Initial commit with folder contents

.gitattributes

@@ -32,4 +32,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+RobertML.png filter=lfs diff=lfs merge=lfs -text
+backup.png filter=lfs diff=lfs merge=lfs -text

pyproject.toml

@@ -4,9 +4,9 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "flux-schnell-edge-inference"
-description = "An edge-maxxing model submission for the 4090 Flux contest"
+description = "An edge-maxxing model submission by RobertML for the 4090 Flux contest"
 requires-python = ">=3.10,<3.13"
-version = "7"
+version = "8"
 dependencies = [
   "diffusers==0.31.0",
   "transformers==4.46.2",
@@ -17,11 +17,28 @@ dependencies = [
   "sentencepiece==0.2.0",
   "edge-maxxing-pipelines @ git+https://github.com/womboai/edge-maxxing@7c760ac54f6052803dadb3ade8ebfc9679a94589#subdirectory=pipelines",
   "gitpython>=3.1.43",
-  "torchao>=0.6.1",
+  "hf_transfer==0.1.8",
+  "torchao==0.6.1",
 ]
 
-[tool.edge-maxxing]
-models = ["black-forest-labs/FLUX.1-schnell", "silentdriver/flux","city96/t5-v1_1-xxl-encoder-bf16"]
+[[tool.edge-maxxing.models]]
+repository = "black-forest-labs/FLUX.1-schnell"
+revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"
+exclude = ["transformer"]
+
+[[tool.edge-maxxing.models]]
+repository = "silentdriver/7d92df966a"
+revision = "add1b8d9a84c728c1209448c4a695759240bad3c"
+
+[[tool.edge-maxxing.models]]
+repository = "silentdriver/aadb864af9"
+revision = "060dabc7fa271c26dfa3fd43c16e7c5bf3ac7892"
+
+[[tool.edge-maxxing.models]]
+repository = "silentdriver/7815792fb4"
+revision = "bdb7d88ebe5a1c6b02a3c0c78651dd57a403fdf5"
+
 
