app16.py

import os
if os.environ.get("SPACES_ZERO_GPU") is not None:
    import spaces
else:
    class spaces:
        @staticmethod
        def GPU(func):
            def wrapper(*args, **kwargs):
                return func(*args, **kwargs)
            return wrapper

import gradio as gr
import json
import logging
import argparse
import torch
import torchvision
from os import path
from PIL import Image
import numpy as np
import spaces
import copy
import random
import time
from torchvision import transforms
from dataclasses import dataclass

import math
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Union
from huggingface_hub import hf_hub_download, snapshot_download
from diffusers import DiffusionPipeline, AutoencoderTiny, AutoPipelineForImage2Image
from diffusers.models.transformers import FluxTransformer2DModel
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
import safetensors.torch
from safetensors.torch import load_file
import random
from tqdm import tqdm
from einops import rearrange, repeat
from torch import Tensor, nn
from pipeline import FluxWithCFGPipeline
from diffusers.models.autoencoders import AutoencoderKL
from transformers import CLIPModel, CLIPProcessor, CLIPTextModel, CLIPTokenizer, CLIPConfig, T5EncoderModel, T5Tokenizer
import gc
import warnings
model_path = snapshot_download(repo_id="Kijai/OpenFLUX-comfy")
cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path

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

#torch.backends.cuda.matmul.allow_tf32 = True

# Load LoRAs from JSON file
with open('loras.json', 'r') as f:
    loras = json.load(f)

dtype = torch.bfloat16

#clipmodel = 'norm'
#if clipmodel == "long":
#    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
#    config = CLIPConfig.from_pretrained(model_id)
#    maxtokens = 77
#if clipmodel == "norm":
#    model_id = "zer0int/CLIP-GmP-ViT-L-14"
#    config = CLIPConfig.from_pretrained(model_id)
#    maxtokens = 77
#clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda")
#clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True)

#pipe.tokenizer = clip_processor.tokenizer
#pipe.text_encoder = clip_model.text_model
#pipe.tokenizer_max_length = maxtokens
#pipe.text_encoder.dtype = torch.bfloat16

class HFEmbedder(nn.Module):
    def __init__(self, version: str, max_length: int, **hf_kwargs):
        super().__init__()
        self.is_clip = version.startswith("openai")
        self.max_length = max_length
        self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"

        if self.is_clip:
            self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
            self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
        else:
            self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
            self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)

        self.hf_module = self.hf_module.eval().requires_grad_(False)

    def forward(self, text: list[str]) -> Tensor:
        batch_encoding = self.tokenizer(
            text,
            truncation=True,
            max_length=self.max_length,
            return_length=False,
            return_overflowing_tokens=False,
            padding="max_length",
            return_tensors="pt",
        )

        outputs = self.hf_module(
            input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
            attention_mask=None,
            output_hidden_states=False,
        )
        return outputs[self.output_key]
    
device = "cuda"
t5 = HFEmbedder("DeepFloyd/t5-v1_1-xxl", max_length=512, torch_dtype=torch.bfloat16).to(device)
clip = HFEmbedder("openai/clip-vit-large-patch14", max_length=77, torch_dtype=torch.bfloat16).to(device)
ae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16).to(device)
# quantize(t5, weights=qfloat8)
# freeze(t5)


# ---------------- Model ----------------


def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
    q, k = apply_rope(q, k, pe)

    x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
    # x = rearrange(x, "B H L D -> B L (H D)")
    x = x.permute(0, 2, 1, 3).reshape(x.size(0), x.size(2), -1)

    return x


def rope(pos, dim, theta):
    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
    omega = 1.0 / (theta ** scale)

    # out = torch.einsum("...n,d->...nd", pos, omega)
    out = pos.unsqueeze(-1) * omega.unsqueeze(0)

    cos_out = torch.cos(out)
    sin_out = torch.sin(out)
    out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)

    # out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
    b, n, d, _ = out.shape
    out = out.view(b, n, d, 2, 2)

    return out.float()


def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)


class EmbedND(nn.Module):
    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
        super().__init__()
        self.dim = dim
        self.theta = theta
        self.axes_dim = axes_dim

    def forward(self, ids: Tensor) -> Tensor:
        n_axes = ids.shape[-1]
        emb = torch.cat(
            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
            dim=-3,
        )

        return emb.unsqueeze(1)


def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
    """
    Create sinusoidal timestep embeddings.
    :param t: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an (N, D) Tensor of positional embeddings.
    """
    t = time_factor * t
    half = dim // 2
    
