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backend/README.md

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+<h2 align="center">W.I.P Backend for Forge</h2>

backend/args.py

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+import argparse
+import enum
+
+
+class EnumAction(argparse.Action):
+    """Argparse `action` for handling Enum"""
+
+    def __init__(self, **kwargs):
+        enum_type = kwargs.pop("type", None)
+        assert issubclass(enum_type, enum.Enum)
+
+        choices = tuple(e.value for e in enum_type)
+        kwargs.setdefault("choices", choices)
+        kwargs.setdefault("metavar", f"[{','.join(list(choices))}]")
+
+        super(EnumAction, self).__init__(**kwargs)
+        self._enum = enum_type
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        value = self._enum(values)
+        setattr(namespace, self.dest, value)
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("--gpu-device-id", type=int, default=None, metavar="DEVICE_ID")
+
+fp_group = parser.add_mutually_exclusive_group()
+fp_group.add_argument("--all-in-fp32", action="store_true")
+fp_group.add_argument("--all-in-fp16", action="store_true")
+
+fpunet_group = parser.add_mutually_exclusive_group()
+fpunet_group.add_argument("--unet-in-bf16", action="store_true")
+fpunet_group.add_argument("--unet-in-fp16", action="store_true")
+fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true")
+fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true")
+
+fpvae_group = parser.add_mutually_exclusive_group()
+fpvae_group.add_argument("--vae-in-fp16", action="store_true")
+fpvae_group.add_argument("--vae-in-fp32", action="store_true")
+fpvae_group.add_argument("--vae-in-bf16", action="store_true")
+
+parser.add_argument("--vae-in-cpu", action="store_true")
+
+fpte_group = parser.add_mutually_exclusive_group()
+fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true")
+fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true")
+fpte_group.add_argument("--clip-in-fp16", action="store_true")
+fpte_group.add_argument("--clip-in-fp32", action="store_true")
+
+parser.add_argument("--clip-in-cpu", action="store_true")
+
+attn_group = parser.add_mutually_exclusive_group()
+attn_group.add_argument("--attention-split", action="store_true")
+attn_group.add_argument("--attention-pytorch", action="store_true")
+
+upcast = parser.add_mutually_exclusive_group()
+upcast.add_argument("--force-upcast-attention", action="store_true")
+upcast.add_argument("--disable-attention-upcast", action="store_true")
+
+parser.add_argument("--xformers", action="store_true", help="install xformers for cross attention")
+parser.add_argument("--sage", action="store_true", help="install sageattention")
+parser.add_argument("--flash", action="store_true", help="install flash_attn")
+parser.add_argument("--nunchaku", action="store_true", help="install nunchaku for SVDQ inference")
+parser.add_argument("--bnb", action="store_true", help="install bitsandbytes for 4-bit inference")
+parser.add_argument("--onnxruntime-gpu", action="store_true", help="install nightly onnxruntime-gpu with cu130 support")
+
+parser.add_argument("--disable-xformers", action="store_true")
+parser.add_argument("--disable-sage", action="store_true")
+parser.add_argument("--disable-flash", action="store_true")
+
+parser.add_argument("--force-xformers-vae", action="store_true")
+
+parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1)
+parser.add_argument("--disable-ipex-hijack", action="store_true")
+
+vram_group = parser.add_mutually_exclusive_group()
+vram_group.add_argument("--always-gpu", action="store_true")
+vram_group.add_argument("--always-high-vram", action="store_true")
+vram_group.add_argument("--always-normal-vram", action="store_true")
+vram_group.add_argument("--always-low-vram", action="store_true")
+vram_group.add_argument("--always-no-vram", action="store_true")
+vram_group.add_argument("--always-cpu", action="store_true")
+
+parser.add_argument("--always-offload-from-vram", action="store_true")
+parser.add_argument("--pytorch-deterministic", action="store_true")
+
+parser.add_argument("--cuda-malloc", action="store_true")
+parser.add_argument("--cuda-stream", action="store_true")
+parser.add_argument("--pin-shared-memory", action="store_true")
+
+parser.add_argument("--disable-gpu-warning", action="store_true")
+parser.add_argument("--fast-fp16", action="store_true")
+
+parser.add_argument("--mmap-torch-files", action="store_true")
+parser.add_argument("--disable-mmap", action="store_true")
+
+
+class SageAttentionFuncs(enum.Enum):
+    auto = "auto"
+    fp16_triton = "fp16_triton"
+    fp16_cuda = "fp16_cuda"
+    fp8_cuda = "fp8_cuda"
+
+
+class Sage_quantization_backend(enum.Enum):
+    cuda = "cuda"
+    triton = "triton"
+
+
+class Sage_qk_quant_gran(enum.Enum):
+    per_warp = "per_warp"
+    per_thread = "per_thread"
+
+
+class Sage_pv_accum_dtype(enum.Enum):
+    fp16 = "fp16"
+    fp32 = "fp32"
+    fp16fp32 = "fp16+fp32"
+    fp32fp32 = "fp32+fp32"
+
+
+parser.add_argument("--sage2-function", type=SageAttentionFuncs, default=SageAttentionFuncs.auto, action=EnumAction)
+parser.add_argument("--sage-quantization-backend", type=Sage_quantization_backend, default=Sage_quantization_backend.triton, action=EnumAction)
+parser.add_argument("--sage-quant-gran", type=Sage_qk_quant_gran, default=Sage_qk_quant_gran.per_thread, action=EnumAction)
+parser.add_argument("--sage-accum-dtype", type=Sage_pv_accum_dtype, default=Sage_pv_accum_dtype.fp32, action=EnumAction)
+
+
+args, _ = parser.parse_known_args()
+
+# TODO: Stop using this to hack every problem...
+dynamic_args = dict(
+    embedding_dir=None,
+    forge_unet_storage_dtype=None,
+    kontext=False,
+    edit=False,
+    nunchaku=False,
+    ref_latents=[],
+    concat_latent=None,
+)
+"""
+Some parameters that are used throughout the Webui
+- embedding_dir: `str` - set in modules/sd_models/forge_model_reload
+- forge_unet_storage_dtype: `torch.dtype` - set in modules/sd_models/forge_model_reload
+- kontext: `bool` - Flux Kontext
+- edit: `bool` - Qwen-Image-Edit
+- nunchaku: `bool` - Nunchaku (SVDQ) Models
+- ref_latents: `list[torch.Tensor]` - Reference Latent(s) for Flux Kontext & Qwen-Image-Edit
+- concat_latent: `torch.Tensor` - Input Latent for Wan 2.2 I2V
+"""

