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update

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This commit is contained in:
Dhruv Nair
2025-06-12 03:22:03 +02:00
parent f20e4afbaa
commit 6a94ef7388
1 changed files with 8 additions and 23 deletions
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31
src/diffusers/pipelines/flux/pipeline_flux.py
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@@ -191,7 +191,6 @@ class FluxPipeline(
transformer: FluxTransformer2DModel,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
variant: str = "flux",
):
super().__init__()
@@ -214,17 +213,6 @@ class FluxPipeline(
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = 128
if variant not in {"flux", "chroma"}:
raise ValueError("`variant` must be `'flux' or `'chroma'`.")
self.variant = variant
def _get_chroma_attn_mask(self, length: torch.Tensor, max_sequence_length: int) -> torch.Tensor:
attention_mask = torch.zeros((length.shape[0], max_sequence_length), dtype=torch.bool, device=length.device)
for i, n_tokens in enumerate(length):
n_tokens = torch.max(n_tokens + 1, max_sequence_length)
attention_mask[i, :n_tokens] = True
return attention_mask
def _get_t5_prompt_embeds(
self,
@@ -248,7 +236,7 @@ class FluxPipeline(
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
@@ -262,10 +250,7 @@ class FluxPipeline(
f" {max_sequence_length} tokens: {removed_text}"
)
text_inputs.attention_mask[:, : text_inputs.length + 1] = 1.0
prompt_embeds = self.text_encoder_2(
text_input_ids.to(device), output_hidden_states=False, attention_mask=text_inputs.attention_mask.to(device)
)[0]
prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
dtype = self.text_encoder_2.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
@@ -702,11 +687,11 @@ class FluxPipeline(
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 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.
Guidance scale as defined in [Classifier-Free Diffusion
Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
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*):
@@ -715,7 +700,7 @@ class FluxPipeline(
latents (`torch.FloatTensor`, *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`.
tensor will be generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *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.