    # Do not block CUDA steam, but having about 1e-4 differences with Flux official codes:
    # freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)

    # Block CUDA steam, but consistent with official codes:
    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(t.device)

    args = t[:, None].float() * freqs[None]
    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
    if dim % 2:
        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    if torch.is_floating_point(t):
        embedding = embedding.to(t)
    return embedding


class MLPEmbedder(nn.Module):
    def __init__(self, in_dim: int, hidden_dim: int):
        super().__init__()
        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
        self.silu = nn.SiLU()
        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)

    def forward(self, x: Tensor) -> Tensor:
        return self.out_layer(self.silu(self.in_layer(x)))


class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.scale = nn.Parameter(torch.ones(dim))

    def forward(self, x: Tensor):
        x_dtype = x.dtype
        x = x.float()
        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
        return (x * rrms).to(dtype=x_dtype) * self.scale


class QKNorm(torch.nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.query_norm = RMSNorm(dim)
        self.key_norm = RMSNorm(dim)

    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
        q = self.query_norm(q)
        k = self.key_norm(k)
        return q.to(v), k.to(v)


class SelfAttention(nn.Module):
    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.norm = QKNorm(head_dim)
        self.proj = nn.Linear(dim, dim)

    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
        qkv = self.qkv(x)
        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = qkv.shape
        qkv = qkv.view(B, L, 3, self.num_heads, -1)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)
        q, k = self.norm(q, k, v)
        x = attention(q, k, v, pe=pe)
        x = self.proj(x)
        return x


@dataclass
class ModulationOut:
    shift: Tensor
    scale: Tensor
    gate: Tensor


class Modulation(nn.Module):
    def __init__(self, dim: int, double: bool):
        super().__init__()
        self.is_double = double
        self.multiplier = 6 if double else 3
        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)

    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)

        return (
            ModulationOut(*out[:3]),
            ModulationOut(*out[3:]) if self.is_double else None,
        )


class DoubleStreamBlock(nn.Module):
    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False):
        super().__init__()

        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.num_heads = num_heads
        self.hidden_size = hidden_size
        self.img_mod = Modulation(hidden_size, double=True)
        self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)

        self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.img_mlp = nn.Sequential(
            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
            nn.GELU(approximate="tanh"),
            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
        )

        self.txt_mod = Modulation(hidden_size, double=True)
        self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias)

        self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.txt_mlp = nn.Sequential(
            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
            nn.GELU(approximate="tanh"),
            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
        )

    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
        img_mod1, img_mod2 = self.img_mod(vec)
        txt_mod1, txt_mod2 = self.txt_mod(vec)

        # prepare image for attention
        img_modulated = self.img_norm1(img)
        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
        img_qkv = self.img_attn.qkv(img_modulated)
         # img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = img_qkv.shape
        H = self.num_heads
        D = img_qkv.shape[-1] // (3 * H)
        img_q, img_k, img_v = img_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)

        # prepare txt for attention
        txt_modulated = self.txt_norm1(txt)
        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
        txt_qkv = self.txt_attn.qkv(txt_modulated)
        # txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        B, L, _ = txt_qkv.shape
        txt_q, txt_k, txt_v = txt_qkv.view(B, L, 3, H, D).permute(2, 0, 3, 1, 4)
        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)

        # run actual attention
        q = torch.cat((txt_q, img_q), dim=2)
        k = torch.cat((txt_k, img_k), dim=2)
        v = torch.cat((txt_v, img_v), dim=2)

        attn = attention(q, k, v, pe=pe)
        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]

        # calculate the img bloks
        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)

        # calculate the txt bloks
        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
        return img, txt


class SingleStreamBlock(nn.Module):
    """
    A DiT block with parallel linear layers as described in
    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
    """

    def __init__(
        self,
        hidden_size: int,
        num_heads: int,
        mlp_ratio: float = 4.0,
        qk_scale: float | None = None,
    ):
        super().__init__()
        self.hidden_dim = hidden_size
        self.num_heads = num_heads
        head_dim = hidden_size // num_heads
        self.scale = qk_scale or head_dim**-0.5

