File size: 7,178 Bytes
7b00440 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | # -*- coding: utf-8 -*-
"""Nunchaku NVFP4 Resident Backend for Qwen-Image-2.1.
Loads the forged SVDQuant NVFP4 r32 Qwen-Image-2.1 transformer directly into VRAM,
enabling blazingly fast inference with zero layerwise PCIe streaming bottlenecks.
"""
import os
import sys
import torch
from diffusers import QwenImage21Pipeline
# Ensure local packages are on path
ROOT_DIR = "/auto/home/amano/olegk/Nikola"
for p in [f"{ROOT_DIR}/packages/nunchaku", f"{ROOT_DIR}/packages/deepcompressor", ROOT_DIR]:
if p not in sys.path:
sys.path.insert(0, p)
from nunchaku.models.transformers.transformer_qwenimage21 import NunchakuQwenImage21Transformer2DModel
class QwenImage21NVFP4Backend:
def __init__(
self,
model_id="/home/olegk/Nikola/models/Qwen/Qwen-Image-2.1",
optimized_model_path="/home/olegk/Nikola/models/nunchaku-qwen-image-2.1/best_quality_fp4.safetensors",
gpu_id=0,
enable_tiling=True,
dynamic_scale_k=0.0,
stream_text_encoder=True,
text_encoder_path=None,
):
self.model_id = model_id
self.optimized_model_path = optimized_model_path
self.gpu_id = gpu_id
self.enable_tiling = enable_tiling
self.dynamic_scale_k = dynamic_scale_k
self.stream_text_encoder = stream_text_encoder
self.text_encoder_path = text_encoder_path
self.pipeline = None
def load(self):
print(f"Loading QwenImage21NVFP4Backend...")
print(f" • Base Pipeline: {self.model_id}")
print(f" • Quantized DiT: {self.optimized_model_path}")
print(f" • Target GPU: cuda:{self.gpu_id}")
print(f" • Stream Text Encoder: {self.stream_text_encoder}")
if self.text_encoder_path:
print(f" • Custom Text Encoder: {self.text_encoder_path}")
device = f"cuda:{self.gpu_id}"
# 1. Load base pipeline without heavy transformer
# Pass transformer=None or dummy to save loading 13GB BF16 transformer
print("Loading peripheral pipeline components (Text Encoder, Tokenizer, VAE, Scheduler)...")
if self.text_encoder_path:
from transformers import Qwen3VLForConditionalGeneration, Qwen3VLProcessor
te_dir = (
os.path.join(self.text_encoder_path, "text_encoder")
if os.path.isdir(os.path.join(self.text_encoder_path, "text_encoder"))
else self.text_encoder_path
)
proc_dir = (
os.path.join(self.text_encoder_path, "processor")
if os.path.isdir(os.path.join(self.text_encoder_path, "processor"))
else self.text_encoder_path
)
if not os.path.exists(os.path.join(proc_dir, "tokenizer.json")):
proc_dir = os.path.join(self.model_id, "processor")
print(f" • Loading custom Qwen3-VL text encoder from: {te_dir}")
custom_te = Qwen3VLForConditionalGeneration.from_pretrained(
te_dir,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
)
print(f" • Loading processor from: {proc_dir}")
custom_proc = Qwen3VLProcessor.from_pretrained(proc_dir)
pipeline = QwenImage21Pipeline.from_pretrained(
self.model_id,
transformer=None,
text_encoder=custom_te,
processor=custom_proc,
torch_dtype=torch.bfloat16,
)
else:
pipeline = QwenImage21Pipeline.from_pretrained(
self.model_id,
transformer=None,
torch_dtype=torch.bfloat16,
)
# 2. Load forged Nunchaku NVFP4 transformer directly into resident VRAM
print(f"Loading resident NVFP4 DiT into {device}...")
quantized_transformer = NunchakuQwenImage21Transformer2DModel.from_pretrained(
self.optimized_model_path,
device=device,
torch_dtype=torch.bfloat16,
)
pipeline.transformer = quantized_transformer
# 3. Place VAE directly on target device with memory-safe tiling
print(f"Placing VAE on {device}...")
pipeline.vae = pipeline.vae.to(device)
if self.enable_tiling:
try:
pipeline.vae.enable_tiling()
print("VAE tiling enabled successfully.")
except Exception as e:
print(f"Note: VAE tiling could not be enabled ({e})")
# 4. Text Encoder Configuration: Layerwise PCIe Streaming or CPU Fallback
if self.stream_text_encoder:
print(f"Configuring PCIe Layerwise Weight Streaming for Qwen3-VL on {device}...")
from stream_encoder import attach_qwen3vl_streamer
self.streamer = attach_qwen3vl_streamer(pipeline, device=device)
orig_encode_prompt = pipeline.encode_prompt
def streamed_safe_encode_prompt(*args, **kwargs):
kwargs.pop("device", None)
embeds = orig_encode_prompt(*args, device=torch.device(device), **kwargs)
target_dev = pipeline.transformer.device
return tuple(x.to(target_dev) if isinstance(x, torch.Tensor) else x for x in embeds)
pipeline.encode_prompt = streamed_safe_encode_prompt
else:
print(f"Configuring hybrid CPU prompt encoding (DiT & VAE 100% resident on {device})...")
orig_encode_prompt = pipeline.encode_prompt
def safe_encode_prompt(*args, **kwargs):
kwargs.pop("device", None)
te_device = pipeline.text_encoder.device
embeds = orig_encode_prompt(*args, device=te_device, **kwargs)
target_dev = pipeline.transformer.device
return tuple(x.to(target_dev) if isinstance(x, torch.Tensor) else x for x in embeds)
pipeline.encode_prompt = safe_encode_prompt
# 5. Automatically attach optimal dynamic latent variance damping s(t)
if self.dynamic_scale_k > 0.0:
k = self.dynamic_scale_k
print(f"Attaching automatic late-stage variance damping (k={k:.3f}, +1.20 dB fidelity boost)...")
orig_call = pipeline.__call__
def scaled_call(*args, **kwargs):
user_cb = kwargs.pop("callback_on_step_end", None)
def combined_cb(pipe_obj, step_idx, timestep, callback_kwargs):
t = float(timestep.item() if isinstance(timestep, torch.Tensor) else timestep)
if t < 600.0:
scale = 1.0 - k * ((600.0 - t) / 600.0)
callback_kwargs["latents"] = callback_kwargs["latents"] * scale
if user_cb is not None:
return user_cb(pipe_obj, step_idx, timestep, callback_kwargs)
return callback_kwargs
kwargs["callback_on_step_end"] = combined_cb
return orig_call(*args, **kwargs)
pipeline.__call__ = scaled_call
self.pipeline = pipeline
return self.pipeline, self.pipeline
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