backend/attention.py

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+import math
+
+import einops
+import torch
+
+from backend import memory_management
+from backend.args import SageAttentionFuncs, args
+from modules.errors import display_once
+
+if memory_management.xformers_enabled() or args.force_xformers_vae:
+    import xformers
+    import xformers.ops
+
+    try:
+        x_vers = xformers.__version__
+    except Exception:
+        BROKEN_XFORMERS = True
+    else:
+        BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
+
+IS_SAGE_2 = False
+"""SageAttention 2 has looser restrictions, allowing it to work on more models (e.g. SD1)"""
+
+if memory_management.sage_enabled():
+    import importlib.metadata
+
+    from sageattention import sageattn
+
+    IS_SAGE_2 = importlib.metadata.version("sageattention").startswith("2")
+
+if memory_management.flash_enabled():
+    from flash_attn import flash_attn_func
+
+    @torch.library.custom_op("flash_attention::flash_attn", mutates_args=())
+    def flash_attn_wrapper(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
+        return flash_attn_func(q, k, v, dropout_p=dropout_p, causal=causal)
+
+    @flash_attn_wrapper.register_fake
+    def flash_attn_fake(q, k, v, dropout_p=0.0, causal=False):
+        return q.new_empty(q.shape)
+
+
+def get_xformers_flash_attention_op(q, k, v):
+    try:
+        flash_attention_op = xformers.ops.MemoryEfficientAttentionFlashAttentionOp
+        fw, bw = flash_attention_op
+        if fw.supports(xformers.ops.fmha.Inputs(query=q, key=k, value=v, attn_bias=None)):
+            return flash_attention_op
+    except Exception as e:
+        display_once(e, "get_xformers_flash_attention_op")
+
+    return None
+
+
+FORCE_UPCAST_ATTENTION_DTYPE = memory_management.force_upcast_attention_dtype()
+
+
+def get_attn_precision(attn_precision, current_dtype):
+    if args.disable_attention_upcast:
+        return None
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
+        return FORCE_UPCAST_ATTENTION_DTYPE.get(current_dtype, attn_precision)
+    return attn_precision
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d
+
+
+if memory_management.is_nvidia():
+    SDP_BATCH_LIMIT = 2**15
+else:
+    SDP_BATCH_LIMIT = 2**31
+
+
+# ========== Diffusion ========== #
+
+
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head**-0.5
+
+    h = heads
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(),
+            (q, k, v),
+        )
+
+    if attn_precision == torch.float32:
+        sim = torch.einsum("b i d, b j d -> b i j", q.float(), k.float()) * scale
+    else:
+        sim = torch.einsum("b i d, b j d -> b i j", q, k) * scale
+
+    del q, k
+
+    if exists(mask):
+        if mask.dtype == torch.bool:
+            mask = einops.rearrange(mask, "b ... -> b (...)")
+            max_neg_value = -torch.finfo(sim.dtype).max
+            mask = einops.repeat(mask, "b j -> (b h) () j", h=h)
+            sim.masked_fill_(~mask, max_neg_value)
+        else:
+            if len(mask.shape) == 2:
+                bs = 1
+            else:
+                bs = mask.shape[0]
+            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+            sim.add_(mask)
+
+    sim = sim.softmax(dim=-1)
+    out = torch.einsum("b i j, b j d -> b i d", sim.to(v.dtype), v)
+
+    if skip_output_reshape:
+        return out.unsqueeze(0).reshape(b, heads, -1, dim_head)
+    else:
+        return out.unsqueeze(0).reshape(b, heads, -1, dim_head).permute(0, 2, 1, 3).reshape(b, -1, heads * dim_head)
+
+
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    attn_precision = get_attn_precision(attn_precision, q.dtype)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    scale = dim_head**-0.5
+
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head).contiguous(),
+            (q, k, v),
+        )
+
+    r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+
+    mem_free_total = memory_management.get_free_memory(q.device)
+
+    if attn_precision == torch.float32:
+        element_size = 4
+        upcast = True
+    else:
+        element_size = q.element_size()
+        upcast = False
+
+    gb = 1024**3
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
+    modifier = 3
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+    if steps > 64:
+        max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
+        raise RuntimeError(f"Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). " f"Need: {mem_required / 64 / gb:0.1f}GB free, Have:{mem_free_total / gb:0.1f}GB free")
+
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
+    first_op_done = False
+    cleared_cache = False
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                if upcast:
+                    with torch.autocast(enabled=False, device_type="cuda"):
+                        s1 = torch.einsum("b i d, b j d -> b i j", q[:, i:end].float(), k.float()) * scale
+                else:
+                    s1 = torch.einsum("b i d, b j d -> b i j", q[:, i:end], k) * scale
+
+                if mask is not None:
+                    if len(mask.shape) == 2:
+                        s1 += mask[i:end]
+                    else:
+                        if mask.shape[1] == 1:
+                            s1 += mask
+                        else:
+                            s1 += mask[:, i:end]
+
+                s2 = s1.softmax(dim=-1).to(v.dtype)
+                del s1
+                first_op_done = True
+
+                r1[:, i:end] = torch.einsum("b i j, b j d -> b i d", s2, v)
+                del s2
+            break
+        except memory_management.OOM_EXCEPTION as e:
+            if first_op_done == False:
+                memory_management.soft_empty_cache(True)
+                if cleared_cache == False:
+                    cleared_cache = True
+                    print(f"[Out of Memory Error] emptying cache and trying again...")
+                    continue
+                steps *= 2
+                if steps > 64:
+                    raise e
+                print(f"[Out of Memory Error] increasing steps and trying again {steps}...")
+            else:
+                raise e
+
+    del q, k, v
+
+    if skip_output_reshape:
+        return r1.unsqueeze(0).reshape(b, heads, -1, dim_head)
+    else:
+        return r1.unsqueeze(0).reshape(b, heads, -1, dim_head).permute(0, 2, 1, 3).reshape(b, -1, heads * dim_head)
+
+
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    b = q.shape[0]
+    dim_head = q.shape[-1]
+    disabled_xformers = False
+
+    if BROKEN_XFORMERS and b * heads > 65535:
+        disabled_xformers = True
+
+    if not disabled_xformers:
+        disabled_xformers = torch.jit.is_tracing() or torch.jit.is_scripting()
+
+    if disabled_xformers:
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+
+    if skip_reshape:
+        q, k, v = map(
+            lambda t: t.permute(0, 2, 1, 3),
+            (q, k, v),
+        )
+    else:
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+        pad = 8 - mask.shape[-1] % 8
+        mask_out = torch.empty([mask.shape[0], mask.shape[1], q.shape[1], mask.shape[-1] + pad], dtype=q.dtype, device=q.device)
+        mask_out[..., : mask.shape[-1]] = mask
+        mask = mask_out[..., : mask.shape[-1]]
+        mask = mask.expand(b, heads, -1, -1)
+
+    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
+
+    if skip_output_reshape:
+        return out.permute(0, 2, 1, 3)
+    else:
+        return out.reshape(b, -1, heads * dim_head)
+
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    if SDP_BATCH_LIMIT >= b:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+        if skip_output_reshape:
+            return out
+        else:
+            return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+
+    out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
+
+    for i in range(0, b, SDP_BATCH_LIMIT):
+        m = mask
