txt2img

Dockerfile

@@ -36,4 +36,5 @@ WORKDIR $HOME/app
 # Copy the current directory contents into the container at $HOME/app setting the owner to the user
 COPY --chown=user . $HOME/app
 
-CMD ["uvicorn", "app-img2img:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["uvicorn", "app-img2img:app", "--host", "0.0.0.0", "--port", "7860"]
+# CMD ["uvicorn", "app-txt2img:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -20,12 +20,22 @@ You need CUDA and Python
 `SAFETY_CHECKER`:  disabled if you want NSFW filter off  
 `MAX_QUEUE_SIZE`: limit number of users on current app instance
 
+### image to image
 ```bash
 python -m venv venv 
 source venv/bin/activate 
 pip install -r requirements.txt
 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload
 ```
+
+### text to image
+
+```bash
+python -m venv venv 
+source venv/bin/activate 
+pip install -r requirements.txt
+uvicorn "app-txt2img:app" --host 0.0.0.0 --port 7860 --reload
+```
 or with environment variables
 ```bash
 TIMEOUT=120 SAFETY_CHECKER=True MAX_QUEUE_SIZE=4 uvicorn "app-img2img:app" --host 0.0.0.0 --port 7860 --reload

app-img2img.py

@@ -67,7 +67,7 @@ def predict(input_image, prompt, guidance_scale=8.0, strength=0.5, seed=2159232)
         strength=strength,
         num_inference_steps=num_inference_steps,
         guidance_scale=guidance_scale,
-        lcm_origin_steps=20,
+        lcm_origin_steps=50,
         output_type="pil",
     )
     nsfw_content_detected = (

app-txt2img.py

@@ -0,0 +1,205 @@
+import asyncio
+import json
+import logging
+import traceback
+from pydantic import BaseModel
+
+from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+
+from diffusers import DiffusionPipeline, AutoencoderTiny
+from compel import Compel
+import torch
+from PIL import Image
+import numpy as np
+import gradio as gr
+import io
+import uuid
+import os
+import time
+
+MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
+TIMEOUT = float(os.environ.get("TIMEOUT", 0))
+SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
+
+print(f"TIMEOUT: {TIMEOUT}")
+print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
+print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
+
+if SAFETY_CHECKER == "True":
+    pipe = DiffusionPipeline.from_pretrained(
+        "SimianLuo/LCM_Dreamshaper_v7",
+        custom_pipeline="latent_consistency_txt2img.py",
+        custom_revision="main",
+    )
+else:
+    pipe = DiffusionPipeline.from_pretrained(
+        "SimianLuo/LCM_Dreamshaper_v7",
+        safety_checker=None,
+        custom_pipeline="latent_consistency_txt2img.py",
+        custom_revision="main",
+    )
+pipe.vae = AutoencoderTiny.from_pretrained(
+    "madebyollin/taesd", torch_dtype=torch.float16, use_safetensors=True
+)
+pipe.set_progress_bar_config(disable=True)
+pipe.to(torch_device="cuda", torch_dtype=torch.float16)
+pipe.unet.to(memory_format=torch.channels_last)
+compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder, truncate_long_prompts=False)
+pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+user_queue_map = {}
+
+# warmup trigger compilation
+pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
+
+def predict(prompt, guidance_scale=8.0, seed=2159232):
+    generator = torch.manual_seed(seed)
+    prompt_embeds = compel_proc(prompt)
+    # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
+    num_inference_steps = 8
+    results = pipe(
+        prompt_embeds=prompt_embeds,
+        generator=generator,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        lcm_origin_steps=50,
+        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
+    return results.images[0]
+
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class InputParams(BaseModel):
+    prompt: str
+    seed: int
+    guidance_scale: float
+
+
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await websocket.accept()
+    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
+        print("Server is full")
+        await websocket.send_json({"status": "error", "message": "Server is full"})
+        await websocket.close()
+        return
+
+    try:
+        uid = str(uuid.uuid4())
+        print(f"New user connected: {uid}")
+        await websocket.send_json(
+            {"status": "success", "message": "Connected", "userId": uid}
+        )
+        user_queue_map[uid] = {
+            "queue": asyncio.Queue(),
+        }
+        await websocket.send_json(
+            {"status": "start", "message": "Start Streaming", "userId": uid}
+        )
+        await handle_websocket_data(websocket, uid)
+    except WebSocketDisconnect as e:
+        logging.error(f"WebSocket Error: {e}, {uid}")
+        traceback.print_exc()
+    finally:
+        print(f"User disconnected: {uid}")
+        queue_value = user_queue_map.pop(uid, None)
+        queue = queue_value.get("queue", None)
+        if queue:
+            while not queue.empty():
+                try:
+                    queue.get_nowait()
+                except asyncio.QueueEmpty:
+                    continue
+
+
+@app.get("/queue_size")
+async def get_queue_size():
+    queue_size = len(user_queue_map)
+    return JSONResponse({"queue_size": queue_size})
+
+
+@app.get("/stream/{user_id}")
+async def stream(user_id: uuid.UUID):
+    uid = str(user_id)
+    try:
+        user_queue = user_queue_map[uid]
+        queue = user_queue["queue"]
+
+        async def generate():
+            while True:
+                params = await queue.get()
+                if params is None:
+                    continue
+                
+                image = predict(params.prompt, params.guidance_scale, params.seed)
+                if image is None:
+                    continue
+                frame_data = io.BytesIO()
+                image.save(frame_data, format="JPEG")
