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| Original file line number | Diff line number | Diff line change |
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| <!--Copyright 2024 The HuggingFace Team. All rights reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
| the License. You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| specific language governing permissions and limitations under the License. | ||
| --> | ||
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| # SD3Transformer2D | ||
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| This class is useful when *only* loading weights into a [`SD3Transformer2DModel`]. If you need to load weights into the text encoder or a text encoder and SD3Transformer2DModel, check [`SD3LoraLoaderMixin`](lora#diffusers.loaders.SD3LoraLoaderMixin) class instead. | ||
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| The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs. | ||
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| <Tip> | ||
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| To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide. | ||
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| </Tip> | ||
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| ## SD3Transformer2DLoadersMixin | ||
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| [[autodoc]] loaders.transformer_sd3.SD3Transformer2DLoadersMixin | ||
| - all | ||
| - _load_ip_adapter_weights |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,89 @@ | ||
| <!--Copyright 2024 The HuggingFace Team, The Black Forest Team. All rights reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
| the License. You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| specific language governing permissions and limitations under the License. | ||
| --> | ||
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| # FluxControlInpaint | ||
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| FluxControlInpaintPipeline is an implementation of Inpainting for Flux.1 Depth/Canny models. It is a pipeline that allows you to inpaint images using the Flux.1 Depth/Canny models. The pipeline takes an image and a mask as input and returns the inpainted image. | ||
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| FLUX.1 Depth and Canny [dev] is a 12 billion parameter rectified flow transformer capable of generating an image based on a text description while following the structure of a given input image. **This is not a ControlNet model**. | ||
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| | Control type | Developer | Link | | ||
| | -------- | ---------- | ---- | | ||
| | Depth | [Black Forest Labs](https://huggingface.co/black-forest-labs) | [Link](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev) | | ||
| | Canny | [Black Forest Labs](https://huggingface.co/black-forest-labs) | [Link](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev) | | ||
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| <Tip> | ||
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| Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. For an exhaustive list of resources, check out [this gist](https://gist.github.com/sayakpaul/b664605caf0aa3bf8585ab109dd5ac9c). | ||
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| </Tip> | ||
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| ```python | ||
| import torch | ||
| from diffusers import FluxControlInpaintPipeline | ||
| from diffusers.models.transformers import FluxTransformer2DModel | ||
| from transformers import T5EncoderModel | ||
| from diffusers.utils import load_image, make_image_grid | ||
| from image_gen_aux import DepthPreprocessor # https://github.com/huggingface/image_gen_aux | ||
| from PIL import Image | ||
| import numpy as np | ||
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| pipe = FluxControlInpaintPipeline.from_pretrained( | ||
| "black-forest-labs/FLUX.1-Depth-dev", | ||
| torch_dtype=torch.bfloat16, | ||
| ) | ||
| # use following lines if you have GPU constraints | ||
| # --------------------------------------------------------------- | ||
| transformer = FluxTransformer2DModel.from_pretrained( | ||
| "sayakpaul/FLUX.1-Depth-dev-nf4", subfolder="transformer", torch_dtype=torch.bfloat16 | ||
| ) | ||
| text_encoder_2 = T5EncoderModel.from_pretrained( | ||
| "sayakpaul/FLUX.1-Depth-dev-nf4", subfolder="text_encoder_2", torch_dtype=torch.bfloat16 | ||
| ) | ||
| pipe.transformer = transformer | ||
| pipe.text_encoder_2 = text_encoder_2 | ||
| pipe.enable_model_cpu_offload() | ||
| # --------------------------------------------------------------- | ||
| pipe.to("cuda") | ||
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| prompt = "a blue robot singing opera with human-like expressions" | ||
| image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png") | ||
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| head_mask = np.zeros_like(image) | ||
| head_mask[65:580,300:642] = 255 | ||
| mask_image = Image.fromarray(head_mask) | ||
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| processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf") | ||
| control_image = processor(image)[0].convert("RGB") | ||
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| output = pipe( | ||
| prompt=prompt, | ||
| image=image, | ||
| control_image=control_image, | ||
| mask_image=mask_image, | ||
| num_inference_steps=30, | ||
| strength=0.9, | ||
| guidance_scale=10.0, | ||
| generator=torch.Generator().manual_seed(42), | ||
| ).images[0] | ||
| make_image_grid([image, control_image, mask_image, output.resize(image.size)], rows=1, cols=4).save("output.png") | ||
| ``` | ||
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| ## FluxControlInpaintPipeline | ||
| [[autodoc]] FluxControlInpaintPipeline | ||
| - all | ||
| - __call__ | ||
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| ## FluxPipelineOutput | ||
| [[autodoc]] pipelines.flux.pipeline_output.FluxPipelineOutput |
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