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Support flux 2 klein kv cache model: Use the FluxKVCache node. (#12905)
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comfyanonymous
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@ -44,6 +44,22 @@ class FluxParams:
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txt_norm: bool = False
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def invert_slices(slices, length):
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sorted_slices = sorted(slices)
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result = []
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current = 0
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for start, end in sorted_slices:
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if current < start:
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result.append((current, start))
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current = max(current, end)
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if current < length:
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result.append((current, length))
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return result
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class Flux(nn.Module):
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"""
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Transformer model for flow matching on sequences.
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@ -138,6 +154,7 @@ class Flux(nn.Module):
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y: Tensor,
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guidance: Tensor = None,
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control = None,
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timestep_zero_index=None,
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transformer_options={},
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attn_mask: Tensor = None,
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) -> Tensor:
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@ -164,10 +181,6 @@ class Flux(nn.Module):
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txt = self.txt_norm(txt)
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txt = self.txt_in(txt)
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vec_orig = vec
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if self.params.global_modulation:
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vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(vec_orig))
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if "post_input" in patches:
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for p in patches["post_input"]:
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out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids, "transformer_options": transformer_options})
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@ -182,6 +195,24 @@ class Flux(nn.Module):
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else:
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pe = None
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vec_orig = vec
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txt_vec = vec
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extra_kwargs = {}
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if timestep_zero_index is not None:
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modulation_dims = []
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batch = vec.shape[0] // 2
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vec_orig = vec_orig.reshape(2, batch, vec.shape[1]).movedim(0, 1)
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invert = invert_slices(timestep_zero_index, img.shape[1])
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for s in invert:
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modulation_dims.append((s[0], s[1], 0))
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for s in timestep_zero_index:
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modulation_dims.append((s[0], s[1], 1))
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extra_kwargs["modulation_dims_img"] = modulation_dims
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txt_vec = vec[:batch]
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if self.params.global_modulation:
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vec = (self.double_stream_modulation_img(vec_orig), self.double_stream_modulation_txt(txt_vec))
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blocks_replace = patches_replace.get("dit", {})
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transformer_options["total_blocks"] = len(self.double_blocks)
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transformer_options["block_type"] = "double"
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@ -195,7 +226,8 @@ class Flux(nn.Module):
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vec=args["vec"],
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pe=args["pe"],
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attn_mask=args.get("attn_mask"),
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transformer_options=args.get("transformer_options"))
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transformer_options=args.get("transformer_options"),
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**extra_kwargs)
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return out
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out = blocks_replace[("double_block", i)]({"img": img,
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@ -213,7 +245,8 @@ class Flux(nn.Module):
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vec=vec,
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pe=pe,
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attn_mask=attn_mask,
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transformer_options=transformer_options)
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transformer_options=transformer_options,
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**extra_kwargs)
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if control is not None: # Controlnet
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control_i = control.get("input")
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@ -230,6 +263,12 @@ class Flux(nn.Module):
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if self.params.global_modulation:
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vec, _ = self.single_stream_modulation(vec_orig)
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extra_kwargs = {}
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if timestep_zero_index is not None:
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lambda a: 0 if a == 0 else a + txt.shape[1]
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modulation_dims_combined = list(map(lambda x: (0 if x[0] == 0 else x[0] + txt.shape[1], x[1] + txt.shape[1], x[2]), modulation_dims))
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extra_kwargs["modulation_dims"] = modulation_dims_combined
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transformer_options["total_blocks"] = len(self.single_blocks)
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transformer_options["block_type"] = "single"
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transformer_options["img_slice"] = [txt.shape[1], img.shape[1]]
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@ -242,7 +281,8 @@ class Flux(nn.Module):
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vec=args["vec"],
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pe=args["pe"],
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attn_mask=args.get("attn_mask"),
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transformer_options=args.get("transformer_options"))
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transformer_options=args.get("transformer_options"),
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**extra_kwargs)
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return out
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out = blocks_replace[("single_block", i)]({"img": img,
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@ -253,7 +293,7 @@ class Flux(nn.Module):
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{"original_block": block_wrap})
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img = out["img"]
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else:
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img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
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img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options, **extra_kwargs)
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if control is not None: # Controlnet
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control_o = control.get("output")
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@ -264,7 +304,11 @@ class Flux(nn.Module):
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img = img[:, txt.shape[1] :, ...]
