PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis with OpenVINO

This Jupyter notebook can be launched after a local installation only.

This paper introduces PIXART-α, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators, reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost. To achieve this goal, three core designs are proposed: 1. Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; 2. Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; 3. High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning.

Table of contents:

• Prerequisites

• Load the original model

• Convert the model to OpenVINO IR

  • Convert text encoder

  • Convert transformer

  • Convert VAE decoder

• Compiling models

• Building the pipeline

• Quantization

  • Prepare calibration dataset

  • Run quantization

    • Run Hybrid Quantization

    • Run Weights Compression

  • Compare model file sizes

  • Compare inference time of the FP16 and optimized pipelines

• Interactive inference

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

Prerequisites

%pip install -q "diffusers>=0.14.0" sentencepiece "datasets>=2.14.6" "transformers>=4.25.1" "gradio>=4.19" "torch>=2.1" Pillow opencv-python --extra-index-url https://download.pytorch.org/whl/cpu
%pip install -Uq "openvino>=2024.3.0"

Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.

import requests
from pathlib import Path


if not Path("pixart_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/pixart/pixart_helper.py")
    open("pixart_helper.py", "w").write(r.text)

if not Path("pixart_quantization_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/pixart/pixart_quantization_helper.py")
    open("pixart_quantization_helper.py", "w").write(r.text)

Load and run the original pipeline

We use PixArt-LCM-XL-2-1024-MS that uses LCMs. LCMs is a diffusion distillation method which predict PF-ODE's solution directly in latent space, achieving super fast inference with few steps.

import torch
from diffusers import PixArtAlphaPipeline


pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-LCM-XL-2-1024-MS", use_safetensors=True)

prompt = "A small cactus with a happy face in the Sahara desert."
generator = torch.Generator().manual_seed(42)

image = pipe(prompt, guidance_scale=0.0, num_inference_steps=4, generator=generator).images[0]

2024-11-05 02:30:04.644117: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-11-05 02:30:04.680089: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-05 02:30:05.360275: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT

Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]

You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the legacy (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set legacy=False. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in huggingface/transformers#24565
Some weights of the model checkpoint were not used when initializing PixArtTransformer2DModel:
 ['caption_projection.y_embedding']

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image

Convert the model to OpenVINO IR

Let’s define the conversion function for PyTorch modules. We use ov.convert_model function to obtain OpenVINO Intermediate Representation object and ov.save_model function to save it as XML file.

import torch
import openvino as ov


def convert(model: torch.nn.Module, xml_path: str, example_input):
    xml_path = Path(xml_path)
    if not xml_path.exists():
        xml_path.parent.mkdir(parents=True, exist_ok=True)
        model.eval()
        with torch.no_grad():
            converted_model = ov.convert_model(model, example_input=example_input)
        ov.save_model(converted_model, xml_path)

        # cleanup memory
        torch._C._jit_clear_class_registry()
        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
        torch.jit._state._clear_class_state()

PixArt-α consists of pure transformer blocks for latent diffusion: It can directly generate 1024px images from text prompts within a single sampling process.

.

During inference it uses text encoder T5EncoderModel, transformer Transformer2DModel and VAE decoder AutoencoderKL. Let’s convert the models from the pipeline one by one.

from pixart_helper import TEXT_ENCODER_PATH, TRANSFORMER_OV_PATH, VAE_DECODER_PATH

Convert text encoder

example_input = {
    "input_ids": torch.zeros(1, 120, dtype=torch.int64),
    "attention_mask": torch.zeros(1, 120, dtype=torch.int64),
}

convert(pipe.text_encoder, TEXT_ENCODER_PATH, example_input)

WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.base has been moved to tensorflow.python.trackable.base. The old module will be deleted in version 2.11.

[ WARNING ]  Please fix your imports. Module %s has been moved to %s. The old module will be deleted in version %s.
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/transformers/modeling_utils.py:5006: FutureWarning: _is_quantized_training_enabled is going to be deprecated in transformers 4.39.0. Please use model.hf_quantizer.is_trainable instead
  warnings.warn(
loss_type=None was set in the config but it is unrecognised.Using the default loss: ForCausalLMLoss.