+        if mask is not None:
+            if mask.shape[0] > 1:
+                m = mask[i : i + SDP_BATCH_LIMIT]
+
+        out[i : i + SDP_BATCH_LIMIT] = (
+            torch.nn.functional.scaled_dot_product_attention(
+                q[i : i + SDP_BATCH_LIMIT],
+                k[i : i + SDP_BATCH_LIMIT],
+                v[i : i + SDP_BATCH_LIMIT],
+                attn_mask=m,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+            .transpose(1, 2)
+            .reshape(-1, q.shape[2], heads * dim_head)
+        )
+
+    return out
+
+
+if IS_SAGE_2 and args.sage2_function is not SageAttentionFuncs.auto:
+    from functools import partial
+
+    import sageattention
+
+    _function = getattr(sageattention, f"sageattn_qk_int8_pv_{args.sage2_function.value}")
+    if args.sage2_function is SageAttentionFuncs.fp16_triton:
+        sageattn = partial(_function, quantization_backend=args.sage_quantization_backend.value)
+    else:
+        sageattn = partial(_function, qk_quant_gran=args.sage_quant_gran.value, pv_accum_dtype=args.sage_accum_dtype.value)
+
+
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+        tensor_layout = "HND"
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        tensor_layout = "NHD"
+
+    if (IS_SAGE_2 and dim_head > 128) or ((not IS_SAGE_2) and (dim_head not in (64, 96, 128))):
+        if memory_management.xformers_enabled():
+            return attention_xformers(q, k, v, heads, mask, attn_precision, skip_reshape, skip_output_reshape, **kwargs)
+        else:
+            return attention_pytorch(q, k, v, heads, mask, attn_precision, skip_reshape, skip_output_reshape, **kwargs)
+
+    if not skip_reshape:
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    try:
+        out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
+    except Exception as e:
+        display_once(e, "attention_sage")
+        if tensor_layout == "NHD":
+            q, k, v = map(
+                lambda t: t.transpose(1, 2),
+                (q, k, v),
+            )
+        if memory_management.xformers_enabled():
+            return attention_xformers(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
+        else:
+            return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
+
+    if tensor_layout == "HND":
+        if skip_output_reshape:
+            return out
+        else:
+            return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+
+    else:
+        if skip_output_reshape:
+            return out.transpose(1, 2)
+        else:
+            return out.reshape(b, -1, heads * dim_head)
+
+
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
+
+    if mask is not None:
+        # add a batch dimension if there isn't already one
+        if mask.ndim == 2:
+            mask = mask.unsqueeze(0)
+        # add a heads dimension if there isn't already one
+        if mask.ndim == 3:
+            mask = mask.unsqueeze(1)
+
+    try:
+        assert mask is None
+        out = flash_attn_wrapper(
+            q.transpose(1, 2),
+            k.transpose(1, 2),
+            v.transpose(1, 2),
+            dropout_p=0.0,
+            causal=False,
+        ).transpose(1, 2)
+    except Exception as e:
+        display_once(e, "attention_flash")
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
+
+    if skip_output_reshape:
+        return out
+    else:
+        return out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+
+
+if memory_management.sage_enabled():
+    attention_function = attention_sage
+    match args.sage2_function:
+        case SageAttentionFuncs.auto:
+            print(f"Using SageAttention {'2' if IS_SAGE_2 else ''}")
+        case SageAttentionFuncs.fp16_triton:
+            print("Using SageAttention (fp16 Triton)")
+        case SageAttentionFuncs.fp16_cuda:
+            print("Using SageAttention (fp16 CUDA)")
+        case SageAttentionFuncs.fp8_cuda:
+            print("Using SageAttention (fp8 CUDA)")
+
+elif memory_management.flash_enabled():
+    print("Using FlashAttention")
+    attention_function = attention_flash
+elif memory_management.xformers_enabled():
+    print("Using xformers Cross Attention")
+    attention_function = attention_xformers
+elif memory_management.pytorch_attention_enabled():
+    print("Using PyTorch Cross Attention")
+    attention_function = attention_pytorch
+elif args.attention_split:
+    print("Using Split Optimization for Cross Attention")
+    attention_function = attention_split
+else:
+    print("Using Basic Cross Attention")
+    attention_function = attention_basic
+
+
+# ========== VAE ========== #
+
+
+def slice_attention_single_head_spatial(q, k, v):
+    r1 = torch.zeros_like(k, device=q.device)
+    scale = int(q.shape[-1]) ** (-0.5)
+
+    mem_free_total = memory_management.get_free_memory(q.device)
+
+    tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
+    modifier = 3 if q.element_size() == 2 else 2.5
+    mem_required = tensor_size * modifier
+    steps = 1
+
+    if mem_required > mem_free_total:
+        steps = 2 ** (math.ceil(math.log(mem_required / mem_free_total, 2)))
+
+    while True:
+        try:
+            slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
+            for i in range(0, q.shape[1], slice_size):
+                end = i + slice_size
+                s1 = torch.bmm(q[:, i:end], k) * scale
+
+                s2 = torch.nn.functional.softmax(s1, dim=2).permute(0, 2, 1)
+                del s1
+
+                r1[:, :, i:end] = torch.bmm(v, s2)
+                del s2
+            break
+        except memory_management.OOM_EXCEPTION as e:
+            memory_management.soft_empty_cache(True)
+            steps *= 2
+            if steps > 128:
+                raise e
+            print("out of memory error, increasing steps and trying again {}".format(steps))
+
+    return r1
+
+
+def normal_attention_single_head_spatial(q, k, v):
+    # compute attention
+    orig_shape = q.shape
+    b = orig_shape[0]
+    c = orig_shape[1]
+
+    q = q.reshape(b, c, -1)
+    q = q.permute(0, 2, 1)  # b,hw,c
+    k = k.reshape(b, c, -1)  # b,c,hw
+    v = v.reshape(b, c, -1)
+
+    r1 = slice_attention_single_head_spatial(q, k, v)
+    h_ = r1.reshape(orig_shape)
+    del r1
+    return h_
+
+
+def xformers_attention_single_head_spatial(q, k, v):
+    # compute attention
+    orig_shape = q.shape
+    B = orig_shape[0]
+    C = orig_shape[1]
+    q, k, v = map(
+        lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=get_xformers_flash_attention_op(q, k, v))
+        out = out.transpose(1, 2).reshape(orig_shape)
+    except NotImplementedError:
+        out = slice_attention_single_head_spatial(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
+    return out
+
+
+def pytorch_attention_single_head_spatial(q, k, v):
+    # compute attention
+    orig_shape = q.shape
+    B = orig_shape[0]
+    C = orig_shape[1]
+    q, k, v = map(
+        lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(),
+        (q, k, v),
+    )
+
+    try:
+        out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
+        out = out.transpose(2, 3).reshape(orig_shape)
+    except memory_management.OOM_EXCEPTION as e:
+        display_once(e, "pytorch_attention_single_head_spatial")
+        out = slice_attention_single_head_spatial(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(orig_shape)
+    return out
+
+
+if memory_management.xformers_enabled_vae() or args.force_xformers_vae:
+    print("Using xformers Attention for VAE")
+    attention_function_single_head_spatial = xformers_attention_single_head_spatial
+elif memory_management.pytorch_attention_enabled():
+    print("Using PyTorch Attention for VAE")
+    attention_function_single_head_spatial = pytorch_attention_single_head_spatial
+else:
+    print("Using Split Attention for VAE")
+    attention_function_single_head_spatial = normal_attention_single_head_spatial

