text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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 | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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(
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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 ... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
# Copied from diffusers.pipelines.flux.pipeline_flux_img2img.FluxImg2ImgPipeline.check_inputs
def check_inputs(
self,
prompt,
prompt_2,
strength,
height,
width,
prompt_embeds=None,
pooled_prompt_embeds=None,
callback_on_step_end_tensor_inputs=N... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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 ... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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)}") | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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`."
)
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
latent_image_id_height, latent_image_id_width, latent_image_id_channels = latent_image_ids.shape
latent_image_ids = latent_image_ids.reshape(
latent_image_id_height * latent_image_id_width, latent_image_id_channels
)
return latent_image_ids.to(device=device, dtype=dtype)
@stat... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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) // (vae_scale_factor * 2))
width = 2 * (int(width) // (vae_scale_factor * 2))
latents = latents.view(batch_size, height //... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
# Copied from diffusers.pipelines.flux.pipeline_flux_img2img.FluxImg2ImgPipeline.prepare_latents
def prepare_latents(
self,
image,
timestep,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
image = image.to(device=device, dtype=dtype)
image_latents = self._encode_vae_image(image=image, generator=generator)
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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
latents = self.scheduler.scale_noise(image_latents, timestep, noise)
latents = self._pack_latents(latents, batch_size, num_channels_latents, height, width)
return latents, latent_image_ids
# Copied from diffusers.p... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
if image_batch_size == 1:
repeat_by = batch_size
else:
# image batch size is the same as prompt batch size
repeat_by = num_images_per_prompt
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classif... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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,
control_image: PipelineImageInput = None,
height: Optional... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
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 for generation. | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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.
control_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `Lis... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
images must be passed as a list such that each element of the list can be correctly batched for input
to a single ControlNet.
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The height in pixels of the generated image. This is set... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1
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 ... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
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.
num_images_per_prompt (`in... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
tensor will ge 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 fro... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
Whether or not to return a [`~pipelines.flux.FluxPipelineOutput`] instead of a plain tuple.
joint_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[dif... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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_sequenc... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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 ... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
# 2. Preprocess image
init_image = self.image_processor.preprocess(image, height=height, width=width)
init_image = init_image.to(dtype=torch.float32)
# 3. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
# 3. Prepare text embeddings
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(
prompt=pr... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
if control_image.ndim == 4:
control_image = self.vae.encode(control_image).latent_dist.sample(generator=generator)
control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
height_control_image, width_control_image = control_image.shape[2:]
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
guidan... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
if XLA_AVAILABLE:
xm.mark_step()
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
class FluxPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or... | 165 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_output.py |
class FluxPriorReduxPipelineOutput(BaseOutput):
"""
Output class for Flux Prior Redux pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL... | 166 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_output.py |
class FluxFillPipeline(
DiffusionPipeline,
FluxLoraLoaderMixin,
FromSingleFileMixin,
TextualInversionLoaderMixin,
):
r"""
The Flux Fill pipeline for image inpainting/outpainting.
Reference: https://blackforestlabs.ai/flux-1-tools/ | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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.
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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,
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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:... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
def prepare_mask_latents(
self,
mask,
masked_image,
batch_size,
num_channels_latents,
num_images_per_prompt,
height,
width,
dtype,
device,
generator,
):
# 1. calculate the height and width of the latents
# VAE ap... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 3. duplicate mask and masked_image_latents for each generation per prompt, using mps friendly method
batch_size = batch_size * num_images_per_prompt
if mask.shape[0] < batch_size:
if not batch_size % mask.shape[0] == 0:
raise ValueError(
"The passed mask... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 4. pack the masked_image_latents
# batch_size, num_channels_latents, height, width -> batch_size, height//2 * width//2 , num_channels_latents*4
masked_image_latents = self._pack_latents(
masked_image_latents,
batch_size,
num_channels_latents,
height,
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 6. pack the mask:
# batch_size, 64, height, width -> batch_size, height//2 * width//2 , 64*2*2
mask = self._pack_latents(
mask,
batch_size,
self.vae_scale_factor * self.vae_scale_factor,
height,
width,
)
mask = mask.to(device=... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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(
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 ... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
prompt_embeds=None,
pooled_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
image=None,
mask_image=None,
masked_image_latents=Non... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 ... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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)}") | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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`."
)
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
@staticmethod
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._prepare_latent_image_ids
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(h... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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,... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 enable_vae_slicing(self):
r"""
Enable sliced VAE decodi... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
""... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
# VAE applies 8x compression on... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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."
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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: Optional[torch.FloatTensor] = None,
mask_image: Optional[torch.FloatTensor] = None,
m... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
are repainted while black pixels are preserved. If `mas... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
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):
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
guidance_scale (`float`, *optional*, defaults to 7.0):
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 ... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
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`.
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
The output format of the generate image. Choose between
[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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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 ... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
self._interrupt = False
# 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):
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 3. Prepare prompt embeddings
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(
prompt=... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 4. Prepare latent variables
num_channels_latents = self.vae.config.latent_channels
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
dev... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
height, width = image.shape[-2:]
mask, masked_image_latents = self.prepare_mask_latents(
mask_image,
masked_image,
batch_size,
num_channels_latents,
num_images_per_prompt,
height,
width,
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
# 6. 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... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.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
# 7. Denoising loop
with self.progress_bar(... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
noise_pred = self.transformer(
hidden_states=torch.cat((latents, masked_image_latents), dim=2),
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
if callback_on_... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
if XLA_AVAILABLE:
xm.mark_step()
# 8. Post-process the image
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents = (latents / self.vae.config.scaling_fact... | 167 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_fill.py |
class ReduxImageEncoderOutput(BaseOutput):
image_embeds: Optional[torch.Tensor] = None | 168 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/modeling_flux.py |
class ReduxImageEncoder(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
redux_dim: int = 1152,
txt_in_features: int = 4096,
) -> None:
super().__init__()
self.redux_up = nn.Linear(redux_dim, txt_in_features * 3)
self.redux_down = nn.Linear(t... | 169 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/modeling_flux.py |
class FluxPipeline(
DiffusionPipeline,
FluxLoraLoaderMixin,
FromSingleFileMixin,
TextualInversionLoaderMixin,
FluxIPAdapterMixin,
):
r"""
The Flux pipeline for text-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.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.
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.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... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->transformer->vae"
_optional_components = ["image_encoder", "feature_extractor"]
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: Autoencod... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.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,
image_encoder=image_encoder,
f... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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: Optional[torch.dtype] = None,
):
device = device or self._execution_device
... | 170 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux.py |
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