Convert transformer

class TransformerWrapper(torch.nn.Module):
    def __init__(self, transformer):
        super().__init__()
        self.transformer = transformer

    def forward(self, hidden_states=None, timestep=None, encoder_hidden_states=None, encoder_attention_mask=None, resolution=None, aspect_ratio=None):
        return self.transformer.forward(
            hidden_states,
            timestep=timestep,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            added_cond_kwargs={"resolution": resolution, "aspect_ratio": aspect_ratio},
        )


example_input = {
    "hidden_states": torch.rand([2, 4, 128, 128], dtype=torch.float32),
    "timestep": torch.tensor([999, 999]),
    "encoder_hidden_states": torch.rand([2, 120, 4096], dtype=torch.float32),
    "encoder_attention_mask": torch.rand([2, 120], dtype=torch.float32),
    "resolution": torch.tensor([[1024.0, 1024.0], [1024.0, 1024.0]]),
    "aspect_ratio": torch.tensor([[1.0], [1.0]]),
}


w_transformer = TransformerWrapper(pipe.transformer)
convert(w_transformer, TRANSFORMER_OV_PATH, example_input)

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/embeddings.py:219: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if self.height != height or self.width != width:
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/attention_processor.py:682: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if current_length != target_length:
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/attention_processor.py:697: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if attention_mask.shape[0] < batch_size * head_size:

Convert VAE decoder

class VAEDecoderWrapper(torch.nn.Module):
    def __init__(self, vae):
        super().__init__()
        self.vae = vae

    def forward(self, latents):
        return self.vae.decode(latents, return_dict=False)


convert(VAEDecoderWrapper(pipe.vae), VAE_DECODER_PATH, (torch.zeros((1, 4, 128, 128))))

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:146: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  assert hidden_states.shape[1] == self.channels
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
  if hidden_states.shape[0] >= 64:

Compiling models

Select device from dropdown list for running inference using OpenVINO.

import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)
open("notebook_utils.py", "w").write(r.text)

from notebook_utils import device_widget

device = device_widget()

device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

core = ov.Core()

compiled_model = core.compile_model(TRANSFORMER_OV_PATH, device.value)
compiled_vae = core.compile_model(VAE_DECODER_PATH, device.value)
compiled_text_encoder = core.compile_model(TEXT_ENCODER_PATH, device.value)

Building the pipeline

Let’s create callable wrapper classes for compiled models to allow interaction with original pipelines. Note that all of wrapper classes return torch.Tensors instead of np.arrays.

from collections import namedtuple

EncoderOutput = namedtuple("EncoderOutput", "last_hidden_state")


class TextEncoderWrapper(torch.nn.Module):
    def __init__(self, text_encoder, dtype):
        super().__init__()
        self.text_encoder = text_encoder
        self.dtype = dtype

    def forward(self, input_ids=None, attention_mask=None):
        inputs = {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        last_hidden_state = self.text_encoder(inputs)[0]
        return EncoderOutput(torch.from_numpy(last_hidden_state))

class TransformerWrapper(torch.nn.Module):
    def __init__(self, transformer, config):
        super().__init__()
        self.transformer = transformer
        self.config = config

    def forward(
        self,
        hidden_states=None,
        timestep=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        resolution=None,
        aspect_ratio=None,
        added_cond_kwargs=None,
        **kwargs
    ):
        inputs = {
            "hidden_states": hidden_states,
            "timestep": timestep,
            "encoder_hidden_states": encoder_hidden_states,
            "encoder_attention_mask": encoder_attention_mask,
        }
        resolution = added_cond_kwargs["resolution"]
        aspect_ratio = added_cond_kwargs["aspect_ratio"]
        if resolution is not None:
            inputs["resolution"] = resolution
            inputs["aspect_ratio"] = aspect_ratio
        outputs = self.transformer(inputs)[0]

