|
from diffusers import ( |
|
StableDiffusionControlNetImg2ImgPipeline, |
|
ControlNetModel, |
|
LCMScheduler, |
|
AutoencoderTiny, |
|
) |
|
from compel import Compel |
|
import torch |
|
from pipelines.utils.canny_gpu import SobelOperator |
|
|
|
try: |
|
import intel_extension_for_pytorch as ipex |
|
except: |
|
pass |
|
|
|
import psutil |
|
from config import Args |
|
from pydantic import BaseModel, Field |
|
from PIL import Image |
|
import math |
|
import time |
|
|
|
|
|
taesd_model = "madebyollin/taesd" |
|
controlnet_model = "thibaud/controlnet-sd21-canny-diffusers" |
|
base_model = "stabilityai/sd-turbo" |
|
|
|
default_prompt = "Portrait of The Joker halloween costume, face painting, with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece" |
|
page_content = """ |
|
<h1 class="text-3xl font-bold">Real-Time SDv2.1 Turbo</h1> |
|
<h3 class="text-xl font-bold">Image-to-Image ControlNet</h3> |
|
<p class="text-sm"> |
|
This demo showcases |
|
<a |
|
href="https://huggingface.co/stabilityai/sd-turbo" |
|
target="_blank" |
|
class="text-blue-500 underline hover:no-underline">SD Turbo</a> |
|
Image to Image pipeline using |
|
<a |
|
href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/sdxl_turbo" |
|
target="_blank" |
|
class="text-blue-500 underline hover:no-underline">Diffusers</a |
|
> with a MJPEG stream server. |
|
</p> |
|
<p class="text-sm text-gray-500"> |
|
Change the prompt to generate different images, accepts <a |
|
href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" |
|
target="_blank" |
|
class="text-blue-500 underline hover:no-underline">Compel</a |
|
> syntax. |
|
</p> |
|
""" |
|
|
|
|
|
class Pipeline: |
|
class Info(BaseModel): |
|
name: str = "controlnet+sd15Turbo" |
|
title: str = "SDv1.5 Turbo + Controlnet" |
|
description: str = "Generates an image from a text prompt" |
|
input_mode: str = "image" |
|
page_content: str = page_content |
|
|
|
class InputParams(BaseModel): |
|
prompt: str = Field( |
|
default_prompt, |
|
title="Prompt", |
|
field="textarea", |
|
id="prompt", |
|
) |
|
seed: int = Field( |
|
4402026899276587, min=0, title="Seed", field="seed", hide=True, id="seed" |
|
) |
|
steps: int = Field( |
|
1, min=1, max=15, title="Steps", field="range", hide=True, id="steps" |
|
) |
|
width: int = Field( |
|
512, min=2, max=15, title="Width", disabled=True, hide=True, id="width" |
|
) |
|
height: int = Field( |
|
512, min=2, max=15, title="Height", disabled=True, hide=True, id="height" |
|
) |
|
guidance_scale: float = Field( |
|
1.21, |
|
min=0, |
|
max=10, |
|
step=0.001, |
|
title="Guidance Scale", |
|
field="range", |
|
hide=True, |
|
id="guidance_scale", |
|
) |
|
strength: float = Field( |
|
0.8, |
|
min=0.10, |
|
max=1.0, |
|
step=0.001, |
|
title="Strength", |
|
field="range", |
|
hide=True, |
|
id="strength", |
|
) |
|
controlnet_scale: float = Field( |
|
0.325, |
|
min=0, |
|
max=1.0, |
|
step=0.001, |
|
title="Controlnet Scale", |
|
field="range", |
|
hide=True, |
|
id="controlnet_scale", |
|
) |
|
controlnet_start: float = Field( |
|
0.0, |
|
min=0, |
|
max=1.0, |
|
step=0.001, |
|
title="Controlnet Start", |
|
field="range", |
|
hide=True, |
|
id="controlnet_start", |
|
) |
|
controlnet_end: float = Field( |
|
1.0, |
|
min=0, |
|
max=1.0, |
|
step=0.001, |
|
title="Controlnet End", |
|
field="range", |
|
hide=True, |
|
id="controlnet_end", |
|
) |
|
canny_low_threshold: float = Field( |
|
0.31, |
|
min=0, |
|
max=1.0, |
|
step=0.001, |
|
title="Canny Low Threshold", |
|
field="range", |
|
hide=True, |
|
id="canny_low_threshold", |
|
) |
|
canny_high_threshold: float = Field( |
|
0.125, |
|
min=0, |
|
max=1.0, |
|
step=0.001, |
|
title="Canny High Threshold", |
|
field="range", |
|
hide=True, |
|
