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Add FluxDisableGuidance node to disable using the guidance embed.
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@ -109,8 +109,7 @@ class Flux(nn.Module):
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img = self.img_in(img)
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vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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if guidance is not None:
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
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vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
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@ -186,7 +185,7 @@ class Flux(nn.Module):
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img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
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return img
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def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
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def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
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bs, c, h, w = x.shape
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patch_size = self.patch_size
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x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
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@ -240,8 +240,7 @@ class HunyuanVideo(nn.Module):
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vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
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if self.params.guidance_embed:
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if guidance is None:
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raise ValueError("Didn't get guidance strength for guidance distilled model.")
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if guidance is not None:
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vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
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if txt_mask is not None and not torch.is_floating_point(txt_mask):
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@ -314,7 +313,7 @@ class HunyuanVideo(nn.Module):
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img = img.reshape(initial_shape)
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return img
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def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
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def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
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bs, c, t, h, w = x.shape
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patch_size = self.patch_size
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t_len = ((t + (patch_size[0] // 2)) // patch_size[0])
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@ -812,7 +812,10 @@ class Flux(BaseModel):
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(h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
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attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
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out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
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out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
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guidance = kwargs.get("guidance", 3.5)
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if guidance is not None:
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out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
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return out
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class GenmoMochi(BaseModel):
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@ -869,7 +872,10 @@ class HunyuanVideo(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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out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
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guidance = kwargs.get("guidance", 6.0)
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if guidance is not None:
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out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
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return out
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class CosmosVideo(BaseModel):
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@ -38,7 +38,26 @@ class FluxGuidance:
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return (c, )
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class FluxDisableGuidance:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"conditioning": ("CONDITIONING", ),
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}}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "append"
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CATEGORY = "advanced/conditioning/flux"
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DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
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def append(self, conditioning):
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c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
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return (c, )
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NODE_CLASS_MAPPINGS = {
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"CLIPTextEncodeFlux": CLIPTextEncodeFlux,
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"FluxGuidance": FluxGuidance,
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"FluxDisableGuidance": FluxDisableGuidance,
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}
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