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Running
on
A100
| from diffusers import ( | |
| StableDiffusionControlNetImg2ImgPipeline, | |
| ControlNetModel, | |
| LCMScheduler, | |
| AutoencoderTiny, | |
| ) | |
| from compel import Compel | |
| 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 | |
| taesd_model = "madebyollin/taesd" | |
| controlnet_model = "lllyasviel/control_v11p_sd15_canny" | |
| # base model with activation token, it will prepend the prompt with the activation token | |
| base_models = { | |
| "plasmo/woolitize": "woolitize", | |
| "nitrosocke/Ghibli-Diffusion": "ghibli style", | |
| "nitrosocke/mo-di-diffusion": "modern disney style", | |
| } | |
| lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" | |
| 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" | |
| page_content = """ | |
| <h1 class="text-3xl font-bold">Real-Time Latent Consistency Model SDv1.5</h1> | |
| <h3 class="text-xl font-bold">LCM + LoRA + Controlnet + Canny</h3> | |
| <p class="text-sm"> | |
| This demo showcases | |
| <a | |
| href="https://huggingface.co/blog/lcm_lora" | |
| target="_blank" | |
| class="text-blue-500 underline hover:no-underline">LCM LoRA</a> | |
| + ControlNet + Image to Imasge pipeline using | |
| <a | |
| href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm#performing-inference-with-lcm" | |
| target="_blank" | |
| class="text-blue-500 underline hover:no-underline">Diffusers</a | |
| > with a MJPEG stream server. | |
| </p> | |
| <p class="text-sm text-gray-500"> | |
| Change the prompt to generate different images, accepts <a | |
| href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" | |
| target="_blank" | |
| class="text-blue-500 underline hover:no-underline">Compel</a | |
| > syntax. | |
| </p> | |
| """ | |
| class Pipeline: | |
| class Info(BaseModel): | |
| name: str = "controlnet+loras+sd15" | |
| title: str = "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", | |
| ) | |
| base_model_id: str = Field( | |
| "plasmo/woolitize", | |
| title="Base Model", | |
| values=list(base_models.keys()), | |
| field="select", | |
| id="base_model_id", | |
| ) | |
| seed: int = Field( | |
| 2159232, min=0, title="Seed", field="seed", hide=True, id="seed" | |
| ) | |
| steps: int = Field( | |
| 1, min=1, max=15, title="Steps", field="range", hide=True, id="steps" | |
| ) | |
| width: int = Field( | |
| 768, min=2, max=15, title="Width", disabled=True, hide=True, id="width" | |
| ) | |
| height: int = Field( | |
| 768, min=2, max=15, title="Height", disabled=True, hide=True, id="height" | |
| ) | |
| guidance_scale: float = Field( | |
| 1.0, | |
| min=0, | |
| max=2, | |
| 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", | |
| ) | |
| controlnet_scale: float = Field( | |
| 0.8, | |
| 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) | |
| self.pipes = {} | |
| if args.safety_checker: | |
| for base_model_id in base_models.keys(): | |
| pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( | |
| base_model_id, | |
| controlnet=controlnet_canny, | |
| ) | |
| self.pipes[base_model_id] = pipe | |
| else: | |
| for base_model_id in base_models.keys(): | |
| pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( | |
| base_model_id, | |
| safety_checker=None, | |
| controlnet=controlnet_canny, | |
| ) | |
| self.pipes[base_model_id] = pipe | |
| self.canny_torch = SobelOperator(device=device) | |
| for pipe in self.pipes.values(): | |
| pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) | |
| pipe.set_progress_bar_config(disable=True) | |
| if device.type != "mps": | |
| pipe.unet.to(memory_format=torch.channels_last) | |
| if args.taesd: | |
| pipe.vae = AutoencoderTiny.from_pretrained( | |
| taesd_model, torch_dtype=torch_dtype, use_safetensors=True | |
| ).to(device) | |
| # Load LCM LoRA | |
| pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") | |
| pipe.to(device=device, dtype=torch_dtype).to(device) | |
| if args.compel: | |
| self.compel_proc = Compel( | |
| tokenizer=pipe.tokenizer, | |
| text_encoder=pipe.text_encoder, | |
| truncate_long_prompts=False, | |
| ) | |
| if args.torch_compile: | |
| pipe.unet = torch.compile( | |
| pipe.unet, mode="reduce-overhead", fullgraph=True | |
| ) | |
| pipe.vae = torch.compile( | |
| pipe.vae, mode="reduce-overhead", fullgraph=True | |
| ) | |
| 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) | |
| pipe = self.pipes[params.base_model_id] | |
| activation_token = base_models[params.base_model_id] | |
| prompt = f"{activation_token} {params.prompt}" | |
| prompt_embeds = None | |
| prompt = params.prompt | |
| if hasattr(self, "compel_proc"): | |
| prompt_embeds = self.compel_proc(prompt) | |
| prompt = None | |
| 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 = pipe( | |
| image=params.image, | |
| control_image=control_image, | |
| prompt=prompt, | |
| prompt_embeds=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 | |