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update

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This commit is contained in:
Dhruv Nair
2025-06-17 11:26:02 +02:00
parent 79b55007ef
commit 57df5f9234
2 changed files with 11 additions and 12 deletions
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4
src/diffusers/pipelines/chroma/pipeline_chroma.py
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@@ -599,9 +599,9 @@ class ChromaPipeline(
negative_prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
num_inference_steps: int = 35,
sigmas: Optional[List[float]] = None,
guidance_scale: float = 3.5,
guidance_scale: float = 5.0,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,

19
src/diffusers/pipelines/chroma/pipeline_chroma_img2img.py
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@@ -53,9 +53,8 @@ EXAMPLE_DOC_STRING = """
>>> from diffusers import ChromaTransformer2DModel, ChromaImg2ImgPipeline
>>> from transformers import AutoModel, Autotokenizer
>>> transformer = ChromaTransformer2DModel.from_single_file(
... "chroma-unlocked-v35-detail-calibrated.safetensors", torch_dtype=torch.bfloat16
... )
>>> ckpt_path = "https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors"
>>> transformer = ChromaTransformer2DModel.from_single_file(ckpt_path, torch_dtype=torch.bfloat16)
>>> text_encoder -= AutoModel.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="text_encoder_2")
>>> tokenizer = AutoTokenizer.from_pretrained("black-forest-labs/FLUX.1-schnell", subfolder="tokenizer_2")
>>> pipe = ChromaImg2ImgPipeline.from_pretrained(
@@ -65,13 +64,13 @@ EXAMPLE_DOC_STRING = """
... tokenizer=tokenizer,
... torch_dtype=torch.bfloat16,
... )
>>> pipe.to("cuda")
>>> pipe.enable_model_cpu_offload()
>>> image = load_image(
... "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
... )
>>> prompt = "A cat holding a sign that says hello world"
>>> image = pipe(prompt, image=image, num_inference_steps=28, guidance_scale=4.0, strength=0.85).images[0]
>>> image.save("chroma.png")
>>> prompt = "a scenic fastasy landscape with a river and mountains in the background, vibrant colors, detailed, high resolution"
>>> image = pipe(prompt, image=image, num_inference_steps=35, guidance_scale=5.0, strength=0.9).images[0]
>>> image.save("chroma-img2img.png")
```
"""
@@ -688,10 +687,10 @@ class ChromaImg2ImgPipeline(
image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
num_inference_steps: int = 35,
sigmas: Optional[List[float]] = None,
guidance_scale: float = 3.5,
strength: float = 0.8,
guidance_scale: float = 5.0,
strength: float = 0.9,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,