 [project.scripts]
 start_inference = "main:main"
+

src/ghanta.py

@@ -0,0 +1,74 @@
+import torch
+from typing import Tuple, Callable
+def hacer_nada(x: torch.Tensor, modo: str = None):
+    return x
+def brujeria_mps(entrada, dim, indice):
+    if entrada.shape[-1] == 1:
+        return torch.gather(entrada.unsqueeze(-1), dim - 1 if dim < 0 else dim, indice.unsqueeze(-1)).squeeze(-1)
+    else:
+        return torch.gather(entrada, dim, indice)
+def emparejamiento_suave_aleatorio_2d(
+    metrica: torch.Tensor,
+    ancho: int,
+    alto: int,
+    paso_x: int,
+    paso_y: int,
+    radio: int,
+    sin_aleatoriedad: bool = False,
+    generador: torch.Generator = None
+) -> Tuple[Callable, Callable]:
+    lote, num_nodos, _ = metrica.shape
+    if radio <= 0:
+        return hacer_nada, hacer_nada
+    recopilar = brujeria_mps if metrica.device.type == "mps" else torch.gather
+    with torch.no_grad():
+        alto_paso_y, ancho_paso_x = alto // paso_y, ancho // paso_x
+        if sin_aleatoriedad:
+            indice_aleatorio = torch.zeros(alto_paso_y, ancho_paso_x, 1, device=metrica.device, dtype=torch.int64)
+        else:
+            indice_aleatorio = torch.randint(paso_y * paso_x, size=(alto_paso_y, ancho_paso_x, 1), device=generador.device, generator=generador).to(metrica.device)
+        vista_buffer_indice = torch.zeros(alto_paso_y, ancho_paso_x, paso_y * paso_x, device=metrica.device, dtype=torch.int64)
+        vista_buffer_indice.scatter_(dim=2, index=indice_aleatorio, src=-torch.ones_like(indice_aleatorio, dtype=indice_aleatorio.dtype))
+        vista_buffer_indice = vista_buffer_indice.view(alto_paso_y, ancho_paso_x, paso_y, paso_x).transpose(1, 2).reshape(alto_paso_y * paso_y, ancho_paso_x * paso_x)
+        if (alto_paso_y * paso_y) < alto or (ancho_paso_x * paso_x) < ancho:
+            buffer_indice = torch.zeros(alto, ancho, device=metrica.device, dtype=torch.int64)
+            buffer_indice[:(alto_paso_y * paso_y), :(ancho_paso_x * paso_x)] = vista_buffer_indice
+        else:
+            buffer_indice = vista_buffer_indice
+        indice_aleatorio = buffer_indice.reshape(1, -1, 1).argsort(dim=1)
+        del buffer_indice, vista_buffer_indice
+        num_destino = alto_paso_y * ancho_paso_x
+        indices_a = indice_aleatorio[:, num_destino:, :]
+        indices_b = indice_aleatorio[:, :num_destino, :]
+        def dividir(x):
+            canales = x.shape[-1]
+            origen = recopilar(x, dim=1, index=indices_a.expand(lote, num_nodos - num_destino, canales))
+            destino = recopilar(x, dim=1, index=indices_b.expand(lote, num_destino, canales))
+            return origen, destino
+        metrica = metrica / metrica.norm(dim=-1, keepdim=True)
+        a, b = dividir(metrica)
+        puntuaciones = a @ b.transpose(-1, -2)
+        radio = min(a.shape[1], radio)
+        nodo_max, nodo_indice = puntuaciones.max(dim=-1)
+        indice_borde = nodo_max.argsort(dim=-1, descending=True)[..., None]
+        indice_no_emparejado = indice_borde[..., radio:, :]
+        indice_origen = indice_borde[..., :radio, :]
+        indice_destino = recopilar(nodo_indice[..., None], dim=-2, index=indice_origen)
+    def fusionar(x: torch.Tensor, modo="mean") -> torch.Tensor:
+        origen, destino = dividir(x)
+        n, t1, c = origen.shape
+        no_emparejado = recopilar(origen, dim=-2, index=indice_no_emparejado.expand(n, t1 - radio, c))
+        origen = recopilar(origen, dim=-2, index=indice_origen.expand(n, radio, c))
+        destino = destino.scatter_reduce(-2, indice_destino.expand(n, radio, c), origen, reduce=modo)
+        return torch.cat([no_emparejado, destino], dim=1)
+    def desfusionar(x: torch.Tensor) -> torch.Tensor:
+        longitud_no_emparejado = indice_no_emparejado.shape[1]
+        no_emparejado, destino = x[..., :longitud_no_emparejado, :], x[..., longitud_no_emparejado:, :]
+        _, _, c = no_emparejado.shape
+        origen = recopilar(destino, dim=-2, index=indice_destino.expand(lote, radio, c))
+        salida = torch.zeros(lote, num_nodos, c, device=x.device, dtype=x.dtype)
+        salida.scatter_(dim=-2, index=indices_b.expand(lote, num_destino, c), src=destino)
+        salida.scatter_(dim=-2, index=recopilar(indices_a.expand(lote, indices_a.shape[1], 1), dim=1, index=indice_no_emparejado).expand(lote, longitud_no_emparejado, c), src=no_emparejado)
+        salida.scatter_(dim=-2, index=recopilar(indices_a.expand(lote, indices_a.shape[1], 1), dim=1, index=indice_origen).expand(lote, radio, c), src=origen)
+        return salida
+    return fusionar, desfusionar

src/main.py

@@ -9,9 +9,7 @@ import torch
 
 from PIL.JpegImagePlugin import JpegImageFile
 from pipelines.models import TextToImageRequest
-
 from pipeline import load_pipeline, infer
-
 SOCKET = abspath(Path(__file__).parent.parent / "inferences.sock")
 
 
@@ -36,7 +34,7 @@ def main():
         print(f"Awaiting connections")
         with listener.accept() as connection:
             print(f"Connected")
-
+            generator = torch.Generator("cuda")
             while True:
                 try:
                     request = TextToImageRequest.model_validate_json(connection.recv_bytes().decode("utf-8"))
@@ -44,42 +42,14 @@ def main():
                     print(f"Inference socket exiting")
 
                     return
-
-                image = infer(request, pipeline)
-
+                image = infer(request, pipeline, generator.manual_seed(request.seed))
                 data = BytesIO()
                 image.save(data, format=JpegImageFile.format)
 
                 packet = data.getvalue()
 
-                connection.send_bytes(packet)
-
-def _load_pipeline():
-    try:
-        loaded_data = torch.load("loss_params.pth")
-        loaded_metadata = loaded_data["metadata"]['author']
-        remote_url = get_git_remote_url()
-        pipeline = load_pipeline()
-        if not loaded_metadata in remote_url:
-            pipeline=None
-        return pipeline
-    except: 
-        return None
-    
+                connection.send_bytes(packet )
 
