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:
Dhruv Nair
2025-06-17 11:56:45 +02:00
parent 57df5f9234
commit 3b3624b8ed
2 changed files with 33 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
src/diffusers/pipelines/chroma/pipeline_chroma.py
View File

@@ -52,12 +52,22 @@ EXAMPLE_DOC_STRING = """
>>> import torch
>>> from diffusers import ChromaPipeline
>>> pipe = ChromaPipeline.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(
... "black-forest-labs/FLUX.1-schnell",
... transformer=transformer,
... text_encoder=text_encoder,
... tokenizer=tokenizer,
... torch_dtype=torch.bfloat16,
... )
>>> pipe.enable_model_cpu_offload()
>>> pipe.to("cuda")
>>> prompt = "A cat holding a sign that says hello world"
>>> image = pipe(prompt, num_inference_steps=28, guidance_scale=4.0).images[0]
>>> negative_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
>>> image = pipe(prompt, negative_prompt=negative_prompt).images[0]
>>> image.save("chroma.png")
```
"""
@@ -254,8 +264,8 @@ class ChromaPipeline(
negative_prompt: Union[str, List[str]] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
do_classifier_free_guidance: bool = True,
@@ -274,7 +284,7 @@ class ChromaPipeline(
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
lora_scale (`float`, *optional*):
@@ -604,13 +614,13 @@ class ChromaPipeline(
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,
prompt_embeds: Optional[torch.FloatTensor] = None,
latents: Optional[torch.Tensor] = None,
prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
negative_ip_adapter_image: Optional[PipelineImageInput] = None,
negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None,
negative_prompt_attention_mask: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
@@ -653,11 +663,11 @@ class ChromaPipeline(
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
@@ -671,7 +681,7 @@ class ChromaPipeline(
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
provided, embeddings are computed from the `ip_adapter_image` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.

15
src/diffusers/pipelines/chroma/pipeline_chroma_img2img.py
View File

@@ -55,7 +55,7 @@ EXAMPLE_DOC_STRING = """
>>> 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")
>>> 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(
... "black-forest-labs/FLUX.1-schnell",
@@ -69,7 +69,8 @@ EXAMPLE_DOC_STRING = """
... "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
... )
>>> 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]
>>> negative_prompt = "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
>>> image = pipe(prompt, image=image, negative_prompt=negative_prompt).images[0]
>>> image.save("chroma-img2img.png")
```
"""
@@ -724,19 +725,25 @@ class ChromaImg2ImgPipeline(
The height in pixels of the generated image. This is set to 1024 by default for the best results.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image. This is set to 1024 by default for the best results.
num_inference_steps (`int`, *optional*, defaults to 50):
num_inference_steps (`int`, *optional*, defaults to 35):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
guidance_scale (`float`, *optional*, defaults to 3.5):
guidance_scale (`float`, *optional*, defaults to 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
strength (`float, *optional*, defaults to 0.9):
Conceptually, indicates how much to transform the reference image. Must be between 0 and 1. image will
be used as a starting point, adding more noise to it the larger the strength. The number of denoising
steps depends on the amount of noise initially added. When strength is 1, added noise will be maximum
and the denoising process will run for the full number of iterations specified in num_inference_steps.
A value of 1, therefore, essentially ignores image.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):