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| from diffusers import ( | |
| AutoPipelineForImage2Image, | |
| AutoencoderTiny, | |
| ) | |
| 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 | |
| import math | |
| from sfast.compilers.stable_diffusion_pipeline_compiler import ( | |
| compile, | |
| CompilationConfig, | |
| ) | |
| base_model = "stabilityai/sd-turbo" | |
| taesd_model = "madebyollin/taesd" | |
| default_prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux" | |
| default_negative_prompt = "blurry, low quality, render, 3D, oversaturated" | |
| page_content = """ | |
| <h1 class="text-3xl font-bold">Real-Time SD-Turbo</h1> | |
| <h3 class="text-xl font-bold">Image-to-Image</h3> | |
| <p class="text-sm"> | |
| This demo showcases | |
| <a | |
| href="https://huggingface.co/stabilityai/sdxl-turbo" | |
| target="_blank" | |
| class="text-blue-500 underline hover:no-underline">SDXL 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 = "img2img" | |
| title: str = "Image-to-Image SDXL" | |
| 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", | |
| ) | |
| negative_prompt: str = Field( | |
| default_negative_prompt, | |
| title="Negative Prompt", | |
| field="textarea", | |
| id="negative_prompt", | |
| hide=True, | |
| ) | |
| 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( | |
| 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" | |
| ) | |
| 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.taesd: | |
| self.pipe.vae = AutoencoderTiny.from_pretrained( | |
| taesd_model, torch_dtype=torch_dtype, use_safetensors=True | |
| ).to(device) | |
| if args.sfast: | |
| print("\nRunning sfast compile\n") | |
| from sfast.compilers.stable_diffusion_pipeline_compiler import ( | |
| compile, | |
| CompilationConfig, | |
| ) | |
| config = CompilationConfig.Default() | |
| config.enable_xformers = True | |
| config.enable_triton = True | |
| config.enable_cuda_graph = True | |
| self.pipe = compile(self.pipe, config=config) | |
| if args.onediff: | |
| print("\nRunning onediff compile\n") | |
| from onediff.infer_compiler import oneflow_compile | |
| self.pipe.unet = oneflow_compile(self.pipe.unet) | |
| self.pipe.vae.encoder = oneflow_compile(self.pipe.vae.encoder) | |
| self.pipe.vae.decoder = oneflow_compile(self.pipe.vae.decoder) | |
| 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) | |
| 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))], | |
| ) | |
| if args.compel: | |
| from compel import Compel | |
| self.pipe.compel_proc = Compel( | |
| tokenizer=self.pipe.tokenizer, | |
| text_encoder=self.pipe.text_encoder, | |
| truncate_long_prompts=True, | |
| ) | |
| def predict(self, params: "Pipeline.InputParams") -> Image.Image: | |
| generator = torch.manual_seed(params.seed) | |
| steps = params.steps | |
| strength = params.strength | |
| if int(steps * strength) < 1: | |
| steps = math.ceil(1 / max(0.10, strength)) | |
| prompt = params.prompt | |
| prompt_embeds = None | |
| if hasattr(self.pipe, "compel_proc"): | |
| prompt_embeds = self.pipe.compel_proc( | |
| [params.prompt, params.negative_prompt] | |
| ) | |
| prompt = None | |
| results = self.pipe( | |
| image=params.image, | |
| prompt_embeds=prompt_embeds, | |
| prompt=prompt, | |
| negative_prompt=params.negative_prompt, | |
| generator=generator, | |
| strength=strength, | |
| num_inference_steps=steps, | |
| guidance_scale=1.1, | |
| 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 | |