AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-29 07:22:12 +03:00
Code Activity

update

Browse Source
This commit is contained in:
DN6
2025-06-19 14:20:40 +05:30
parent fb57c76aa1
commit 907ecf72b1
1 changed files with 49 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files

66
docs/source/en/api/pipelines/chroma.md
View File

@@ -27,9 +27,36 @@ Chroma can use all the same optimizations as Flux.
</Tip>
## Inference (Single File)
## Inference
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 Diffusers version of Chroma is based on the `unlocked-v37` version of the original model, which is available in the [Chroma repository](https://huggingface.co/lodestones/Chroma).
```python
import torch
from diffusers import ChromaPipeline
pipe = ChromaPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16)
pipe.enabe_model_cpu_offload()
prompt = [
"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."
]
negative_prompt = ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
generator=torch.Generator("cpu").manual_seed(433),
num_inference_steps=40,
guidance_scale=3.0,
num_images_per_prompt=1,
).images[0]
image.save("chroma.png")
```
## Loading from a single file
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.
The following example demonstrates how to run Chroma from a single file.
@@ -38,30 +65,29 @@ 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"
model_id = "lodestones/Chroma"
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)
transformer = ChromaTransformer2DModel.from_single_file("https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors", torch_dtype=dtype)
pipe = ChromaPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=dtype)
pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
prompt = [
"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."
]
negative_prompt = ["low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"]
image = pipe(
prompt,
guidance_scale=4.0,
output_type="pil",
num_inference_steps=26,
generator=torch.Generator("cpu").manual_seed(0)
prompt=prompt,
negative_prompt=negative_prompt,
generator=torch.Generator("cpu").manual_seed(433),
num_inference_steps=40,
guidance_scale=3.0,
).images[0]
image.save("image.png")
image.save("chroma-single-file.png")
```
## ChromaPipeline
@@ -69,3 +95,9 @@ image.save("image.png")
[[autodoc]] ChromaPipeline
- all
- __call__
## ChromaImg2ImgPipeline
[[autodoc]] ChromaImg2ImgPipeline
- all
- __call__