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sayakpaul
2026-01-12 15:39:12 +05:30
parent d77d61bae4
commit 8063353819
5 changed files with 5 additions and 5 deletions
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2
docs/source/en/using-diffusers/custom_pipeline_overview.md
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@@ -121,7 +121,7 @@ from showone_unet_3d_condition import ShowOneUNet3DConditionModel
unet = ShowOneUNet3DConditionModel.from_pretrained(pipeline_id, subfolder="unet")
```
3. Load the custom pipeline code (already implemented in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py)). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Like the custom UNet, Any code required for `TextToVideIFPipeline` should be placed in `pipeline_t2v_base_pixel.py`.
3. Load the custom pipeline code (already implemented in [pipeline_t2v_base_pixel.py](https://huggingface.co/sayakpaul/show-1-base-with-code/blob/main/pipeline_t2v_base_pixel.py)). This script contains a custom `TextToVideoIFPipeline` class for generating videos from text. Like the custom UNet, any code required for `TextToVideIFPipeline` should be placed in `pipeline_t2v_base_pixel.py`.
Initialize `TextToVideoIFPipeline` with `ShowOneUNet3DConditionModel`.

2
docs/source/en/using-diffusers/other-formats.md
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@@ -103,7 +103,7 @@ model = UNet2DConditionModel.from_single_file(ckpt_path, upcast_attention=True)
### Local files
The [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. For example, Any single file checkpoint based on the Stable Diffusion XL base model is configured from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
The [`~loaders.FromSingleFileMixin.from_single_file`] method attempts to configure a pipeline or model by inferring the model type from the keys in the checkpoint file. For example, any single file checkpoint based on the Stable Diffusion XL base model is configured from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
If you're working with local files, download the config files with the [`~huggingface_hub.snapshot_download`] method and the model checkpoint with [`~huggingface_hub.hf_hub_download`]. These files are downloaded to your [cache directory](https://huggingface.co/docs/huggingface_hub/en/guides/manage-cache), but you can download them to a specific directory with the `local_dir` argument.

2
examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py
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@@ -945,7 +945,7 @@ class TokenEmbeddingsHandler:
new_token_embeddings = embeds.weight.data[train_ids]
# New tokens for each text encoder are saved under "clip_l" (for text_encoder 0),
# Note: When loading with diffusers, Any name can work - simply specify in inference
# Note: When loading with diffusers, any name can work - simply specify in inference
tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings
# tensors[f"text_encoders_{idx}"] = new_token_embeddings

2
examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py
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@@ -801,7 +801,7 @@ class TokenEmbeddingsHandler:
# New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), "clip_g" (for
# text_encoder 1) to keep compatible with the ecosystem.
# Note: When loading with diffusers, Any name can work - simply specify in inference
# Note: When loading with diffusers, any name can work - simply specify in inference
tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings
# tensors[f"text_encoders_{idx}"] = new_token_embeddings

2
examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py
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@@ -966,7 +966,7 @@ class TokenEmbeddingsHandler:
# New tokens for each text encoder are saved under "clip_l" (for text_encoder 0), "clip_g" (for
# text_encoder 1) to keep compatible with the ecosystem.
# Note: When loading with diffusers, Any name can work - simply specify in inference
# Note: When loading with diffusers, any name can work - simply specify in inference
tensors[idx_to_text_encoder_name[idx]] = new_token_embeddings
# tensors[f"text_encoders_{idx}"] = new_token_embeddings