        return [torch.from_numpy(outputs)]

class VAEWrapper(torch.nn.Module):
    def __init__(self, vae, config):
        super().__init__()
        self.vae = vae
        self.config = config

    def decode(self, latents=None, **kwargs):
        inputs = {
            "latents": latents,
        }

        outs = self.vae(inputs)
        outs = namedtuple("VAE", "sample")(torch.from_numpy(outs[0]))

        return outs

And insert wrappers instances in the pipeline:

pipe.__dict__["_internal_dict"]["_execution_device"] = pipe._execution_device  # this is to avoid some problem that can occur in the pipeline

pipe.register_modules(
    text_encoder=TextEncoderWrapper(compiled_text_encoder, pipe.text_encoder.dtype),
    transformer=TransformerWrapper(compiled_model, pipe.transformer.config),
    vae=VAEWrapper(compiled_vae, pipe.vae.config),
)

generator = torch.Generator().manual_seed(42)

image = pipe(prompt=prompt, guidance_scale=0.0, num_inference_steps=4, generator=generator).images[0]

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute _execution_device directly via 'PixArtAlphaPipeline' object attribute is deprecated. Please access '_execution_device' over 'PixArtAlphaPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
  deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)

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image

Quantization

NNCF enables post-training quantization by adding quantization layers into model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. Quantized operations are executed in INT8 instead of FP32/FP16 making model inference faster.

According to PixArt-LCM-XL-2-1024-MS structure, Transformer2DModel is used in the cycle repeating inference on each diffusion step, while other parts of pipeline take part only once. Quantizing the rest of the pipeline does not significantly improve inference performance but can lead to a substantial degradation of accuracy. That’s why we use only weight compression in 4-bits for the text encoder and vae decoder to reduce the memory footprint. Now we will show you how to optimize pipeline using NNCF to reduce memory and computation cost.

Please select below whether you would like to run quantization to improve model inference speed.

NOTE: Quantization is time and memory consuming operation. Running quantization code below may take some time.

from notebook_utils import quantization_widget

to_quantize = quantization_widget()

to_quantize

Checkbox(value=True, description='Quantization')

Let’s load skip magic extension to skip quantization if to_quantize is not selected

# Fetch `skip_kernel_extension` module
r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
)
open("skip_kernel_extension.py", "w").write(r.text)

optimized_pipe = None

%load_ext skip_kernel_extension

Prepare calibration dataset

We use a portion of google-research-datasets/conceptual_captions dataset from Hugging Face as calibration data. We use prompts below to guide image generation and to determine what not to include in the resulting image.

To collect intermediate model inputs for calibration we should customize CompiledModel.

%%skip not $to_quantize.value

from pixart_quantization_helper import INT8_TRANSFORMER_OV_PATH, INT4_TEXT_ENCODER_PATH, INT4_VAE_DECODER_PATH, collect_calibration_data

if not INT8_TRANSFORMER_OV_PATH.exists():
    subset_size = 100
    calibration_data = collect_calibration_data(pipe, subset_size=subset_size)

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/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute _execution_device directly via 'PixArtAlphaPipeline' object attribute is deprecated. Please access '_execution_device' over 'PixArtAlphaPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
  deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)

Run Hybrid Quantization

For the Transformer2DModel model we apply quantization in hybrid mode which means that we quantize: (1) weights of MatMul and Embedding layers and (2) activations of other layers. The steps are the following:

