Upload app.py

app.py

@@ -1,735 +1,743 @@
-import gradio as gr
-from PIL import Image
-from torchvision import transforms
-from dataclasses import dataclass
-import math
-from typing import Callable
-import os 
-import spaces
-
-import torch
-import random
-from tqdm import tqdm
-from einops import rearrange, repeat
-from diffusers import AutoencoderKL
-from torch import Tensor, nn
-from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
-from safetensors.torch import load_file
-dtype = torch.bfloat16
-from huggingface_hub import snapshot_download
-model_path  = snapshot_download(repo_id="nyanko7/flux-dev-de-distill")
-device = "cuda" if torch.cuda.is_available() else "cpu"
-# ---------------- Encoders ----------------
-
-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()
-
-
-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,
-            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
-    
-
-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}/consolidated_s6700.safetensors")
-    sd = {k.replace("model.", ""): v for k, v in sd.items()}
-    result = model.load_state_dict(sd)
-    
-@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,
-    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)
-        
-    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) 
-    
-    # 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)
-    
-    # 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
-
-def create_demo():
-    with gr.Blocks(theme="bethecloud/storj_theme") as demo:
-        with gr.Row():
-            with gr.Column():
-                prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world")
-                neg_prompt = gr.Textbox(label="Negative Prompt", value="bad photo")
-                num_steps = gr.Slider(minimum=1, maximum=50, step=1, label="num_steps", value=28)
-                width = gr.Slider(minimum=128, maximum=2048, step=64, label="Width", value=1024)
-                height = gr.Slider(minimum=128, maximum=2048, step=64, label="Height", value=1024)
-                guidance = gr.Slider(minimum=1.0, maximum=5.0, step=0.1, label="Guidance", value=3.5)
-                seed = gr.Number(label="Seed", precision=-1)
-                do_img2img = gr.Checkbox(label="Image to Image", value=False)
-                init_image = gr.Image(label="Input Image", visible=False)
-                image2image_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Noising strength", value=0.8, visible=False)
-                resize_img = gr.Checkbox(label="Resize image", value=True, visible=False)
-                generate_button = gr.Button("Generate")
-            
-            with gr.Column():
-                output_image = gr.Image(label="Generated Image")
-                output_seed = gr.Text(label="Used Seed")
-
-        do_img2img.change(
-            fn=lambda x: [gr.update(visible=x), gr.update(visible=x), gr.update(visible=x)],
-            inputs=[do_img2img],
-            outputs=[init_image, image2image_strength, resize_img]
-        )
-
-        generate_button.click(
-            fn=generate_image,
-            inputs=[prompt, neg_prompt, num_steps,width, height, guidance, seed, do_img2img, init_image, image2image_strength, resize_img],
-            outputs=[output_image, output_seed]
-        )
-        
-        examples = [
-            "a tiny astronaut hatching from an egg on the moon",
-            "a cat holding a sign that says hello world",
-            "an anime illustration of a wiener schnitzel",
-        ]
-
-    return demo
-
-demo = create_demo()
+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
+from PIL import Image
+from torchvision import transforms
+from dataclasses import dataclass
+import math
+from typing import Callable
+
+import torch
+import random
+from tqdm import tqdm
+from einops import rearrange, repeat
+from diffusers import AutoencoderKL
+from torch import Tensor, nn
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5Tokenizer
+from safetensors.torch import load_file
+dtype = torch.bfloat16
+from huggingface_hub import snapshot_download
+model_path  = snapshot_download(repo_id="nyanko7/flux-dev-de-distill")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+# ---------------- Encoders ----------------
+
+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("camenduru/FLUX.1-dev-diffusers", 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()
+
+
+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,
+            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
+    
+
+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}/consolidated_s6700.safetensors")
+    sd = {k.replace("model.", ""): v for k, v in sd.items()}
+    result = model.load_state_dict(sd)
+    
+@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,
+    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)
+        
+    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) 
+    
+    # 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)
+    
+    # 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
+
+def create_demo():
+    with gr.Blocks(theme="bethecloud/storj_theme") as demo:
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Prompt", value="A cat holding a sign that says hello world")
+                neg_prompt = gr.Textbox(label="Negative Prompt", value="bad photo")
+                num_steps = gr.Slider(minimum=1, maximum=50, step=1, label="num_steps", value=28)
+                width = gr.Slider(minimum=128, maximum=2048, step=64, label="Width", value=1024)
+                height = gr.Slider(minimum=128, maximum=2048, step=64, label="Height", value=1024)
+                guidance = gr.Slider(minimum=1.0, maximum=5.0, step=0.1, label="Guidance", value=3.5)
+                seed = gr.Number(label="Seed", precision=-1)
+                do_img2img = gr.Checkbox(label="Image to Image", value=False)
+                init_image = gr.Image(label="Input Image", visible=False)
+                image2image_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Noising strength", value=0.8, visible=False)
+                resize_img = gr.Checkbox(label="Resize image", value=True, visible=False)
+                generate_button = gr.Button("Generate")
+            
+            with gr.Column():
+                output_image = gr.Image(label="Generated Image")
+                output_seed = gr.Text(label="Used Seed")
+
+        do_img2img.change(
+            fn=lambda x: [gr.update(visible=x), gr.update(visible=x), gr.update(visible=x)],
+            inputs=[do_img2img],
+            outputs=[init_image, image2image_strength, resize_img]
+        )
+
+        generate_button.click(
+            fn=generate_image,
+            inputs=[prompt, neg_prompt, num_steps,width, height, guidance, seed, do_img2img, init_image, image2image_strength, resize_img],
+            outputs=[output_image, output_seed]
+        )
+        
+        examples = [
+            "a tiny astronaut hatching from an egg on the moon",
+            "a cat holding a sign that says hello world",
+            "an anime illustration of a wiener schnitzel",
+        ]
+
+    return demo
+
+demo = create_demo()
 demo.launch(share=True)