text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.inp... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
dtype = self.text_encoder_2.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_input... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, n... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
device = device or self._execution_device | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# We only use the pooled prompt output from the CLIPTextModel
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds = self._get_t5_prompt_embeds(
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if self.text_encoder_2 is not None:
if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale)
dtype = self.text_encoder.dtype if self.text_encoder ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt
):
image_embeds = []
if ip_adapter_image_embeds is None:
if not isinstance(ip_adapter_image, list):
ip_adapter_image = [ip_adapter_image]
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
image_embeds.append(single_image_embeds[None, :])
else:
for single_image_embeds in ip_adapter_image_embeds:
image_embeds.append(single_image_embeds)
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
callback_... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
@staticmethod
def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
latent_image_ids = torch.zeros(height, width, 3)
latent_image_ids[..., 1] = latent_image_ids[..., 1] + torch.arange(height)[:, None]
latent_image_ids[..., 2] = latent_image_ids[..., 2] + torch.arange(width... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
return latents
@staticmethod
def _unpack_latents(latents, height, width, vae_scale_factor):
batch_size, num_patches, channels = latents.shape
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE deco... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height an... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
negative_prompt: Union[str, List[str]] = None,
negative_prompt_2: Optional[Union[str, List[str]]] = ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str]... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokeni... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
true_cfg_scale (`float`, *optional*, defaults to 1.0):
When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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 7.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
Pre-generated image embeddings ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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*):
Pre-generated negative text embeddings. Ca... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
joint_attention_kwargs (`dict`, *optional*):
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specifie... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
Examples:
Returns:
[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
images.
"""
height = height or ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
prompt_embeds=prompt_embeds,
negative_prompt_emb... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
has_neg_prompt = negative_prompt is not None or (
negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
)
do_true_cfg = ... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
prompt=negative_prompt,
prompt_2=negative_prompt_2,
prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=negative_pooled_prompt_embeds,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=ma... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# 4. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
image_seq_len = latents.shape[1]
mu = calculate_shift(
image_seq_len,
self.scheduler.config.get("base_image_seq_len", 256),
self.schedu... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
if (ip_adapter_image is not None or ip_adapter_imag... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
image_embeds = None
negative_image_embeds = None
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
image_embeds = self.prepare_ip_adapter_image_embeds(
ip_adapter_image,
ip_adapter_image_embeds,
device,
bat... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if image_embeds is not None:
self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = self.t... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
if do_true_cfg:
if negative_image_embeds is not None:
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
neg_noise_pred = self.transformer(
hidden_states=latents,
timestep=timeste... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if XLA_AVAILABLE:
xm.mark_step()
if output_type == "latent":
image = la... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
class FluxInpaintPipeline(DiffusionPipeline, FluxLoraLoaderMixin):
r"""
The Flux pipeline for image inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
Args:
transformer ([`FluxTransformer2DModel`]):
Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Toke... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(se... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
do_binarize=True,
do_convert_grayscale=True,
)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = 128 | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
self,
prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
max_sequence_length: int = 512,
device: Optional[torch.device] = None,
dtype:... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_length=False,
return_overflowing_tokens=False,
return_tensors="pt",
)
text_input_ids = text_inputs.inp... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
dtype = self.text_encoder_2.dtype
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer_max_length,
truncation=True,
return_overflowing_tokens=False,
return_length=False,
return_tensors="pt",
)
text_input_ids = text_input... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# Use pooled output of CLIPTextModel
prompt_embeds = prompt_embeds.pooler_output
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, n... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
used in all text-encoders
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
lora_scale (`float`, *optional*):
A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
"""
device = device or self._execution_device | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# set lora scale so that monkey patched LoRA
# function of text encoder can correctly access it
if lora_scale is not None and isinstance(self, FluxLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is not None and... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# We only use the pooled prompt output from the CLIPTextModel
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
)
prompt_embeds = self._get_t5_prompt_embeds(
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if self.text_encoder_2 is not None:
if isinstance(self, FluxLoraLoaderMixin) and USE_PEFT_BACKEND:
# Retrieve the original scale by scaling back the LoRA layers
unscale_lora_layers(self.text_encoder_2, lora_scale)
dtype = self.text_encoder.dtype if self.text_encoder ... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_inpaint.StableDiffusion3InpaintPipeline._encode_vae_image
def _encode_vae_image(self, image: torch.Tensor, generator: torch.Generator):
if isinstance(generator, list):
image_latents = [
retrieve_late... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3_img2img.StableDiffusion3Img2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(num_inference_steps * strength, n... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
def check_inputs(
self,
prompt,
prompt_2,
image,
mask_image,
strength,
height,
width,
output_type,
prompt_embeds=None,
pooled_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
padding_mask_crop=None,
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt_2 is not None and prompt_embeds is not ... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if prompt_embeds is not None and pooled_prompt_embeds is None:
raise ValueError(
"If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
)
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if max_sequence_length is not None and max_sequence_length > 512:
raise ValueError(f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}")
@staticmethod
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
def _prepare_latent_imag... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
@staticmethod
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._pack_latents
def _pack_latents(latents, batch_size, num_channels_latents, height, width):
latents = latents.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
latents = latents.permute(0, 2, 4, 1,... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
latents = latents.view(batch_size, height // 2, width // 2, channels // 4, 2, 2)
latents = latents.permute(0, 3, 1, 4, 2, 5)
latents = latents.reshape(batch_size, channels // (2 * 2), height, width)
return latents
def prepare_latents(
self,
image,
timestep,
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# VAE applies 8x compression on images but we must also account for packing which requires
