| 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 |
| except: |
| pass |
|
|
| import psutil |
| from config import Args |
| from pydantic import BaseModel, Field |
| from PIL import Image |
| import math |
| import time |
|
|
| |
| taesd_model = "madebyollin/taesd" |
| controlnet_model = "thibaud/controlnet-sd21-canny-diffusers" |
| base_model = "stabilityai/sd-turbo" |
|
|
| default_prompt = "Portrait of The Joker halloween costume, face painting, 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 SDv2.1 Turbo</h1> |
| <h3 class="text-xl font-bold">Image-to-Image ControlNet</h3> |
| <p class="text-sm"> |
| This demo showcases |
| <a |
| href="https://huggingface.co/stabilityai/sd-turbo" |
| target="_blank" |
| class="text-blue-500 underline hover:no-underline">SD Turbo</a> |
| Image to Image pipeline using |
| <a |
| href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/sdxl_turbo" |
| 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+sd15Turbo" |
| title: str = "SDv1.5 Turbo + 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", |
| ) |
| seed: int = Field( |
| 4402026899276587, 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( |
| 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( |
| 1.21, |
| min=0, |
| max=10, |
| step=0.001, |
| title="Guidance Scale", |
| field="range", |
| hide=True, |
| id="guidance_scale", |
| ) |
| strength: float = Field( |
| 0.8, |
| min=0.10, |
| max=1.0, |
| step=0.001, |
| title="Strength", |
| field="range", |
| hide=True, |
| id="strength", |
| ) |
| controlnet_scale: float = Field( |
| 0.2, |
| 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: |
| self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( |
| base_model, |
| controlnet=controlnet_canny, |
| ) |
| else: |
| self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( |
| base_model, |
| controlnet=controlnet_canny, |
| safety_checker=None, |
| ) |
|
|
| if args.use_taesd: |
| self.pipe.vae = AutoencoderTiny.from_pretrained( |
| taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
| ).to(device) |
| self.canny_torch = SobelOperator(device=device) |
|
|
| 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, |
| text_encoder=self.pipe.text_encoder, |
| truncate_long_prompts=True, |
| ) |
| if args.use_taesd: |
| self.pipe.vae = AutoencoderTiny.from_pretrained( |
| taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
| ).to(device) |
|
|
| 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", |
| 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) |
| prompt_embeds = self.pipe.compel_proc(params.prompt) |
| 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)) |
| last_time = time.time() |
| results = self.pipe( |
| image=params.image, |
| control_image=control_image, |
| 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, |
| ) |
| print(f"Time taken: {time.time() - last_time}") |
|
|
| 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: |
| |
| 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 |
|
|
