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  1. 2
      .gitignore
  2. 56
      requirements.txt
  3. 0
      utils/__init__.py
  4. 98
      utils/viewer.py
  5. 663
      utils/wrapper.py

2
.gitignore vendored
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.venv
engines
/__pycache__
/utils/__pycache__

56
requirements.txt
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accelerate==1.8.1
antlr4-python3-runtime==4.9.3
certifi==2022.12.7
charset-normalizer==2.1.1
colorama==0.4.6
colored==2.3.0
coloredlogs==15.0.1
cuda-bindings==12.9.0
cuda-python==12.9.0
diffusers==0.24.0
filelock==3.13.1
fire==0.7.0
flatbuffers==25.2.10
fsspec==2024.6.1
huggingface-hub==0.25.2
humanfriendly==10.0
idna==3.4
importlib_metadata==8.7.0
Jinja2==3.1.4
MarkupSafe==2.1.5
mpmath==1.3.0
networkx==3.3
numpy==1.26.4
oauthlib==3.3.1
omegaconf==2.3.0
onnx==1.15.0
onnx_graphsurgeon==0.5.8
onnxruntime==1.16.3
packaging==25.0
pillow==11.0.0
polygraphy==0.49.24
protobuf==3.20.2
psutil==7.0.0
PyOpenGL==3.1.9
pyreadline3==3.5.4
python-osc==1.9.3
pywin32==311
PyYAML==6.0.2
regex==2024.11.6
requests==2.28.1
requests-oauthlib==2.0.0
safetensors==0.5.3
SpoutGL==0.1.1
streamdiffusion @ git+https://github.com/cumulo-autumn/StreamDiffusion.git@b623251dc055e1fd858d53509aa43e09dfc5cdc0
sympy==1.13.3
termcolor==3.1.0
tokenizers==0.15.2
torch==2.1.0+cu121
torchvision==0.16.0+cu121
tqdm==4.67.1
transformers==4.35.2
twython==3.9.1
typing_extensions==4.12.2
urllib3==1.26.13
xformers==0.0.22.post7
zipp==3.23.0

0
utils/__init__.py
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98
utils/viewer.py
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import os
import sys
import threading
import time
import tkinter as tk
from multiprocessing import Queue
from typing import List
from PIL import Image, ImageTk
from streamdiffusion.image_utils import postprocess_image
sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
def update_image(image_data: Image.Image, label: tk.Label) -> None:
"""
Update the image displayed on a Tkinter label.
Parameters
----------
image_data : Image.Image
The image to be displayed.
label : tk.Label
The labels where the image will be updated.
"""
width = 512
height = 512
tk_image = ImageTk.PhotoImage(image_data, size=width)
label.configure(image=tk_image, width=width, height=height)
label.image = tk_image # keep a reference
def _receive_images(
queue: Queue, fps_queue: Queue, label: tk.Label, fps_label: tk.Label
) -> None:
"""
Continuously receive images from a queue and update the labels.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
label : tk.Label
The label to update with images.
fps_label : tk.Label
The label to show fps.
"""
while True:
try:
if not queue.empty():
label.after(
0,
update_image,
postprocess_image(queue.get(block=False), output_type="pil")[0],
label,
)
if not fps_queue.empty():
fps_label.config(text=f"FPS: {fps_queue.get(block=False):.2f}")
time.sleep(0.0005)
except KeyboardInterrupt:
return
def receive_images(queue: Queue, fps_queue: Queue) -> None:
"""
Setup the Tkinter window and start the thread to receive images.
Parameters
----------
queue : Queue
The queue to receive images from.
fps_queue : Queue
The queue to put the calculated fps.
