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 # type: ignore 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 = """

Real-Time SDv2.1 Turbo

Image-to-Image ControlNet

This demo showcases SDXL Turbo Image to Image pipeline using Diffusers with a MJPEG stream server.

Change the prompt to generate different images, accepts Compel syntax.

""" 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( 0.2, 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.use_taesd: self.pipe.vae = AutoencoderTiny.from_pretrained( taesd_model, torch_dtype=torch_dtype, use_safetensors=True ).to(device) 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 psutil.virtual_memory().total < 64 * 1024**3: self.pipe.enable_attention_slicing() self.pipe.compel_proc = Compel( tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder, truncate_long_prompts=True, ) if args.use_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_embeds = self.pipe.compel_proc(params.prompt) 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)) last_time = time.time() results = self.pipe( image=params.image, control_image=control_image, 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, ) print(f"Time taken: {time.time() - last_time}") 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: # paste control_image on top of result_image 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