+                frame_data = frame_data.getvalue()
+                if frame_data is not None and len(frame_data) > 0:
+                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
+
+                await asyncio.sleep(1.0 / 120.0)
+
+        return StreamingResponse(
+            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
+        )
+    except Exception as e:
+        logging.error(f"Streaming Error: {e}, {user_queue_map}")
+        traceback.print_exc()
+        return HTTPException(status_code=404, detail="User not found")
+
+
+async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
+    uid = str(user_id)
+    user_queue = user_queue_map[uid]
+    queue = user_queue["queue"]
+    if not queue:
+        return HTTPException(status_code=404, detail="User not found")
+    last_time = time.time()
+    try:
+        while True:
+            params = await websocket.receive_json()
+            params = InputParams(**params)
+            while not queue.empty():
+                try:
+                    queue.get_nowait()
+                except asyncio.QueueEmpty:
+                    continue
+            await queue.put(params)
+            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
+                await websocket.send_json(
+                    {
+                        "status": "timeout",
+                        "message": "Your session has ended",
+                        "userId": uid,
+                    }
+                )
+                await websocket.close()
+                return
+
+    except Exception as e:
+        logging.error(f"Error: {e}")
+        traceback.print_exc()
+
+
+app.mount("/", StaticFiles(directory="txt2img", html=True), name="public")

latent_consistency_txt2img.py

@@ -0,0 +1,836 @@
+# Copyright 2023 Stanford University Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
+# and https://github.com/hojonathanho/diffusion
+
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
+
+from diffusers import (
+    AutoencoderKL,
+    ConfigMixin,
+    DiffusionPipeline,
+    SchedulerMixin,
+    UNet2DConditionModel,
+    logging,
+)
+from diffusers.configuration_utils import register_to_config
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import (
+    StableDiffusionSafetyChecker,
+)
+from diffusers.utils import BaseOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class LatentConsistencyModelPipeline(DiffusionPipeline):
+    _optional_components = ["scheduler"]
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: "LCMScheduler",
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPImageProcessor,
+        requires_safety_checker: bool = True,
+    ):
+        super().__init__()
+
+        scheduler = (
+            scheduler
+            if scheduler is not None
+            else LCMScheduler(
+                beta_start=0.00085,
+                beta_end=0.0120,
+                beta_schedule="scaled_linear",
+                prediction_type="epsilon",
+            )
+        )
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+
+    def _encode_prompt(
+        self,
+        prompt,
+        device,
+        num_images_per_prompt,
+        prompt_embeds: None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+        """
+
+        if prompt is not None and isinstance(prompt, str):
+            pass
+        elif prompt is not None and isinstance(prompt, list):
+            len(prompt)
+        else:
+            prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            text_inputs = self.tokenizer(
+                prompt,
+                padding="max_length",
+                max_length=self.tokenizer.model_max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+            text_input_ids = text_inputs.input_ids
+            untruncated_ids = self.tokenizer(
+                prompt, padding="longest", return_tensors="pt"
+            ).input_ids
+
+            if untruncated_ids.shape[-1] >= text_input_ids.shape[
+                -1
+            ] and not torch.equal(text_input_ids, untruncated_ids):
+                removed_text = self.tokenizer.batch_decode(
+                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
+                )
+                logger.warning(
+                    "The following part of your input was truncated because CLIP can only handle sequences up to"
+                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+                )
+
+            if (
+                hasattr(self.text_encoder.config, "use_attention_mask")
+                and self.text_encoder.config.use_attention_mask
+            ):
+                attention_mask = text_inputs.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            prompt_embeds = self.text_encoder(
+                text_input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            prompt_embeds = prompt_embeds[0]
+
+        if self.text_encoder is not None:
+            prompt_embeds_dtype = self.text_encoder.dtype
+        elif self.unet is not None:
+            prompt_embeds_dtype = self.unet.dtype
+        else:
+            prompt_embeds_dtype = prompt_embeds.dtype
+
+        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
+
+        bs_embed, seq_len, _ = prompt_embeds.shape
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(
+            bs_embed * num_images_per_prompt, seq_len, -1
+        )
+
+        # Don't need to get uncond prompt embedding because of LCM Guided Distillation
+        return prompt_embeds
+
+    def run_safety_checker(self, image, device, dtype):
+        if self.safety_checker is None:
+            has_nsfw_concept = None
+        else:
+            if torch.is_tensor(image):
+                feature_extractor_input = self.image_processor.postprocess(
