handler.py

import base64
import json
import sys
from collections import defaultdict
from io import BytesIO
from pprint import pprint
from typing import Any, Dict, List
import os

import torch
from diffusers import (
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    EulerAncestralDiscreteScheduler,
)
from safetensors.torch import load_file
from torch import autocast

# https://huggingface.co/philschmid/stable-diffusion-v1-4-endpoints
# https://huggingface.co/docs/inference-endpoints/guides/custom_handler


# if local avoid repo url
LOCAL = False
PREFIX_URL = ""
if not LOCAL:
    PREFIX_URL = "https://huggingface.co/isatis/kw/tree/main/"


# set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

if device.type != "cuda":
    raise ValueError("need to run on GPU")


class EndpointHandler:
    print(os.getcwd())

    LORA_PATHS = {
        "hairdetailer": PREFIX_URL + "lora/hairdetailer.safetensors",
        "lora_leica": PREFIX_URL + "lora/lora_leica.safetensors",
        "epiNoiseoffset_v2": PREFIX_URL + "lora/epiNoiseoffset_v2.safetensors",
        "MBHU-TT2FRS": PREFIX_URL + "lora/MBHU-TT2FRS.safetensors",
        "ShinyOiledSkin_v20": PREFIX_URL + "lora/ShinyOiledSkin_v20-LoRA.safetensors",
        "polyhedron_new_skin_v1.1": PREFIX_URL
        + "lora/polyhedron_new_skin_v1.1.safetensors",
        "detailed_eye-10": PREFIX_URL + "lora/detailed_eye-10.safetensors",
        "add_detail": PREFIX_URL + "lora/add_detail.safetensors",
        "MuscleGirl_v1": PREFIX_URL + "lora/MuscleGirl_v1.safetensors",
    }

    TEXTUAL_INVERSION = [
        {
            "weight_name": PREFIX_URL + "embeddings/EasyNegative.safetensors",
            "token": "easynegative",
        },
        {
            "weight_name": PREFIX_URL + "embeddings/EasyNegative.safetensors",
            "token": "EasyNegative",
        },
        {"weight_name": PREFIX_URL + "embeddings/badhandv4.pt", "token": "badhandv4"},
        {
            "weight_name": PREFIX_URL + "embeddings/bad-artist-anime.pt",
            "token": "bad-artist-anime",
        },
        {"weight_name": PREFIX_URL + "embeddings/NegfeetV2.pt", "token": "NegfeetV2"},
        {
            "weight_name": PREFIX_URL + "embeddings/ng_deepnegative_v1_75t.pt",
            "token": "ng_deepnegative_v1_75t",
        },
        {
            "weight_name": PREFIX_URL + "embeddings/ng_deepnegative_v1_75t.pt",
            "token": "NG_DeepNegative_V1_75T",
        },
        {
            "weight_name": PREFIX_URL + "embeddings/bad-hands-5.pt",
            "token": "bad-hands-5",
        },
    ]

    def __init__(self, path="."):
        # load the optimized model
        self.pipe = DiffusionPipeline.from_pretrained(
            path,
            custom_pipeline="lpw_stable_diffusion",  # avoid 77 token limit
            torch_dtype=torch.float16,  # accelerate render
        )
        self.pipe = self.pipe.to(device)

        # DPM++ 2M SDE Karras
        # increase step to avoid high contrast num_inference_steps=30
        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(
            self.pipe.scheduler.config,
            use_karras_sigmas=True,
            algorithm_type="sde-dpmsolver++",
        )

        # Mode boulardus
        self.pipe.safety_checker = None

        # Load negative embeddings to avoid bad hands, etc
        self.load_embeddings()

        # Load default Lora models
        self.pipe = self.load_selected_loras(
            [
                ("polyhedron_new_skin_v1.1", 0.35),  # nice Skin
                ("detailed_eye-10", 0.3),  # nice eyes
                ("add_detail", 0.4),  # detailed pictures
                ("MuscleGirl_v1", 0.3),  # shape persons
            ],
        )

        # boosts performance by another 20%
        self.pipe.enable_xformers_memory_efficient_attention()
        self.pipe.enable_attention_slicing()

    def load_lora(self, pipeline, lora_path, lora_weight=0.5):
        state_dict = load_file(lora_path)
        LORA_PREFIX_UNET = "lora_unet"
        LORA_PREFIX_TEXT_ENCODER = "lora_te"

        alpha = lora_weight
        visited = []

        for key in state_dict:
            state_dict[key] = state_dict[key].to(device)

