Image Generation with Stable Diffusion and IP-Adapter#
This Jupyter notebook can be launched after a local installation only.
IP-Adapter is an effective and
lightweight adapter that adds image prompting capabilities to a
diffusion model. This adapter works by decoupling the cross-attention
layers of the image and text features. All the other model components
are frozen and only the embedded image features in the UNet are trained.
As a result, IP-Adapter files are typically only ~100MBs.
In this tutorial, we will consider how to convert and run Stable Diffusion pipeline with loading IP-Adapter. We will use stable-diffusion-v1.5 as base model and apply official IP-Adapter weights. Also for speedup generation process we will use LCM-LoRA
Table of contents:
Installation Instructions#
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#
import platform
%pip install -q "torch>=2.1" transformers accelerate "diffusers>=0.24.0" "openvino>=2023.3.0" "gradio>=4.19" opencv-python "peft>=0.6.2" "protobuf>=3.20" --extra-index-url https://download.pytorch.org/whl/cpu
if platform.system() != "Windows":
%pip install -q "matplotlib>=3.4"
else:
%pip install -q "matplotlib>=3.4,<3.7"
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
botocore 1.35.36 requires urllib3<1.27,>=1.25.4; python_version < "3.10", but you have urllib3 2.2.3 which is incompatible.
Note: you may need to restart the kernel to use updated packages.
Note: you may need to restart the kernel to use updated packages.
Prepare Diffusers pipeline#
First of all, we should collect all components of our pipeline together. To work with Stable Diffusion, we will use HuggingFace Diffusers library. To experiment with Stable Diffusion models, Diffusers exposes the StableDiffusionPipeline similar to the other Diffusers pipelines. Additionally, the pipeline supports load adapters that extend Stable Diffusion functionality such as Low-Rank Adaptation (LoRA), PEFT, IP-Adapter, and Textual Inversion. You can find more information about supported adapters in diffusers documentation.
In this tutorial, we will focus on ip-adapter. IP-Adapter can be
integrated into diffusion pipeline using load_ip_adapter method.
IP-Adapter allows you to use both image and text to condition the image
generation process. For adjusting the text prompt and image prompt
condition ratio, we can use set_ip_adapter_scale() method. If you
only use the image prompt, you should set the scale to 1.0. You can
lower the scale to get more generation diversity, but it’ll be less
aligned with the prompt. scale=0.5 can achieve good results when you use
both text and image prompts.
As discussed before, we will also use LCM LoRA for speeding generation
process. You can find more information about LCM LoRA in this
notebook.
For applying LCM LoRA, we should use load_lora_weights method.
Additionally, LCM requires using LCMScheduler for efficient generation.
from pathlib import Path
from diffusers import AutoPipelineForText2Image
from transformers import CLIPVisionModelWithProjection
from diffusers.utils import load_image
from diffusers import LCMScheduler
stable_diffusion_id = "botp/stable-diffusion-v1-5"
ip_adapter_id = "h94/IP-Adapter"
ip_adapter_weight_name = "ip-adapter_sd15.bin"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
models_dir = Path("model")
load_original_pipeline = not all(
[
(models_dir / model_name).exists()
for model_name in [
"text_encoder.xml",
"image_encoder.xml",
"unet.xml",
"vae_decoder.xml",
"vae_encoder.xml",
]
]
)
def get_pipeline_components(
stable_diffusion_id,
ip_adapter_id,
ip_adapter_weight_name,
lcm_lora_id,
ip_adapter_scale=0.6,
):
image_encoder = CLIPVisionModelWithProjection.from_pretrained("h94/IP-Adapter", subfolder="models/image_encoder")
pipeline = AutoPipelineForText2Image.from_pretrained(stable_diffusion_id, image_encoder=image_encoder)
pipeline.load_lora_weights(lcm_lora_id)
pipeline.fuse_lora()
pipeline.load_ip_adapter(ip_adapter_id, subfolder="models", weight_name=ip_adapter_weight_name)
pipeline.set_ip_adapter_scale(0.6)
scheduler = LCMScheduler.from_pretrained(stable_diffusion_id, subfolder="scheduler")
return (
pipeline.tokenizer,
pipeline.feature_extractor,
scheduler,
pipeline.text_encoder,
pipeline.image_encoder,
pipeline.unet,
pipeline.vae,
)
if load_original_pipeline:
(
tokenizer,
feature_extractor,
scheduler,
text_encoder,
image_encoder,
unet,
vae,
) = get_pipeline_components(stable_diffusion_id, ip_adapter_id, ip_adapter_weight_name, lcm_lora_id)
scheduler.save_pretrained(models_dir / "scheduler")
else:
tokenizer, feature_extractor, scheduler, text_encoder, image_encoder, unet, vae = (
None,
None,
None,
None,
None,
None,
None,
)
2024-11-05 05:11:37.931642: 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 05:11:37.965522: 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 05:11:38.628517: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
Loading pipeline components...: 0%| | 0/7 [00:00<?, ?it/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/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use CLIPImageProcessor instead.