id="canny_high_threshold", |
|
) |
|
debug_canny: bool = Field( |
|
False, |
|
title="Debug Canny", |
|
field="checkbox", |
|
hide=True, |
|
id="debug_canny", |
|
) |
|
|
|
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): |
|
controlnet_canny = ControlNetModel.from_pretrained( |
|
controlnet_model, torch_dtype=torch_dtype |
|
).to(device) |
|
|
|
self.pipes = {} |
|
|
|
if args.safety_checker: |
|
self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( |
|
base_model, |
|
controlnet=controlnet_canny, |
|
) |
|
else: |
|
self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( |
|
base_model, |
|
controlnet=controlnet_canny, |
|
safety_checker=None, |
|
) |
|
|
|
if args.taesd: |
|
self.pipe.vae = AutoencoderTiny.from_pretrained( |
|
taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
|
).to(device) |
|
|
|
if args.sfast: |
|
from sfast.compilers.stable_diffusion_pipeline_compiler import ( |
|
compile, |
|
CompilationConfig, |
|
) |
|
|
|
config = CompilationConfig.Default() |
|
config.enable_xformers = True |
|
config.enable_triton = True |
|
config.enable_cuda_graph = True |
|
self.pipe = compile(self.pipe, config=config) |
|
|
|
self.canny_torch = SobelOperator(device=device) |
|
|
|
self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config) |
|
self.pipe.set_progress_bar_config(disable=True) |
|
self.pipe.to(device=device, dtype=torch_dtype).to(device) |
|
if device.type != "mps": |
|
self.pipe.unet.to(memory_format=torch.channels_last) |
|
|
|
if args.compel: |
|
from compel import Compel |
|
|
|
self.pipe.compel_proc = Compel( |
|
tokenizer=self.pipe.tokenizer, |
|
text_encoder=self.pipe.text_encoder, |
|
truncate_long_prompts=True, |
|
) |
|
|
|
if args.taesd: |
|
self.pipe.vae = AutoencoderTiny.from_pretrained( |
|
taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
|
).to(device) |
|
|
|
if args.torch_compile: |
|
self.pipe.unet = torch.compile( |
|
self.pipe.unet, mode="reduce-overhead", fullgraph=True |
|
) |
|
self.pipe.vae = torch.compile( |
|
self.pipe.vae, mode="reduce-overhead", fullgraph=True |
|
) |
|
self.pipe( |
|
prompt="warmup", |
|
image=[Image.new("RGB", (768, 768))], |
|
control_image=[Image.new("RGB", (768, 768))], |
|
) |
|
|
|
def predict(self, params: "Pipeline.InputParams") -> Image.Image: |
|
generator = torch.manual_seed(params.seed) |
|
prompt = params.prompt |
|
prompt_embeds = None |
|
if hasattr(self.pipe, "compel_proc"): |
|
prompt_embeds = self.pipe.compel_proc( |
|
[params.prompt, params.negative_prompt] |
|
) |
|
prompt = None |
|
control_image = self.canny_torch( |
|
params.image, params.canny_low_threshold, params.canny_high_threshold |
|
) |
|
steps = params.steps |
|
strength = params.strength |
|
if int(steps * strength) < 1: |
|
steps = math.ceil(1 / max(0.10, strength)) |
|
results = self.pipe( |
|
image=params.image, |
|
control_image=control_image, |
|
prompt=prompt, |
|
prompt_embeds=prompt_embeds, |
|
generator=generator, |
|
strength=strength, |
|
num_inference_steps=steps, |
|
guidance_scale=params.guidance_scale, |
|
width=params.width, |
|
height=params.height, |
|
output_type="pil", |
|
controlnet_conditioning_scale=params.controlnet_scale, |
|
control_guidance_start=params.controlnet_start, |
|
control_guidance_end=params.controlnet_end, |
|
) |
|
nsfw_content_detected = ( |
|
results.nsfw_content_detected[0] |
|
if "nsfw_content_detected" in results |
|
else False |
|
) |
|
if nsfw_content_detected: |
|
return None |
|
result_image = results.images[0] |
|
if params.debug_canny: |
|
|
|
w0, h0 = (200, 200) |
|
control_image = control_image.resize((w0, h0)) |
|
w1, h1 = result_image.size |
|
result_image.paste(control_image, (w1 - w0, h1 - h0)) |
|
|
|
return result_image |
|
|