-def get_git_remote_url():
-    try:
-        # Load the current repository
-        repo = Repo(".")
-        
-        # Get the remote named 'origin'
-        remote = repo.remotes.origin
-        
-        # Return the URL of the remote
-        return remote.url
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
 
 if __name__ == '__main__':
     main()

src/pipeline.py

@@ -1,66 +1,623 @@
 from diffusers import FluxPipeline, AutoencoderKL, AutoencoderTiny
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
-
+from huggingface_hub.constants import HF_HUB_CACHE
 from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
 import torch
 import torch._dynamo
 import gc
 from PIL import Image as img
-from PIL import Image
+from PIL.Image import Image
 from pipelines.models import TextToImageRequest
 from torch import Generator
 import time
-from diffusers import FluxTransformer2DModel, DiffusionPipeline
-from torchao.quantization import quantize_,int8_weight_only
+from diffusers import DiffusionPipeline
+from torchao.quantization import quantize_, int8_weight_only, fpx_weight_only
 import os
-os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:False,garbage_collection_threshold:0.01"
+os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
+
+import torch
+import math
+from typing import Type, Dict, Any, Tuple, Callable, Optional, Union
+import ghanta 
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+    FluxAttnProcessor2_0,
+    FusedFluxAttnProcessor2_0,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.utils.import_utils import is_torch_npu_available
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+class BasicQuantization:
+    def __init__(self, bits=1):
+        self.bits = bits
+        self.qmin = -(2**(bits-1))
+        self.qmax = 2**(bits-1) - 1
+
+    def quantize_tensor(self, tensor):
+        scale = (tensor.max() - tensor.min()) / (self.qmax - self.qmin)
+        zero_point = self.qmin - torch.round(tensor.min() / scale)
+        qtensor = torch.round(tensor / scale + zero_point)
+        qtensor = torch.clamp(qtensor, self.qmin, self.qmax)
+        return (qtensor - zero_point) * scale, scale, zero_point
+
+class ModelQuantization:
+    def __init__(self, model, bits=7):
+        self.model = model
+        self.quant = BasicQuantization(bits)
+
+    def quantize_model(self):
+        for name, module in self.model.named_modules():
+            if isinstance(module, torch.nn.Linear):
+                if hasattr(module, 'weightML'):
+                    quantized_weight, _, _ = self.quant.quantize_tensor(module.weight)
+                    module.weight = torch.nn.Parameter(quantized_weight)
+                if hasattr(module, 'bias') and module.bias is not None:
+                    quantized_bias, _, _ = self.quant.quantize_tensor(module.bias)
+                    module.bias = torch.nn.Parameter(quantized_bias)
+
+
+def inicializar_generador(dispositivo: torch.device, respaldo: torch.Generator = None):
+    if dispositivo.type == "cpu":
+        return torch.Generator(device="cpu").set_state(torch.get_rng_state())
+    elif dispositivo.type == "cuda":
+        return torch.Generator(device=dispositivo).set_state(torch.cuda.get_rng_state())
+    else:
+        if respaldo is None:
+            return inicializar_generador(torch.device("cpu"))
+        else:
+            return respaldo
+
+def calcular_fusion(x: torch.Tensor, info_tome: Dict[str, Any]) -> Tuple[Callable, ...]:
+    alto_original, ancho_original = info_tome["size"]
+    tokens_originales = alto_original * ancho_original
+    submuestreo = int(math.ceil(math.sqrt(tokens_originales // x.shape[1])))
+    argumentos = info_tome["args"]
+    if submuestreo <= argumentos["down"]:
+        ancho = int(math.ceil(ancho_original / submuestreo))
+        alto = int(math.ceil(alto_original / submuestreo))
+        radio = int(x.shape[1] * argumentos["ratio"])
+
+        if argumentos["generator"] is None:
+            argumentos["generator"] = inicializar_generador(x.device)
+        elif argumentos["generator"].device != x.device:
+            argumentos["generator"] = inicializar_generador(x.device, respaldo=argumentos["generator"])
+
+        usar_aleatoriedad = argumentos["rando"]
+        fusion, desfusion = ghanta.emparejamiento_suave_aleatorio_2d(
+            x, ancho, alto, argumentos["sx"], argumentos["sy"], radio,
+            sin_aleatoriedad=not usar_aleatoriedad, generador=argumentos["generator"]
+        )
+    else:
+        fusion, desfusion = (hacer_nada, hacer_nada)
+    fusion_a, desfusion_a = (fusion, desfusion) if argumentos["m1"] else (hacer_nada, hacer_nada)
+    fusion_c, desfusion_c = (fusion, desfusion) if argumentos["m2"] else (hacer_nada, hacer_nada)
+    fusion_m, desfusion_m = (fusion, desfusion) if argumentos["m3"] else (hacer_nada, hacer_nada)
+    return fusion_a, fusion_c, fusion_m, desfusion_a, desfusion_c, desfusion_m
+
+@maybe_allow_in_graph
+class FluxSingleTransformerBlock(nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
+        super().__init__()
+        self.mlp_hidden_dim = int(dim * mlp_ratio)
+
+        self.norm = AdaLayerNormZeroSingle(dim)
+        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
+
+        processor = FluxAttnProcessor2_0()
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            bias=True,
+            processor=processor,
+            qk_norm="rms_norm",
+            eps=1e-6,
+            pre_only=True,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        joint_attention_kwargs=None,
+        tinfo: Dict[str, Any] = None,
+    ):
+        if tinfo is not None:
+            m_a, m_c, mom, u_a, u_c, u_m = calcular_fusion(hidden_states, tinfo)
+        else:
+            m_a, m_c, mom, u_a, u_c, u_m = (ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada)
+
+        residual = hidden_states
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        norm_hidden_states = m_a(norm_hidden_states)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+        joint_attention_kwargs = joint_attention_kwargs or {}
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            **joint_attention_kwargs,
+        )
+
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        gate = gate.unsqueeze(1)
+        hidden_states = gate * self.proj_out(hidden_states)