warnings.warn(
Convert PyTorch models#
Starting from 2023.0 release, OpenVINO supports PyTorch models directly
via Model Conversion API. ov.convert_model function accepts instance
of PyTorch model and example inputs for tracing and returns object of
ov.Model class, ready to use or save on disk using ov.save_model
function.
The pipeline consists of four important parts:
Image Encoder to create image condition for IP-Adapter.
Text Encoder to create condition to generate an image from a text prompt.
U-Net for step-by-step denoising latent image representation.
Autoencoder (VAE) for decoding latent space to image.
Let us convert each part:
Image Encoder#
IP-Adapter relies on an image encoder to generate the image features.
Usually
CLIPVisionModelWithProjection
is used as Image Encoder. For preprocessing input image, Image Encoder
uses CLIPImageProcessor named feature extractor in pipeline. The
image encoder accept resized and normalized image processed by feature
extractor as input and returns image embeddings.
import openvino as ov
import torch
import gc
def cleanup_torchscript_cache():
"""
Helper for removing cached model representation
"""
torch._C._jit_clear_class_registry()
torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()
torch.jit._state._clear_class_state()
IMAGE_ENCODER_PATH = models_dir / "image_encoder.xml"
if not IMAGE_ENCODER_PATH.exists():
with torch.no_grad():
ov_model = ov.convert_model(
image_encoder,
example_input=torch.zeros((1, 3, 224, 224)),
input=[-1, 3, 224, 224],
)
ov.save_model(ov_model, IMAGE_ENCODER_PATH)
feature_extractor.save_pretrained(models_dir / "feature_extractor")
del ov_model
cleanup_torchscript_cache()
del image_encoder
del feature_extractor
gc.collect();
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. /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/models/clip/modeling_clip.py:243: 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 not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
U-net#
U-Net model gradually denoises latent image representation guided by text encoder hidden state.
Generally, U-Net model conversion process remain the same like in Stable
Diffusion, expect additional input that accept image embeddings
generated by Image Encoder. In Stable Diffusion pipeline, this data
provided into model using dictionary added_cond_kwargs and key
image_embeds inside it. After OpenVINO conversion, this input will
be decomposed from dictionary. In some cases, such decomposition may
lead to loosing information about input shape and data type. We can
restore it manually as demonstrated in the code bellow.
U-Net model inputs:
sample- latent image sample from previous step. Generation process has not been started yet, so you will use random noise.timestep- current scheduler step.encoder_hidden_state- hidden state of text encoder.image_embeds- hidden state of image encoder.
Model predicts the sample state for the next step.
UNET_PATH = models_dir / "unet.xml"
if not UNET_PATH.exists():
inputs = {
"sample": torch.randn((2, 4, 64, 64)),
"timestep": torch.tensor(1),
"encoder_hidden_states": torch.randn((2, 77, 768)),
"added_cond_kwargs": {"image_embeds": torch.ones((2, 1024))},
}
with torch.no_grad():
ov_model = ov.convert_model(unet, example_input=inputs)
# dictionary with added_cond_kwargs will be decomposed during conversion
# in some cases decomposition may lead to losing data type and shape information
# We need to recover it manually after the conversion
ov_model.inputs[-1].get_node().set_element_type(ov.Type.f32)
ov_model.validate_nodes_and_infer_types()
ov.save_model(ov_model, UNET_PATH)
del ov_model
cleanup_torchscript_cache()
del unet
gc.collect();
/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/unets/unet_2d_condition.py:1111: 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 dim % default_overall_up_factor != 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/models/embeddings.py:1801: FutureWarning: You have passed a tensor as image_embeds.This is deprecated and will be removed in a future release. Please make sure to update your script to pass image_embeds as a list of tensors to suppress this warning.
deprecate("image_embeds not a list", "1.0.0", deprecation_message, standard_warn=False)
/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/downsampling.py:136: 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/downsampling.py:145: 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:147: 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:
VAE Encoder and Decoder#
The VAE model has two parts, an encoder and a decoder. The encoder is used to convert the image into a low dimensional latent representation, which will serve as the input to the U-Net model. The decoder, conversely, transforms the latent representation back into an image.
