from diffusers import ( AutoPipelineForImage2Image, AutoencoderTiny, ) from compel import Compel 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 base_model = "SimianLuo/LCM_Dreamshaper_v7" taesd_model = "madebyollin/taesd" 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" class Pipeline: class Info(BaseModel): name: str = "img2img" title: str = "Image-to-Image LCM" description: str = "Generates an image from a text prompt" input_mode: str = "image" class InputParams(BaseModel): prompt: str = Field( default_prompt, title="Prompt", field="textarea", id="prompt", ) 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( 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( 0.2, min=0, max=20, step=0.001, title="Guidance Scale", field="range", hide=True, id="guidance_scale", ) strength: float = Field( 0.5, min=0.25, max=1.0, step=0.001, title="Strength", field="range", hide=True, id="strength", ) def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): if args.safety_checker: self.pipe = AutoPipelineForImage2Image.from_pretrained(base_model) else: self.pipe = AutoPipelineForImage2Image.from_pretrained( base_model, safety_checker=None, ) if args.use_taesd: self.pipe.vae = AutoencoderTiny.from_pretrained( taesd_model, torch_dtype=torch_dtype, use_safetensors=True ) self.pipe.set_progress_bar_config(disable=True) self.pipe.to(device=device, dtype=torch_dtype) if device.type != "mps": self.pipe.unet.to(memory_format=torch.channels_last) # check if computer has less than 64GB of RAM using sys or os if psutil.virtual_memory().total < 64 * 1024**3: self.pipe.enable_attention_slicing() if args.torch_compile: print("Running 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))], ) self.compel_proc = Compel( tokenizer=self.pipe.tokenizer, text_encoder=self.pipe.text_encoder, truncate_long_prompts=False, ) def predict(self, params: "Pipeline.InputParams") -> Image.Image: generator = torch.manual_seed(params.seed) prompt_embeds = self.compel_proc(params.prompt) results = self.pipe( image=params.image, prompt_embeds=prompt_embeds, generator=generator, strength=params.strength, 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