+                    image, output_type="pil"
+                )
+            else:
+                feature_extractor_input = self.image_processor.numpy_to_pil(image)
+            safety_checker_input = self.feature_extractor(
+                feature_extractor_input, return_tensors="pt"
+            ).to(device)
+            image, has_nsfw_concept = self.safety_checker(
+                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
+            )
+        return image, has_nsfw_concept
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        latents=None,
+        generator=None,
+    ):
+        shape = (
+            batch_size,
+            num_channels_latents,
+            height // self.vae_scale_factor,
+            width // self.vae_scale_factor,
+        )
+        if generator is None:
+            generator = torch.Generator()
+            generator.manual_seed(torch.randint(0, 2 ** 32, (1,)).item())
+
+        if latents is None:
+            latents = torch.randn(shape, dtype=dtype, generator=generator).to(device)
+        else:
+            latents = latents.to(device)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def get_w_embedding(self, w, embedding_dim=512, dtype=torch.float32):
+        """
+        see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
+        Args:
+        timesteps: torch.Tensor: generate embedding vectors at these timesteps
+        embedding_dim: int: dimension of the embeddings to generate
+        dtype: data type of the generated embeddings
+        Returns:
+        embedding vectors with shape `(len(timesteps), embedding_dim)`
+        """
+        assert len(w.shape) == 1
+        w = w * 1000.0
+
+        half_dim = embedding_dim // 2
+        emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
+        emb = w.to(dtype)[:, None] * emb[None, :]
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+        if embedding_dim % 2 == 1:  # zero pad
+            emb = torch.nn.functional.pad(emb, (0, 1))
+        assert emb.shape == (w.shape[0], embedding_dim)
+        return emb
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        height: Optional[int] = 768,
+        width: Optional[int] = 768,
+        guidance_scale: float = 7.5,
+        num_images_per_prompt: Optional[int] = 1,
+        latents: Optional[torch.FloatTensor] = None,
+        generator: Optional[torch.Generator] = None,
+        num_inference_steps: int = 4,
+        lcm_origin_steps: int = 50,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        # 0. Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # do_classifier_free_guidance = guidance_scale > 0.0  # In LCM Implementation:  cfg_noise = noise_cond + cfg_scale * (noise_cond - noise_uncond) , (cfg_scale > 0.0 using CFG)
+
+        # 3. Encode input prompt
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            prompt_embeds=prompt_embeds,
+        )
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, lcm_origin_steps)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variable
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            latents,
+            generator
+        )
+        bs = batch_size * num_images_per_prompt
+
+        # 6. Get Guidance Scale Embedding
+        w = torch.tensor(guidance_scale).repeat(bs)
+        w_embedding = self.get_w_embedding(w, embedding_dim=256).to(
+            device=device, dtype=latents.dtype
+        )
+
+        # 7. LCM MultiStep Sampling Loop:
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                ts = torch.full((bs,), t, device=device, dtype=torch.long)
+                latents = latents.to(prompt_embeds.dtype)
+
+                # model prediction (v-prediction, eps, x)
+                model_pred = self.unet(
+                    latents,
+                    ts,
+                    timestep_cond=w_embedding,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents, denoised = self.scheduler.step(
+                    model_pred, i, t, latents, return_dict=False
+                )
+
+                # # call the callback, if provided
+                # if i == len(timesteps) - 1:
+                progress_bar.update()
+
+        denoised = denoised.to(prompt_embeds.dtype)
+        if not output_type == "latent":
+            image = self.vae.decode(
+                denoised / self.vae.config.scaling_factor, return_dict=False
+            )[0]
+            image, has_nsfw_concept = self.run_safety_checker(
+                image, device, prompt_embeds.dtype
+            )
+        else:
+            image = denoised
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(
+            image, output_type=output_type, do_denormalize=do_denormalize
+        )
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(
+            images=image, nsfw_content_detected=has_nsfw_concept
+        )
+
+
+@dataclass
+# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
+class LCMSchedulerOutput(BaseOutput):
+    """
+    Output class for the scheduler's `step` function output.
+    Args:
+        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the
+            denoising loop.
+        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
+            The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
+            `pred_original_sample` can be used to preview progress or for guidance.
+    """
+
+    prev_sample: torch.FloatTensor
+    denoised: Optional[torch.FloatTensor] = None
+
+
+# Copied from diffusers.schedulers.scheduling_ddpm.betas_for_alpha_bar
+def betas_for_alpha_bar(
+    num_diffusion_timesteps,
+    max_beta=0.999,
+    alpha_transform_type="cosine",
+):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of
+    (1-beta) over time from t = [0,1].