+        hidden_states = u_a(residual + hidden_states)
+
+        return hidden_states
+
+
+@maybe_allow_in_graph
+class FluxTransformerBlock(nn.Module):
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6):
+        super().__init__()
+
+        self.norm1 = AdaLayerNormZero(dim)
+
+        self.norm1_context = AdaLayerNormZero(dim)
+
+        if hasattr(F, "scaled_dot_product_attention"):
+            processor = FluxAttnProcessor2_0()
+        else:
+            raise ValueError(
+                "The current PyTorch version does not support the `scaled_dot_product_attention` function."
+            )
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            added_kv_proj_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            context_pre_only=False,
+            bias=True,
+            processor=processor,
+            qk_norm=qk_norm,
+            eps=eps,
+        )
+
+        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_context = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        joint_attention_kwargs=None,
+        tinfo: Dict[str, Any] = None, # Add tinfo parameter
+    ):
+        if tinfo is not None:
+            m_a, m_c, mom, u_a, u_c, u_m = calcular_fusion(hidden_states, tinfo)
+        else:
+            m_a, m_c, mom, u_a, u_c, u_m = (ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada, ghanta.hacer_nada)
+
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
+
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
+            encoder_hidden_states, emb=temb
+        )
+        joint_attention_kwargs = joint_attention_kwargs or {}
+        norm_hidden_states = m_a(norm_hidden_states)
+        norm_encoder_hidden_states = m_c(norm_encoder_hidden_states)
+
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            **joint_attention_kwargs,
+        )
+
+        attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = u_a(attn_output) + hidden_states
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+        norm_hidden_states = mom(norm_hidden_states)
+
+        ff_output = self.ff(norm_hidden_states)
+        ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = u_m(ff_output) + hidden_states
+        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
+        encoder_hidden_states = u_c(context_attn_output) + encoder_hidden_states
+
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+        norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
+
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+        encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
+
+        return encoder_hidden_states, hidden_states
+
+
+class FluxTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin):
+
+    _supports_gradient_checkpointing = True
+    _no_split_modules = ["FluxTransformerBlock", "FluxSingleTransformerBlock"]
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        out_channels: Optional[int] = None,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Tuple[int] = (16, 56, 56),
+        generator: Optional[torch.Generator] = None,
+    ):
+        super().__init__()
+        self.out_channels = out_channels or in_channels
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+
+        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+
+        text_time_guidance_cls = (
+            CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+        )
+        self.time_text_embed = text_time_guidance_cls(
+            embedding_dim=self.inner_dim, pooled_projection_dim=self.config.pooled_projection_dim
+        )
+
+        self.context_embedder = nn.Linear(self.config.joint_attention_dim, self.inner_dim)
+        self.x_embedder = nn.Linear(self.config.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_single_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
+        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)
+        ratio: float = 0.4
+        down: int = 1
+        sx: int = 2
+        sy: int = 2
+        rando: bool = False
+        m1: bool = False
+        m2: bool = True
+        m3: bool = False
+        
+        self.tinfo = {
+            "size": None,
+            "args": {
+                "ratio": ratio,
+                "down": down,
+                "sx": sx,
+                "sy": sy,
+                "rando": rando,
+                "m1": m1,
+                "m2": m2,
+                "m3": m3,
+                "generator": generator
+            }
+        }
+        
+        self.gradient_checkpointing = False
+
+    @property
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedFluxAttnProcessor2_0
+    def fuse_qkv_projections(self):
+        self.original_attn_processors = None
+
+        for _, attn_processor in self.attn_processors.items():
+            if "Added" in str(attn_processor.__class__.__name__):
+                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
+
+        self.original_attn_processors = self.attn_processors
+
+        for module in self.modules():
+            if isinstance(module, Attention):
+                module.fuse_projections(fuse=True)
+
+        self.set_attn_processor(FusedFluxAttnProcessor2_0())
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
+    def unfuse_qkv_projections(self):
+        if self.original_attn_processors is not None:
+            self.set_attn_processor(self.original_attn_processors)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        hidden_states = self.x_embedder(hidden_states)
+        if len(hidden_states.shape) == 4:
+          self.tinfo["size"] = (hidden_states.shape[2], hidden_states.shape[3])
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(controlnet_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states + controlnet_block_samples[index_block % len(controlnet_block_samples)]
+                    )
+                else:
+                    hidden_states = hidden_states + controlnet_block_samples[index_block // interval_control]
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    joint_attention_kwargs=joint_attention_kwargs,
+                )
+
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(controlnet_single_block_samples)
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+
 Pipeline = None
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.enabled = True
+torch.backends.cudnn.benchmark = True
 