backend/diffusion_engine/base.py

@@ -0,0 +1,77 @@
+from backend import utils
+
+
+class ForgeObjects:
+    def __init__(self, unet, clip, vae, clipvision):
+        self.unet = unet
+        self.clip = clip
+        self.vae = vae
+        self.clipvision = clipvision
+
+    def shallow_copy(self):
+        return ForgeObjects(self.unet, self.clip, self.vae, self.clipvision)
+
+
+class ForgeDiffusionEngine:
+    matched_guesses = []
+
+    def __init__(self, estimated_config, huggingface_components):
+        self.model_config = estimated_config
+        self.is_inpaint = estimated_config.inpaint_model()
+
+        self.forge_objects = None
+        self.forge_objects_original = None
+        self.forge_objects_after_applying_lora = None
+
+        self.current_lora_hash = str([])
+
+        self.fix_for_webui_backward_compatibility()
+
+    def set_clip_skip(self, clip_skip):
+        pass
+
+    def get_first_stage_encoding(self, x):
+        return x
+
+    def get_learned_conditioning(self, prompt: list[str]):
+        raise NotImplementedError
+
+    def encode_first_stage(self, x):
+        raise NotImplementedError
+
+    def decode_first_stage(self, x):
+        raise NotImplementedError
+
+    def get_prompt_lengths_on_ui(self, prompt):
+        return 0, 75
+
+    def is_webui_legacy_model(self):
+        return self.is_sd1 or self.is_sdxl
+
+    def fix_for_webui_backward_compatibility(self):
+        self.tiling_enabled = False
+        self.first_stage_model = None
+        self.cond_stage_model = None
+        self.use_distilled_cfg_scale = False
+        self.use_shift = False
+        self.is_sd1 = False
+        self.is_sdxl = False
+        self.is_flux = False  # affects the usage of TAESD
+        self.is_wan = False  # affects the usage of WanVAE (B, C, T, H, W)
+
+    def save_unet(self, filename):
+        import safetensors.torch as sf
+
+        sd = utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model)
+        sf.save_file(sd, filename)
+        return filename
+
+    def save_checkpoint(self, filename):
+        import safetensors.torch as sf
+
+        sd = {}
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="text_encoders."))
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="vae."))
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/chroma.py

@@ -0,0 +1,59 @@
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionFlux
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.t5_engine import T5TextProcessingEngine
+
+
+class Chroma(ForgeDiffusionEngine):
+    matched_guesses = [model_list.Chroma]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"t5xxl": huggingface_components["text_encoder"]}, tokenizer_dict={"t5xxl": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+        k_predictor = PredictionFlux(mu=1.0)
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+            min_length=-1,
+            min_padding=1,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.is_flux = True
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        return self.text_processing_engine_t5(prompt)
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/flux.py

@@ -0,0 +1,114 @@
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from modules.prompt_parser import SdConditioning
+
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management
+from backend.args import dynamic_args
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionFlux
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from backend.text_processing.t5_engine import T5TextProcessingEngine
+
+
+class Flux(ForgeDiffusionEngine):
+    matched_guesses = [model_list.Flux, model_list.FluxSchnell]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"clip_l": huggingface_components["text_encoder"], "t5xxl": huggingface_components["text_encoder_2"]}, tokenizer_dict={"clip_l": huggingface_components["tokenizer"], "t5xxl": huggingface_components["tokenizer_2"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        if "schnell" in estimated_config.huggingface_repo.lower():
+            k_predictor = PredictionFlux(mu=1.0)
+        else:
+            k_predictor = PredictionFlux(
+                seq_len=4096,
+                base_seq_len=256,
+                max_seq_len=4096,
+                base_shift=0.5,
+                max_shift=1.15,
+            )
+            self.use_distilled_cfg_scale = True
+
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args["embedding_dir"],
+            embedding_key="clip_l",
+            embedding_expected_shape=768,
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=True,
+            final_layer_norm=True,
+        )
+
+        self.text_processing_engine_t5 = T5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.t5xxl,
+            tokenizer=clip.tokenizer.t5xxl,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.is_flux = True
+
+        self.ref_latents = []
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: "SdConditioning"):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond_l, pooled_l = self.text_processing_engine_l(prompt)
+        cond_t5 = self.text_processing_engine_t5(prompt)
+        cond = dict(crossattn=cond_t5, vector=pooled_l)
+
+        if self.use_distilled_cfg_scale:
+            distilled_cfg_scale = getattr(prompt, "distilled_cfg_scale", 3.5) or 3.5
+            cond["guidance"] = torch.FloatTensor([distilled_cfg_scale] * len(prompt))
+            print(f"Distilled CFG Scale: {distilled_cfg_scale}")
+        else:
+            print("Distilled CFG Scale is ignored for Schnell")
+
+        if not prompt.is_negative_prompt:
+            if dynamic_args["kontext"] and self.ref_latents:
+                dynamic_args["ref_latents"] = self.ref_latents.copy()
+                self.ref_latents.clear()
+            else:
+                dynamic_args["ref_latents"].clear()
+                self.ref_latents.clear()
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(255, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        self.ref_latents.append(sample.cpu())
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/lumina.py

@@ -0,0 +1,62 @@
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionDiscreteFlow
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.gemma_engine import GemmaTextProcessingEngine
+
+
+class Lumina2(ForgeDiffusionEngine):
+    matched_guesses = [model_list.Lumina2]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"gemma2": huggingface_components["text_encoder"]}, tokenizer_dict={"gemma2": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        k_predictor = PredictionDiscreteFlow(estimated_config)
+
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_gemma = GemmaTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.gemma2,
+            tokenizer=clip.tokenizer.gemma2,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.use_shift = True
+        self.is_flux = True
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        shift = getattr(prompt, "distilled_cfg_scale", 6.0)
+        self.forge_objects.unet.model.predictor.set_parameters(shift=shift)
+        return self.text_processing_engine_gemma(prompt)
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_gemma.tokenize([prompt])[0])
+        return token_count, max(999, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/qwen.py

@@ -0,0 +1,118 @@
+import math
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from modules.prompt_parser import SdConditioning
+
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management
+from backend.args import dynamic_args
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionDiscreteFlow
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.qwen_engine import QwenTextProcessingEngine
+from modules.shared import opts
+
+
+class QwenImage(ForgeDiffusionEngine):
+    matched_guesses = [model_list.QwenImage]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"qwen25_7b": huggingface_components["text_encoder"]}, tokenizer_dict={"qwen25_7b": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"], is_wan=True)
+        vae.first_stage_model.latent_format = self.model_config.latent_format
+
+        k_predictor = PredictionDiscreteFlow(estimated_config)
+
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_qwen = QwenTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.qwen25_7b,
+            tokenizer=clip.tokenizer.qwen25_7b,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.is_wan = True
+
+        self.images_vl = []
+        self.ref_latents = []
+        self.image_prompt = ""
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: "SdConditioning"):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        if not prompt.is_negative_prompt:
+            if self.image_prompt:
+                return self.get_learned_conditioning_with_image(prompt)
+            else:
+                dynamic_args["ref_latents"].clear()
+                self.ref_latents.clear()
+                self.image_prompt = ""
+        return self.text_processing_engine_qwen(prompt)
+
+    @torch.inference_mode()
+    def get_learned_conditioning_with_image(self, prompt: list[str]):
+        cond = self.text_processing_engine_qwen([self.image_prompt + "".join(prompt)], images=self.images_vl)
+        self.images_vl.clear()
+        dynamic_args["ref_latents"] = self.ref_latents.copy()
+        self.ref_latents.clear()
+        self.image_prompt = ""
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_qwen.tokenize([prompt])[0])
+        return token_count, max(999, token_count)
+
+    @torch.inference_mode()
+    def encode_vision(self, image: torch.Tensor):
+        samples = image.movedim(-1, 1)  # b, c, h, w
+
+        total = int(384 * 384)
+        scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+        width = round(samples.shape[3] * scale_by)
+        height = round(samples.shape[2] * scale_by)
+
+        s = torch.nn.functional.interpolate(samples, size=(height, width), mode="area")
+        self.images_vl.append(s.movedim(1, -1))
+
+        if opts.qwen_vae_resize:
+            total = int(1024 * 1024)
+            scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+            width = round(samples.shape[3] * scale_by / 32.0) * 32
+            height = round(samples.shape[2] * scale_by / 32.0) * 32
+
+            s = torch.nn.functional.interpolate(samples, size=(height, width), mode="area")
+        else:
+            s = samples.clone()
+        sample = self.forge_objects.vae.encode(s.movedim(1, -1)[:, :, :, :3])
+        self.ref_latents.append(self.forge_objects.vae.first_stage_model.process_in(sample))
+
+        self.image_prompt += f"Picture {len(self.images_vl)}: <|vision_start|><|image_pad|><|vision_end|>"
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        if x.size(0) > 1:
+            x = x[0].unsqueeze(0)  # enforce batch_size of 1
+        start_image = x.movedim(1, -1) * 0.5 + 0.5
+        self.encode_vision(start_image)
+        sample = self.forge_objects.vae.encode(start_image)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 2) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/sd15.py