+    Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up
+    to that part of the diffusion process.
+    Args:
+        num_diffusion_timesteps (`int`): the number of betas to produce.
+        max_beta (`float`): the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+        alpha_transform_type (`str`, *optional*, default to `cosine`): the type of noise schedule for alpha_bar.
+                     Choose from `cosine` or `exp`
+    Returns:
+        betas (`np.ndarray`): the betas used by the scheduler to step the model outputs
+    """
+    if alpha_transform_type == "cosine":
+
+        def alpha_bar_fn(t):
+            return math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    elif alpha_transform_type == "exp":
+
+        def alpha_bar_fn(t):
+            return math.exp(t * -12.0)
+
+    else:
+        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}")
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar_fn(t2) / alpha_bar_fn(t1), max_beta))
+    return torch.tensor(betas, dtype=torch.float32)
+
+
+def rescale_zero_terminal_snr(betas):
+    """
+    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+    Args:
+        betas (`torch.FloatTensor`):
+            the betas that the scheduler is being initialized with.
+    Returns:
+        `torch.FloatTensor`: rescaled betas with zero terminal SNR
+    """
+    # Convert betas to alphas_bar_sqrt
+    alphas = 1.0 - betas
+    alphas_cumprod = torch.cumprod(alphas, dim=0)
+    alphas_bar_sqrt = alphas_cumprod.sqrt()
+
+    # Store old values.
+    alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
+    alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
+
+    # Shift so the last timestep is zero.
+    alphas_bar_sqrt -= alphas_bar_sqrt_T
+
+    # Scale so the first timestep is back to the old value.
+    alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
+
+    # Convert alphas_bar_sqrt to betas
+    alphas_bar = alphas_bar_sqrt**2  # Revert sqrt
+    alphas = alphas_bar[1:] / alphas_bar[:-1]  # Revert cumprod
+    alphas = torch.cat([alphas_bar[0:1], alphas])
+    betas = 1 - alphas
+
+    return betas
+
+
+class LCMScheduler(SchedulerMixin, ConfigMixin):
+    """
+    `LCMScheduler` extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
+    non-Markovian guidance.
+    This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic
+    methods the library implements for all schedulers such as loading and saving.
+    Args:
+        num_train_timesteps (`int`, defaults to 1000):
+            The number of diffusion steps to train the model.
+        beta_start (`float`, defaults to 0.0001):
+            The starting `beta` value of inference.
+        beta_end (`float`, defaults to 0.02):
+            The final `beta` value.
+        beta_schedule (`str`, defaults to `"linear"`):
+            The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
+            `linear`, `scaled_linear`, or `squaredcos_cap_v2`.
+        trained_betas (`np.ndarray`, *optional*):
+            Pass an array of betas directly to the constructor to bypass `beta_start` and `beta_end`.
+        clip_sample (`bool`, defaults to `True`):
+            Clip the predicted sample for numerical stability.
+        clip_sample_range (`float`, defaults to 1.0):
+            The maximum magnitude for sample clipping. Valid only when `clip_sample=True`.
+        set_alpha_to_one (`bool`, defaults to `True`):
+            Each diffusion step uses the alphas product value at that step and at the previous one. For the final step
+            there is no previous alpha. When this option is `True` the previous alpha product is fixed to `1`,
+            otherwise it uses the alpha value at step 0.
+        steps_offset (`int`, defaults to 0):
+            An offset added to the inference steps. You can use a combination of `offset=1` and
+            `set_alpha_to_one=False` to make the last step use step 0 for the previous alpha product like in Stable
+            Diffusion.
+        prediction_type (`str`, defaults to `epsilon`, *optional*):
+            Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
+            `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
+            Video](https://imagen.research.google/video/paper.pdf) paper).
+        thresholding (`bool`, defaults to `False`):
+            Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
+            as Stable Diffusion.
+        dynamic_thresholding_ratio (`float`, defaults to 0.995):
+            The ratio for the dynamic thresholding method. Valid only when `thresholding=True`.
+        sample_max_value (`float`, defaults to 1.0):
+            The threshold value for dynamic thresholding. Valid only when `thresholding=True`.
+        timestep_spacing (`str`, defaults to `"leading"`):
+            The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
+            Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
+        rescale_betas_zero_snr (`bool`, defaults to `False`):
+            Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
+            dark samples instead of limiting it to samples with medium brightness. Loosely related to
+            [`--offset_noise`](https://github.com/huggingface/diffusers/blob/74fd735eb073eb1d774b1ab4154a0876eb82f055/examples/dreambooth/train_dreambooth.py#L506).