During latent diffusion training, the encoder is used to get the latent representations (latents) of the images for the forward diffusion process, which applies more and more noise at each step. During inference, the denoised latents generated by the reverse diffusion process are converted back into images using the VAE decoder. When you run inference for Text-to-Image, there is no initial image as a starting point. You can skip this step and directly generate initial random noise. VAE encoder is used in Image-to-Image generation pipelines for creating initial latent state based on input image. The main difference between IP-Adapter encoded image and VAE encoded image that the first is used as addition into input prompt making connection between text and image during conditioning, while the second used as Unet sample initialization and does not give guarantee preserving some attributes of initial image. It is still can be useful to use both ip-adapter and VAE image in pipeline, we can discuss it in inference examples.
VAE_DECODER_PATH = models_dir / "vae_decoder.xml"
VAE_ENCODER_PATH = models_dir / "vae_encoder.xml"
if not VAE_DECODER_PATH.exists():
class VAEDecoderWrapper(torch.nn.Module):
def __init__(self, vae):
super().__init__()
self.vae = vae
def forward(self, latents):
return self.vae.decode(latents)
vae_decoder = VAEDecoderWrapper(vae)
with torch.no_grad():
ov_model = ov.convert_model(vae_decoder, example_input=torch.ones([1, 4, 64, 64]))
ov.save_model(ov_model, VAE_DECODER_PATH)
del ov_model
cleanup_torchscript_cache()
del vae_decoder
if not VAE_ENCODER_PATH.exists():
class VAEEncoderWrapper(torch.nn.Module):
def __init__(self, vae):
super().__init__()
self.vae = vae
def forward(self, image):
return self.vae.encode(x=image)["latent_dist"].sample()
vae_encoder = VAEEncoderWrapper(vae)
vae_encoder.eval()
image = torch.zeros((1, 3, 512, 512))
with torch.no_grad():
ov_model = ov.convert_model(vae_encoder, example_input=image)
ov.save_model(ov_model, VAE_ENCODER_PATH)
del ov_model
cleanup_torchscript_cache()
del vae
gc.collect();
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1303: TracerWarning: Trace had nondeterministic nodes. Did you forget call .eval() on your model? Nodes:
%2506 : Float(1, 4, 64, 64, strides=[16384, 4096, 64, 1], requires_grad=0, device=cpu) = aten::randn(%2500, %2501, %2502, %2503, %2504, %2505) # /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/utils/torch_utils.py:81:0
This may cause errors in trace checking. To disable trace checking, pass check_trace=False to torch.jit.trace()
_check_trace(
/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/torch/jit/_trace.py:1303: TracerWarning: Output nr 1. of the traced function does not match the corresponding output of the Python function. Detailed error:
Tensor-likes are not close!
Mismatched elements: 10398 / 16384 (63.5%)
Greatest absolute difference: 0.0014954805374145508 at index (0, 1, 0, 63) (up to 1e-05 allowed)
Greatest relative difference: 0.004146566421115057 at index (0, 3, 63, 59) (up to 1e-05 allowed)
_check_trace(
Text Encoder#
The text-encoder is responsible for transforming the input prompt, for example, “a photo of an astronaut riding a horse” into an embedding space that can be understood by the U-Net. It is usually a simple transformer-based encoder that maps a sequence of input tokens to a sequence of latent text embeddings.
The input of the text encoder is tensor input_ids, which contains
indexes of tokens from text processed by the tokenizer and padded to the
maximum length accepted by the model. Model outputs are two tensors:
last_hidden_state - hidden state from the last MultiHeadAttention
layer in the model and pooler_out - pooled output for whole model
hidden states.