@@ -0,0 +1,78 @@
+import safetensors.torch as sf
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management, utils
+from backend.args import dynamic_args
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+
+
+class StableDiffusion(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SD15]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(model_dict={"clip_l": huggingface_components["text_encoder"]}, tokenizer_dict={"clip_l": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config)
+
+        self.text_processing_engine = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args["embedding_dir"],
+            embedding_key="clip_l",
+            embedding_expected_shape=768,
+            text_projection=False,
+            minimal_clip_skip=1,
+            clip_skip=1,
+            return_pooled=False,
+            final_layer_norm=True,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sd1 = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        cond = self.text_processing_engine(prompt)
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine.process_texts([prompt])
+        return token_count, self.text_processing_engine.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
+        sd.update(model_list.SD15.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="")))
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model."))
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/sdxl.py

@@ -0,0 +1,228 @@
+import safetensors.torch as sf
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management, utils
+from backend.args import dynamic_args
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.nn.unet import Timestep
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.classic_engine import ClassicTextProcessingEngine
+from modules.shared import opts
+
+
+class StableDiffusionXL(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SDXL]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(model_dict={"clip_l": huggingface_components["text_encoder"], "clip_g": huggingface_components["text_encoder_2"]}, tokenizer_dict={"clip_l": huggingface_components["tokenizer"], "clip_g": huggingface_components["tokenizer_2"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config)
+
+        self.text_processing_engine_l = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_l,
+            tokenizer=clip.tokenizer.clip_l,
+            embedding_dir=dynamic_args["embedding_dir"],
+            embedding_key="clip_l",
+            embedding_expected_shape=2048,
+            text_projection=False,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=False,
+            final_layer_norm=False,
+        )
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args["embedding_dir"],
+            embedding_key="clip_g",
+            embedding_expected_shape=2048,
+            text_projection=True,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.embedder = Timestep(256)
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sdxl = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_l.clip_skip = clip_skip
+        self.text_processing_engine_g.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+
+        cond_l = self.text_processing_engine_l(prompt)
+        cond_g, clip_pooled = self.text_processing_engine_g(prompt)
+
+        width = getattr(prompt, "width", 1024) or 1024
+        height = getattr(prompt, "height", 1024) or 1024
+        is_negative_prompt = getattr(prompt, "is_negative_prompt", False)
+
+        crop_w = opts.sdxl_crop_left
+        crop_h = opts.sdxl_crop_top
+        target_width = width
+        target_height = height
+
+        out = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])), self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), self.embedder(torch.Tensor([target_height])), self.embedder(torch.Tensor([target_width]))]
+
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == "" for x in prompt)
+
+        if force_zero_negative_prompt:
+            clip_pooled = torch.zeros_like(clip_pooled)
+            cond_l = torch.zeros_like(cond_l)
+            cond_g = torch.zeros_like(cond_g)
+
+        # Ensure cond_l and cond_g have the same size
+        max_len = max(cond_l.shape[1], cond_g.shape[1])
+        cond_l = torch.cat([cond_l, cond_l.new_zeros(cond_l.size(0), max_len - cond_l.shape[1], cond_l.size(2))], dim=1)
+        cond_g = torch.cat([cond_g, cond_g.new_zeros(cond_g.size(0), max_len - cond_g.shape[1], cond_g.size(2))], dim=1)
+
+        cond = dict(
+            crossattn=torch.cat([cond_l, cond_g], dim=2),
+            vector=torch.cat([clip_pooled, flat], dim=1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine_l.process_texts([prompt])
+        return token_count, self.text_processing_engine_l.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
+        sd.update(model_list.SDXL.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="")))
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model."))
+        sf.save_file(sd, filename)
+        return filename
+
+
+class StableDiffusionXLRefiner(ForgeDiffusionEngine):
+    matched_guesses = [model_list.SDXLRefiner]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+
+        clip = CLIP(
+            model_dict={"clip_g": huggingface_components["text_encoder"]},
+            tokenizer_dict={
+                "clip_g": huggingface_components["tokenizer"],
+            },
+        )
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        unet = UnetPatcher.from_model(model=huggingface_components["unet"], diffusers_scheduler=huggingface_components["scheduler"], config=estimated_config)
+
+        self.text_processing_engine_g = ClassicTextProcessingEngine(
+            text_encoder=clip.cond_stage_model.clip_g,
+            tokenizer=clip.tokenizer.clip_g,
+            embedding_dir=dynamic_args["embedding_dir"],
+            embedding_key="clip_g",
+            embedding_expected_shape=2048,
+            text_projection=True,
+            minimal_clip_skip=2,
+            clip_skip=2,
+            return_pooled=True,
+            final_layer_norm=False,
+        )
+
+        self.embedder = Timestep(256)
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        # WebUI Legacy
+        self.is_sdxl = True
+
+    def set_clip_skip(self, clip_skip):
+        self.text_processing_engine_g.clip_skip = clip_skip
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+
+        cond_g, clip_pooled = self.text_processing_engine_g(prompt)
+
+        width = getattr(prompt, "width", 1024) or 1024
+        height = getattr(prompt, "height", 1024) or 1024
+        is_negative_prompt = getattr(prompt, "is_negative_prompt", False)
+
+        crop_w = opts.sdxl_crop_left
+        crop_h = opts.sdxl_crop_top
+        aesthetic = opts.sdxl_refiner_low_aesthetic_score if is_negative_prompt else opts.sdxl_refiner_high_aesthetic_score
+
+        out = [self.embedder(torch.Tensor([height])), self.embedder(torch.Tensor([width])), self.embedder(torch.Tensor([crop_h])), self.embedder(torch.Tensor([crop_w])), self.embedder(torch.Tensor([aesthetic]))]
+
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1).to(clip_pooled)
+
+        force_zero_negative_prompt = is_negative_prompt and all(x == "" for x in prompt)
+
+        if force_zero_negative_prompt:
+            clip_pooled = torch.zeros_like(clip_pooled)
+            cond_g = torch.zeros_like(cond_g)
+
+        cond = dict(
+            crossattn=cond_g,
+            vector=torch.cat([clip_pooled, flat], dim=1),
+        )
+
+        return cond
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        _, token_count = self.text_processing_engine_g.process_texts([prompt])
+        return token_count, self.text_processing_engine_g.get_target_prompt_token_count(token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)
+
+    def save_checkpoint(self, filename):
+        sd = {}
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.unet.model.diffusion_model, prefix="model.diffusion_model."))
+        sd.update(model_list.SDXLRefiner.process_clip_state_dict_for_saving(self, utils.get_state_dict_after_quant(self.forge_objects.clip.cond_stage_model, prefix="")))
+        sd.update(utils.get_state_dict_after_quant(self.forge_objects.vae.first_stage_model, prefix="first_stage_model."))
+        sf.save_file(sd, filename)
+        return filename

