revert and fix again...
I'll clean this later
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@@ -1266,7 +1266,6 @@ class WanVideoSampler:
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positive_prompt = text_embeds["prompt_embeds"][prompt_index]
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img_emb = image_embeds.get("image_embeds", None)
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partial_img_emb = None
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if img_emb is not None:
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print("img_emb shape", img_emb.shape)
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partial_img_emb = img_emb[:, c, :, :]
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@@ -1276,7 +1275,7 @@ class WanVideoSampler:
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# Model inference - returns [frames, channels, height, width]
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noise_pred_cond = transformer(
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partial_latent_model_input,
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y=partial_img_emb,
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y=[partial_img_emb],
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t=timestep,
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current_step=i,
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is_uncond=False,
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@@ -1286,7 +1285,7 @@ class WanVideoSampler:
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if cfg[i] != 1.0:
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noise_pred_uncond = transformer(
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partial_latent_model_input,
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y=partial_img_emb,
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y=[partial_img_emb],
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t=timestep,
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current_step=i,
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is_uncond=True,
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@@ -1323,14 +1322,14 @@ class WanVideoSampler:
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t=timestep,
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current_step=i,
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is_uncond=False,
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y=image_embeds.get("image_embeds", None),
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y=[image_embeds["image_embeds"]] if "image_embeds" in image_embeds else [],
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context = [text_embeds["prompt_embeds"][0]],
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**args
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)[0].to(intermediate_device)
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if cfg[i] != 1.0:
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noise_pred_uncond = transformer(
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latent_model_input,
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y=image_embeds.get("image_embeds", None),
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y=[image_embeds["image_embeds"]] if "image_embeds" in image_embeds else [],
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t=timestep,
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current_step=i,
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is_uncond=True,
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@@ -609,12 +609,13 @@ class WanModel(ModelMixin, ConfigMixin):
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"""
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if self.model_type == 'i2v':
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assert clip_fea is not None and y is not None
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# params
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#device = self.patch_embedding.weight.device
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if freqs.device != device:
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freqs = freqs.to(device)
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if y is not None:
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x = torch.cat([x, y], dim=0)
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if y:
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x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
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# embeddings
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x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
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