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Merge branch 'comfyanonymous:master' into master

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patientx 2025-08-28 02:22:15 +03:00 committed by GitHub
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9 changed files with 55 additions and 12 deletions
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15
comfy/k_diffusion/sampling.py
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@ -853,6 +853,11 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
return x
@torch.no_grad()
def sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@torch.no_grad()
def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
"""DPM-Solver++(3M) SDE."""
@ -925,6 +930,16 @@ def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, di
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
@torch.no_grad()
def sample_dpmpp_2m_sde_heun_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='heun'):
if len(sigmas) <= 1:
return x
extra_args = {} if extra_args is None else extra_args
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
return sample_dpmpp_2m_sde_heun(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
@torch.no_grad()
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
if len(sigmas) <= 1:

4
comfy/ldm/flux/model.py
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@ -158,7 +158,7 @@ class Flux(nn.Module):
if i < len(control_i):
add = control_i[i]
if add is not None:
img += add
img[:, :add.shape[1]] += add
if img.dtype == torch.float16:
img = torch.nan_to_num(img, nan=0.0, posinf=65504, neginf=-65504)
@ -189,7 +189,7 @@ class Flux(nn.Module):
if i < len(control_o):
add = control_o[i]
if add is not None:
img[:, txt.shape[1] :, ...] += add
img[:, txt.shape[1] : txt.shape[1] + add.shape[1], ...] += add
img = img[:, txt.shape[1] :, ...]

2
comfy/ldm/qwen_image/model.py
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@ -459,7 +459,7 @@ class QwenImageTransformer2DModel(nn.Module):
if i < len(control_i):
add = control_i[i]
if add is not None:
hidden_states += add
hidden_states[:, :add.shape[1]] += add
hidden_states = self.norm_out(hidden_states, temb)
hidden_states = self.proj_out(hidden_states)

6
comfy/ldm/wan/model.py
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@ -1255,6 +1255,7 @@ class WanModel_S2V(WanModel):
audio_emb = None
# embeddings
bs, _, time, height, width = x.shape
x = self.patch_embedding(x.float()).to(x.dtype)
if control_video is not None:
x = x + self.cond_encoder(control_video)
@ -1272,11 +1273,12 @@ class WanModel_S2V(WanModel):
if reference_latent is not None:
ref = self.patch_embedding(reference_latent.float()).to(x.dtype)
ref = ref.flatten(2).transpose(1, 2)
freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=30, device=x.device, dtype=x.dtype)
freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=max(30, time + 9), device=x.device, dtype=x.dtype)
ref = ref + cond_mask_weight[1]
x = torch.cat([x, ref], dim=1)
freqs = torch.cat([freqs, freqs_ref], dim=1)
t = torch.cat([t, torch.zeros((t.shape[0], reference_latent.shape[-3]), device=t.device, dtype=t.dtype)], dim=1)
del ref, freqs_ref
if reference_motion is not None:
motion_encoded, freqs_motion = self.frame_packer(reference_motion, self)
@ -1286,6 +1288,7 @@ class WanModel_S2V(WanModel):
t = torch.repeat_interleave(t, 2, dim=1)
t = torch.cat([t, torch.zeros((t.shape[0], 3), device=t.device, dtype=t.dtype)], dim=1)
del motion_encoded, freqs_motion
# time embeddings
e = self.time_embedding(
@ -1296,7 +1299,6 @@ class WanModel_S2V(WanModel):
# context
context = self.text_embedding(context)
patches_replace = transformer_options.get("patches_replace", {})
blocks_replace = patches_replace.get("dit", {})
for i, block in enumerate(self.blocks):

