from diffusers import ( DiffusionPipeline, LCMScheduler, AutoencoderKL, ) from compel import Compel, ReturnedEmbeddingsType import torch 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 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" default_prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux" default_negative_prompt = "blurry, low quality, render, 3D, oversaturated" page_content = """

Real-Time Latent Consistency Model

Text-to-Image SDXL + LCM + LoRA

This demo showcases LCM LoRA Text 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 = "LCM+Lora+SDXL" title: str = "Text-to-Image SDXL + LCM + LoRA" description: str = "Generates an image from a text prompt" page_content: str = page_content input_mode: str = "text" 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( 4, min=2, max=15, title="Steps", field="range", hide=True, id="steps" ) width: int = Field( 1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width" ) height: int = Field( 1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height" ) guidance_scale: float = Field( 1.0, min=0, max=20, step=0.001, title="Guidance Scale", field="range", hide=True, id="guidance_scale", ) def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype ) if args.safety_checker: self.pipe = DiffusionPipeline.from_pretrained( model_id, vae=vae, ) else: self.pipe = DiffusionPipeline.from_pretrained( model_id, safety_checker=None, vae=vae, ) # Load LCM LoRA self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") 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, 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.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", ) def predict(self, params: "Pipeline.InputParams") -> Image.Image: generator = torch.manual_seed(params.seed) prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc( [params.prompt, params.negative_prompt] ) results = self.pipe( 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], generator=generator, num_inference_steps=params.steps, guidance_scale=params.guidance_scale, width=params.width, height=params.height, output_type="pil", ) 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] return result_image