1. Create a calibration dataset for quantization.

2. Collect operations with weights.

3. Run nncf.compress_model() to compress only the model weights.

4. Run nncf.quantize() on the compressed model with weighted operations ignored by providing ignored_scope parameter.

5. Save the INT8 model using openvino.save_model() function.

%%skip not $to_quantize.value

import nncf
from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters
from nncf.quantization.advanced_parameters import AdvancedQuantizationParameters
from pixart_quantization_helper import get_quantization_ignored_scope

if not INT8_TRANSFORMER_OV_PATH.exists():
    model = core.read_model(TRANSFORMER_OV_PATH)
    ignored_scope = get_quantization_ignored_scope(model)
    # The convolution operations will be fully quantized
    compressed_model = nncf.compress_weights(model, ignored_scope=nncf.IgnoredScope(types=['Convolution']))
    quantized_model = nncf.quantize(
        model=compressed_model,
        calibration_dataset=nncf.Dataset(calibration_data),
        subset_size=subset_size,
        ignored_scope=nncf.IgnoredScope(names=ignored_scope),
        model_type=nncf.ModelType.TRANSFORMER,
        # Disable SQ because MatMul weights are already compressed
        advanced_parameters=AdvancedQuantizationParameters(smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=-1))
    )
    ov.save_model(quantized_model, INT8_TRANSFORMER_OV_PATH)

INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, tensorflow, onnx, openvino
INFO:nncf:1 ignored nodes were found by types in the NNCFGraph
INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 100% (290 / 290)            │ 100% (290 / 290)                       │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

Output()

INFO:nncf:290 ignored nodes were found by names in the NNCFGraph
INFO:nncf:Not adding activation input quantizer for operation: 9 __module.transformer.caption_projection.linear_1/aten::linear/MatMul
18 __module.transformer.caption_projection.linear_1/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 44 __module.transformer.caption_projection.linear_2/aten::linear/MatMul
171 __module.transformer.caption_projection.linear_2/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 110 __module.transformer.transformer_blocks.0.attn2.to_k/aten::linear/MatMul
263 __module.transformer.transformer_blocks.0.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 111 __module.transformer.transformer_blocks.0.attn2.to_v/aten::linear/MatMul
264 __module.transformer.transformer_blocks.0.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 112 __module.transformer.transformer_blocks.1.attn2.to_k/aten::linear/MatMul
265 __module.transformer.transformer_blocks.1.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 113 __module.transformer.transformer_blocks.1.attn2.to_v/aten::linear/MatMul
266 __module.transformer.transformer_blocks.1.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 114 __module.transformer.transformer_blocks.10.attn2.to_k/aten::linear/MatMul
267 __module.transformer.transformer_blocks.10.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 115 __module.transformer.transformer_blocks.10.attn2.to_v/aten::linear/MatMul
268 __module.transformer.transformer_blocks.10.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 116 __module.transformer.transformer_blocks.11.attn2.to_k/aten::linear/MatMul
269 __module.transformer.transformer_blocks.11.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 117 __module.transformer.transformer_blocks.11.attn2.to_v/aten::linear/MatMul
270 __module.transformer.transformer_blocks.11.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 118 __module.transformer.transformer_blocks.12.attn2.to_k/aten::linear/MatMul
271 __module.transformer.transformer_blocks.12.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 119 __module.transformer.transformer_blocks.12.attn2.to_v/aten::linear/MatMul
272 __module.transformer.transformer_blocks.12.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 120 __module.transformer.transformer_blocks.13.attn2.to_k/aten::linear/MatMul
273 __module.transformer.transformer_blocks.13.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 121 __module.transformer.transformer_blocks.13.attn2.to_v/aten::linear/MatMul
274 __module.transformer.transformer_blocks.13.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 122 __module.transformer.transformer_blocks.14.attn2.to_k/aten::linear/MatMul
275 __module.transformer.transformer_blocks.14.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 123 __module.transformer.transformer_blocks.14.attn2.to_v/aten::linear/MatMul
276 __module.transformer.transformer_blocks.14.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 124 __module.transformer.transformer_blocks.15.attn2.to_k/aten::linear/MatMul
277 __module.transformer.transformer_blocks.15.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 125 __module.transformer.transformer_blocks.15.attn2.to_v/aten::linear/MatMul
278 __module.transformer.transformer_blocks.15.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 126 __module.transformer.transformer_blocks.16.attn2.to_k/aten::linear/MatMul
279 __module.transformer.transformer_blocks.16.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 127 __module.transformer.transformer_blocks.16.attn2.to_v/aten::linear/MatMul
280 __module.transformer.transformer_blocks.16.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 128 __module.transformer.transformer_blocks.17.attn2.to_k/aten::linear/MatMul
281 __module.transformer.transformer_blocks.17.attn2.to_k/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 129 __module.transformer.transformer_blocks.17.attn2.to_v/aten::linear/MatMul
282 __module.transformer.transformer_blocks.17.attn2.to_v/aten::linear/Add