"""
root = tk.Tk()
root.title("Image Viewer")
label = tk.Label(root)
fps_label = tk.Label(root, text="FPS: 0")
label.grid(column=0)
fps_label.grid(column=1)
def on_closing():
print("window closed")
root.quit() # stop event loop
return
thread = threading.Thread(
target=_receive_images, args=(queue, fps_queue, label, fps_label), daemon=True
)
thread.start()
try:
root.protocol("WM_DELETE_WINDOW", on_closing)
root.mainloop()
except KeyboardInterrupt:
return

663
utils/wrapper.py
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import gc
import os
from pathlib import Path
import traceback
from typing import List, Literal, Optional, Union, Dict
import numpy as np
import torch
from diffusers import AutoencoderTiny, StableDiffusionPipeline
from PIL import Image
from streamdiffusion import StreamDiffusion
from streamdiffusion.image_utils import postprocess_image
torch.set_grad_enabled(False)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
class StreamDiffusionWrapper:
def __init__(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
mode: Literal["img2img", "txt2img"] = "img2img",
output_type: Literal["pil", "pt", "np", "latent"] = "pil",
lcm_lora_id: Optional[str] = None,
vae_id: Optional[str] = None,
device: Literal["cpu", "cuda"] = "cuda",
dtype: torch.dtype = torch.float16,
frame_buffer_size: int = 1,
width: int = 512,
height: int = 512,
warmup: int = 10,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
do_add_noise: bool = True,
device_ids: Optional[List[int]] = None,
use_lcm_lora: bool = True,
use_tiny_vae: bool = True,
enable_similar_image_filter: bool = False,
similar_image_filter_threshold: float = 0.98,
similar_image_filter_max_skip_frame: int = 10,
use_denoising_batch: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
seed: int = 2,
use_safety_checker: bool = False,
engine_dir: Optional[Union[str, Path]] = "engines",
):
"""
Initializes the StreamDiffusionWrapper.
Parameters
----------
model_id_or_path : str
The model id or path to load.
t_index_list : List[int]
The t_index_list to use for inference.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by default None.
Keys are the LoRA names and values are the LoRA scales.
Example: {'LoRA_1' : 0.5 , 'LoRA_2' : 0.7 ,...}
mode : Literal["img2img", "txt2img"], optional
txt2img or img2img, by default "img2img".
output_type : Literal["pil", "pt", "np", "latent"], optional
The output type of image, by default "pil".
lcm_lora_id : Optional[str], optional
The lcm_lora_id to load, by default None.
If None, the default LCM-LoRA
("latent-consistency/lcm-lora-sdv1-5") will be used.
vae_id : Optional[str], optional
The vae_id to load, by default None.
If None, the default TinyVAE
("madebyollin/taesd") will be used.
device : Literal["cpu", "cuda"], optional
The device to use for inference, by default "cuda".
dtype : torch.dtype, optional
The dtype for inference, by default torch.float16.
frame_buffer_size : int, optional
The frame buffer size for denoising batch, by default 1.
width : int, optional
The width of the image, by default 512.
height : int, optional
The height of the image, by default 512.
warmup : int, optional
The number of warmup steps to perform, by default 10.
acceleration : Literal["none", "xformers", "tensorrt"], optional
The acceleration method, by default "tensorrt".
do_add_noise : bool, optional
Whether to add noise for following denoising steps or not,
by default True.
device_ids : Optional[List[int]], optional
The device ids to use for DataParallel, by default None.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA or not, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE or not, by default True.
enable_similar_image_filter : bool, optional
Whether to enable similar image filter or not,
by default False.
similar_image_filter_threshold : float, optional
The threshold for similar image filter, by default 0.98.
similar_image_filter_max_skip_frame : int, optional
The max skip frame for similar image filter, by default 10.
use_denoising_batch : bool, optional
Whether to use denoising batch or not, by default True.
cfg_type : Literal["none", "full", "self", "initialize"],
optional
The cfg_type for img2img mode, by default "self".
You cannot use anything other than "none" for txt2img mode.
seed : int, optional
The seed, by default 2.
use_safety_checker : bool, optional
Whether to use safety checker or not, by default False.
"""
self.sd_turbo = "turbo" in model_id_or_path
if mode == "txt2img":
if cfg_type != "none":
raise ValueError(
f"txt2img mode accepts only cfg_type = 'none', but got {cfg_type}"
)
if use_denoising_batch and frame_buffer_size > 1:
if not self.sd_turbo:
raise ValueError(
"txt2img mode cannot use denoising batch with frame_buffer_size > 1."
)
if mode == "img2img":
if not use_denoising_batch:
raise NotImplementedError(
"img2img mode must use denoising batch for now."