        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
        # qkv and mlp_in
        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
        # proj and mlp_out
        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)

        self.norm = QKNorm(head_dim)

        self.hidden_size = hidden_size
        self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)

        self.mlp_act = nn.GELU(approximate="tanh")
        self.modulation = Modulation(hidden_size, double=False)

    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
        mod, _ = self.modulation(vec)
        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)

        # q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
        qkv = qkv.view(qkv.size(0), qkv.size(1), 3, self.num_heads, self.hidden_size // self.num_heads)
        q, k, v = qkv.permute(2, 0, 3, 1, 4)
        q, k = self.norm(q, k, v)

        # compute attention
        attn = attention(q, k, v, pe=pe)
        # compute activation in mlp stream, cat again and run second linear layer
        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
        return x + mod.gate * output
    

class LastLayer(nn.Module):
    def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
        super().__init__()
        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))

    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
        x = self.linear(x)
        return x
   
   
class FluxParams:
    in_channels: int = 64
    vec_in_dim: int = 768
    context_in_dim: int = 4096
    hidden_size: int = 3072
    mlp_ratio: float = 4.0
    num_heads: int = 24
    depth: int = 19
    depth_single_blocks: int = 38
    axes_dim: list = [16, 56, 56]
    theta: int = 10_000
    qkv_bias: bool = True
    guidance_embed: bool = True


class Flux(nn.Module):
    """
    Transformer model for flow matching on sequences.
    """

    def __init__(self, params = FluxParams()):
        super().__init__()

        self.params = params
        self.in_channels = params.in_channels
        self.out_channels = self.in_channels
        if params.hidden_size % params.num_heads != 0:
            raise ValueError(
                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
            )
        pe_dim = params.hidden_size // params.num_heads
        if sum(params.axes_dim) != pe_dim:
            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
        self.hidden_size = params.hidden_size
        self.num_heads = params.num_heads
        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
        # self.guidance_in = (
        #     MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
        # )
        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)

        self.double_blocks = nn.ModuleList(
            [
                DoubleStreamBlock(
                    self.hidden_size,
                    self.num_heads,
                    mlp_ratio=params.mlp_ratio,
                    qkv_bias=params.qkv_bias,
                )
                for _ in range(params.depth)
            ]
        )

        self.single_blocks = nn.ModuleList(
            [
                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio)
                for _ in range(params.depth_single_blocks)
            ]
        )

        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)

    def forward(
        self,
        img: Tensor,
        img_ids: Tensor,
        txt: Tensor,
        txt_ids: Tensor,
        timesteps: Tensor,
        y: Tensor,
        guidance: Tensor | None = None,
        use_guidance_vec = True,
    ) -> Tensor:
        if img.ndim != 3 or txt.ndim != 3:
            raise ValueError("Input img and txt tensors must have 3 dimensions.")

        # running on sequences img
        img = self.img_in(img)
        vec = self.time_in(timestep_embedding(timesteps, 256))
        # if self.params.guidance_embed and use_guidance_vec:
        #     if guidance is None:
        #         raise ValueError("Didn't get guidance strength for guidance distilled model.")
        #     vec = vec + self.guidance_in(timestep_embedding(guidance, 256))
        vec = vec + self.vector_in(y)
        txt = self.txt_in(txt)

        ids = torch.cat((txt_ids, img_ids), dim=1)
        pe = self.pe_embedder(ids)

        for block in self.double_blocks:
            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)

        img = torch.cat((txt, img), 1)
        for block in self.single_blocks:
            img = block(img, vec=vec, pe=pe)
        img = img[:, txt.shape[1] :, ...]

        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
        return img


def prepare(t5: HFEmbedder, clip: HFEmbedder, img: Tensor, prompt: str | list[str]) -> dict[str, Tensor]:
    bs, c, h, w = img.shape
    if bs == 1 and not isinstance(prompt, str):
        bs = len(prompt)

    img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
    if img.shape[0] == 1 and bs > 1:
        img = repeat(img, "1 ... -> bs ...", bs=bs)

    img_ids = torch.zeros(h // 2, w // 2, 3)
    img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
    img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
    img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)

    if isinstance(prompt, str):
        prompt = [prompt]
    txt = t5(prompt)
    if txt.shape[0] == 1 and bs > 1:
        txt = repeat(txt, "1 ... -> bs ...", bs=bs)
    txt_ids = torch.zeros(bs, txt.shape[1], 3)