INFO:nncf:Not adding activation input quantizer for operation: 130 __module.transformer.transformer_blocks.18.attn2.to_k/aten::linear/MatMul
283 __module.transformer.transformer_blocks.18.attn2.to_k/aten::linear/Add

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INFO:nncf:Not adding activation input quantizer for operation: 932 __module.transformer.adaln_single.emb.timestep_embedder.linear_1/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1624 __module.transformer.adaln_single.emb.timestep_embedder.linear_2/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 934 __module.transformer.adaln_single.emb.resolution_embedder.linear_1/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1625 __module.transformer.adaln_single.emb.resolution_embedder.linear_2/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 935 __module.transformer.adaln_single.emb.aspect_ratio_embedder.linear_1/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1626 __module.transformer.adaln_single.emb.aspect_ratio_embedder.linear_2/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1096 __module.transformer.transformer_blocks.21.attn1.to_v/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1873 __module.transformer.transformer_blocks.21.attn1.to_out.0/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1097 __module.transformer.transformer_blocks.21.attn2.to_q/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 639 __module.transformer.transformer_blocks.21.attn2.to_out.0/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 767 __module.transformer.transformer_blocks.21.ff.net.0.proj/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1545 __module.transformer.transformer_blocks.21.ff.net.2/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1103 __module.transformer.transformer_blocks.22.attn1.to_k/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 640 __module.transformer.transformer_blocks.22.attn2.to_out.0/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 775 __module.transformer.transformer_blocks.22.ff.net.0.proj/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 791 __module.transformer.transformer_blocks.24.ff.net.0.proj/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1130 __module.transformer.transformer_blocks.25.attn1.to_k/aten::linear/MatMul
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INFO:nncf:Not adding activation input quantizer for operation: 1456 __module.transformer.proj_out/aten::linear/MatMul
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Output()

Run Weights Compression

Quantizing of the T5EncoderModel and AutoencoderKL does not significantly improve inference performance but can lead to a substantial degradation of accuracy. The weight compression will be applied to footprint reduction.

%%skip not $to_quantize.value

if not INT4_TEXT_ENCODER_PATH.exists():
    text_encoder = core.read_model(TEXT_ENCODER_PATH)
    compressed_text_encoder = nncf.compress_weights(text_encoder, mode=nncf.CompressWeightsMode.INT4_SYM)
    ov.save_model(compressed_text_encoder, INT4_TEXT_ENCODER_PATH)

if not INT4_VAE_DECODER_PATH.exists():
    vae_decoder = core.read_model(VAE_DECODER_PATH)
    compressed_vae_decoder = nncf.compress_weights(vae_decoder, mode=nncf.CompressWeightsMode.INT4_SYM)
    ov.save_model(compressed_vae_decoder, INT4_VAE_DECODER_PATH)

INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 3% (3 / 194)                │ 0% (0 / 191)                           │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 97% (191 / 194)             │ 100% (191 / 191)                       │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

Output()

INFO:nncf:Statistics of the bitwidth distribution:
┍━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑
│   Num bits (N) │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │
┝━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥
│              8 │ 98% (37 / 40)               │ 0% (0 / 3)                             │
├────────────────┼─────────────────────────────┼────────────────────────────────────────┤
│              4 │ 2% (3 / 40)                 │ 100% (3 / 3)                           │
┕━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙

Output()

Let’s compare the images generated by the original and optimized pipelines.