)
self.device = device
self.dtype = dtype
self.width = width
self.height = height
self.mode = mode
self.output_type = output_type
self.frame_buffer_size = frame_buffer_size
self.batch_size = (
len(t_index_list) * frame_buffer_size
if use_denoising_batch
else frame_buffer_size
)
self.use_denoising_batch = use_denoising_batch
self.use_safety_checker = use_safety_checker
self.stream: StreamDiffusion = self._load_model(
model_id_or_path=model_id_or_path,
lora_dict=lora_dict,
lcm_lora_id=lcm_lora_id,
vae_id=vae_id,
t_index_list=t_index_list,
acceleration=acceleration,
warmup=warmup,
do_add_noise=do_add_noise,
use_lcm_lora=use_lcm_lora,
use_tiny_vae=use_tiny_vae,
cfg_type=cfg_type,
seed=seed,
engine_dir=engine_dir,
)
if device_ids is not None:
self.stream.unet = torch.nn.DataParallel(
self.stream.unet, device_ids=device_ids
)
if enable_similar_image_filter:
self.stream.enable_similar_image_filter(similar_image_filter_threshold, similar_image_filter_max_skip_frame)
def prepare(
self,
prompt: str,
negative_prompt: str = "",
num_inference_steps: int = 50,
guidance_scale: float = 1.2,
delta: float = 1.0,
) -> None:
"""
Prepares the model for inference.
Parameters
----------
prompt : str
The prompt to generate images from.
num_inference_steps : int, optional
The number of inference steps to perform, by default 50.
guidance_scale : float, optional
The guidance scale to use, by default 1.2.
delta : float, optional
The delta multiplier of virtual residual noise,
by default 1.0.
"""
self.stream.prepare(
prompt,
negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
delta=delta,
)
def __call__(
self,
image: Optional[Union[str, Image.Image, torch.Tensor]] = None,
prompt: Optional[str] = None,
) -> Union[Image.Image, List[Image.Image]]:
"""
Performs img2img or txt2img based on the mode.
Parameters
----------
image : Optional[Union[str, Image.Image, torch.Tensor]]
The image to generate from.
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
Union[Image.Image, List[Image.Image]]
The generated image.
"""
if self.mode == "img2img":
return self.img2img(image, prompt)
else:
return self.txt2img(prompt)
def txt2img(
self, prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs txt2img.
Parameters
----------
prompt : Optional[str]
The prompt to generate images from.
Returns
-------
Union[Image.Image, List[Image.Image]]
The generated image.
"""
if prompt is not None:
self.stream.update_prompt(prompt)
if self.sd_turbo:
image_tensor = self.stream.txt2img_sd_turbo(self.batch_size)
else:
image_tensor = self.stream.txt2img(self.frame_buffer_size)
image = self.postprocess_image(image_tensor, output_type=self.output_type)
if self.use_safety_checker:
safety_checker_input = self.feature_extractor(
image, return_tensors="pt"
).to(self.device)
_, has_nsfw_concept = self.safety_checker(
images=image_tensor.to(self.dtype),
clip_input=safety_checker_input.pixel_values.to(self.dtype),
)
image = self.nsfw_fallback_img if has_nsfw_concept[0] else image
return image
def img2img(
self, image: Union[str, Image.Image, torch.Tensor], prompt: Optional[str] = None
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Performs img2img.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to generate from.
Returns
-------
Image.Image
The generated image.
"""
if prompt is not None:
self.stream.update_prompt(prompt)
if isinstance(image, str) or isinstance(image, Image.Image):
image = self.preprocess_image(image)
image_tensor = self.stream(image)
image = self.postprocess_image(image_tensor, output_type=self.output_type)
if self.use_safety_checker:
safety_checker_input = self.feature_extractor(
image, return_tensors="pt"
).to(self.device)
_, has_nsfw_concept = self.safety_checker(
images=image_tensor.to(self.dtype),
clip_input=safety_checker_input.pixel_values.to(self.dtype),
)
image = self.nsfw_fallback_img if has_nsfw_concept[0] else image
return image
def preprocess_image(self, image: Union[str, Image.Image]) -> torch.Tensor:
"""
Preprocesses the image.
Parameters
----------
image : Union[str, Image.Image, torch.Tensor]
The image to preprocess.
Returns
-------
torch.Tensor
The preprocessed image.
"""
if isinstance(image, str):
image = Image.open(image).convert("RGB").resize((self.width, self.height))
if isinstance(image, Image.Image):
image = image.convert("RGB").resize((self.width, self.height))
return self.stream.image_processor.preprocess(
image, self.height, self.width
).to(device=self.device, dtype=self.dtype)
def postprocess_image(
self, image_tensor: torch.Tensor, output_type: str = "pil"
) -> Union[Image.Image, List[Image.Image], torch.Tensor, np.ndarray]:
"""
Postprocesses the image.
Parameters
----------
image_tensor : torch.Tensor
The image tensor to postprocess.