TEXT_ENCODER_PATH = models_dir / "text_encoder.xml"
if not TEXT_ENCODER_PATH.exists():
with torch.no_grad():
ov_model = ov.convert_model(
text_encoder,
example_input=torch.ones([1, 77], dtype=torch.long),
input=[
(1, 77),
],
)
ov.save_model(ov_model, TEXT_ENCODER_PATH)
del ov_model
cleanup_torchscript_cache()
tokenizer.save_pretrained(models_dir / "tokenizer")
del text_encoder
del tokenizer
/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_attn_mask_utils.py:88: 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 input_shape[-1] > 1 or self.sliding_window is not None:
/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_attn_mask_utils.py:164: 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 past_key_values_length > 0:
Prepare OpenVINO inference pipeline#
As shown on diagram below, the only difference between original Stable
Diffusion pipeline and IP-Adapter Stable Diffusion pipeline only in
additional conditioning by image processed via Image Encoder.
The stable diffusion model with ip-adapter takes a latent image representation, a text prompt is transformed to text embeddings via CLIP text encoder and ip-adapter image is transformed to image embeddings via CLIP Image Encoder. Next, the U-Net iteratively denoises the random latent image representations while being conditioned on the text and image embeddings. The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm.
The denoising process is repeated given number of times (by default 4 taking into account that we use LCM) to step-by-step retrieve better latent image representations. When complete, the latent image representation is decoded by the decoder part of the variational auto encoder (VAE).
import inspect
from typing import List, Optional, Union, Dict, Tuple
import numpy as np
import PIL
import cv2
import torch
from transformers import CLIPTokenizer, CLIPImageProcessor
from diffusers import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.pipeline_output import (
StableDiffusionPipelineOutput,
)
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
def scale_fit_to_window(dst_width: int, dst_height: int, image_width: int, image_height: int):
"""
Preprocessing helper function for calculating image size for resize with peserving original aspect ratio
and fitting image to specific window size
Parameters:
dst_width (int): destination window width
dst_height (int): destination window height
image_width (int): source image width
image_height (int): source image height
Returns:
result_width (int): calculated width for resize
result_height (int): calculated height for resize
"""
im_scale = min(dst_height / image_height, dst_width / image_width)
return int(im_scale * image_width), int(im_scale * image_height)
def randn_tensor(
shape: Union[Tuple, List],
generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
dtype: Optional["torch.dtype"] = None,
):
"""A helper function to create random tensors on the desired `device` with the desired `dtype`. When
passing a list of generators, you can seed each batch size individually.
"""
batch_size = shape[0]
rand_device = torch.device("cpu")
# make sure generator list of length 1 is treated like a non-list
if isinstance(generator, list) and len(generator) == 1:
generator = generator[0]
if isinstance(generator, list):
shape = (1,) + shape[1:]
latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype) for i in range(batch_size)]
latents = torch.cat(latents, dim=0)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype)
return latents
def preprocess(image: PIL.Image.Image, height, width):
"""
Image preprocessing function. Takes image in PIL.Image format, resizes it to keep aspect ration and fits to model input window 512x512,
then converts it to np.ndarray and adds padding with zeros on right or bottom side of image (depends from aspect ratio), after that
converts data to float32 data type and change range of values from [0, 255] to [-1, 1], finally, converts data layout from planar NHWC to NCHW.
The function returns preprocessed input tensor and padding size, which can be used in postprocessing.
Parameters:
image (PIL.Image.Image): input image
Returns:
image (np.ndarray): preprocessed image tensor
meta (Dict): dictionary with preprocessing metadata info
"""
src_width, src_height = image.size
dst_width, dst_height = scale_fit_to_window(height, width, src_width, src_height)
image = np.array(image.resize((dst_width, dst_height), resample=PIL.Image.Resampling.LANCZOS))[None, :]
pad_width = width - dst_width
pad_height = height - dst_height
pad = ((0, 0), (0, pad_height), (0, pad_width), (0, 0))
image = np.pad(image, pad, mode="constant")
image = image.astype(np.float32) / 255.0
image = 2.0 * image - 1.0
image = image.transpose(0, 3, 1, 2)
return image, {"padding": pad, "src_width": src_width, "src_height": src_height}
class OVStableDiffusionPipeline(DiffusionPipeline):
def __init__(
self,
vae_decoder: ov.Model,
text_encoder: ov.Model,
tokenizer: CLIPTokenizer,
unet: ov.Model,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
image_encoder: ov.Model,
feature_extractor: CLIPImageProcessor,
vae_encoder: ov.Model,
):
"""
Pipeline for text-to-image generation using Stable Diffusion and IP-Adapter with OpenVINO
Parameters:
vae_decoder (ov.Model):
Variational Auto-Encoder (VAE) Model to decode images to and from latent representations.
