controlnetLoraSD15.py

frontend/src/lib/components/PipelineOptions.svelte

@@ -6,6 +6,7 @@
   import SeedInput from './SeedInput.svelte';
   import TextArea from './TextArea.svelte';
   import Checkbox from './Checkbox.svelte';
+  import Selectlist from './Selectlist.svelte';
   import { pipelineValues } from '$lib/store';
 
   export let pipelineParams: FieldProps[];
@@ -25,6 +26,8 @@
         <TextArea {params} bind:value={$pipelineValues[params.id]}></TextArea>
       {:else if params.field === FieldType.CHECKBOX}
         <Checkbox {params} bind:value={$pipelineValues[params.id]}></Checkbox>
+      {:else if params.field === FieldType.SELECT}
+        <Selectlist {params} bind:value={$pipelineValues[params.id]}></Selectlist>
       {/if}
     {/each}
   {/if}
@@ -45,6 +48,8 @@
           <TextArea {params} bind:value={$pipelineValues[params.id]}></TextArea>
         {:else if params.field === FieldType.CHECKBOX}
           <Checkbox {params} bind:value={$pipelineValues[params.id]}></Checkbox>
+        {:else if params.field === FieldType.SELECT}
+          <Selectlist {params} bind:value={$pipelineValues[params.id]}></Selectlist>
         {/if}
       {/each}
     {/if}

frontend/src/lib/components/Selectlist.svelte

@@ -0,0 +1,24 @@
+<script lang="ts">
+  import type { FieldProps } from '$lib/types';
+  import { onMount } from 'svelte';
+  export let value = '';
+  export let params: FieldProps;
+  onMount(() => {
+    value = String(params?.default);
+  });
+</script>
+
+<div class="grid max-w-md grid-cols-4 items-center justify-items-start gap-3">
+  <label for="model-list" class="font-medium">{params?.title} </label>
+  {#if params?.values}
+    <select
+      bind:value
+      id="model-list"
+      class="cursor-pointer rounded-md border-2 border-gray-500 p-2 font-light dark:text-black"
+    >
+      {#each params.values as model, i}
+        <option value={model} selected={i === 0}>{model}</option>
+      {/each}
+    </select>
+  {/if}
+</div>

frontend/src/lib/lcmLive.ts

@@ -36,7 +36,6 @@ export const lcmLiveActions = {
                 };
                 websocket.onmessage = (event) => {
                     const data = JSON.parse(event.data);
-                    console.log("WS: ", data);
                     switch (data.status) {
                         case "connected":
                             const userId = data.userId;

frontend/src/lib/types.ts

@@ -3,6 +3,7 @@ export const enum FieldType {
     SEED = "seed",
     TEXTAREA = "textarea",
     CHECKBOX = "checkbox",
+    SELECT = "select",
 }
 export const enum PipelineMode {
     IMAGE = "image",
@@ -20,6 +21,7 @@ export interface FieldProps {
     disabled?: boolean;
     hide?: boolean;
     id: string;
+    values?: string[];
 }
 export interface PipelineInfo {
     title: {

frontend/src/routes/+page.svelte

@@ -9,12 +9,7 @@
   import PipelineOptions from '$lib/components/PipelineOptions.svelte';
   import Spinner from '$lib/icons/spinner.svelte';
   import { lcmLiveStatus, lcmLiveActions, LCMLiveStatus } from '$lib/lcmLive';
-  import {
-    mediaStreamActions,
-    mediaStreamStatus,
-    onFrameChangeStore,
-    MediaStreamStatusEnum
-  } from '$lib/mediaStream';
+  import { mediaStreamActions, onFrameChangeStore } from '$lib/mediaStream';
   import { getPipelineValues, deboucedPipelineValues } from '$lib/store';
 
   let pipelineParams: FieldProps[];
@@ -44,9 +39,7 @@
   }
 
   $: isLCMRunning = $lcmLiveStatus !== LCMLiveStatus.DISCONNECTED;
-  $: {
-    console.log('lcmLiveStatus', $lcmLiveStatus);
-  }
+
   let disabled = false;
   async function toggleLcmLive() {
     if (!isLCMRunning) {

pipelines/controlnetLoraSD15.py

@@ -0,0 +1,238 @@
+from diffusers import (
+    StableDiffusionControlNetImg2ImgPipeline,
+    ControlNetModel,
+    LCMScheduler,
+)
+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
+
+taesd_model = "madebyollin/taesd"
+controlnet_model = "lllyasviel/control_v11p_sd15_canny"
+base_models = [
+    "plasmo/woolitize",
+    "nitrosocke/Ghibli-Diffusion",
+    "nitrosocke/mo-di-diffusion",
+]
+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"
+
+
+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"
+
+    class InputParams(BaseModel):
+        prompt: str = Field(
+            default_prompt,
+            title="Prompt",
+            field="textarea",
+            id="prompt",
+        )
+        model_id: str = Field(
+            "plasmo/woolitize",
+            title="Base Models List",
+            values=base_models,
+            field="select",
+            id="model_id",
+        )
+        seed: int = Field(
+            2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
+        )
+        steps: int = Field(
+            4, min=2, 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(
+            0.2,
+            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 model_id in base_models:
+                pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+                    model_id,
+                    controlnet=controlnet_canny,
+                )
+            self.pipes[model_id] = pipe
+        else:
+            for model_id in base_models:
+                pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
+                    model_id,
+                    safety_checker=None,
+                    controlnet=controlnet_canny,
+                )
+                self.pipes[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)
+            pipe.to(device=device, dtype=torch_dtype).to(device)
+
+            if psutil.virtual_memory().total < 64 * 1024**3:
+                pipe.enable_attention_slicing()
+
+            # Load LCM LoRA
+            pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
+            pipe.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)
+        print(f"Using model: {params.model_id}")
+        pipe = self.pipes[params.model_id]
+
+        prompt_embeds = pipe.compel_proc(params.prompt)
+        control_image = self.canny_torch(
+            params.image, params.canny_low_threshold, params.canny_high_threshold
+        )
+
+        results = pipe(
+            image=params.image,
+            control_image=control_image,
+            prompt_embeds=prompt_embeds,
+            generator=generator,
+            strength=params.strength,
+            num_inference_steps=params.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