+    """
+
+    # _compatibles = [e.name for e in KarrasDiffusionSchedulers]
+    order = 1
+
+    @register_to_config
+    def __init__(
+        self,
+        num_train_timesteps: int = 1000,
+        beta_start: float = 0.0001,
+        beta_end: float = 0.02,
+        beta_schedule: str = "linear",
+        trained_betas: Optional[Union[np.ndarray, List[float]]] = None,
+        clip_sample: bool = True,
+        set_alpha_to_one: bool = True,
+        steps_offset: int = 0,
+        prediction_type: str = "epsilon",
+        thresholding: bool = False,
+        dynamic_thresholding_ratio: float = 0.995,
+        clip_sample_range: float = 1.0,
+        sample_max_value: float = 1.0,
+        timestep_spacing: str = "leading",
+        rescale_betas_zero_snr: bool = False,
+    ):
+        if trained_betas is not None:
+            self.betas = torch.tensor(trained_betas, dtype=torch.float32)
+        elif beta_schedule == "linear":
+            self.betas = torch.linspace(
+                beta_start, beta_end, num_train_timesteps, dtype=torch.float32
+            )
+        elif beta_schedule == "scaled_linear":
+            # this schedule is very specific to the latent diffusion model.
+            self.betas = (
+                torch.linspace(
+                    beta_start**0.5,
+                    beta_end**0.5,
+                    num_train_timesteps,
+                    dtype=torch.float32,
+                )
+                ** 2
+            )
+        elif beta_schedule == "squaredcos_cap_v2":
+            # Glide cosine schedule
+            self.betas = betas_for_alpha_bar(num_train_timesteps)
+        else:
+            raise NotImplementedError(
+                f"{beta_schedule} does is not implemented for {self.__class__}"
+            )
+
+        # Rescale for zero SNR
+        if rescale_betas_zero_snr:
+            self.betas = rescale_zero_terminal_snr(self.betas)
+
+        self.alphas = 1.0 - self.betas
+        self.alphas_cumprod = torch.cumprod(self.alphas, dim=0)
+
+        # At every step in ddim, we are looking into the previous alphas_cumprod
+        # For the final step, there is no previous alphas_cumprod because we are already at 0
+        # `set_alpha_to_one` decides whether we set this parameter simply to one or
+        # whether we use the final alpha of the "non-previous" one.
+        self.final_alpha_cumprod = (
+            torch.tensor(1.0) if set_alpha_to_one else self.alphas_cumprod[0]
+        )
+
+        # standard deviation of the initial noise distribution
+        self.init_noise_sigma = 1.0
+
+        # setable values
+        self.num_inference_steps = None
+        self.timesteps = torch.from_numpy(
+            np.arange(0, num_train_timesteps)[::-1].copy().astype(np.int64)
+        )
+
+    def scale_model_input(
+        self, sample: torch.FloatTensor, timestep: Optional[int] = None
+    ) -> torch.FloatTensor:
+        """
+        Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
+        current timestep.
+        Args:
+            sample (`torch.FloatTensor`):
+                The input sample.
+            timestep (`int`, *optional*):
+                The current timestep in the diffusion chain.
+        Returns:
+            `torch.FloatTensor`:
+                A scaled input sample.
+        """
+        return sample
+
+    def _get_variance(self, timestep, prev_timestep):
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        alpha_prod_t_prev = (
+            self.alphas_cumprod[prev_timestep]
+            if prev_timestep >= 0
+            else self.final_alpha_cumprod
+        )
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        variance = (beta_prod_t_prev / beta_prod_t) * (
+            1 - alpha_prod_t / alpha_prod_t_prev
+        )
+
+        return variance
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler._threshold_sample
+    def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        "Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the
+        prediction of x_0 at timestep t), and if s > 1, then we threshold xt0 to the range [-s, s] and then divide by
+        s. Dynamic thresholding pushes saturated pixels (those near -1 and 1) inwards, thereby actively preventing
+        pixels from saturation at each step. We find that dynamic thresholding results in significantly better
+        photorealism as well as better image-text alignment, especially when using very large guidance weights."
+        https://arxiv.org/abs/2205.11487
+        """
+        dtype = sample.dtype
+        batch_size, channels, height, width = sample.shape
+
+        if dtype not in (torch.float32, torch.float64):
+            sample = (
+                sample.float()
+            )  # upcast for quantile calculation, and clamp not implemented for cpu half
+
+        # Flatten sample for doing quantile calculation along each image
+        sample = sample.reshape(batch_size, channels * height * width)
+
+        abs_sample = sample.abs()  # "a certain percentile absolute pixel value"
+
+        s = torch.quantile(abs_sample, self.config.dynamic_thresholding_ratio, dim=1)
+        s = torch.clamp(
+            s, min=1, max=self.config.sample_max_value
+        )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
+
+        s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
+        sample = (
+            torch.clamp(sample, -s, s) / s
+        )  # "we threshold xt0 to the range [-s, s] and then divide by s"
+
+        sample = sample.reshape(batch_size, channels, height, width)
+        sample = sample.to(dtype)
+
+        return sample
+
+    def set_timesteps(
+        self,
+        num_inference_steps: int,
+        lcm_origin_steps: int,
+        device: Union[str, torch.device] = None,
+    ):
+        """
+        Sets the discrete timesteps used for the diffusion chain (to be run before inference).