backend/diffusion_engine/wan.py

@@ -0,0 +1,121 @@
+import torch
+from huggingface_guess import model_list
+from huggingface_guess.utils import resize_to_batch_size
+
+from backend import args, memory_management
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionDiscreteFlow
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.umt5_engine import UMT5TextProcessingEngine
+
+# get_learned_conditioning is not called in the Refiner pass;
+# so we store the desired shift value for the low_noise model
+refiner_shift: float = None
+
+
+class Wan(ForgeDiffusionEngine):
+    matched_guesses = [model_list.WAN21_T2V, model_list.WAN21_I2V]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"umt5xxl": huggingface_components["text_encoder"]}, tokenizer_dict={"umt5xxl": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"], is_wan=True)
+        vae.first_stage_model.latent_format = self.model_config.latent_format
+
+        k_predictor = PredictionDiscreteFlow(estimated_config)
+
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_t5 = UMT5TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.umt5xxl,
+            tokenizer=clip.tokenizer.umt5xxl,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.use_shift = True
+        self.is_wan = True
+
+        global refiner_shift
+        if refiner_shift is not None:
+            self.forge_objects.unet.model.predictor.set_parameters(shift=refiner_shift)
+            refiner_shift = None
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        global refiner_shift
+        shift = getattr(prompt, "distilled_cfg_scale", 8.0)
+        self.forge_objects.unet.model.predictor.set_parameters(shift=shift)
+        refiner_shift = shift
+        return self.text_processing_engine_t5(prompt)
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_t5.tokenize([prompt])[0])
+        return token_count, max(510, token_count)
+
+    @torch.inference_mode()
+    def image_to_video(self, length: int, start_image: torch.Tensor, noise: torch.Tensor):
+        _, h, w, c = start_image.shape
+
+        _image = torch.ones((length, h, w, c), device=start_image.device, dtype=start_image.dtype) * 0.5
+        _image[: start_image.shape[0]] = start_image
+
+        concat_latent_image = self.forge_objects.vae.encode(_image[:, :, :, :3])
+        mask = torch.ones((1, 1, noise.shape[2], concat_latent_image.shape[-2], concat_latent_image.shape[-1]), device=start_image.device, dtype=start_image.dtype)
+        mask[:, :, : ((start_image.shape[0] - 1) // 4) + 1] = 0.0
+
+        image = concat_latent_image
+
+        extra_channels = self.forge_objects.unet.model.diffusion_model.in_dim - 16  # 20
+
+        for i in range(0, image.shape[1], 16):
+            image[:, i : i + 16] = self.forge_objects.vae.first_stage_model.process_in(image[:, i : i + 16])
+        image = resize_to_batch_size(image, noise.shape[0])
+
+        if image.shape[1] > (extra_channels - 4):
+            image = image[:, : (extra_channels - 4)]
+
+        if mask.shape[1] != 4:
+            mask = torch.mean(mask, dim=1, keepdim=True)
+        mask = (1.0 - mask).to(image)
+        if mask.shape[-3] < noise.shape[-3]:
+            mask = torch.nn.functional.pad(mask, (0, 0, 0, 0, 0, noise.shape[-3] - mask.shape[-3]), mode="constant", value=0)
+        if mask.shape[1] == 1:
+            mask = mask.repeat(1, 4, 1, 1, 1)
+        mask = resize_to_batch_size(mask, noise.shape[0])
+
+        _concat_mask_index = 0  # TODO
+
+        if _concat_mask_index != 0:
+            z = torch.cat((image[:, :_concat_mask_index], mask, image[:, _concat_mask_index:]), dim=1)
+        else:
+            z = torch.cat((mask, image), dim=1)
+
+        args.dynamic_args["concat_latent"] = z
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        length, c, h, w = x.shape
+        assert c == 3
+        if length > 1:
+            x = x[0].unsqueeze(0)  # enforce batch_size of 1
+        start_image = x.movedim(1, -1) * 0.5 + 0.5
+        latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, h // 8, w // 8], device=self.forge_objects.vae.device)
+        self.image_to_video(length, start_image, latent)
+        sample = self.forge_objects.vae.first_stage_model.process_in(latent)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 2) * 2.0 - 1.0
+        return sample.to(x)

backend/diffusion_engine/zimage.py

@@ -0,0 +1,62 @@
+import torch
+from huggingface_guess import model_list
+
+from backend import memory_management
+from backend.diffusion_engine.base import ForgeDiffusionEngine, ForgeObjects
+from backend.modules.k_prediction import PredictionDiscreteFlow
+from backend.patcher.clip import CLIP
+from backend.patcher.unet import UnetPatcher
+from backend.patcher.vae import VAE
+from backend.text_processing.qwen3_engine import Qwen3TextProcessingEngine
+
+
+class ZImage(ForgeDiffusionEngine):
+    matched_guesses = [model_list.ZImage]
+
+    def __init__(self, estimated_config, huggingface_components):
+        super().__init__(estimated_config, huggingface_components)
+        self.is_inpaint = False
+
+        clip = CLIP(model_dict={"qwen3": huggingface_components["text_encoder"]}, tokenizer_dict={"qwen3": huggingface_components["tokenizer"]})
+
+        vae = VAE(model=huggingface_components["vae"])
+
+        k_predictor = PredictionDiscreteFlow(estimated_config)
+
+        unet = UnetPatcher.from_model(model=huggingface_components["transformer"], diffusers_scheduler=None, k_predictor=k_predictor, config=estimated_config)
+
+        self.text_processing_engine_gemma = Qwen3TextProcessingEngine(
+            text_encoder=clip.cond_stage_model.qwen3,
+            tokenizer=clip.tokenizer.qwen3,
+        )
+
+        self.forge_objects = ForgeObjects(unet=unet, clip=clip, vae=vae, clipvision=None)
+        self.forge_objects_original = self.forge_objects.shallow_copy()
+        self.forge_objects_after_applying_lora = self.forge_objects.shallow_copy()
+
+        self.use_shift = True
+        self.is_flux = True
+
+    @torch.inference_mode()
+    def get_learned_conditioning(self, prompt: list[str]):
+        memory_management.load_model_gpu(self.forge_objects.clip.patcher)
+        shift = getattr(prompt, "distilled_cfg_scale", 3.0)
+        self.forge_objects.unet.model.predictor.set_parameters(shift=shift)
+        return self.text_processing_engine_gemma(prompt)
+
+    @torch.inference_mode()
+    def get_prompt_lengths_on_ui(self, prompt):
+        token_count = len(self.text_processing_engine_gemma.tokenize([prompt])[0])
+        return token_count, max(999, token_count)
+
+    @torch.inference_mode()
+    def encode_first_stage(self, x):
+        sample = self.forge_objects.vae.encode(x.movedim(1, -1) * 0.5 + 0.5)
+        sample = self.forge_objects.vae.first_stage_model.process_in(sample)
+        return sample.to(x)
+
+    @torch.inference_mode()
+    def decode_first_stage(self, x):
+        sample = self.forge_objects.vae.first_stage_model.process_out(x)
+        sample = self.forge_objects.vae.decode(sample).movedim(-1, 1) * 2.0 - 1.0
+        return sample.to(x)

backend/huggingface/Chroma/model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": "FluxPipeline",
+  "_diffusers_version": "0.30.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "ChromaTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

backend/huggingface/Chroma/scheduler/scheduler_config.json

@@ -0,0 +1,11 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.30.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "use_dynamic_shifting": false
+}

backend/huggingface/Chroma/text_encoder/config.json

@@ -0,0 +1,22 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}

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backend/huggingface/Chroma/tokenizer/special_tokens_map.json

