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5efb81fa71
* Add `ChromaInpaintPipeline` * Set `attention_mask` to `dtype=torch.bool` for `ChromaInpaintPipeline`. * Revert `.gitignore`.
108 lines
4.1 KiB
Markdown
108 lines
4.1 KiB
Markdown
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# Chroma
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<div class="flex flex-wrap space-x-1">
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<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
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<img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
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</div>
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Chroma is a text to image generation model based on Flux.
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Original model checkpoints for Chroma can be found here:
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* High-resolution finetune: [lodestones/Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD)
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* Base model: [lodestones/Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base)
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* Original repo with progress checkpoints: [lodestones/Chroma](https://huggingface.co/lodestones/Chroma) (loading this repo with `from_pretrained` will load a Diffusers-compatible version of the `unlocked-v37` checkpoint)
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> [!TIP]
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> Chroma can use all the same optimizations as Flux.
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## Inference
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```python
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import torch
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from diffusers import ChromaPipeline
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pipe = ChromaPipeline.from_pretrained("lodestones/Chroma1-HD", torch_dtype=torch.bfloat16)
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pipe.enable_model_cpu_offload()
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prompt = [
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"A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
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]
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negative_prompt = ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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generator=torch.Generator("cpu").manual_seed(433),
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num_inference_steps=40,
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guidance_scale=3.0,
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num_images_per_prompt=1,
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).images[0]
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image.save("chroma.png")
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```
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## Loading from a single file
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To use updated model checkpoints that are not in the Diffusers format, you can use the `ChromaTransformer2DModel` class to load the model from a single file in the original format. This is also useful when trying to load finetunes or quantized versions of the models that have been published by the community.
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The following example demonstrates how to run Chroma from a single file.
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Then run the following example
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```python
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import torch
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from diffusers import ChromaTransformer2DModel, ChromaPipeline
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model_id = "lodestones/Chroma1-HD"
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dtype = torch.bfloat16
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transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma1-HD/blob/main/Chroma1-HD.safetensors", torch_dtype=dtype)
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pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
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pipe.enable_model_cpu_offload()
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prompt = [
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"A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done."
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]
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negative_prompt = ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
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image = pipe(
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prompt=prompt,
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negative_prompt=negative_prompt,
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generator=torch.Generator("cpu").manual_seed(433),
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num_inference_steps=40,
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guidance_scale=3.0,
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).images[0]
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image.save("chroma-single-file.png")
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```
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## ChromaPipeline
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[[autodoc]] ChromaPipeline
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- all
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- __call__
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## ChromaImg2ImgPipeline
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[[autodoc]] ChromaImg2ImgPipeline
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- all
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- __call__
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## ChromaInpaintPipeline
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[[autodoc]] ChromaInpaintPipeline
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- all
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- __call__
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