text_encoder (ov.Model):CLIPImageProcessor
Frozen text-encoder. Stable Diffusion uses the text portion of
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the clip-vit-large-patch14(https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer (CLIPTokenizer):
Tokenizer of class CLIPTokenizer(https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
unet (ov.Model): Conditional U-Net architecture to denoise the encoded image latents.
scheduler (SchedulerMixin):
A scheduler to be used in combination with unet to denoise the encoded image latents
image_encoder (ov.Model):
IP-Adapter image encoder for embedding input image as input prompt for generation
feature_extractor :
"""
super().__init__()
self.scheduler = scheduler
self.vae_decoder = vae_decoder
self.image_encoder = image_encoder
self.text_encoder = text_encoder
self.unet = unet
self.height = 512
self.width = 512
self.vae_scale_factor = 8
self.tokenizer = tokenizer
self.vae_encoder = vae_encoder
self.feature_extractor = feature_extractor
def __call__(
self,
prompt: Union[str, List[str]],
ip_adapter_image: PIL.Image.Image,
image: PIL.Image.Image = None,
num_inference_steps: Optional[int] = 4,
negative_prompt: Union[str, List[str]] = None,
guidance_scale: Optional[float] = 0.5,
eta: Optional[float] = 0.0,
output_type: Optional[str] = "pil",
height: Optional[int] = None,
width: Optional[int] = None,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
strength: float = 1.0,
**kwargs,
):
"""
Function invoked when calling the pipeline for generation.
Parameters:
prompt (str or List[str]):
The prompt or prompts to guide the image generation.
image (PIL.Image.Image, *optional*, None):
Intinal image for generation.
num_inference_steps (int, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
negative_prompt (str or List[str]):https://user-images.githubusercontent.com/29454499/258651862-28b63016-c5ff-4263-9da8-73ca31100165.jpeg
The negative prompt or prompts to guide the image generation.
guidance_scale (float, *optional*, defaults to 7.5):
Guidance scale as defined in Classifier-Free Diffusion Guidance(https://arxiv.org/abs/2207.12598).
guidance_scale is defined as `w` of equation 2.
Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality.
eta (float, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[DDIMScheduler], will be ignored for others.
output_type (`str`, *optional*, defaults to "pil"):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): PIL.Image.Image or np.array.
height (int, *optional*, 512):
Generated image height
width (int, *optional*, 512):
Generated image width
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
Returns:
Dictionary with keys:
sample - the last generated image PIL.Image.Image or np.arrayhttps://huggingface.co/latent-consistency/lcm-lora-sdv1-5
iterations - *optional* (if gif=True) images for all diffusion steps, List of PIL.Image.Image or np.array.
"""
do_classifier_free_guidance = guidance_scale > 1.0
# get prompt text embeddings
text_embeddings = self._encode_prompt(
prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
)
# get ip-adapter image embeddings
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image)
if do_classifier_free_guidance:
image_embeds = np.concatenate([negative_image_embeds, image_embeds])
# set timesteps
accepts_offset = "offset" in set(inspect.signature(self.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
latent_timestep = timesteps[:1]
# get the initial random noise unless the user supplied it
latents, meta = self.prepare_latents(
1,
4,
height or self.height,
width or self.width,
generator=generator,
latents=latents,
image=image,
latent_timestep=latent_timestep,
)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if you are doing classifier free guidance
latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet([latent_model_input, t, text_embeddings, image_embeds])[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred[0], noise_pred[1]
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
torch.from_numpy(noise_pred),
t,
torch.from_numpy(latents),
**extra_step_kwargs,
)["prev_sample"].numpy()
# scale and decode the image latents with vae
image = self.vae_decoder(latents * (1 / 0.18215))[0]
image = self.postprocess_image(image, meta, output_type)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=False)
def _encode_prompt(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt: Union[str, List[str]] = None,
):
"""
Encodes the prompt into text encoder hidden states.
