initial chroma docs

docs/source/en/api/pipelines/chroma.md

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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.
+-->
+
+# Chroma
+
+<div class="flex flex-wrap space-x-1">
+  <img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
+  <img alt="MPS" src="https://img.shields.io/badge/MPS-000000?style=flat&logo=apple&logoColor=white%22">
+</div>
+
+Chroma is a text to image generation model based on Flux.
+
+Original model checkpoints for Chroma can be found [here](https://huggingface.co/lodestones/Chroma).
+
+<Tip>
+
+Chroma can use all the same optimizations as Flux.
+
+
+### Inference
+
+```python
+import torch
+from diffusers import ChromaPipeline
+
+pipe = ChromaPipeline.from_pretrained("chroma-diffusers-repo", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+out = pipe(
+    prompt=prompt,
+    guidance_scale=4.0,
+    height=1024,
+    width=1024,
+    num_inference_steps=26,
+).images[0]
+out.save("image.png")
+```
+
+## Single File Loading for the `ChromaTransformer2DModel`
+
+The `ChromaTransformer2DModel` supports loading checkpoints 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.
+
+The following example demonstrates how to run Chroma from a single file.
+
+Then run the following example
+
+```python
+import torch
+from diffusers import ChromaTransformer2DModel, ChromaPipeline
+from transformers import T5EncoderModel
+
+bfl_repo = "black-forest-labs/FLUX.1-dev"
+dtype = torch.bfloat16
+
+transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v35.safetensors", torch_dtype=dtype)
+
+text_encoder = T5EncoderModel.from_pretrained(bfl_repo, subfolder="text_encoder_2", torch_dtype=dtype)
+tokenizer = T5Tokenizer.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype)
+
+pipe = ChromaPipeline.from_pretrained(bfl_repo, transformer=transformer, text_encoder=text_encoder, tokenizer=tokenizer, torch_dtype=dtype)
+
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    guidance_scale=4.0,
+    output_type="pil",
+    num_inference_steps=26,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+
+image.save("image.png")
+```
+
+## ChromaPipeline
+
+[[autodoc]] ChromaPipeline
+	- all
+	- __call__