from diffusers import DiffusionPipeline, AutoencoderTiny, LCMScheduler 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 = "wavymulder/Analog-Diffusion" lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" taesd_model = "madebyollin/taesd" default_prompt = "Analog style photograph of young Harrison Ford as Han Solo, star wars behind the scenes" class Pipeline: class Info(BaseModel): name: str = "txt2imglora" title: str = "txt2imglora" description: str = "Generates an image from a text prompt" input_mode: str = "text" class InputParams(BaseModel): prompt: str = Field( default_prompt, title="Prompt", field="textarea", id="prompt", ) seed: int = Field( 8638236174640251, 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=4, 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): if args.safety_checker: self.pipe = DiffusionPipeline.from_pretrained(base_model) else: self.pipe = DiffusionPipeline.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.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config) self.pipe.set_progress_bar_config(disable=True) self.pipe.to(device=device, dtype=torch_dtype) 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: 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", num_inference_steps=1, guidance_scale=8.0) self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") 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( prompt_embeds=prompt_embeds, 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 return results.images[0]