@@ -0,0 +1,125 @@
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+    "normalized": false,
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+    "single_word": false
+  }
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backend/huggingface/Chroma/tokenizer/tokenizer_config.json

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+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
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+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

backend/huggingface/Chroma/vae/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.30.0.dev0",
+  "_name_or_path": "../checkpoints/flux-dev",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

backend/huggingface/Qwen/Qwen-Image/model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": "QwenImagePipeline",
+  "_diffusers_version": "0.34.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "tokenizer": [
+    "transformers",
+    "Qwen2Tokenizer"
+  ],
+  "transformer": [
+    "diffusers",
+    "QwenImageTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLQwenImage"
+  ]
+}

backend/huggingface/Qwen/Qwen-Image/scheduler/scheduler_config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.34.0.dev0",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "invert_sigmas": false,
+  "max_image_seq_len": 8192,
+  "max_shift": 0.9,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "shift_terminal": 0.02,
+  "stochastic_sampling": false,
+  "time_shift_type": "exponential",
+  "use_beta_sigmas": false,
+  "use_dynamic_shifting": true,
+  "use_exponential_sigmas": false,
+  "use_karras_sigmas": false
+}

backend/huggingface/Qwen/Qwen-Image/text_encoder/config.json

@@ -0,0 +1,135 @@
+{
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 28,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "text_config": {
+    "architectures": [
+      "Qwen2_5_VLForConditionalGeneration"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 3584,
+    "image_token_id": null,
+    "initializer_range": 0.02,
+    "intermediate_size": 18944,
+    "layer_types": [
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention",
+      "full_attention"
+    ],
+    "max_position_embeddings": 128000,
+    "max_window_layers": 28,
+    "model_type": "qwen2_5_vl_text",
+    "num_attention_heads": 28,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 4,
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": {
+      "mrope_section": [
+        16,
+        24,
+        24
+      ],
+      "rope_type": "default",
+      "type": "default"
+    },
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "torch_dtype": "float32",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "video_token_id": null,
+    "vision_end_token_id": 151653,
+    "vision_start_token_id": 151652,
+    "vision_token_id": 151654,
+    "vocab_size": 152064
+  },
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "video_token_id": 151656,
+  "vision_config": {
+    "depth": 32,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
+    ],
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "in_channels": 3,
+    "in_chans": 3,
+    "initializer_range": 0.02,
+    "intermediate_size": 3420,
+    "model_type": "qwen2_5_vl",
+    "num_heads": 16,
+    "out_hidden_size": 3584,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "torch_dtype": "float32",
+    "window_size": 112
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 152064
+}

backend/huggingface/Qwen/Qwen-Image/text_encoder/generation_config.json

@@ -0,0 +1,14 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.1,
+  "top_k": 1,
+  "top_p": 0.001,
+  "transformers_version": "4.53.1"
+}

backend/huggingface/Qwen/Qwen-Image/tokenizer/added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

backend/huggingface/Qwen/Qwen-Image/tokenizer/special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/Qwen/Qwen-Image/tokenizer/tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

backend/huggingface/Qwen/Qwen-Image/transformer/config.json

@@ -0,0 +1,18 @@
+{
+  "_class_name": "QwenImageTransformer2DModel",
+  "_diffusers_version": "0.34.0.dev0",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    16,
+    56,
+    56
+  ],
+  "guidance_embeds": false,
+  "in_channels": 64,
+  "joint_attention_dim": 3584,
+  "num_attention_heads": 24,
+  "num_layers": 60,
+  "out_channels": 16,
+  "patch_size": 2,
+  "pooled_projection_dim": 768
+}

backend/huggingface/Qwen/Qwen-Image/vae/config.json

@@ -0,0 +1,56 @@
+{
+  "_class_name": "AutoencoderKLQwenImage",
+  "_diffusers_version": "0.34.0.dev0",
+  "attn_scales": [],
+  "base_dim": 96,
+  "dim_mult": [
+    1,
+    2,
+    4,
+    4
+  ],
+  "dropout": 0.0,
+  "latents_mean": [
+    -0.7571,
+    -0.7089,
+    -0.9113,
+    0.1075,
+    -0.1745,
+    0.9653,
+    -0.1517,
+    1.5508,
+    0.4134,
+    -0.0715,
+    0.5517,
+    -0.3632,
+    -0.1922,
+    -0.9497,
+    0.2503,
+    -0.2921
+  ],
+  "latents_std": [
+    2.8184,
+    1.4541,
+    2.3275,
+    2.6558,
+    1.2196,
+    1.7708,
+    2.6052,
+    2.0743,
+    3.2687,
+    2.1526,
+    2.8652,
+    1.5579,
+    1.6382,
+    1.1253,
+    2.8251,
+    1.916
+  ],
+  "num_res_blocks": 2,
+  "temporal_downsample": [
+    false,
+    true,
+    true
+  ],
+  "z_dim": 16
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/model_index.json

@@ -0,0 +1,24 @@
+{
+    "_class_name": "ZImagePipeline",
+    "_diffusers_version": "0.36.0.dev0",
+    "scheduler": [
+        "diffusers",
+        "FlowMatchEulerDiscreteScheduler"
+    ],
+    "text_encoder": [
+        "transformers",
+        "Qwen3Model"
+    ],
+    "tokenizer": [
+        "transformers",
+        "Qwen2Tokenizer"
+    ],
+    "transformer": [
+        "diffusers",
+        "ZImageTransformer2DModel"
+    ],
+    "vae": [
+        "diffusers",
+        "AutoencoderKL"
+    ]
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/scheduler/scheduler_config.json

@@ -0,0 +1,7 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version": "0.36.0.dev0",
+  "num_train_timesteps": 1000,
+  "use_dynamic_shifting": false,
+  "shift": 3.0
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/text_encoder/config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/text_encoder/generation_config.json

@@ -0,0 +1,13 @@
+{
+    "bos_token_id": 151643,
+    "do_sample": true,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "pad_token_id": 151643,
+    "temperature": 0.6,
+    "top_k": 20,
+    "top_p": 0.95,
+    "transformers_version": "4.51.0"
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/tokenizer/tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/transformer/config.json

