVisVDenoiser, CompVisDenoiser
from k_diffusion.sampling import get_sigmas_karras, BrownianTreeNoiseSampler
from toolkit.models.flux import bypass_flux_guidance, restore_flux_guidance
from diffusers.image_processor import PipelineImageInput
from PIL import Image
@@ -29,278 +24,6 @@ if is_torch_xla_available():
else:
XLA_AVAILABLE = False
class StableDiffusionKDiffusionXLPipeline(StableDiffusionXLPipeline):
def __init__(
self,
vae: 'AutoencoderKL',
text_encoder: 'CLIPTextModel',
text_encoder_2: 'CLIPTextModelWithProjection',
tokenizer: 'CLIPTokenizer',
tokenizer_2: 'CLIPTokenizer',
unet: 'UNet2DConditionModel',
scheduler: 'KarrasDiffusionSchedulers',
force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None,
):
super().__init__(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
scheduler=scheduler,
)
raise NotImplementedError("This pipeline is not implemented yet")
# self.sampler = None
# scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
# model = ModelWrapper(unet, scheduler.alphas_cumprod)
# if scheduler.config.prediction_type == "v_prediction":
# self.k_diffusion_model = CompVisVDenoiser(model)
# else:
# self.k_diffusion_model = CompVisDenoiser(model)
def set_scheduler(self, scheduler_type: str):
library = importlib.import_module("k_diffusion")
sampling = getattr(library, "sampling")
self.sampler = getattr(sampling, scheduler_type)
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
denoising_end: Optional[float] = None,
guidance_scale: float = 5.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
original_size: Optional[Tuple[int, int]] = None,
crops_coords_top_left: Tuple[int, int] = (0, 0),
target_size: Optional[Tuple[int, int]] = None,
use_karras_sigmas: bool = False,
):
# 0. Default height and width to unet
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
original_size = original_size or (height, width)
target_size = target_size or (height, width)
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
callback_steps,
negative_prompt,
negative_prompt_2,
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = (
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Prepare added time ids & embeddings
add_text_embeds = pooled_prompt_embeds
add_time_ids = self._get_add_time_ids(
original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
# 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
# 7.1 Apply denoising_end
if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (denoising_end * self.scheduler.config.num_train_timesteps)
)
)
num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
timesteps = timesteps[:num_inference_steps]
# 5. Prepare sigmas
if use_karras_sigmas:
sigma_min: float = self.k_diffusion_model.sigmas[0].item()
sigma_max: float = self.k_diffusion_model.sigmas[-1].item()
sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max)
sigmas = sigmas.to(device)
else:
sigmas = self.scheduler.sigmas
sigmas = sigmas.to(prompt_embeds.dtype)
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
latents = latents * sigmas[0]
self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device)
self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device)
# 7. Define model function
def model_fn(x, t):
latent_model_input = torch.cat([x] * 2)
t = torch.cat([t] * 2)
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
# noise_pred = self.unet(
# latent_model_input,
# t,
# encoder_hidden_states=prompt_embeds,
# cross_attention_kwargs=cross_attention_kwargs,
# added_cond_kwargs=added_cond_kwargs,
# return_dict=False,
# )[0]
noise_pred = self.k_diffusion_model(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,)[0]
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
# 8. Run k-diffusion solver
sampler_kwargs = {}
# should work without it
noise_sampler_seed = None
if "noise_sampler" in inspect.signature(self.sampler).parameters:
min_sigma, max_sigma = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(latents, min_sigma, max_sigma, noise_sampler_seed)
sampler_kwargs["noise_sampler"] = noise_sampler
latents = self.sampler(model_fn, latents, sigmas, **sampler_kwargs)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (image,)
return StableDiffusionXLPipelineOutput(images=image)
class CustomStableDiffusionXLPipeline(StableDiffusionXLPipeline):
def predict_noise(

28
toolkit/sampler.py
View File

@@ -20,8 +20,6 @@ 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:
@@ -185,29 +183,3 @@ def get_sampler(
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")

10
toolkit/stable_diffusion_model.py
View File

@@ -42,7 +42,7 @@ from toolkit.train_tools import get_torch_dtype, apply_noise_offset
from einops import rearrange, repeat
import torch
from toolkit.pipelines import CustomStableDiffusionXLPipeline, CustomStableDiffusionPipeline, \
StableDiffusionKDiffusionXLPipeline, StableDiffusionXLRefinerPipeline, FluxWithCFGPipeline, \
StableDiffusionXLRefinerPipeline, FluxWithCFGPipeline, \
FluxAdvancedControlPipeline
from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, T2IAdapter, DDPMScheduler, \
StableDiffusionXLAdapterPipeline, StableDiffusionAdapterPipeline, DiffusionPipeline, PixArtTransformer2DModel, \
@@ -1212,10 +1212,7 @@ class StableDiffusion:
except:
pass
if sampler.startswith("sample_") and self.is_xl:
# using kdiffusion
Pipe = StableDiffusionKDiffusionXLPipeline
elif self.is_xl:
if self.is_xl:
Pipe = StableDiffusionXLPipeline
elif self.is_v3:
Pipe = StableDiffusion3Pipeline
@@ -1348,9 +1345,6 @@ class StableDiffusion:
# disable progress bar
pipeline.set_progress_bar_config(disable=True)
if sampler.startswith("sample_"):
pipeline.set_scheduler(sampler)
refiner_pipeline = None
if self.refiner_unet:
# build refiner pipeline