+        Args:
+            num_inference_steps (`int`):
+                The number of diffusion steps used when generating samples with a pre-trained model.
+        """
+
+        if num_inference_steps > self.config.num_train_timesteps:
+            raise ValueError(
+                f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
+                f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
+                f" maximal {self.config.num_train_timesteps} timesteps."
+            )
+
+        self.num_inference_steps = num_inference_steps
+
+        # LCM Timesteps Setting:  # Linear Spacing
+        c = self.config.num_train_timesteps // lcm_origin_steps
+        lcm_origin_timesteps = (
+            np.asarray(list(range(1, lcm_origin_steps + 1))) * c - 1
+        )  # LCM Training  Steps Schedule
+        skipping_step = len(lcm_origin_timesteps) // num_inference_steps
+        timesteps = lcm_origin_timesteps[::-skipping_step][
+            :num_inference_steps
+        ]  # LCM Inference Steps Schedule
+
+        self.timesteps = torch.from_numpy(timesteps.copy()).to(device)
+
+    def get_scalings_for_boundary_condition_discrete(self, t):
+        self.sigma_data = 0.5  # Default: 0.5
+
+        # By dividing 0.1: This is almost a delta function at t=0.
+        c_skip = self.sigma_data**2 / ((t / 0.1) ** 2 + self.sigma_data**2)
+        c_out = (t / 0.1) / ((t / 0.1) ** 2 + self.sigma_data**2) ** 0.5
+        return c_skip, c_out
+
+    def step(
+        self,
+        model_output: torch.FloatTensor,
+        timeindex: int,
+        timestep: int,
+        sample: torch.FloatTensor,
+        eta: float = 0.0,
+        use_clipped_model_output: bool = False,
+        generator=None,
+        variance_noise: Optional[torch.FloatTensor] = None,
+        return_dict: bool = True,
+    ) -> Union[LCMSchedulerOutput, Tuple]:
+        """
+        Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
+        process from the learned model outputs (most often the predicted noise).
+        Args:
+            model_output (`torch.FloatTensor`):
+                The direct output from learned diffusion model.
+            timestep (`float`):
+                The current discrete timestep in the diffusion chain.
+            sample (`torch.FloatTensor`):
+                A current instance of a sample created by the diffusion process.
+            eta (`float`):
+                The weight of noise for added noise in diffusion step.
+            use_clipped_model_output (`bool`, defaults to `False`):
+                If `True`, computes "corrected" `model_output` from the clipped predicted original sample. Necessary
+                because predicted original sample is clipped to [-1, 1] when `self.config.clip_sample` is `True`. If no
+                clipping has happened, "corrected" `model_output` would coincide with the one provided as input and
+                `use_clipped_model_output` has no effect.
+            generator (`torch.Generator`, *optional*):
+                A random number generator.
+            variance_noise (`torch.FloatTensor`):
+                Alternative to generating noise with `generator` by directly providing the noise for the variance
+                itself. Useful for methods such as [`CycleDiffusion`].
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] or `tuple`.
+        Returns:
+            [`~schedulers.scheduling_utils.LCMSchedulerOutput`] or `tuple`:
+                If return_dict is `True`, [`~schedulers.scheduling_lcm.LCMSchedulerOutput`] is returned, otherwise a
+                tuple is returned where the first element is the sample tensor.
+        """
+        if self.num_inference_steps is None:
+            raise ValueError(
+                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
+            )
+
+        # 1. get previous step value
+        prev_timeindex = timeindex + 1
+        if prev_timeindex < len(self.timesteps):
+            prev_timestep = self.timesteps[prev_timeindex]
+        else:
+            prev_timestep = timestep
+
+        # 2. compute alphas, betas
+        alpha_prod_t = self.alphas_cumprod[timestep]
+        alpha_prod_t_prev = (
+            self.alphas_cumprod[prev_timestep]
+            if prev_timestep >= 0
+            else self.final_alpha_cumprod
+        )
+
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        # 3. Get scalings for boundary conditions
+        c_skip, c_out = self.get_scalings_for_boundary_condition_discrete(timestep)
+
+        # 4. Different Parameterization:
+        parameterization = self.config.prediction_type
+
+        if parameterization == "epsilon":  # noise-prediction
+            pred_x0 = (sample - beta_prod_t.sqrt() * model_output) / alpha_prod_t.sqrt()
+
+        elif parameterization == "sample":  # x-prediction
+            pred_x0 = model_output
+
+        elif parameterization == "v_prediction":  # v-prediction
+            pred_x0 = alpha_prod_t.sqrt() * sample - beta_prod_t.sqrt() * model_output
+
+        # 4. Denoise model output using boundary conditions
+        denoised = c_out * pred_x0 + c_skip * sample
+
+        # 5. Sample z ~ N(0, I), For MultiStep Inference
+        # Noise is not used for one-step sampling.
