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| 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 = "controlnet" | |
| title: str = "Text-to-Image LCM + LoRa" | |
| 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) | |
| 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: | |
| 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] | |