Parameters:
prompt (str or list(str)): prompt to be encoded
num_images_per_prompt (int): number of images that should be generated per prompt
do_classifier_free_guidance (bool): whether to use classifier free guidance or not
negative_prompt (str or list(str)): negative prompt to be encoded.
Returns:
text_embeddings (np.ndarray): text encoder hidden states
"""
batch_size = len(prompt) if isinstance(prompt, list) else 1
# tokenize input prompts
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
text_input_ids = text_inputs.input_ids
text_embeddings = self.text_encoder(text_input_ids)[0]
# duplicate text embeddings for each generation per prompt
if num_images_per_prompt != 1:
bs_embed, seq_len, _ = text_embeddings.shape
text_embeddings = np.tile(text_embeddings, (1, num_images_per_prompt, 1))
text_embeddings = np.reshape(text_embeddings, (bs_embed * num_images_per_prompt, seq_len, -1))
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
max_length = text_input_ids.shape[-1]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
else:
uncond_tokens = negative_prompt
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="np",
)
uncond_embeddings = self.text_encoder(uncond_input.input_ids)[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_embeddings.shape[1]
uncond_embeddings = np.tile(uncond_embeddings, (1, num_images_per_prompt, 1))
uncond_embeddings = np.reshape(uncond_embeddings, (batch_size * num_images_per_prompt, seq_len, -1))
# For classifier-free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
text_embeddings = np.concatenate([uncond_embeddings, text_embeddings])
return text_embeddings
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype=torch.float32,
generator=None,
latents=None,
image=None,
latent_timestep=None,
):
shape = (
batch_size,
num_channels_latents,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, dtype=dtype)
if image is None:
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents.numpy(), {}
input_image, meta = preprocess(image, height, width)
image_latents = self.vae_encoder(input_image)[0]
image_latents = image_latents * 0.18215
latents = self.scheduler.add_noise(torch.from_numpy(image_latents), latents, latent_timestep).numpy()
return latents, meta
def postprocess_image(self, image: np.ndarray, meta: Dict, output_type: str = "pil"):
"""
Postprocessing for decoded image. Takes generated image decoded by VAE decoder, unpad it to initial image size (if required),
normalize and convert to [0, 255] pixels range. Optionally, converts it from np.ndarray to PIL.Image format
Parameters:
image (np.ndarray):
Generated image
meta (Dict):
Metadata obtained on the latents preparing step can be empty
output_type (str, *optional*, pil):
Output format for result, can be pil or numpy
Returns:
image (List of np.ndarray or PIL.Image.Image):
Post-processed images
"""
if "padding" in meta:
pad = meta["padding"]
(_, end_h), (_, end_w) = pad[1:3]
h, w = image.shape[2:]
unpad_h = h - end_h
unpad_w = w - end_w
image = image[:, :, :unpad_h, :unpad_w]
image = np.clip(image / 2 + 0.5, 0, 1)
image = np.transpose(image, (0, 2, 3, 1))
# 9. Convert to PIL
if output_type == "pil":
image = self.numpy_to_pil(image)
if "src_height" in meta:
orig_height, orig_width = meta["src_height"], meta["src_width"]
image = [img.resize((orig_width, orig_height), PIL.Image.Resampling.LANCZOS) for img in image]
else:
if "src_height" in meta:
orig_height, orig_width = meta["src_height"], meta["src_width"]
image = [cv2.resize(img, (orig_width, orig_width)) for img in image]
return image
def encode_image(self, image, num_images_per_prompt=1):
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image_embeds = self.image_encoder(image)[0]
if num_images_per_prompt > 1:
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = np.zeros(image_embeds.shape)
return image_embeds, uncond_image_embeds
def get_timesteps(self, num_inference_steps: int, strength: float):
"""
Helper function for getting scheduler timesteps for generation
In case of image-to-image generation, it updates number of steps according to strength
Parameters:
num_inference_steps (int):
number of inference steps for generation
strength (float):
value between 0.0 and 1.0, that controls the amount of noise that is added to the input image.
Values that approach 1.0 allow for lots of variations but will also produce images that are not semantically consistent with the input.
"""
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
Run model inference#
Now let’s configure our pipeline and take a look on generation results.
