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from diffusers import DiffusionPipeline, AutoencoderTiny, LCMScheduler |
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from compel import Compel |
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import torch |
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try: |
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import intel_extension_for_pytorch as ipex |
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except: |
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pass |
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import psutil |
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from config import Args |
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from pydantic import BaseModel, Field |
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from PIL import Image |
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base_model = "wavymulder/Analog-Diffusion" |
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lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" |
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taesd_model = "madebyollin/taesd" |
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default_prompt = "Analog style photograph of young Harrison Ford as Han Solo, star wars behind the scenes" |
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page_content = """ |
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<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model SDv1.5</h1> |
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<h3 class="text-xl font-bold">Text-to-Image LCM + LoRa</h3> |
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<p class="text-sm"> |
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This demo showcases |
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<a |
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href="https://huggingface.co/blog/lcm_lora" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">LCM</a> |
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Image to Image pipeline using |
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<a |
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href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm#performing-inference-with-lcm" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">Diffusers</a |
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> with a MJPEG stream server. Featuring <a |
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href="https://huggingface.co/wavymulder/Analog-Diffusion" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">Analog-Diffusion</a> |
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</p> |
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<p class="text-sm text-gray-500"> |
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Change the prompt to generate different images, accepts <a |
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href="https://github.com/damian0815/compel/blob/main/doc/syntax.md" |
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target="_blank" |
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class="text-blue-500 underline hover:no-underline">Compel</a |
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> syntax. |
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</p> |
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""" |
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class Pipeline: |
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class Info(BaseModel): |
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name: str = "controlnet" |
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title: str = "Text-to-Image LCM + LoRa" |
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description: str = "Generates an image from a text prompt" |
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input_mode: str = "text" |
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page_content: str = page_content |
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class InputParams(BaseModel): |
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prompt: str = Field( |
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default_prompt, |
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title="Prompt", |
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field="textarea", |
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id="prompt", |
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) |
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seed: int = Field( |
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8638236174640251, min=0, title="Seed", field="seed", hide=True, id="seed" |
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) |
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steps: int = Field( |
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4, min=2, max=15, title="Steps", field="range", hide=True, id="steps" |
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) |
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width: int = Field( |
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512, min=2, max=15, title="Width", disabled=True, hide=True, id="width" |
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) |
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height: int = Field( |
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512, min=2, max=15, title="Height", disabled=True, hide=True, id="height" |
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) |
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guidance_scale: float = Field( |
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0.2, |
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min=0, |
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max=4, |
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step=0.001, |
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title="Guidance Scale", |
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field="range", |
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hide=True, |
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id="guidance_scale", |
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) |
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def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): |
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if args.safety_checker: |
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self.pipe = DiffusionPipeline.from_pretrained(base_model) |
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else: |
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self.pipe = DiffusionPipeline.from_pretrained( |
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base_model, safety_checker=None |
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) |
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if args.taesd: |
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self.pipe.vae = AutoencoderTiny.from_pretrained( |
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taesd_model, torch_dtype=torch_dtype, use_safetensors=True |
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).to(device) |
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self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe.set_progress_bar_config(disable=True) |
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self.pipe.to(device=device, dtype=torch_dtype) |
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if device.type != "mps": |
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self.pipe.unet.to(memory_format=torch.channels_last) |
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if psutil.virtual_memory().total < 64 * 1024**3: |
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self.pipe.enable_attention_slicing() |
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if args.torch_compile: |
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self.pipe.unet = torch.compile( |
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self.pipe.unet, mode="reduce-overhead", fullgraph=True |
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) |
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self.pipe.vae = torch.compile( |
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self.pipe.vae, mode="reduce-overhead", fullgraph=True |
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) |
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self.pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0) |
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self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm") |
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self.compel_proc = Compel( |
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tokenizer=self.pipe.tokenizer, |
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text_encoder=self.pipe.text_encoder, |
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truncate_long_prompts=False, |
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) |
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def predict(self, params: "Pipeline.InputParams") -> Image.Image: |
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generator = torch.manual_seed(params.seed) |
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prompt_embeds = self.compel_proc(params.prompt) |
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results = self.pipe( |
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prompt_embeds=prompt_embeds, |
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generator=generator, |
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num_inference_steps=params.steps, |
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guidance_scale=params.guidance_scale, |
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width=params.width, |
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height=params.height, |
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output_type="pil", |
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) |
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nsfw_content_detected = ( |
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results.nsfw_content_detected[0] |
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if "nsfw_content_detected" in results |
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else False |
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) |
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if nsfw_content_detected: |
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return None |
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return results.images[0] |
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