Returns
-------
Union[Image.Image, List[Image.Image]]
The postprocessed image.
"""
if self.frame_buffer_size > 1:
return postprocess_image(image_tensor.cpu(), output_type=output_type)
else:
return postprocess_image(image_tensor.cpu(), output_type=output_type)[0]
def _load_model(
self,
model_id_or_path: str,
t_index_list: List[int],
lora_dict: Optional[Dict[str, float]] = None,
lcm_lora_id: Optional[str] = None,
vae_id: Optional[str] = None,
acceleration: Literal["none", "xformers", "tensorrt"] = "tensorrt",
warmup: int = 10,
do_add_noise: bool = True,
use_lcm_lora: bool = True,
use_tiny_vae: bool = True,
cfg_type: Literal["none", "full", "self", "initialize"] = "self",
seed: int = 2,
engine_dir: Optional[Union[str, Path]] = "engines",
) -> StreamDiffusion:
"""
Loads the model.
This method does the following:
1. Loads the model from the model_id_or_path.
2. Loads and fuses the LCM-LoRA model from the lcm_lora_id if needed.
3. Loads the VAE model from the vae_id if needed.
4. Enables acceleration if needed.
5. Prepares the model for inference.
6. Load the safety checker if needed.
Parameters
----------
model_id_or_path : str
The model id or path to load.
t_index_list : List[int]
The t_index_list to use for inference.
lora_dict : Optional[Dict[str, float]], optional
The lora_dict to load, by default None.
Keys are the LoRA names and values are the LoRA scales.
Example: {'LoRA_1' : 0.5 , 'LoRA_2' : 0.7 ,...}
lcm_lora_id : Optional[str], optional
The lcm_lora_id to load, by default None.
vae_id : Optional[str], optional
The vae_id to load, by default None.
acceleration : Literal["none", "xfomers", "sfast", "tensorrt"], optional
The acceleration method, by default "tensorrt".
warmup : int, optional
The number of warmup steps to perform, by default 10.
do_add_noise : bool, optional
Whether to add noise for following denoising steps or not,
by default True.
use_lcm_lora : bool, optional
Whether to use LCM-LoRA or not, by default True.
use_tiny_vae : bool, optional
Whether to use TinyVAE or not, by default True.
cfg_type : Literal["none", "full", "self", "initialize"],
optional
The cfg_type for img2img mode, by default "self".
You cannot use anything other than "none" for txt2img mode.
seed : int, optional
The seed, by default 2.
Returns
-------
StreamDiffusion
The loaded model.
"""
try: # Load from local directory
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_pretrained(
model_id_or_path,
).to(device=self.device, dtype=self.dtype)
except ValueError: # Load from huggingface
pipe: StableDiffusionPipeline = StableDiffusionPipeline.from_single_file(
model_id_or_path,
).to(device=self.device, dtype=self.dtype)
except Exception: # No model found
traceback.print_exc()
print("Model load has failed. Doesn't exist.")
exit()
stream = StreamDiffusion(
pipe=pipe,
t_index_list=t_index_list,
torch_dtype=self.dtype,
width=self.width,
height=self.height,
do_add_noise=do_add_noise,
frame_buffer_size=self.frame_buffer_size,
use_denoising_batch=self.use_denoising_batch,
cfg_type=cfg_type,
)
if not self.sd_turbo:
if use_lcm_lora:
if lcm_lora_id is not None:
stream.load_lcm_lora(
pretrained_model_name_or_path_or_dict=lcm_lora_id
)
else:
stream.load_lcm_lora()
stream.fuse_lora()
if lora_dict is not None:
for lora_name, lora_scale in lora_dict.items():
stream.load_lora(lora_name)
stream.fuse_lora(lora_scale=lora_scale)
print(f"Use LoRA: {lora_name} in weights {lora_scale}")
if use_tiny_vae:
if vae_id is not None:
stream.vae = AutoencoderTiny.from_pretrained(vae_id).to(
device=pipe.device, dtype=pipe.dtype
)
else:
stream.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd").to(
device=pipe.device, dtype=pipe.dtype
)
try:
if acceleration == "xformers":
stream.pipe.enable_xformers_memory_efficient_attention()
if acceleration == "tensorrt":
from polygraphy import cuda
from streamdiffusion.acceleration.tensorrt import (
TorchVAEEncoder,
compile_unet,
compile_vae_decoder,
compile_vae_encoder,
)
from streamdiffusion.acceleration.tensorrt.engine import (
AutoencoderKLEngine,
UNet2DConditionModelEngine,