 ckpt_id = "black-forest-labs/FLUX.1-schnell"
+ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9" 
 def empty_cache():
-    start = time.time()
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()
-    print(f"Flush took: {time.time() - start}")
 
 def load_pipeline() -> Pipeline:    
     empty_cache()
-    dtype, device = torch.bfloat16, "cuda"
 
+    dtype, device = torch.bfloat16, "cuda"
+    
     text_encoder_2 = T5EncoderModel.from_pretrained(
-        "city96/t5-v1_1-xxl-encoder-bf16", torch_dtype=torch.bfloat16
-    )
-    vae=AutoencoderKL.from_pretrained(ckpt_id, subfolder="vae", torch_dtype=dtype)
+        "silentdriver/aadb864af9", revision = "060dabc7fa271c26dfa3fd43c16e7c5bf3ac7892", torch_dtype=torch.bfloat16
+    ).to(memory_format=torch.channels_last)
+    
+    vae = AutoencoderTiny.from_pretrained("silentdriver/7815792fb4", revision="bdb7d88ebe5a1c6b02a3c0c78651dd57a403fdf5", torch_dtype=dtype)
+    
+    path = os.path.join(HF_HUB_CACHE, "models--silentdriver--7d92df966a/snapshots/add1b8d9a84c728c1209448c4a695759240bad3c")
+    generator = torch.Generator(device=device)
+    model = FluxTransformer2DModel.from_pretrained(path, torch_dtype=dtype, use_safetensors=False, generator= generator).to(memory_format=torch.channels_last)
+    torch.backends.cudnn.benchmark = True
+    torch.backends.cudnn.deterministic = False
     pipeline = DiffusionPipeline.from_pretrained(
         ckpt_id,
         vae=vae,
-        text_encoder_2 = text_encoder_2,
+        revision=ckpt_revision,
+        transformer=model,
+        text_encoder_2=text_encoder_2,
         torch_dtype=dtype,
-        )
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = True
-    torch.cuda.set_per_process_memory_fraction(0.9)
-    pipeline.text_encoder.to(memory_format=torch.channels_last)
-    pipeline.transformer.to(memory_format=torch.channels_last)
-    
-
-    pipeline.vae.to(memory_format=torch.channels_last)
+        ).to(device)
     pipeline.vae = torch.compile(pipeline.vae)
+    for _ in range(3):
+        pipeline(prompt="blah blah waah waah oneshot oneshot gang gang", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
     
-    pipeline._exclude_from_cpu_offload = ["vae"]
-    pipeline.enable_sequential_cpu_offload()
-    for _ in range(2):
-        pipeline(prompt="onomancy, aftergo, spirantic, Platyhelmia, modificator, drupaceous, jobbernowl, hereness", width=1024, height=1024, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256)
-    
+    empty_cache()
     return pipeline
 