@@ -0,0 +1,31 @@
+{
+  "_class_name": "ZImageTransformer2DModel",
+  "_diffusers_version": "0.36.0.dev0",
+  "all_f_patch_size": [
+    1
+  ],
+  "all_patch_size": [
+    2
+  ],
+  "axes_dims": [
+    32,
+    48,
+    48
+  ],
+  "axes_lens": [
+    1536,
+    512,
+    512
+  ],
+  "cap_feat_dim": 2560,
+  "dim": 3840,
+  "in_channels": 16,
+  "n_heads": 30,
+  "n_kv_heads": 30,
+  "n_layers": 30,
+  "n_refiner_layers": 2,
+  "norm_eps": 1e-05,
+  "qk_norm": true,
+  "rope_theta": 256.0,
+  "t_scale": 1000.0
+}

backend/huggingface/Tongyi-MAI/Z-Image-Turbo/vae/config.json

@@ -0,0 +1,38 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.36.0.dev0",
+  "_name_or_path": "flux-dev",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/image_encoder/config.json

@@ -0,0 +1,23 @@
+{
+  "_name_or_path": "laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
+  "architectures": [
+    "CLIPVisionModelWithProjection"
+  ],
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "gelu",
+  "hidden_size": 1280,
+  "image_size": 224,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 5120,
+  "layer_norm_eps": 1e-05,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 32,
+  "patch_size": 14,
+  "projection_dim": 1024,
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0"
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/image_processor/preprocessor_config.json

@@ -0,0 +1,28 @@
+{
+  "crop_size": {
+    "height": 224,
+    "width": 224
+  },
+  "do_center_crop": false,
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "CLIPImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "WanImageToVideoPipeline",
+  "_diffusers_version": "0.33.0.dev0",
+  "image_encoder": [
+    "transformers",
+    "CLIPVisionModelWithProjection"
+  ],
+  "image_processor": [
+    "transformers",
+    "CLIPImageProcessor"
+  ],
+  "scheduler": [
+    "diffusers",
+    "UniPCMultistepScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "UMT5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "WanTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan"
+  ]
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/scheduler/scheduler_config.json

@@ -0,0 +1,28 @@
+{
+  "_class_name": "UniPCMultistepScheduler",
+  "_diffusers_version": "0.33.0.dev0",
+  "beta_end": 0.02,
+  "beta_schedule": "linear",
+  "beta_start": 0.0001,
+  "disable_corrector": [],
+  "dynamic_thresholding_ratio": 0.995,
+  "final_sigmas_type": "zero",
+  "flow_shift": 5.0,
+  "lower_order_final": true,
+  "num_train_timesteps": 1000,
+  "predict_x0": true,
+  "prediction_type": "flow_prediction",
+  "rescale_betas_zero_snr": false,
+  "sample_max_value": 1.0,
+  "solver_order": 2,
+  "solver_p": null,
+  "solver_type": "bh2",
+  "steps_offset": 0,
+  "thresholding": false,
+  "timestep_spacing": "linspace",
+  "trained_betas": null,
+  "use_beta_sigmas": false,
+  "use_exponential_sigmas": false,
+  "use_flow_sigmas": true,
+  "use_karras_sigmas": false
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/text_encoder/config.json

@@ -0,0 +1,34 @@
+{
+  "_name_or_path": "google/umt5-xxl",
+  "architectures": [
+    "UMT5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "scalable_attention": true,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.0.dev0",
+  "use_cache": true,
+  "vocab_size": 256384
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/tokenizer/special_tokens_map.json

@@ -0,0 +1,332 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
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+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
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+    "<extra_id_296>",
+    "<extra_id_297>",
+    "<extra_id_298>",
+    "<extra_id_299>"
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+  "bos_token": {
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+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
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+    "single_word": false
+  },
+  "pad_token": {
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+    "rstrip": false,
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+  },
+  "unk_token": {
+    "content": "<unk>",
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+    "rstrip": false,
+    "single_word": false
+  }
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/tokenizer/tokenizer_config.json

@@ -0,0 +1,2749 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
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+    "<extra_id_269>",
+    "<extra_id_270>",
+    "<extra_id_271>",
+    "<extra_id_272>",
+    "<extra_id_273>",
+    "<extra_id_274>",
+    "<extra_id_275>",
+    "<extra_id_276>",
+    "<extra_id_277>",
+    "<extra_id_278>",
+    "<extra_id_279>",
+    "<extra_id_280>",
+    "<extra_id_281>",
+    "<extra_id_282>",
+    "<extra_id_283>",
+    "<extra_id_284>",
+    "<extra_id_285>",
+    "<extra_id_286>",
+    "<extra_id_287>",
+    "<extra_id_288>",
+    "<extra_id_289>",
+    "<extra_id_290>",
+    "<extra_id_291>",
+    "<extra_id_292>",
+    "<extra_id_293>",
+    "<extra_id_294>",
+    "<extra_id_295>",
+    "<extra_id_296>",
+    "<extra_id_297>",
+    "<extra_id_298>",
+    "<extra_id_299>"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 300,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/transformer/config.json

@@ -0,0 +1,23 @@
+{
+  "_class_name": "WanTransformer3DModel",
+  "_diffusers_version": "0.33.0.dev0",
+  "added_kv_proj_dim": 5120,
+  "attention_head_dim": 128,
+  "cross_attn_norm": true,
+  "eps": 1e-06,
+  "ffn_dim": 13824,
+  "freq_dim": 256,
+  "image_dim": 1280,
+  "in_channels": 36,
+  "num_attention_heads": 40,
+  "num_layers": 40,
+  "out_channels": 16,
+  "patch_size": [
+    1,
+    2,
+    2
+  ],
+  "qk_norm": "rms_norm_across_heads",
+  "rope_max_seq_len": 1024,
+  "text_dim": 4096
+}

backend/huggingface/Wan-AI/Wan2.1-I2V-14B/vae/config.json

@@ -0,0 +1,56 @@
+{
+  "_class_name": "AutoencoderKLWan",
+  "_diffusers_version": "0.33.0.dev0",
+  "attn_scales": [],
+  "base_dim": 96,
+  "dim_mult": [
+    1,
+    2,
+    4,
+    4
+  ],
+  "dropout": 0.0,
+  "latents_mean": [
+    -0.7571,
+    -0.7089,
+    -0.9113,
+    0.1075,
+    -0.1745,
+    0.9653,
+    -0.1517,
+    1.5508,
+    0.4134,
+    -0.0715,
+    0.5517,
+    -0.3632,
+    -0.1922,
+    -0.9497,
+    0.2503,
+    -0.2921
+  ],
+  "latents_std": [
+    2.8184,
+    1.4541,
+    2.3275,
+    2.6558,
+    1.2196,
+    1.7708,
+    2.6052,
+    2.0743,
+    3.2687,
+    2.1526,
+    2.8652,
+    1.5579,
+    1.6382,
+    1.1253,
+    2.8251,
+    1.916
+  ],
+  "num_res_blocks": 2,
+  "temporal_downsample": [
+    false,
+    true,
+    true
+  ],
+  "z_dim": 16
+}

backend/huggingface/Wan-AI/Wan2.1-T2V-14B/model_index.json

@@ -0,0 +1,24 @@
+{
+  "_class_name": "WanPipeline",
+  "_diffusers_version": "0.33.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "UniPCMultistepScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "UMT5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "WanTransformer3DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKLWan"
+  ]
+}