Select inference device#
Select inference device from dropdown list.
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')
from transformers import AutoTokenizer
core = ov.Core()
ov_config = {"INFERENCE_PRECISION_HINT": "f32"} if device.value != "CPU" else {}
vae_decoder = core.compile_model(VAE_DECODER_PATH, device.value, ov_config)
vae_encoder = core.compile_model(VAE_ENCODER_PATH, device.value, ov_config)
text_encoder = core.compile_model(TEXT_ENCODER_PATH, device.value)
image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device.value)
unet = core.compile_model(UNET_PATH, device.value)
scheduler = LCMScheduler.from_pretrained(models_dir / "scheduler")
tokenizer = AutoTokenizer.from_pretrained(models_dir / "tokenizer")
feature_extractor = CLIPImageProcessor.from_pretrained(models_dir / "feature_extractor")
ov_pipe = OVStableDiffusionPipeline(
vae_decoder,
text_encoder,
tokenizer,
unet,
scheduler,
image_encoder,
feature_extractor,
vae_encoder,
)
The config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.
Generation image variation#
If we stay input text prompt empty and provide only ip-adapter image, we can get variation of the same image.
import matplotlib.pyplot as plt
def visualize_results(images, titles):
"""
Helper function for results visualization
Parameters:
orig_img (PIL.Image.Image): original image
processed_img (PIL.Image.Image): processed image after editing
img1_title (str): title for the image on the left
img2_title (str): title for the image on the right
Returns:
fig (matplotlib.pyplot.Figure): matplotlib generated figure contains drawing result
"""
im_w, im_h = images[0].size
is_horizontal = im_h <= im_w
figsize = (10, 15 * len(images)) if is_horizontal else (15 * len(images), 10)
fig, axs = plt.subplots(
1 if is_horizontal else len(images),
len(images) if is_horizontal else 1,
figsize=figsize,
sharex="all",
sharey="all",
)
fig.patch.set_facecolor("white")
list_axes = list(axs.flat)
for a in list_axes:
a.set_xticklabels([])
a.set_yticklabels([])
a.get_xaxis().set_visible(False)
a.get_yaxis().set_visible(False)
a.grid(False)
for image, title, ax in zip(images, titles, list_axes):
ax.imshow(np.array(image))
ax.set_title(title, fontsize=20)
fig.subplots_adjust(wspace=0.0 if is_horizontal else 0.01, hspace=0.01 if is_horizontal else 0.0)
fig.tight_layout()
return fig
generator = torch.Generator(device="cpu").manual_seed(576)
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
result = ov_pipe(
prompt="",
ip_adapter_image=image,
gaidance_scale=1,
negative_prompt="",
num_inference_steps=4,
generator=generator,
)
fig = visualize_results([image, result.images[0]], ["input image", "result"])
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Generation conditioned by image and text#
IP-Adapter allows you to use both image and text to condition the image generation process. Both IP-Adapter image and text prompt serve as extension for each other, for example we can use a text prompt to add “sunglasses” 😎 on previous image.
generator = torch.Generator(device="cpu").manual_seed(576)
result = ov_pipe(
prompt="best quality, high quality, wearing sunglasses",
ip_adapter_image=image,
gaidance_scale=1,
negative_prompt="monochrome, low-res, bad anatomy, worst quality, low quality",
num_inference_steps=4,
generator=generator,
)
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fig = visualize_results([image, result.images[0]], ["input image", "result"])
Generation image blending#
IP-Adapter also works great with Image-to-Image translation. It helps to achieve image blending effect.
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
result = ov_pipe(
prompt="best quality, high quality",
image=image,
ip_adapter_image=ip_image,
gaidance_scale=1,
generator=generator,
strength=0.7,
num_inference_steps=8,
)
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fig = visualize_results([image, ip_image, result.images[0]], ["input image", "ip-adapter image", "result"])
Interactive demo#
Now, you can try model using own images and text prompts.
import requests
from pathlib import Path
if not Path("gradio_helper.py").exists():
r = requests.get(url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-diffusion-ip-adapter/gradio_helper.py")
open("gradio_helper.py", "w").write(r.text)
from gradio_helper import make_demo
demo = make_demo(ov_pipe)
try:
demo.queue().launch(debug=False)
except Exception:
demo.queue().launch(share=True, debug=False)
# 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().