        # directly update weight in diffusers model
        for key in state_dict:
            # as we have set the alpha beforehand, so just skip
            if ".alpha" in key or key in visited:
                continue

            if "text" in key:
                layer_infos = (
                    key.split(".")[0]
                    .split(LORA_PREFIX_TEXT_ENCODER + "_")[-1]
                    .split("_")
                )
                curr_layer = pipeline.text_encoder
            else:
                layer_infos = (
                    key.split(".")[0].split(LORA_PREFIX_UNET + "_")[-1].split("_")
                )
                curr_layer = pipeline.unet

            # find the target layer
            temp_name = layer_infos.pop(0)
            while len(layer_infos) > -1:
                try:
                    curr_layer = curr_layer.__getattr__(temp_name)
                    if len(layer_infos) > 0:
                        temp_name = layer_infos.pop(0)
                    elif len(layer_infos) == 0:
                        break
                except Exception:
                    if len(temp_name) > 0:
                        temp_name += "_" + layer_infos.pop(0)
                    else:
                        temp_name = layer_infos.pop(0)

            # org_forward(x) + lora_up(lora_down(x)) * multiplier
            pair_keys = []
            if "lora_down" in key:
                pair_keys.append(key.replace("lora_down", "lora_up"))
                pair_keys.append(key)
            else:
                pair_keys.append(key)
                pair_keys.append(key.replace("lora_up", "lora_down"))

            # update weight
            if len(state_dict[pair_keys[0]].shape) == 4:
                weight_up = (
                    state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.float32)
                )
                weight_down = (
                    state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.float32)
                )
                curr_layer.weight.data += alpha * torch.mm(
                    weight_up, weight_down
                ).unsqueeze(2).unsqueeze(3)
            else:
                weight_up = state_dict[pair_keys[0]].to(torch.float32)
                weight_down = state_dict[pair_keys[1]].to(torch.float32)
                curr_layer.weight.data += alpha * torch.mm(weight_up, weight_down)

            # update visited list
            for item in pair_keys:
                visited.append(item)

        return pipeline

    def load_embeddings(self):
        """Load textual inversions, avoid bad prompts"""
        for model in EndpointHandler.TEXTUAL_INVERSION:
            self.pipe.load_textual_inversion(
                ".", weight_name=model["weight_name"], token=model["token"]
            )

    def load_selected_loras(self, selections):
        """Load Loras models, can lead to marvelous creations"""
        for model_name, weight in selections:
            lora_path = EndpointHandler.LORA_PATHS[model_name]
            self.pipe = self.load_lora(
                pipeline=self.pipe, lora_path=lora_path, lora_weight=weight
            )
        return self.pipe

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`dict`:. base64 encoded image
        """
        global device

        # Which Lora do we load ?
        # selected_models = [
        #     ("ShinyOiledSkin_v20", 0.3),
        #     ("MBHU-TT2FRS", 0.5),
        #     ("hairdetailer", 0.5),
        #     ("lora_leica", 0.5),
        #     ("epiNoiseoffset_v2", 0.5),
        # ]

        # 1. Verify input arguments
        required_fields = [
            "prompt",
            "negative_prompt",
            "width",
            "num_inference_steps",
            "height",
            "seed",
            "guidance_scale",
        ]

        missing_fields = [field for field in required_fields if field not in data]

        if missing_fields:
            return {
                "flag": "error",
                "message": f"Missing fields: {', '.join(missing_fields)}",
            }

        # Now extract the fields
        prompt = data["prompt"]
        negative_prompt = data["negative_prompt"]
        loras_model = data.pop("loras_model", None)
        seed = data["seed"]
        width = data["width"]
        num_inference_steps = data["num_inference_steps"]
        height = data["height"]
        guidance_scale = data["guidance_scale"]

        # USe this to add automatically some negative prompts
        forced_negative = (
            negative_prompt
            + """easynegative, badhandv4, bad-artist-anime, NegfeetV2, ng_deepnegative_v1_75t, bad-hands-5  """
        )

        # Set the generator seed if provided
        generator = torch.Generator(device="cuda").manual_seed(seed) if seed else None

        # Load the provided Lora models
        if loras_model:
            self.pipe = self.load_selected_loras(loras_model)

        try:
            # 2. Process
            with autocast(device.type):
                image = self.pipe.text2img(
                    prompt=prompt,
                    guidance_scale=guidance_scale,
                    num_inference_steps=num_inference_steps,
                    height=height,
                    width=width,
                    negative_prompt=forced_negative,
                    generator=generator,
                    max_embeddings_multiples=5,
                ).images[0]

            # encode image as base 64
            buffered = BytesIO()
            image.save(buffered, format="JPEG")
            img_str = base64.b64encode(buffered.getvalue())

            # Return the success response
            return {"flag": "success", "image": img_str.decode()}

        except Exception as e:
            # Handle any other exceptions and return an error response
            return {"flag": "error", "message": str(e)}