    vec = clip(prompt)
    if vec.shape[0] == 1 and bs > 1:
        vec = repeat(vec, "1 ... -> bs ...", bs=bs)

    return {
        "img": img,
        "img_ids": img_ids.to(img.device),
        "txt": txt.to(img.device),
        "txt_ids": txt_ids.to(img.device),
        "vec": vec.to(img.device),
    }


def time_shift(mu: float, sigma: float, t: Tensor):
    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)


def get_lin_function(
    x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15
) -> Callable[[float], float]:
    m = (y2 - y1) / (x2 - x1)
    b = y1 - m * x1
    return lambda x: m * x + b


def get_schedule(
    num_steps: int,
    image_seq_len: int,
    base_shift: float = 0.5,
    max_shift: float = 1.15,
    shift: bool = True,
) -> list[float]:
    # extra step for zero
    timesteps = torch.linspace(1, 0, num_steps + 1)

    # shifting the schedule to favor high timesteps for higher signal images
    if shift:
        # eastimate mu based on linear estimation between two points
        mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
        timesteps = time_shift(mu, 1.0, timesteps)

    return timesteps.tolist()
    
@property
def joint_attention_kwargs(self):
    return self._joint_attention_kwargs

def denoise(
    model: Flux,
    # model input
    img: Tensor,
    img_ids: Tensor,
    txt: Tensor,
    txt_ids: Tensor,
    vec: Tensor,
    # sampling parameters
    timesteps: list[float],
    guidance: float = 4.0,
    use_cfg_guidance = False,
):
    # this is ignored for schnell
    guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
    for t_curr, t_prev in tqdm(zip(timesteps[:-1], timesteps[1:])):
        t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
        
        if use_cfg_guidance:
            half_x = img[:len(img)//2]
            img = torch.cat([half_x, half_x], dim=0)
            t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
        
        pred = model(
            img=img,
            img_ids=img_ids,
            txt=txt,
            txt_ids=txt_ids,
            y=vec,
            timesteps=t_vec,
            guidance=guidance_vec,
            joint_attention_kwargs=self.joint_attention_kwargs,
            use_guidance_vec=not use_cfg_guidance,
        )
        
        if use_cfg_guidance:
            uncond, cond = pred.chunk(2, dim=0)
            model_output = uncond + guidance * (cond - uncond)
            pred = torch.cat([model_output, model_output], dim=0)

        img = img + (t_prev - t_curr) * pred

    return img


def unpack(x: Tensor, height: int, width: int) -> Tensor:
    return rearrange(
        x,
        "b (h w) (c ph pw) -> b c (h ph) (w pw)",
        h=math.ceil(height / 16),
        w=math.ceil(width / 16),
        ph=2,
        pw=2,
    )

@dataclass
class SamplingOptions:
    prompt: str
    width: int
    height: int
    guidance: float
    seed: int | None
    joint_attention_kwargs: Any | None 
    

def get_image(image) -> torch.Tensor | None:
    if image is None:
        return None
    image = Image.fromarray(image).convert("RGB")

    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Lambda(lambda x: 2.0 * x - 1.0),
    ])
    img: torch.Tensor = transform(image)
    return img[None, ...]


# ---------------- Demo ----------------

class EmptyInitWrapper(torch.overrides.TorchFunctionMode):
    def __init__(self, device=None):
        self.device = device

    def __torch_function__(self, func, types, args=(), kwargs=None):
        kwargs = kwargs or {}
        if getattr(func, "__module__", None) == "torch.nn.init":
            if "tensor" in kwargs:
                return kwargs["tensor"]
            else:
                return args[0]
        if (
            self.device is not None
            and func in torch.utils._device._device_constructors()
            and kwargs.get("device") is None
        ):
            kwargs["device"] = self.device
        return func(*args, **kwargs)

with EmptyInitWrapper():
    model = Flux().to(dtype=torch.bfloat16, device="cuda")
    
    sd = load_file(f"{model_path}/OpenFlux-fp8_e4m3fn.safetensors")
    sd = {k.replace("model.", ""): v for k, v in sd.items()}
    result = model.load_state_dict(sd)
    
@torch.cuda.empty_cache()