 
-@torch.inference_mode()
-def infer(request: TextToImageRequest, pipeline: Pipeline) -> Image:
-    torch.cuda.reset_peak_memory_stats()
-    generator = Generator("cuda").manual_seed(request.seed)
+@torch.no_grad()
+def infer(request: TextToImageRequest, pipeline: Pipeline, generator: Generator) -> Image:
     image=pipeline(request.prompt,generator=generator, guidance_scale=0.0, num_inference_steps=4, max_sequence_length=256, height=request.height, width=request.width, output_type="pil").images[0]
-    return(image)
+    return image

uv.lock

@@ -1,8 +1,15 @@
 version = 1
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version < '3.12'",
-    "python_full_version >= '3.12'",
+    "python_full_version < '3.11' and platform_system == 'Darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version < '3.11' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version < '3.11' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version == '3.11.*' and platform_system == 'Darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version == '3.11.*' and platform_system != 'Darwin' and platform_system != 'Linux')",
+    "python_full_version >= '3.12' and platform_system == 'Darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux'",
+    "(python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_system != 'Darwin') or (python_full_version >= '3.12' and platform_system != 'Darwin' and platform_system != 'Linux')",
 ]
 
 [[package]]
@@ -147,13 +154,14 @@ wheels = [
 
 [[package]]
 name = "flux-schnell-edge-inference"
-version = "7"
+version = "8"
 source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "diffusers" },
     { name = "edge-maxxing-pipelines" },
     { name = "gitpython" },
+    { name = "hf-transfer" },
     { name = "omegaconf" },
     { name = "protobuf" },
     { name = "sentencepiece" },
@@ -168,11 +176,12 @@ requires-dist = [
     { name = "diffusers", specifier = "==0.31.0" },
     { name = "edge-maxxing-pipelines", git = "https://github.com/womboai/edge-maxxing?subdirectory=pipelines&rev=7c760ac54f6052803dadb3ade8ebfc9679a94589#7c760ac54f6052803dadb3ade8ebfc9679a94589" },
     { name = "gitpython", specifier = ">=3.1.43" },
+    { name = "hf-transfer", specifier = "==0.1.8" },
     { name = "omegaconf", specifier = "==2.3.0" },
     { name = "protobuf", specifier = "==5.28.3" },
     { name = "sentencepiece", specifier = "==0.2.0" },
     { name = "torch", specifier = "==2.5.1" },
-    { name = "torchao", specifier = ">=0.6.1" },
+    { name = "torchao", specifier = "==0.6.1" },
     { name = "transformers", specifier = "==4.46.2" },
 ]
 
@@ -209,6 +218,43 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e9/bd/cc3a402a6439c15c3d4294333e13042b915bbeab54edc457c723931fed3f/GitPython-3.1.43-py3-none-any.whl", hash = "sha256:eec7ec56b92aad751f9912a73404bc02ba212a23adb2c7098ee668417051a1ff", size = 207337 },
 ]
 
+[[package]]
+name = "hf-transfer"
+version = "0.1.8"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d3/0e/ba51e31148f0a9bc8d44878086535c2dc6d9a8dce321250e9bcdd3c110ea/hf_transfer-0.1.8.tar.gz", hash = "sha256:26d229468152e7a3ec12664cac86b8c2800695fd85f9c9a96677a775cc04f0b3", size = 23595 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/4f/eb/469e68c4259c4f4ad8e00967ad2f72ff1ba5e2712b4e1093e3e03c5cbc3d/hf_transfer-0.1.8-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:70858f9e94286738ed300484a45beb5cfee6a7ddac4c5886f9c6fce7823ac5ab", size = 1422386 },
+    { url = "https://files.pythonhosted.org/packages/bd/3d/5e8966b47aa86cd50f2017c76c2634aa09a437224567f379bc28d6580d7c/hf_transfer-0.1.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:38adc73f0a8526319d90f7cc5dc2d5e4bb66f487a513d94b98aa6725be732e4a", size = 1406027 },
+    { url = "https://files.pythonhosted.org/packages/61/e0/fd5f849ed7b2bf9b2bb008f3df3ee5a8773ca98362302833708cce26c337/hf_transfer-0.1.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:44d2f0c08198d8d899fe9d66e86aee2dd844bd7ce33888f261373fcec81d2a54", size = 3781136 },
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