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https://github.com/comfyanonymous/ComfyUI.git
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Merge branch 'comfyanonymous:master' into master
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commit
3f09b4dba5
@ -164,8 +164,11 @@ class IndexListContextHandler(ContextHandlerABC):
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return resized_cond
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def set_step(self, timestep: torch.Tensor, model_options: dict[str]):
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indexes = torch.where(model_options["transformer_options"]["sample_sigmas"] == timestep[0])
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self._step = int(indexes[0])
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mask = torch.isclose(model_options["transformer_options"]["sample_sigmas"], timestep, rtol=0.0001)
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matches = torch.nonzero(mask)
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if torch.numel(matches) == 0:
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raise Exception("No sample_sigmas matched current timestep; something went wrong.")
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self._step = int(matches[0].item())
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def get_context_windows(self, model: BaseModel, x_in: torch.Tensor, model_options: dict[str]) -> list[IndexListContextWindow]:
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full_length = x_in.size(self.dim) # TODO: choose dim based on model
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@ -360,6 +360,7 @@ class QwenImageTransformer2DModel(nn.Module):
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context,
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attention_mask=None,
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guidance: torch.Tensor = None,
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ref_latents=None,
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transformer_options={},
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**kwargs
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):
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@ -370,6 +371,31 @@ class QwenImageTransformer2DModel(nn.Module):
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hidden_states, img_ids, orig_shape = self.process_img(x)
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num_embeds = hidden_states.shape[1]
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if ref_latents is not None:
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h = 0
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w = 0
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index = 0
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index_ref_method = kwargs.get("ref_latents_method", "index") == "index"
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for ref in ref_latents:
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if index_ref_method:
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index += 1
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h_offset = 0
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w_offset = 0
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else:
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index = 1
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h_offset = 0
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w_offset = 0
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if ref.shape[-2] + h > ref.shape[-1] + w:
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w_offset = w
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else:
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h_offset = h
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h = max(h, ref.shape[-2] + h_offset)
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w = max(w, ref.shape[-1] + w_offset)
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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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hidden_states = torch.cat([hidden_states, kontext], dim=1)
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img_ids = torch.cat([img_ids, kontext_ids], dim=1)
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txt_start = round(max(((x.shape[-1] + (self.patch_size // 2)) // self.patch_size), ((x.shape[-2] + (self.patch_size // 2)) // self.patch_size)))
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txt_ids = torch.linspace(txt_start, txt_start + context.shape[1], steps=context.shape[1], device=x.device, dtype=x.dtype).reshape(1, -1, 1).repeat(x.shape[0], 1, 3)
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ids = torch.cat((txt_ids, img_ids), dim=1)
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@ -1331,4 +1331,14 @@ class QwenImage(BaseModel):
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cross_attn = kwargs.get("cross_attn", None)
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if cross_attn is not None:
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out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
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ref_latents = kwargs.get("reference_latents", None)
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if ref_latents is not None:
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latents = []
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for lat in ref_latents:
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latents.append(self.process_latent_in(lat))
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out['ref_latents'] = comfy.conds.CONDList(latents)
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ref_latents_method = kwargs.get("reference_latents_method", None)
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if ref_latents_method is not None:
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out['ref_latents_method'] = comfy.conds.CONDConstant(ref_latents_method)
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return out
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@ -699,7 +699,7 @@ class WanTrackToVideo(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="WanPhantomSubjectToVideo",
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node_id="WanTrackToVideo",
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category="conditioning/video_models",
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inputs=[
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io.Conditioning.Input("positive"),
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@ -1,5 +1,5 @@
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comfyui-frontend-package==1.25.8
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comfyui-workflow-templates==0.1.59
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comfyui-frontend-package==1.25.9
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comfyui-workflow-templates==0.1.60
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comfyui-embedded-docs==0.2.6
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torch
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torchsde
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