@spaces.GPU(duration=70)
@torch.no_grad()
def generate_image(
    prompt, neg_prompt, num_steps, width, height, guidance, seed,
    do_img2img, init_image, image2image_strength, resize_img, lora_scale,
    progress=gr.Progress(track_tqdm=True),
):
    if seed == 0:
        seed = int(random.random() * 1000000)
        
    device = "cuda" if torch.cuda.is_available() else "cpu"
    torch_device = torch.device(device)

    lora_scale = (self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None)
        
    if do_img2img and init_image is not None:
        init_image = get_image(init_image)
        if resize_img:
            init_image = torch.nn.functional.interpolate(init_image, (height, width))
        else:
            h, w = init_image.shape[-2:]
            init_image = init_image[..., : 16 * (h // 16), : 16 * (w // 16)]
            height = init_image.shape[-2]
            width = init_image.shape[-1]
        init_image = ae.encode(init_image.to(torch_device)).latent_dist.sample()
        init_image =  (init_image - ae.config.shift_factor) * ae.config.scaling_factor

    generator = torch.Generator(device=device).manual_seed(seed)
    x = torch.randn(1, 16, 2 * math.ceil(height / 16), 2 * math.ceil(width / 16), device=device, dtype=torch.bfloat16, generator=generator) 
    
    with calculateDuration("Generating image"):
        # Generate image
        image = pipe(
            prompt=f"{prompt} {trigger_word}",
            neg_prompt=negative_prompt,
            num_steps=steps,
            guidance=cfg_scale,
            width=width,
            height=height,
            generator=generator,
            do_img2img=img2img, 
            init_image=upload_image,
            image2image_strength=image2image_strength,
            resize_img=resize_img,
            joint_attention_kwargs={"scale": lora_scale},
        ).images[0]
    return image
    
    # num_steps = 28
    timesteps = get_schedule(num_steps, (x.shape[-1] * x.shape[-2]) // 4, shift=True)

    if do_img2img and init_image is not None:
        t_idx = int((1 - image2image_strength) * num_steps)
        t = timesteps[t_idx]
        timesteps = timesteps[t_idx:]
        x = t * x + (1.0 - t) * init_image.to(x.dtype)

    inp = prepare(t5=t5, clip=clip, img=x, prompt=[neg_prompt, prompt])
    x = denoise(model, **inp, timesteps=timesteps, guidance=guidance, use_cfg_guidance=True, joint_attention_kwargs=self.joint_attention_kwargs)
    
    # with profile(activities=[ProfilerActivity.CPU],record_shapes=True,profile_memory=True) as prof:
    # print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=20))

    x = unpack(x.float(), height, width)
    with torch.autocast(device_type=torch_device.type, dtype=torch.bfloat16):
        x = x = (x / ae.config.scaling_factor) + ae.config.shift_factor 
        x = ae.decode(x).sample

    x = x.clamp(-1, 1)
    x = rearrange(x[0], "c h w -> h w c")
    img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
    
    return img, seed
    

@torch.cuda.empty_cache()

class calculateDuration:
    def __init__(self, activity_name=""):
        self.activity_name = activity_name

    def __enter__(self):
        self.start_time = time.time()
        return self
    
    def __exit__(self, exc_type, exc_value, traceback):
        self.end_time = time.time()
        self.elapsed_time = self.end_time - self.start_time
        if self.activity_name:
            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
        else:
            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")


def update_selection(evt: gr.SelectData, width, height):
    selected_lora = loras[evt.index]
    new_placeholder = f"Type a prompt for {selected_lora['title']}"
    lora_repo = selected_lora["repo"]
    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
    if "aspect" in selected_lora:
        if selected_lora["aspect"] == "portrait":
            width = 768
            height = 1024
        elif selected_lora["aspect"] == "landscape":
            width = 1024
            height = 768
    return (
        gr.update(placeholder=new_placeholder),
        updated_text,
        evt.index,
        width,
        height,
    )

def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, negative_prompt, lora_scale, progress=gr.Progress(track_tqdm=True)):
    if negative_prompt == "":
        negative_prompt = None    
    if selected_index is None:
        raise gr.Error("You must select a LoRA before proceeding.")

    selected_lora = loras[selected_index]
    lora_path = selected_lora["repo"]
    trigger_word = selected_lora["trigger_word"]

    # Load LoRA weights
    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
        if "weights" in selected_lora:
            pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
        else:
            pipe.load_lora_weights(lora_path)
        