)
from streamdiffusion.acceleration.tensorrt.models import (
VAE,
UNet,
VAEEncoder,
)
def create_prefix(
model_id_or_path: str,
max_batch_size: int,
min_batch_size: int,
):
maybe_path = Path(model_id_or_path)
if maybe_path.exists():
return f"{maybe_path.stem}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--mode-{self.mode}"
else:
return f"{model_id_or_path}--lcm_lora-{use_lcm_lora}--tiny_vae-{use_tiny_vae}--max_batch-{max_batch_size}--min_batch-{min_batch_size}--mode-{self.mode}"
engine_dir = Path(engine_dir)
unet_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=stream.trt_unet_batch_size,
min_batch_size=stream.trt_unet_batch_size,
),
"unet.engine",
)
vae_encoder_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
min_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
),
"vae_encoder.engine",
)
vae_decoder_path = os.path.join(
engine_dir,
create_prefix(
model_id_or_path=model_id_or_path,
max_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
min_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
),
"vae_decoder.engine",
)
if not os.path.exists(unet_path):
os.makedirs(os.path.dirname(unet_path), exist_ok=True)
unet_model = UNet(
fp16=True,
device=stream.device,
max_batch_size=stream.trt_unet_batch_size,
min_batch_size=stream.trt_unet_batch_size,
embedding_dim=stream.text_encoder.config.hidden_size,
unet_dim=stream.unet.config.in_channels,
)
compile_unet(
stream.unet,
unet_model,
unet_path + ".onnx",
unet_path + ".opt.onnx",
unet_path,
opt_batch_size=stream.trt_unet_batch_size,
)
if not os.path.exists(vae_decoder_path):
os.makedirs(os.path.dirname(vae_decoder_path), exist_ok=True)
stream.vae.forward = stream.vae.decode
vae_decoder_model = VAE(
device=stream.device,
max_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
min_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
)
compile_vae_decoder(
stream.vae,
vae_decoder_model,
vae_decoder_path + ".onnx",
vae_decoder_path + ".opt.onnx",
vae_decoder_path,
opt_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
)
delattr(stream.vae, "forward")
if not os.path.exists(vae_encoder_path):
os.makedirs(os.path.dirname(vae_encoder_path), exist_ok=True)
vae_encoder = TorchVAEEncoder(stream.vae).to(torch.device("cuda"))
vae_encoder_model = VAEEncoder(
device=stream.device,
max_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
min_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
)
compile_vae_encoder(
vae_encoder,
vae_encoder_model,
vae_encoder_path + ".onnx",
vae_encoder_path + ".opt.onnx",
vae_encoder_path,
opt_batch_size=self.batch_size
if self.mode == "txt2img"
else stream.frame_bff_size,
)
cuda_stream = cuda.Stream()
vae_config = stream.vae.config
vae_dtype = stream.vae.dtype
stream.unet = UNet2DConditionModelEngine(
unet_path, cuda_stream, use_cuda_graph=False
)
stream.vae = AutoencoderKLEngine(
vae_encoder_path,
vae_decoder_path,
cuda_stream,
stream.pipe.vae_scale_factor,
use_cuda_graph=False,
)
setattr(stream.vae, "config", vae_config)
setattr(stream.vae, "dtype", vae_dtype)
gc.collect()
torch.cuda.empty_cache()
print("TensorRT acceleration enabled.")
if acceleration == "sfast":
from streamdiffusion.acceleration.sfast import (
accelerate_with_stable_fast,
)
stream = accelerate_with_stable_fast(stream)
print("StableFast acceleration enabled.")
except Exception:
traceback.print_exc()
print("Acceleration has failed. Falling back to normal mode.")
if seed < 0: # Random seed
seed = np.random.randint(0, 1000000)
stream.prepare(
"",
"",
num_inference_steps=50,
guidance_scale=1.1
if stream.cfg_type in ["full", "self", "initialize"]
else 1.0,
generator=torch.manual_seed(seed),
seed=seed,
)
if self.use_safety_checker:
from transformers import CLIPFeatureExtractor
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
"CompVis/stable-diffusion-safety-checker"
).to(pipe.device)
self.feature_extractor = CLIPFeatureExtractor.from_pretrained(
"openai/clip-vit-base-patch32"
)
self.nsfw_fallback_img = Image.new("RGB", (512, 512), (0, 0, 0))
return stream
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