# latent height and width to be divisible by 2.
height = 2 * (int(height) // (self.vae_scale_factor * 2))
width = 2 * (int(width) // (self.vae_scale_factor * 2))
shape = (batch_size, num_channels_l... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if batch_size > image_latents.shape[0] and batch_size % image_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // image_latents.shape[0]
image_latents = torch.cat([image_latents] * additional_image_per_prompt, dim=0)
elif ba... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
noise = self._pack_latents(noise, batch_size, num_channels_latents, height, width)
image_latents = self._pack_latents(image_latents, batch_size, num_channels_latents, height, width)
latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
return latents, noise, imag... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
def prepare_mask_latents(
self,
mask,
masked_image,
batch_size,
num_channels_latents,
num_images_per_prompt,
height,
width,
dtype,
device,
generator,
):
# VAE applies 8x compression on images but we must also account for... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if masked_image.shape[1] == 16:
masked_image_latents = masked_image
else:
masked_image_latents = retrieve_latents(self.vae.encode(masked_image), generator=generator)
masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask and the required batch size don't match. Masks are suppose... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
f" to a total batch size of {batch_size}, but {masked_image_latents.shape[0]} images were passed."
" Make sure the number of images that you pass is divisible by the total requested batch size."
)
masked_image_latents = masked_image_latents.repeat(batch_size // masked_ima... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# aligning device to prevent device errors when concating it with the latent model input
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype)
masked_image_latents = self._pack_latents(
masked_image_latents,
batch_size,
num_channels_latents,
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
image: PipelineImageInput = None,
mask_image: PipelineImageInput = None,
masked_image_latent... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
):
r"""
Function invoked when calling the pipeline fo... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokeni... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image
latents as `image`, but if passing latents directly it is not encoded again.
mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `Lis... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`):
`Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask
latents tensor will ge generated by `mask_image`.
height (`int`, *optional*, defaults to self.unet.config.sample_size * s... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
with the same aspect ration of the image and contains all masked area, and then expand that area based
on `padding_mask_crop`. The image and mask_image will then be cropped based on the expanded area before
resizing to the original image size for inpainting. This is useful when the maske... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
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*):
... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
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 (`... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
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.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can ... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end ... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
Examples:
Returns:
[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
images.
"""
height = height or ... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
self._interrupt = False
# 2. Preprocess mask and image
if padding_mask_crop is not None:
crops_coords = self.mask_processor.get_crop_region(mask_image, width, height, pad=padding_mask... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
device = self._execution_device
lora_scale = (
self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
)
(
prompt_embeds,
pooled_prompt_embeds,
text_ids,
) = self.encode_prompt(
promp... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# 4.Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
image_seq_len = (int(height) // self.vae_scale_factor // 2) * (int(width) // self.vae_scale_factor // 2)
mu = calculate_shift(
image_seq_len,
self.s... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if num_inference_steps < 1:
raise ValueError(
f"After adjusting the num_inference_steps by strength parameter: {strength}, the number of pipeline"
f"steps is {num_inference_steps} which is < 1 and not appropriate for this pipeline."
)
latent_timestep = tim... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
mask_condition = self.mask_processor.preprocess(
mask_image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords
)
if masked_image_latents is None:
masked_image = init_image * (mask_condition < 0.5)
else:
masked_image = masked_image... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 6. Denoising loop
with self.progress_bar(... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidanc... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
if i < len(timesteps) - 1:
noise_timestep = timesteps[i + 1]
init_latents_proper = self.scheduler.scale_noise(
init_latents_proper, torch.tensor([noise_timestep]), noise
)
latents = (1 - init_mask) * init_latents_proper... | 171 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_inpaint.py |
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