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Flux2KleinBackend.py
FLUX.2 Klein 9B KV Nunchaku + NVFP4 Qwen3 Text Encoder Backend.
Optimized for 16GB GPUs (Blackwell sm_120 / sm_110):
- Distilled 4-step inference with KV-cache for multi-reference editing.
- Sequential GPU stage execution:
Stage 1: Qwen3 NVFP4 Text Encoder on GPU -> offload to CPU.
Stage 2: AutoencoderKLFlux2 VAE on GPU (if image conditioning) -> offload to CPU.
Stage 3: NunchakuFlux2Transformer2DModel (FP4/INT4 SVDQuant) on GPU -> offload to CPU.
Stage 4: AutoencoderKLFlux2 VAE on GPU (decode) -> offload to CPU.
- All models reside on CPU when idle, leaving the GPU with 0% VRAM allocation
for other concurrent workloads.
"""
from __future__ import annotations
import gc
import os
import time
from typing import Any, List, Optional, Union
import numpy as np
import PIL.Image
import torch
from diffusers import AutoencoderKLFlux2, FlowMatchEulerDiscreteScheduler
from diffusers.pipelines.flux2.pipeline_flux2_klein_kv import (
Flux2KleinKVPipeline,
compute_empirical_mu,
retrieve_timesteps,
)
from diffusers.pipelines.flux2.pipeline_output import Flux2PipelineOutput
from nunchaku import NunchakuFlux2Transformer2DModel
from nunchaku.utils import get_precision
from transformers import AutoTokenizer
from NVFP4TextEncoder import load_nvfp4_text_encoder
def _flush():
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
class SequentialFlux2KleinPipeline:
"""Wrapper that runs Flux2KleinKVPipeline stages sequentially on CUDA,
offloading each component to CPU immediately after its stage finishes.
"""
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKLFlux2,
text_encoder: torch.nn.Module,
tokenizer: AutoTokenizer,
transformer: NunchakuFlux2Transformer2DModel,
device: str | torch.device = "cuda",
):
self.scheduler = scheduler
self.vae = vae
self.text_encoder = text_encoder
self.tokenizer = tokenizer
self.transformer = transformer
self.device = torch.device(device)
# Standard diffusers pipeline attributes for compatibility
self.inner_pipe = Flux2KleinKVPipeline(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
is_distilled=True,
)
self.image_processor = self.inner_pipe.image_processor
self.vae_scale_factor = self.inner_pipe.vae_scale_factor
# Expose components dictionary for server sleep / memory introspection
self.components = {
"text_encoder": self.text_encoder,
"transformer": self.transformer,
"vae": self.vae,
"scheduler": self.scheduler,
"tokenizer": self.tokenizer,
}
# Keep everything on CPU at initialization
self.to("cpu")
def to(self, device: str | torch.device):
"""Move components to specified device (typically 'cpu')."""
target = str(device)
if target == "cpu":
for mod in [self.text_encoder, self.transformer, self.vae]:
if isinstance(mod, torch.nn.Module):
try:
mod.to("cpu")
except Exception:
pass
_flush()
return self
@torch.no_grad()
def __call__(
self,
prompt: str | list[str] = "",
image: Union[PIL.Image.Image, List[PIL.Image.Image], None] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 4,
guidance_scale: float = 1.0,
negative_prompt: Optional[str] = None,
num_images_per_prompt: int = 1,
generator: Optional[torch.Generator] = None,
seed: Optional[int] = None,
**kwargs,
) -> Flux2PipelineOutput:
device = self.device
if generator is None:
generator = torch.Generator(device=device)
if seed is not None:
generator.manual_seed(seed)
t_start = time.time()
# ------------------------------------------------------------------
# STAGE 1: Text Encoding (Qwen3 NVFP4 Text Encoder)
# ------------------------------------------------------------------
prompt_embeds = None
text_ids = None
try:
print("[Flux2Klein] Stage 1: Moving Text Encoder to GPU...")
