from diffusers import ( StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, LCMScheduler, AutoencoderKL, AutoencoderTiny, ) from compel import Compel, ReturnedEmbeddingsType 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 controlnet_model = "diffusers/controlnet-canny-sdxl-1.0" model_id = "stabilityai/stable-diffusion-xl-base-1.0" lcm_lora_id = "latent-consistency/lcm-lora-sdxl" taesd_model = "madebyollin/taesdxl" default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece" default_negative_prompt = "blurry, low quality, render, 3D, oversaturated" page_content = """

Real-Time Latent Consistency Model SDXL

SDXL + LCM + LoRA + Controlnet

This demo showcases LCM LoRA + SDXL + Controlnet + 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+loras+sdxl" title: str = "SDXL + LCM + LoRA + 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", ) negative_prompt: str = Field( default_negative_prompt, title="Negative Prompt", field="textarea", id="negative_prompt", hide=True, ) seed: int = Field( 2159232, min=0, title="Seed", field="seed", hide=True, id="seed" ) steps: int = Field( 1, min=1, max=10, 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( 1.0, min=0, max=2.0, step=0.001, title="Guidance Scale", field="range", hide=True, id="guidance_scale", ) strength: float = Field( 1, min=0.25, max=1.0, step=0.0001, title="Strength", field="range", hide=True, id="strength", ) controlnet_scale: float = Field( 0.5, 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) vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype ) if args.safety_checker: self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( model_id, controlnet=controlnet_canny, vae=vae, ) else: self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( model_id, safety_checker=None, controlnet=controlnet_canny, vae=vae, ) self.canny_torch = SobelOperator(device=device) # Load LCM LoRA self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") self.pipe.load_lora_weights( "CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy", ) self.pipe.set_adapters(["lcm", "toy"], adapter_weights=[1.0, 0.8]) 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 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) if device.type != "mps": self.pipe.unet.to(memory_format=torch.channels_last) if args.compel: self.pipe.compel_proc = Compel( tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2], text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2], returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, requires_pooled=[False, 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 negative_prompt = params.negative_prompt prompt_embeds = None pooled_prompt_embeds = None negative_prompt_embeds = None negative_pooled_prompt_embeds = None if hasattr(self.pipe, "compel_proc"): _prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc( [params.prompt, params.negative_prompt] ) prompt = None negative_prompt = None prompt_embeds = _prompt_embeds[0:1] pooled_prompt_embeds = pooled_prompt_embeds[0:1] negative_prompt_embeds = _prompt_embeds[1:2] negative_pooled_prompt_embeds = pooled_prompt_embeds[1:2] 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, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_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: # 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