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319 lines (267 loc) · 10.3 KB
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import node_helpers
import comfy.utils
from typing_extensions import override
from comfy_api.latest import ComfyExtension, io
import comfy.model_management
import torch
import math
import nodes
import comfy.ldm.flux.math
class CLIPTextEncodeFlux(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="CLIPTextEncodeFlux",
category="model/conditioning/flux",
inputs=[
io.Clip.Input("clip"),
io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, clip, clip_l, t5xxl, guidance) -> io.NodeOutput:
tokens = clip.tokenize(clip_l)
tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
encode = execute # TODO: remove
class EmptyFlux2LatentImage(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="EmptyFlux2LatentImage",
display_name="Empty Flux 2 Latent",
category="model/latent/flux",
inputs=[
io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
io.Int.Input("batch_size", default=1, min=1, max=4096),
],
outputs=[
io.Latent.Output(),
],
)
@classmethod
def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
latent = torch.zeros([batch_size, 128, height // 16, width // 16], device=comfy.model_management.intermediate_device())
return io.NodeOutput({"samples": latent})
class FluxGuidance(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FluxGuidance",
category="model/conditioning/flux",
inputs=[
io.Conditioning.Input("conditioning"),
io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, conditioning, guidance) -> io.NodeOutput:
c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
return io.NodeOutput(c)
append = execute # TODO: remove
class FluxDisableGuidance(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FluxDisableGuidance",
category="model/conditioning/flux",
description="This node completely disables the guidance embed on Flux and Flux like models",
inputs=[
io.Conditioning.Input("conditioning"),
],
outputs=[
io.Conditioning.Output(),
],
)
@classmethod
def execute(cls, conditioning) -> io.NodeOutput:
c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
return io.NodeOutput(c)
append = execute # TODO: remove
PREFERRED_KONTEXT_RESOLUTIONS = [
(672, 1568),
(688, 1504),
(720, 1456),
(752, 1392),
(800, 1328),
(832, 1248),
(880, 1184),
(944, 1104),
(1024, 1024),
(1104, 944),
(1184, 880),
(1248, 832),
(1328, 800),
(1392, 752),
(1456, 720),
(1504, 688),
(1568, 672),
]
class FluxKontextImageScale(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FluxKontextImageScale",
category="model/conditioning/flux",
description="This node resizes the image to one that is more optimal for flux kontext.",
inputs=[
io.Image.Input("image"),
],
outputs=[
io.Image.Output(),
],
)
@classmethod
def execute(cls, image) -> io.NodeOutput:
width = image.shape[2]
height = image.shape[1]
aspect_ratio = width / height
_, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERRED_KONTEXT_RESOLUTIONS)
image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
return io.NodeOutput(image)
scale = execute # TODO: remove
class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="FluxKontextMultiReferenceLatentMethod",
display_name="Edit Model Reference Method",
category="model/conditioning/flux",
inputs=[
io.Conditioning.Input("conditioning"),
io.Combo.Input(
"reference_latents_method",
options=["offset", "index", "uxo/uno", "index_timestep_zero"],
advanced=True,
),
],
outputs=[
io.Conditioning.Output(),
],
is_experimental=True,
)
@classmethod
def execute(cls, conditioning, reference_latents_method) -> io.NodeOutput:
if "uxo" in reference_latents_method or "uso" in reference_latents_method:
reference_latents_method = "uxo"
c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method})
return io.NodeOutput(c)
append = execute # TODO: remove
def generalized_time_snr_shift(t, mu: float, sigma: float):
return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
def compute_empirical_mu(image_seq_len: int, num_steps: int) -> float:
a1, b1 = 8.73809524e-05, 1.89833333
a2, b2 = 0.00016927, 0.45666666
if image_seq_len > 4300:
mu = a2 * image_seq_len + b2
return float(mu)
m_200 = a2 * image_seq_len + b2
m_10 = a1 * image_seq_len + b1
a = (m_200 - m_10) / 190.0
b = m_200 - 200.0 * a
mu = a * num_steps + b
return float(mu)
def get_schedule(num_steps: int, image_seq_len: int) -> list[float]:
mu = compute_empirical_mu(image_seq_len, num_steps)
timesteps = torch.linspace(1, 0, num_steps + 1)
timesteps = generalized_time_snr_shift(timesteps, mu, 1.0)
return timesteps
class Flux2Scheduler(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="Flux2Scheduler",
category="model/sampling/schedulers",
inputs=[
io.Int.Input("steps", default=20, min=1, max=4096),
io.Int.Input("width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),
io.Int.Input("height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=1),
],
outputs=[
io.Sigmas.Output(),
],
)
@classmethod
def execute(cls, steps, width, height) -> io.NodeOutput:
seq_len = (width * height / (16 * 16))
sigmas = get_schedule(steps, round(seq_len))
return io.NodeOutput(sigmas)
class KV_Attn_Input:
def __init__(self):
self.cache = {}
def __call__(self, q, k, v, extra_options, **kwargs):
reference_image_num_tokens = extra_options.get("reference_image_num_tokens", [])
if len(reference_image_num_tokens) == 0:
return {}
ref_toks = sum(reference_image_num_tokens)
cache_key = "{}_{}".format(extra_options["block_type"], extra_options["block_index"])
if cache_key in self.cache:
kk, vv = self.cache[cache_key]
# Fix batch size changing.
kk = comfy.utils.repeat_to_batch_size(kk, k.shape[0])
vv = comfy.utils.repeat_to_batch_size(vv, v.shape[0])
self.set_cache = False
return {"q": q, "k": torch.cat((k, kk), dim=2), "v": torch.cat((v, vv), dim=2)}
self.cache[cache_key] = (k[:, :, -ref_toks:].clone(), v[:, :, -ref_toks:].clone())
self.set_cache = True
return {"q": q, "k": k, "v": v}
def cleanup(self):
self.cache = {}
class FluxKVCache(io.ComfyNode):
@classmethod
def define_schema(cls) -> io.Schema:
return io.Schema(
node_id="FluxKVCache",
display_name="Flux KV Cache",
description="Enables KV Cache optimization for reference images on Flux family models.",
category="experimental",
is_experimental=True,
inputs=[
io.Model.Input("model", tooltip="The model to use KV Cache on."),
],
outputs=[
io.Model.Output(tooltip="The patched model with KV Cache enabled."),
],
)
@classmethod
def execute(cls, model: io.Model.Type) -> io.NodeOutput:
m = model.clone()
input_patch_obj = KV_Attn_Input()
def model_input_patch(inputs):
if len(input_patch_obj.cache) > 0:
ref_image_tokens = sum(inputs["transformer_options"].get("reference_image_num_tokens", []))
if ref_image_tokens > 0:
img = inputs["img"]
inputs["img"] = img[:, :-ref_image_tokens]
return inputs
m.set_model_attn1_patch(input_patch_obj)
m.set_model_post_input_patch(model_input_patch)
if hasattr(model.model.diffusion_model, "params"):
m.add_object_patch("diffusion_model.params.default_ref_method", "index_timestep_zero")
else:
m.add_object_patch("diffusion_model.default_ref_method", "index_timestep_zero")
return io.NodeOutput(m)
class FluxExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
CLIPTextEncodeFlux,
FluxGuidance,
FluxDisableGuidance,
FluxKontextImageScale,
FluxKontextMultiReferenceLatentMethod,
EmptyFlux2LatentImage,
Flux2Scheduler,
FluxKVCache,
]
async def comfy_entrypoint() -> FluxExtension:
return FluxExtension()
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