Merge branch 'comfyanonymous:master' into master

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patientx 2024-11-24 14:53:09 +03:00 committed by GitHub
commit 2ab6c68377
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2 changed files with 18 additions and 6 deletions

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@ -287,7 +287,7 @@ class HunYuanDiT(nn.Module):
style=None,
return_dict=False,
control=None,
transformer_options=None,
transformer_options={},
):
"""
Forward pass of the encoder.
@ -315,8 +315,7 @@ class HunYuanDiT(nn.Module):
return_dict: bool
Whether to return a dictionary.
"""
#import pdb
#pdb.set_trace()
patches_replace = transformer_options.get("patches_replace", {})
encoder_hidden_states = context
text_states = encoder_hidden_states # 2,77,1024
text_states_t5 = encoder_hidden_states_t5 # 2,256,2048
@ -364,6 +363,8 @@ class HunYuanDiT(nn.Module):
# Concatenate all extra vectors
c = t + self.extra_embedder(extra_vec) # [B, D]
blocks_replace = patches_replace.get("dit", {})
controls = None
if control:
controls = control.get("output", None)
@ -375,9 +376,20 @@ class HunYuanDiT(nn.Module):
skip = skips.pop() + controls.pop().to(dtype=x.dtype)
else:
skip = skips.pop()
x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D)
else:
x = block(x, c, text_states, freqs_cis_img) # (N, L, D)
skip = None
if ("double_block", layer) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = block(args["img"], args["vec"], args["txt"], args["pe"], args["skip"])
return out
out = blocks_replace[("double_block", layer)]({"img": x, "txt": text_states, "vec": c, "pe": freqs_cis_img, "skip": skip}, {"original_block": block_wrap})
x = out["img"]
else:
x = block(x, c, text_states, freqs_cis_img, skip) # (N, L, D)
if layer < (self.depth // 2 - 1):
skips.append(x)

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@ -10,7 +10,7 @@ class EmptyLTXVLatentVideo:
def INPUT_TYPES(s):
return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
"height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
"length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
"length": ("INT", {"default": 97, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "generate"