    # Set random seed for reproducibility
    with calculateDuration("Randomizing seed"):
        if randomize_seed:
            seed = random.randint(0, 2**32-1)
    
    image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, negative_prompt, lora_scale, progress)
    pipe.to("cpu")
    pipe.unload_lora_weights()
    return image, seed  

run_lora.zerogpu = True

css = '''
#gen_btn{height: 100%}
#title{text-align: center}
#title h1{font-size: 3em; display:inline-flex; align-items:center}
#title img{width: 100px; margin-right: 0.5em}
#gallery .grid-wrap{height: 10vh}
'''
with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
    title = gr.HTML(
        """<h1><img src="https://huggingface.co/AlekseyCalvin/HSTklimbimOPENfluxLora/resolve/main/acs62iv.png" alt="LoRA">OpenFlux LoRAsoon®</h1>""",
        elem_id="title",
    )
    	    # Info blob stating what the app is running
    info_blob = gr.HTML(
        """<div id="info_blob"> SOON®'s curated LoRa Gallery & Art Manufactory Space.|Runs on Ostris' OpenFLUX.1 model + fast-gen LoRA & Zer0int's fine-tuned CLIP-GmP-ViT-L-14*! (*'normal' 77 tokens)| Largely stocked w/our trained LoRAs: Historic Color, Silver Age Poets, Sots Art, more!|</div>"""
    )
        # Info blob stating what the app is running
    info_blob = gr.HTML(
        """<div id="info_blob"> *Auto-planting of prompts with a choice LoRA trigger errors out in this space over flaws yet unclear. In its stead, we pose numbered LoRA-box rows & a matched token cheat-sheet: ungainly & free. So, prephrase your prompts w/: 1-2. HST style autochrome |3. RCA style Communist poster |4. SOTS art |5. HST Austin Osman Spare style |6. Vladimir Mayakovsky |7-8. Marina Tsvetaeva Tsvetaeva_02.CR2 |9. Anna Akhmatova |10. Osip Mandelshtam |11-12. Alexander Blok |13. Blok_02.CR2 |14. LEN Lenin |15. Leon Trotsky |16. Rosa Fluxemburg |17. HST Peterhof photo |18-19. HST |20. HST portrait |21. HST |22. HST 80s Perestroika-era Soviet photo |23-30. HST |31. How2Draw a__ |32. propaganda poster |33. TOK hybrid photo of__ with cartoon of__ |34. 2004 IMG_1099.CR2 photo |35. unexpected photo of |36. flmft |37. 80s yearbook photo |38. TOK portra |39. pficonics |40. retrofuturism |41. wh3r3sw4ld0 |42. amateur photo |43. crisp |44-45. IMG_1099.CR2 |46. FilmFotos |47. ff-collage |48. HST |49-50. AOS |51. cover </div>"""
    )
    selected_index = gr.State(None)
    with gr.Row():
        with gr.Column(scale=3):
            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt!")
    with gr.Row():
        with gr.Column(scale=3):
            negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="List unwanted conditions, open-fluxedly!")
        with gr.Column(scale=1, elem_id="gen_column"):
            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
    with gr.Row():
        with gr.Column(scale=3):
            selected_info = gr.Markdown("")
            gallery = gr.Gallery(
                [(item["image"], item["title"]) for item in loras],
                label="LoRA Inventory",
                allow_preview=False,
                columns=3,
                elem_id="gallery"
            )
            
        with gr.Column(scale=4):
            result = gr.Image(label="Generated Image")

    with gr.Row():
        with gr.Accordion("Advanced Settings", open=True):
            with gr.Column():
                with gr.Row():
                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=1, value=3)
                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=6)
                
                with gr.Row():
                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=768)
                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=768)
                
                with gr.Row():
                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
                    seed = gr.Slider(label="Seed", minimum=0, maximum=2**32-1, step=1, value=0, randomize=True)
                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=0.95)

    gallery.select(
        update_selection,
        inputs=[width, height],
        outputs=[prompt, selected_info, selected_index, width, height]
    )

    gr.on(
        triggers=[generate_button.click, prompt.submit],
        fn=run_lora,
        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, negative_prompt, lora_scale],
        outputs=[result, seed]
    )

warnings.filterwarnings("ignore", category=FutureWarning)
app.queue(default_concurrency_limit=2).launch(show_error=True)
app.launch()