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img = self.final_layer(img, vec_orig) # (N, T, patch_size ** 2 * out_channels)
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extra_kwargs = {}
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if timestep_zero_index is not None:
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extra_kwargs["modulation_dims"] = modulation_dims
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img = self.final_layer(img, vec_orig, **extra_kwargs) # (N, T, patch_size ** 2 * out_channels)
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return img
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def process_img(self, x, index=0, h_offset=0, w_offset=0, transformer_options={}):
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@ -312,13 +356,16 @@ class Flux(nn.Module):
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w_len = ((w_orig + (patch_size // 2)) // patch_size)
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img, img_ids = self.process_img(x, transformer_options=transformer_options)
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img_tokens = img.shape[1]
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timestep_zero_index = None
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if ref_latents is not None:
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ref_num_tokens = []
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h = 0
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w = 0
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index = 0
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ref_latents_method = kwargs.get("ref_latents_method", self.params.default_ref_method)
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timestep_zero = ref_latents_method == "index_timestep_zero"
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for ref in ref_latents:
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if ref_latents_method == "index":
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if ref_latents_method in ("index", "index_timestep_zero"):
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index += self.params.ref_index_scale
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h_offset = 0
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w_offset = 0
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@ -342,6 +389,13 @@ class Flux(nn.Module):
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kontext, kontext_ids = self.process_img(ref, index=index, h_offset=h_offset, w_offset=w_offset)
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img = torch.cat([img, kontext], dim=1)
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img_ids = torch.cat([img_ids, kontext_ids], dim=1)
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ref_num_tokens.append(kontext.shape[1])
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if timestep_zero:
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if index > 0:
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timestep = torch.cat([timestep, timestep * 0], dim=0)
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timestep_zero_index = [[img_tokens, img_ids.shape[1]]]
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transformer_options = transformer_options.copy()
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transformer_options["reference_image_num_tokens"] = ref_num_tokens
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txt_ids = torch.zeros((bs, context.shape[1], len(self.params.axes_dim)), device=x.device, dtype=torch.float32)
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@ -349,6 +403,6 @@ class Flux(nn.Module):
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for i in self.params.txt_ids_dims:
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txt_ids[:, :, i] = torch.linspace(0, context.shape[1] - 1, steps=context.shape[1], device=x.device, dtype=torch.float32)
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out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, transformer_options, attn_mask=kwargs.get("attention_mask", None))
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out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance, control, timestep_zero_index=timestep_zero_index, transformer_options=transformer_options, attn_mask=kwargs.get("attention_mask", None))
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out = out[:, :img_tokens]
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return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=self.patch_size, pw=self.patch_size)[:,:,:h_orig,:w_orig]
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@ -6,6 +6,7 @@ import comfy.model_management
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import torch
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import math
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import nodes
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import comfy.ldm.flux.math
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class CLIPTextEncodeFlux(io.ComfyNode):
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@classmethod
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@ -231,6 +232,68 @@ class Flux2Scheduler(io.ComfyNode):
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sigmas = get_schedule(steps, round(seq_len))
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return io.NodeOutput(sigmas)
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class KV_Attn_Input:
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def __init__(self):
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self.cache = {}
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def __call__(self, q, k, v, extra_options, **kwargs):
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reference_image_num_tokens = extra_options.get("reference_image_num_tokens", [])
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if len(reference_image_num_tokens) == 0:
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return {}
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ref_toks = sum(reference_image_num_tokens)
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cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"])
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if cache_key in self.cache:
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kk, vv = self.cache[cache_key]
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self.set_cache = False
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return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
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self.cache[cache_key] = (k[:, :, -ref_toks:], v[:, :, -ref_toks:])
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self.set_cache = True
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return {"q": q, "k": k, "v": v}
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def cleanup(self):
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self.cache = {}
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class FluxKVCache(io.ComfyNode):
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@classmethod
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def define_schema(cls) -> io.Schema:
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return io.Schema(
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node_id="FluxKVCache",
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display_name="Flux KV Cache",
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description="Enables KV Cache optimization for reference images on Flux family models.",
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category="",
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is_experimental=True,
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inputs=[
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io.Model.Input("model", tooltip="The model to use KV Cache on."),
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],
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outputs=[
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io.Model.Output(tooltip="The patched model with KV Cache enabled."),
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],
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)
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@classmethod
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def execute(cls, model: io.Model.Type) -> io.NodeOutput:
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m = model.clone()
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input_patch_obj = KV_Attn_Input()
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def model_input_patch(inputs):
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if len(input_patch_obj.cache) > 0:
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ref_image_tokens = sum(inputs["transformer_options"].get("reference_image_num_tokens", []))
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if ref_image_tokens > 0:
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img = inputs["img"]
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inputs["img"] = img[:, :-ref_image_tokens]
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return inputs
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m.set_model_attn1_patch(input_patch_obj)
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m.set_model_post_input_patch(model_input_patch)
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if hasattr(model.model.diffusion_model, "params"):
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m.add_object_patch("diffusion_model.params.default_ref_method", "index_timestep_zero")
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else:
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m.add_object_patch("diffusion_model.default_ref_method", "index_timestep_zero")
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return io.NodeOutput(m)
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class FluxExtension(ComfyExtension):
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@override
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@ -243,6 +306,7 @@ class FluxExtension(ComfyExtension):
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FluxKontextMultiReferenceLatentMethod,
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EmptyFlux2LatentImage,
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Flux2Scheduler,
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FluxKVCache,
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]
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