self.text_encoder.to(device)
_flush()
prompt_embeds, text_ids = self.inner_pipe.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=512,
)
# Store CPU copies until Stage 3 to minimize VRAM
prompt_embeds = prompt_embeds.to("cpu")
text_ids = text_ids.to("cpu")
finally:
self.text_encoder.to("cpu")
_flush()
print(f"[Flux2Klein] Stage 1 complete. Allocated VRAM: {torch.cuda.memory_allocated() / 1e9:.3f} GB")
# ------------------------------------------------------------------
# STAGE 2: VAE Conditioning (if reference images provided)
# ------------------------------------------------------------------
condition_images = None
if image is not None:
if not isinstance(image, list):
image = [image]
condition_images = []
for img in image:
self.image_processor.check_image_input(img)
img_w, img_h = img.size
if img_w * img_h > 1024 * 1024:
img = self.image_processor._resize_to_target_area(img, 1024 * 1024)
img_w, img_h = img.size
multiple_of = self.vae_scale_factor * 2
img_w = (img_w // multiple_of) * multiple_of
img_h = (img_h // multiple_of) * multiple_of
img_pre = self.image_processor.preprocess(img, height=img_h, width=img_w, resize_mode="crop")
condition_images.append(img_pre)
height = height or img_h
width = width or img_w
height = height or 1024
width = width or 1024
image_latents = None
image_latent_ids = None
if condition_images is not None:
try:
print(f"[Flux2Klein] Stage 2: Moving VAE to GPU to encode {len(condition_images)} reference image(s)...")
self.vae.to(device)
_flush()
image_latents, image_latent_ids = self.inner_pipe.prepare_image_latents(
images=condition_images,
batch_size=num_images_per_prompt,
generator=generator,
device=device,
dtype=self.vae.dtype,
)
image_latents = image_latents.to("cpu")
image_latent_ids = image_latent_ids.to("cpu")
finally:
self.vae.to("cpu")
_flush()
print(f"[Flux2Klein] Stage 2 complete. Allocated VRAM: {torch.cuda.memory_allocated() / 1e9:.3f} GB")
# ------------------------------------------------------------------
# STAGE 3: Transformer Denoising Loop (Nunchaku FP4/INT4 KV-Cache)
# ------------------------------------------------------------------
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_ids = self.inner_pipe.prepare_latents(
batch_size=num_images_per_prompt,
num_latents_channels=num_channels_latents,
height=height,
width=width,
dtype=self.transformer.dtype,
device=device,
generator=generator,
)
sigmas = np.linspace(1.0, 1.0 / num_inference_steps, num_inference_steps)
image_seq_len = latents.shape[1]
mu = compute_empirical_mu(image_seq_len=image_seq_len, num_steps=num_inference_steps)
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
device,
sigmas=sigmas,
mu=mu,
)
self.scheduler.set_begin_index(0)
kv_cache = None
try:
print("[Flux2Klein] Stage 3: Moving Nunchaku Transformer to GPU...")
self.transformer.to(device)
_flush()
prompt_embeds = prompt_embeds.to(device)
text_ids = text_ids.to(device)
latents = latents.to(device)
latent_ids = latent_ids.to(device)
if image_latents is not None:
image_latents = image_latents.to(device)
image_latent_ids = image_latent_ids.to(device)
for i, t in enumerate(timesteps):
t_step = time.time()
timestep = t.expand(latents.shape[0]).to(latents.dtype)
if i == 0 and image_latents is not None:
# Step 0: Extract KV cache for reference tokens
latent_model_input = torch.cat([image_latents, latents], dim=1).to(self.transformer.dtype)
latent_image_ids = torch.cat([image_latent_ids, latent_ids], dim=1)
noise_pred, kv_cache = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=None,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=None,
return_dict=False,
kv_cache_mode="extract",
num_ref_tokens=image_latents.shape[1],
)
elif kv_cache is not None:
# Steps 1+: Reuse cached reference KV
noise_pred = self.transformer(
hidden_states=latents.to(self.transformer.dtype),
timestep=timestep / 1000,
guidance=None,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_ids,
joint_attention_kwargs=None,
return_dict=False,
kv_cache=kv_cache,
kv_cache_mode="cached",
)[0]
else:
# Standard text-to-image forward
noise_pred = self.transformer(
hidden_states=latents.to(self.transformer.dtype),
timestep=timestep / 1000,
guidance=None,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_ids,
joint_attention_kwargs=None,
return_dict=False,
)[0]
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
print(
f"[Flux2Klein] Step {i+1}/{len(timesteps)} in {time.time() - t_step:.2f}s | "
f"Peak VRAM: {torch.cuda.max_memory_allocated() / 1e9:.2f} GB"
)
if "noise_pred" in locals():
del noise_pred
finally:
if kv_cache is not None:
kv_cache.clear()
self.transformer.to("cpu")
del prompt_embeds, text_ids, image_latents, image_latent_ids
_flush()
print(f"[Flux2Klein] Stage 3 complete. Allocated VRAM: {torch.cuda.memory_allocated() / 1e9:.3f} GB")
# ------------------------------------------------------------------
# STAGE 4: VAE Decoding
# ------------------------------------------------------------------
try:
print("[Flux2Klein] Stage 4: Moving VAE to GPU to decode latents...")