+        if len(self.timesteps) > 1:
+            noise = torch.randn(model_output.shape).to(model_output.device)
+            prev_sample = (
+                alpha_prod_t_prev.sqrt() * denoised + beta_prod_t_prev.sqrt() * noise
+            )
+        else:
+            prev_sample = denoised
+
+        if not return_dict:
+            return (prev_sample, denoised)
+
+        return LCMSchedulerOutput(prev_sample=prev_sample, denoised=denoised)
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.add_noise
+    def add_noise(
+        self,
+        original_samples: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
+        alphas_cumprod = self.alphas_cumprod.to(
+            device=original_samples.device, dtype=original_samples.dtype
+        )
+        timesteps = timesteps.to(original_samples.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        noisy_samples = (
+            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
+        )
+        return noisy_samples
+
+    # Copied from diffusers.schedulers.scheduling_ddpm.DDPMScheduler.get_velocity
+    def get_velocity(
+        self,
+        sample: torch.FloatTensor,
+        noise: torch.FloatTensor,
+        timesteps: torch.IntTensor,
+    ) -> torch.FloatTensor:
+        # Make sure alphas_cumprod and timestep have same device and dtype as sample
+        alphas_cumprod = self.alphas_cumprod.to(
+            device=sample.device, dtype=sample.dtype
+        )
+        timesteps = timesteps.to(sample.device)
+
+        sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
+        sqrt_alpha_prod = sqrt_alpha_prod.flatten()
+        while len(sqrt_alpha_prod.shape) < len(sample.shape):
+            sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1)
+
+        sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5
+        sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()
+        while len(sqrt_one_minus_alpha_prod.shape) < len(sample.shape):
+            sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
+
+        velocity = sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
+        return velocity
+
+    def __len__(self):
+        return self.config.num_train_timesteps

txt2img/index.html

@@ -0,0 +1,218 @@
+<!doctype html>
+<html>
+
+<head>
+    <meta charset="UTF-8">
+    <title>Real-Time Latent Consistency Model</title>
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script
+        src="https://cdnjs.cloudflare.com/ajax/libs/iframe-resizer/4.3.1/iframeResizer.contentWindow.min.js"></script>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <script type="module">
+
+        const seedEl = document.querySelector("#seed");
+        const promptEl = document.querySelector("#prompt");
+        const guidanceEl = document.querySelector("#guidance-scale");
+        const startBtn = document.querySelector("#start");
+        const stopBtn = document.querySelector("#stop");
+        const videoEl = document.querySelector("#webcam");
+        const imageEl = document.querySelector("#player");
+        const queueSizeEl = document.querySelector("#queue_size");
+        const errorEl = document.querySelector("#error");
+
+        function LCMLive(promptEl, liveImage, seedEl, guidanceEl) {
+            let websocket;
+
+            async function start() {
+                return new Promise((resolve, reject) => {
+                    const websocketURL = `${window.location.protocol === "https:" ? "wss" : "ws"
+                        }:${window.location.host}/ws`;
+
+                    const socket = new WebSocket(websocketURL);
+                    socket.onopen = () => {
+                        console.log("Connected to websocket");
+                    };
+                    socket.onclose = () => {
+                        console.log("Disconnected from websocket");
+                        stop();
+                        resolve({ "status": "disconnected" });
+                    };
+                    socket.onerror = (err) => {
+                        console.error(err);
+                        reject(err);
+                    };
+                    socket.onmessage = (event) => {
+                        const data = JSON.parse(event.data);
+                        switch (data.status) {
+                            case "success":
+                                break;
+                            case "start":
+                                const userId = data.userId;
+                                initPromptStream(userId);
+                                break;
+                            case "timeout":
+                                stop();
+                                resolve({ "status": "timeout" });
+                            case "error":
+                                stop();
+                                reject(data.message);
+                        }
+                    };
+                    websocket = socket;
+                })
+            }
+
+            async function promptUpdateStream() {
+                const prompt = promptEl.value;
+                const seed = seedEl.value;
+                const guidance_scale = guidanceEl.value;
+                websocket.send(JSON.stringify({
+                    prompt: prompt,
+                    seed: seed,
+                    guidance_scale: guidance_scale,
+                }));
+            }
+
+            function initPromptStream(userId) {
+                liveImage.src = `/stream/${userId}`;
+                promptEl.addEventListener("input", promptUpdateStream);
+                seedEl.addEventListener("input", promptUpdateStream);
+                guidanceEl.addEventListener("change", promptUpdateStream);
+            }
+
+            async function stop() {
+                websocket.close();
+                promptEl.removeEventListener("input", promptUpdateStream);
+                seedEl.removeEventListener("input", promptUpdateStream);
+                guidanceEl.removeEventListener("change", promptUpdateStream);