25
comfy/model_base.py
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@ -150,6 +150,7 @@ class BaseModel(torch.nn.Module):
logging.debug("adm {}".format(self.adm_channels))
self.memory_usage_factor = model_config.memory_usage_factor
self.memory_usage_factor_conds = ()
self.memory_usage_shape_process = {}
def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
return comfy.patcher_extension.WrapperExecutor.new_class_executor(
@ -350,8 +351,15 @@ class BaseModel(torch.nn.Module):
input_shapes = [input_shape]
for c in self.memory_usage_factor_conds:
shape = cond_shapes.get(c, None)
if shape is not None and len(shape) > 0:
input_shapes += shape
if shape is not None:
if c in self.memory_usage_shape_process:
out = []
for s in shape:
out.append(self.memory_usage_shape_process[c](s))
shape = out
if len(shape) > 0:
input_shapes += shape
if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
dtype = self.get_dtype()
@ -1204,6 +1212,8 @@ class WAN21_Camera(WAN21):
class WAN22_S2V(WAN21):
def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
self.memory_usage_factor_conds = ("reference_latent", "reference_motion")
self.memory_usage_shape_process = {"reference_motion": lambda shape: [shape[0], shape[1], 1.5, shape[-2], shape[-1]]}
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
@ -1224,6 +1234,17 @@ class WAN22_S2V(WAN21):
out['control_video'] = comfy.conds.CONDRegular(self.process_latent_in(control_video))
return out
def extra_conds_shapes(self, **kwargs):
out = {}
ref_latents = kwargs.get("reference_latents", None)
if ref_latents is not None:
out['reference_latent'] = list([1, 16, sum(map(lambda a: math.prod(a.size()), ref_latents)) // 16])
reference_motion = kwargs.get("reference_motion", None)
if reference_motion is not None:
out['reference_motion'] = reference_motion.shape
return out
class WAN22(BaseModel):
def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel)

2
comfy/samplers.py Normal file → Executable file
View File

@ -729,7 +729,7 @@ class Sampler:
KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_2m_sde_heun", "dpmpp_2m_sde_heun_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
"gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"]

3
comfy/weight_adapter/lokr.py
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@ -97,6 +97,9 @@ class LoKrAdapter(WeightAdapterBase):
(mat1, mat2, alpha, None, None, None, None, None, None)
)
def to_train(self):
return LokrDiff(self.weights)
@classmethod
def load(
cls,

8
comfy_extras/nodes_model_patch.py
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@ -89,6 +89,7 @@ class DiffSynthCnetPatch:
self.strength = strength
self.mask = mask
self.encoded_image = model_patch.model.process_input_latent_image(self.encode_latent_cond(image))
self.encoded_image_size = (image.shape[1], image.shape[2])
def encode_latent_cond(self, image):
latent_image = self.vae.encode(image)
@ -106,14 +107,15 @@ class DiffSynthCnetPatch:
x = kwargs.get("x")
img = kwargs.get("img")
block_index = kwargs.get("block_index")
if self.encoded_image is None or self.encoded_image.shape[1:] != img.shape[1:]:
spacial_compression = self.vae.spacial_compression_encode()
spacial_compression = self.vae.spacial_compression_encode()
if self.encoded_image is None or self.encoded_image_size != (x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression):
image_scaled = comfy.utils.common_upscale(self.image.movedim(-1, 1), x.shape[-1] * spacial_compression, x.shape[-2] * spacial_compression, "area", "center")
loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
self.encoded_image = self.model_patch.model.process_input_latent_image(self.encode_latent_cond(image_scaled.movedim(1, -1)))
self.encoded_image_size = (image_scaled.shape[-2], image_scaled.shape[-1])
comfy.model_management.load_models_gpu(loaded_models)
img = img + (self.model_patch.model.control_block(img, self.encoded_image.to(img.dtype), block_index) * self.strength)
img[:, :self.encoded_image.shape[1]] += (self.model_patch.model.control_block(img[:, :self.encoded_image.shape[1]], self.encoded_image.to(img.dtype), block_index) * self.strength)
kwargs['img'] = img
return kwargs

2
comfy_extras/nodes_wan.py
View File

@ -920,7 +920,7 @@ class WanSoundImageToVideo(io.ComfyNode):
audio_embed_bucket = audio_embed_bucket.permute(0, 2, 3, 1)
positive = node_helpers.conditioning_set_values(positive, {"audio_embed": audio_embed_bucket})
negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_embed_bucket})
negative = node_helpers.conditioning_set_values(negative, {"audio_embed": audio_embed_bucket * 0.0})
if ref_image is not None:
ref_image = comfy.utils.common_upscale(ref_image[:1].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)