[shortfin-sd] Add exports and support for scheduled unet, batch sizes. #972
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IanNod
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Feb 17, 2025
| @@ -147,7 +147,7 @@ def forward( | |||
| # 1b. Aug embedding of text_embeds, time_ids | |||
| time_embeds = self.add_time_proj(time_ids.flatten()) | |||
| time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) | |||
| add_embeds = torch.concat([text_embeds, time_embeds], dim=-1).to(emb.dtype) | |||
| add_embeds = torch.cat([text_embeds, time_embeds], dim=-1).to(emb.dtype) | |||
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Is there any reason for this change?
| ): | ||
| super().__init__() | ||
| self.torch_dtype = torch.float16 if precision == "fp16" else torch.float32 | ||
| config_1 = CLIPTextConfig.from_pretrained( |
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Are we grabbing the HF implementation for all these (except punet) models? We should have everything covered in our sharktank models that we should switch to
| ] | ||
| with decompositions.extend_aot_decompositions( | ||
| from_current=True, | ||
| add_ops=decomp_list, |
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This will decompose attention for all models, we want to keep sdpa for punet but not (at least for now) clip or vae
daveliddell
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Feb 18, 2025
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