%%skip not $to_quantize.value

# Disable dynamic quantization due to the performance overhead for Diffusion models
optimized_transformer = core.compile_model(INT8_TRANSFORMER_OV_PATH, device.value, config={"DYNAMIC_QUANTIZATION_GROUP_SIZE":"0"})
optimized_text_encoder = core.compile_model(INT4_TEXT_ENCODER_PATH, device.value)
optimized_vae_decoder = core.compile_model(INT4_VAE_DECODER_PATH, device.value)

optimized_pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-LCM-XL-2-1024-MS", use_safetensors=True)
optimized_pipe.__dict__["_internal_dict"]["_execution_device"] = optimized_pipe._execution_device  # this is to avoid some problem that can occur in the pipeline

optimized_pipe.register_modules(
    text_encoder=TextEncoderWrapper(optimized_text_encoder, optimized_pipe.text_encoder.dtype),
    transformer=TransformerWrapper(optimized_transformer, optimized_pipe.transformer.config),
    vae=VAEWrapper(optimized_vae_decoder, optimized_pipe.vae.config),
)

Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]

Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]

Some weights of the model checkpoint were not used when initializing PixArtTransformer2DModel:
 ['caption_projection.y_embedding']

%%skip not $to_quantize.value

from pixart_quantization_helper import visualize_results

prompt = "A small cactus with a happy face in the Sahara desert."
generator = torch.Generator().manual_seed(42)
opt_image = optimized_pipe(prompt=prompt, guidance_scale=0.0, num_inference_steps=4, generator=generator).images[0]

visualize_results(image, opt_image)

/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/diffusers/configuration_utils.py:140: FutureWarning: Accessing config attribute _execution_device directly via 'PixArtAlphaPipeline' object attribute is deprecated. Please access '_execution_device' over 'PixArtAlphaPipeline's config object instead, e.g. 'scheduler.config._execution_device'.
  deprecate("direct config name access", "1.0.0", deprecation_message, standard_warn=False)

0%|          | 0/4 [00:00<?, ?it/s]

Compare model file sizes

%%skip not $to_quantize.value
from pixart_quantization_helper import compare_models_size

compare_models_size()

transformer_ir compression rate: 1.979
text_encoder compression rate: 4.514
vae_decoder compression rate: 2.012

Compare inference time of the FP16 and optimized pipelines

To measure the inference performance of the FP16 and optimized pipelines, we use mean inference time on 3 samples.

NOTE: For the most accurate performance estimation, it is recommended to run benchmark_app in a terminal/command prompt after closing other applications.

%%skip not $to_quantize.value

from pixart_quantization_helper import compare_perf

compare_perf(pipe, optimized_pipe, validation_size=3)

FP16 pipeline: 43.073 seconds
Optimized pipeline: 41.450 seconds
Performance speed-up: 1.039

Interactive inference

Please select below whether you would like to use the quantized models to launch the interactive demo.

from pixart_helper import get_pipeline_selection_option

use_quantized_models = get_pipeline_selection_option(optimized_pipe)

use_quantized_models

Checkbox(value=True, description='Use quantized models')

pipeline = optimized_pipe if use_quantized_models.value else pipe


def generate(prompt, seed, negative_prompt, num_inference_steps):
    generator = torch.Generator().manual_seed(seed)
    image = pipeline(prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, generator=generator, guidance_scale=0.0).images[0]
    return image


if not Path("gradio_helper.py").exists():
    r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/pixart/gradio_helper.py")
    open("gradio_helper.py", "w").write(r.text)

from gradio_helper import make_demo

demo = make_demo(fn=generate)

try:
    demo.queue().launch(debug=False)
except Exception:
    demo.queue().launch(debug=False, share=True)
# if you are launching remotely, specify server_name and server_port
# demo.launch(server_name='your server name', server_port='server port in int')
# Read more in the docs: https://gradio.app/docs/

Running on local URL:  http://127.0.0.1:7860

To create a public link, set share=True in launch().

# please uncomment and run this cell for stopping gradio interface
# demo.close()