self.vae.to(device)
_flush()
latents = self.inner_pipe._unpack_latents_with_ids(latents, latent_ids)
latents_bn_mean = self.vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
latents_bn_std = torch.sqrt(self.vae.bn.running_var.view(1, -1, 1, 1) + self.vae.config.batch_norm_eps).to(
latents.device, latents.dtype
)
latents = latents * latents_bn_std + latents_bn_mean
latents = self.inner_pipe._unpatchify_latents(latents)
decoded = self.vae.decode(latents, return_dict=False)[0]
out_images = self.image_processor.postprocess(decoded, output_type="pil")
finally:
self.vae.to("cpu")
del latents, latent_ids
_flush()
print(f"[Flux2Klein] Stage 4 complete. Allocated VRAM: {torch.cuda.memory_allocated() / 1e9:.3f} GB")
print(f"[Flux2Klein] Total generation finished in {time.time() - t_start:.2f}s!")
return Flux2PipelineOutput(images=out_images)
class Flux2KleinBackend:
"""Backend factory for FLUX.2 Klein 9B KV Nunchaku."""
def __init__(
self,
model_id: str,
optimized_model_path: Optional[str] = None,
nvfp4_text_encoder_path: Optional[str] = None,
):
self.model_id = model_id
self.optimized_model_path = optimized_model_path
self.nvfp4_text_encoder_path = nvfp4_text_encoder_path
self.pipeline = None
def load(self):
print(f"[Flux2KleinBackend] Initializing backend from base model: {self.model_id}...")
# 1. Resolve optimized transformer path
opt_path = self.optimized_model_path
if not opt_path:
precision = get_precision()
opt_path = os.path.join(
self.model_id,
f"svdq-{precision}_r32-FLUX.2-klein-9b-kv-Nunchaku.safetensors",
)
if not os.path.exists(opt_path):
# Fallback to fp4 if not found directly
opt_path = os.path.join(
self.model_id,
"svdq-fp4_r32-FLUX.2-klein-9b-kv-Nunchaku.safetensors",
)
print(f"[Flux2KleinBackend] Loading Nunchaku transformer from {opt_path}...")
transformer = NunchakuFlux2Transformer2DModel.from_pretrained(
opt_path,
torch_dtype=torch.bfloat16,
)
transformer.to("cpu")
# 2. Resolve NVFP4 text encoder path
enc_path = self.nvfp4_text_encoder_path
if not enc_path:
# Check if running directly from within the text encoder repository (extras/)
candidate_parent = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
if os.path.exists(os.path.join(candidate_parent, "config.json")):
enc_path = candidate_parent
elif os.path.exists(os.path.join(self.model_id, "text_encoder")):
enc_path = os.path.join(self.model_id, "text_encoder")
else:
enc_path = "/path/to/FLUX.2-klein-text-encoder-NVFP4"
print(f"[Flux2KleinBackend] Loading NVFP4 text encoder from {enc_path}...")
text_encoder = load_nvfp4_text_encoder(enc_path, device="cpu", dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(enc_path)
# 3. Load VAE and Scheduler
print(f"[Flux2KleinBackend] Loading VAE and Scheduler from {self.model_id}...")
vae = AutoencoderKLFlux2.from_pretrained(
self.model_id,
subfolder="vae",
torch_dtype=torch.bfloat16,
)
vae.to("cpu")
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
self.model_id,
subfolder="scheduler",
)
# 4. Construct Sequential Pipeline
self.pipeline = SequentialFlux2KleinPipeline(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
device="cuda",
)
print("[Flux2KleinBackend] Pipeline successfully loaded and ready on CPU!")
return self.pipeline, self.pipeline
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