Flux new control models #59
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| self.redux_up = nn.Linear(redux_dim, txt_in_features * 3, dtype=dtype) | ||
| self.redux_down = nn.Linear(txt_in_features * 3, txt_in_features, dtype=dtype) | ||
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| sd = load_sft(redux_path, device=str(device)) |
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maybe wrap this in if redux_path is not None?
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good thought - I'd rather remove the | None = os.getenv("FLUX_REDUX") part of this. the way we're using this model we're always passing in redux_path and we should fail w/o it.
| torch._dynamo.mark_dynamic(img_ids, 1, min=256, max=8100) | ||
| torch._dynamo.mark_dynamic(img_cond, 1, min=256, max=8100) |
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this is only needed if we're compiling the bf16 controlnets, which I don't think we're doing (unless I'm wrong?). not a huge deal either way, more of a thought.
| @@ -386,12 +386,12 @@ def into_bytes(self, x: torch.Tensor, jpeg_quality: int = 99) -> io.BytesIO: | |||
| return im | |||
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| @torch.inference_mode() | |||
| def as_img_tensor(self, x: torch.Tensor) -> io.BytesIO: | |||
| """Converts the image tensor to bytes.""" | |||
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extremely nit but might be worth renaming this method to as_pil_and_np_image or something.
| @@ -378,64 +440,62 @@ def handle_loras( | |||
| self.bf16_lora = lora_weights | |||
| self.bf16_lora_scale = lora_scale | |||
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| def preprocess(self, aspect_ratio: str, megapixels: str = "1") -> Tuple[int, int]: | |||
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need to update this name change in the HotswapPredictor
predict.py
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| timesteps = get_schedule( | ||
| num_inference_steps, (x.shape[-1] * x.shape[-2]) // 4, shift=self.shift | ||
| num_inference_steps, | ||
| # TODO: this has changed in upstream flux to x.shape[1] |
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can probably delete this comment
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