QRCode pipeline

server/pipelines/controlnetLoraSD15QRCode.py

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+from diffusers import (
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    LCMScheduler,
+    AutoencoderTiny,
+)
+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
+import math
+
+taesd_model = "madebyollin/taesd"
+controlnet_model = "monster-labs/control_v1p_sd15_qrcode_monster"
+base_model = "nitrosocke/mo-di-diffusion"
+lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
+default_prompt = "abstract art of a men with curly hair by Pablo Picasso"
+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 + QRCode</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",
+        )
+        seed: int = Field(
+            2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
+        )
+        steps: int = Field(
+            5, 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(
+            1.0,
+            min=0,
+            max=2,
+            step=0.001,
+            title="Guidance Scale",
+            field="range",
+            hide=True,
+            id="guidance_scale",
+        )
+        strength: float = Field(
+            0.6,
+            min=0.25,
+            max=1.0,
+            step=0.001,
+            title="Strength",
+            field="range",
+            hide=True,
+            id="strength",
+        )
+        controlnet_scale: float = Field(
+            1.0,
+            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",
+        )
+        blend: float = Field(
+            0.1,
+            min=0.0,
+            max=1.0,
+            step=0.001,
+            title="Blend",
+            field="range",
+            hide=True,
+            id="blend",
+        )
+
+    def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
+        controlnet_qrcode = ControlNetModel.from_pretrained(
+            controlnet_model, torch_dtype=torch_dtype, subfolder="v2"
+        ).to(device)
+
+        if args.safety_checker:
+            self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+                base_model,
+                controlnet=controlnet_qrcode,
+            )
+        else:
+            self.pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+                base_model,
+                safety_checker=None,
+                controlnet=controlnet_qrcode,
+            )
+
+        self.control_image = Image.open(
+            "qr-code.png").convert("RGB").resize((512, 512))
+
+        self.pipe.scheduler = LCMScheduler.from_config(
+            self.pipe.scheduler.config)
+        self.pipe.set_progress_bar_config(disable=True)
+        if device.type != "mps":
+            self.pipe.unet.to(memory_format=torch.channels_last)
+
+        if args.taesd:
+            self.pipe.vae = AutoencoderTiny.from_pretrained(
+                taesd_model, torch_dtype=torch_dtype, use_safetensors=True
+            ).to(device)
+
+        # Load LCM LoRA
+        self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
+        self.pipe.to(device=device, dtype=torch_dtype).to(device)
+        if args.compel:
+            self.compel_proc = Compel(
+                tokenizer=self.pipe.tokenizer,
+                text_encoder=self.pipe.text_encoder,
+                truncate_long_prompts=False,
+            )
+        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", (512, 512))],
+                control_image=[Image.new("RGB", (512, 512))],
+            )
+
+    def predict(self, params: "Pipeline.InputParams") -> Image.Image:
+        generator = torch.manual_seed(params.seed)
+
+        prompt = f"modern disney style {params.prompt}"
+        prompt_embeds = None
+        prompt = params.prompt
+        if hasattr(self, "compel_proc"):
+            prompt_embeds = self.compel_proc(prompt)
+            prompt = None
+
+        steps = params.steps
+        strength = params.strength
+        if int(steps * strength) < 1:
+            steps = math.ceil(1 / max(0.10, strength))
+
+        blend_qr_image = Image.blend(
+            params.image,
+            self.control_image,
+            alpha=params.blend
+        )
+        results = self.pipe(
+            image=blend_qr_image,
+            control_image=self.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]
+
+        return result_image