+            }
+            return {
+                start,
+                stop
+            }
+        }
+        function toggleMessage(type) {
+            errorEl.hidden = false;
+            switch (type) {
+                case "error":
+                    errorEl.innerText = "To many users are using the same GPU, please try again later.";
+                    errorEl.classList.toggle("bg-red-300", "text-red-900");
+                    break;
+                case "success":
+                    errorEl.innerText = "Your session has ended, please start a new one.";
+                    errorEl.classList.toggle("bg-green-300", "text-green-900");
+                    break;
+            }
+            setTimeout(() => {
+                errorEl.hidden = true;
+            }, 2000);
+        }
+
+
+        const lcmLive = LCMLive(promptEl, imageEl, seedEl, guidanceEl);
+        startBtn.addEventListener("click", async () => {
+            try {
+                startBtn.disabled = true;
+                const res = await lcmLive.start();
+                startBtn.disabled = false;
+                if (res.status === "timeout")
+                    toggleMessage("success")
+            } catch (err) {
+                console.log(err);
+                toggleMessage("error")
+            }
+        });
+        stopBtn.addEventListener("click", () => {
+            lcmLive.stop();
+        });
+        window.addEventListener("beforeunload", () => {
+            lcmLive.stop();
+        });
+        setInterval(() =>
+            fetch("/queue_size")
+                .then((res) => res.json())
+                .then((data) => {
+                    queueSizeEl.innerText = data.queue_size;
+                })
+                .catch((err) => {
+                    console.log(err);
+                })
+            , 5000);
+    </script>
+</head>
+
+<body>
+    <div class="fixed right-2 top-2 p-4 font-bold text-sm rounded-lg max-w-xs text-center" id="error">
+    </div>
+    <main class="container mx-auto px-4 py-4 max-w-4xl flex flex-col gap-4">
+        <article class="text-center max-w-xl mx-auto">
+            <h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
+            <h2 class="text-2xl font-bold mb-4">Text to Image</h2>
+            <p class="text-sm">
+                This demo showcases
+                <a href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7" target="_blank"
+                    class="text-blue-500 underline hover:no-underline">LCM</a> Text to Image model
+                using
+                <a href="https://github.com/huggingface/diffusers/tree/main/examples/community#latent-consistency-pipeline"
+                    target="_blank" class="text-blue-500 underline hover:no-underline">Diffusers</a> with a MJPEG
+                stream server.
+            </p>
+            <p class="text-sm">
+                There are <span id="queue_size" class="font-bold">0</span> user(s) sharing the same GPU, affecting
+                real-time performance. Maximum queue size is 10. <a
+                    href="https://huggingface.co/spaces/radames/Real-Time-Latent-Consistency-Model?duplicate=true"
+                    target="_blank" class="text-blue-500 underline hover:no-underline">Duplicate</a> and run it on your own GPU.
+            </p>
+        </article>
+        <div>
+            <h2 class="font-medium">Prompt</h2>
+            <p class="text-sm text-gray-500">
+                Start your session and type your prompt here, 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>
+            <div class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
+                <textarea type="text" id="prompt" class="font-light w-full px-3 py-2 mx-1  outline-none"
+                    title="Start your session and type your prompt here, you can see the result in real-time."
+                    placeholder="Add your prompt here...">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</textarea>
+            </div>
+
+        </div>
+        <div class="">
+            <details>
+                <summary class="font-medium cursor-pointer">Advanced Options</summary>
+                <div class="grid grid-cols-3 max-w-md items-center gap-3 py-3">
+                    <label class="text-sm font-medium" for="guidance-scale">Guidance Scale
+                    </label>
+                    <input type="range" id="guidance-scale" name="guidance-scale" min="1" max="30" step="0.001"
+                        value="8.0" oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)">
+                    <output class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
+                        8.0</output>
+                    <label class="text-sm font-medium" for="seed">Seed</label>
+                    <input type="number" id="seed" name="seed" value="299792458"
+                        class="font-light border border-gray-700 text-right rounded-md p-2">
+                    <button
+                        onclick="document.querySelector('#seed').value = Math.floor(Math.random() * Number.MAX_SAFE_INTEGER); document.querySelector('#seed').dispatchEvent(new Event('input'));"
+                        class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm">
+                        Rand
+                    </button>
+                </div>
+            </details>
+        </div>
+        <div>
+            <button id="start"
+                class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
+                Start
+            </button>
+            <button id="stop"
+                class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
+                Stop
+            </button>
+        </div>
+        <div class="relative rounded-lg border border-slate-300 overflow-hidden">
+            <img id="player" class="w-full aspect-square rounded-lg "
+                src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNkYAAAAAYAAjCB0C8AAAAASUVORK5CYII=">
+        </div>
+    </main>
+</body>
+
+</html>