text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
# 8. Prepare mask latents
mask_condition = self.mask_processor.preprocess(
mask_image, height=global_height, width=global_width, resize_mode=resize_mode, crops_coords=crops_coords
)
if masked_image_latents is None:
masked_image = init_image * (mask_condition < 0.5)
... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
controlnet_keep.append(keeps[0] if isinstance(sel... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
# predict the noise residual
if isinstance(self.controlnet, FluxMultiControlNetModel):
use_guidance = self.controlnet.nets[0].config.guidance_embeds
else:
use_guidance = self.controlnet.config.guidance_embeds
if use_guidance:
... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
if isinstance(controlnet_keep[i], list):
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
else:
controlnet_cond_scale = controlnet_conditioning_scale
if isinstance(controlnet_cond_scale, list):
... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
hidden_states=latents,
controlnet_cond=control_image,
controlnet_mode=control_mode,
conditioning_scale=cond_scale,
timestep=timestep / 1000,
... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
controlnet_block_sample... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
# For inpainting, we need to apply the mask and add the masked image latents
init_latents_proper = image_latents
init_mask = mask
if i < len(timesteps) - 1:
noise_timestep = timesteps[i + 1]
init_latents_proper = self.scheduler.sca... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
# call the callback, if provided
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t,... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
# Post-processing
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, global_height, global_width, self.vae_scale_factor)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
image = self... | 161 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet_inpainting.py |
class FluxControlNetPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
r"""
The Flux pipeline for text-to-image generation.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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.
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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,
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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,
controlnet=controlnet,
)
s... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for co... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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(
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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 ... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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,
):
if height % (self.vae_scale_factor * 2) != 0 or width % (self.... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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 ... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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)}") | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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`."
)
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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 //... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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."
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# Copied from diffusers.pipelines.controlnet_sd3.pipeline_stable_diffusion_3_controlnet.StableDiffusion3ControlNetPipeline.prepare_image
def prepare_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classif... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
@property
def guidance_scale(self):
return self._guidance_scale
@property
def joint_attention_kwargs(self):
return self._joint_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interru... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 28,
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_in... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
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
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
usually at the expense of lower image quality.
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
The percentage of total steps at which the ControlNet starts applying.
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
width are passed, `image` is resized accordingly. If multiple ControlNets are specified in `init`,
images must be passed as a list such that each element of the list can be correctly batched for input
to a single ControlNet.
controlnet_conditioning_scale (`float` or `List[flo... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.arra... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as sp... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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 ... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
control_guidance_end = l... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_te... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# 3. Prepare control image
num_channels_latents = self.transformer.config.in_channels // 4
if isinstance(self.controlnet, FluxControlNetModel):
control_image = self.prepare_image(
image=control_image,
width=width,
height=height,
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# xlab controlnet has a input_hint_block and instantx controlnet does not
controlnet_blocks_repeat = False if self.controlnet.input_hint_block is None else True
if self.controlnet.input_hint_block is None:
# vae encode
control_image = retrieve_latents(self.vae.enc... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# Here we ensure that `control_mode` has the same length as the control_image.
if control_mode is not None:
if not isinstance(control_mode, int):
raise ValueError(" For `FluxControlNet`, `control_mode` should be an `int` or `None`")
control_mode = torch.te... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
elif isinstance(self.controlnet, FluxMultiControlNetModel):
control_images = []
# xlab controlnet has a input_hint_block and instantx controlnet does not
controlnet_blocks_repeat = False if self.controlnet.nets[0].input_hint_block is None else True
for i, control_image_ i... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
if self.controlnet.nets[0].input_hint_block is None:
# vae encode
control_image_ = retrieve_latents(self.vae.encode(control_image_), generator=generator)
control_image_ = (control_image_ - self.vae.config.shift_factor) * self.vae.config.scaling_factor
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# Here we ensure that `control_mode` has the same length as the control_image.
if isinstance(control_mode, list) and len(control_mode) != len(control_image):
raise ValueError(
"For Multi-ControlNet, `control_mode` must be a list of the same "
+ " lengt... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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,
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# 6. Create tensor stating which controlnets to keep
controlnet_keep = []
for i in range(len(timesteps)):
keeps = [
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
for s, e in zip(control_guidance_start, control_guidance_end)
]
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
if isinstance(self.controlnet, FluxMultiControlNetModel):
use_guidance = self.controlnet.nets[0].config.guidance_embeds
else:
use_guidance = self.controlnet.config.guidance_embeds
guidance = torch.tensor([guidance_scale], device=device) if use_gui... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
# controlnet
controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
hidden_states=latents,
controlnet_cond=control_image,
controlnet_mode=control_mode,
conditioning_scale=cond_scale,
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
noise_pred = self.transformer(
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
controlnet_block_sample... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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_... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.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
... | 162 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_controlnet.py |
class FluxPriorReduxPipeline(DiffusionPipeline):
r"""
The Flux Redux pipeline for image-to-image generation.
Reference: https://blackforestlabs.ai/flux-1-tools/ | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
Args:
image_encoder ([`SiglipVisionModel`]):
SIGLIP vision model to encode the input image.
feature_extractor ([`SiglipImageProcessor`]):
Image processor for preprocessing images for the SIGLIP model.
image_embedder ([`ReduxImageEncoder`]):
Redux image encoder... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`T5TokenizerFast`, *optional*):
Second Tokenizer of class
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
""" | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
model_cpu_offload_seq = "image_encoder->image_embedder"
_optional_components = [
"text_encoder",
"tokenizer",
"text_encoder_2",
"tokenizer_2",
]
_callback_tensor_inputs = []
def __init__(
self,
image_encoder: SiglipVisionModel,
feature_extractor: ... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
self.register_modules(
image_encoder=image_encoder,
feature_extractor=feature_extractor,
image_embedder=image_embedder,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
)
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
def check_inputs(
self,
image,
prompt,
prompt_2,
prompt_embeds=None,
pooled_prompt_embeds=None,
prompt_embeds_scale=1.0,
pooled_prompt_embeds_scale=1.0,
):
if prompt is not None and prompt_embeds is not None:
raise ValueError(
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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)}")
if prompt is not None and (isinstance(prompt, list) and isinstance(image, list) and len(prompt) != len(image)):
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
f"number of weights must be equal to number of images, but {len(prompt_embeds_scale)} weights were provided and {len(image)} images"
) | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
image = self.feature_extractor.preprocess(
images=image, do_resize=True, return_tensors="pt", do_convert_rgb=True
)
image = image.to(device=device, dtype=dtype)
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if isinstance(self, TextualInversionLoaderMixin):
prompt = self.maybe_convert_prompt(prompt, self.tokenizer_2)
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated beca... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
# Copied from diffusers.pipelines.flux.pipeline_flux.FluxPipeline._get_clip_prompt_embeds
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
):
device = device or self._execution_device
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untrunca... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt)
prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
return prompt_embeds
# Copied from diffusers.pipelines.flux.pipeli... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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 | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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(
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.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 ... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
Args:
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 be used as the starting point. For both
numpy array and pytorch tensor, the expec... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
Examples:
Returns:
[`~pipelines.flux.FluxPriorReduxPipelineOutput`] or `tuple`:
[`~pipelines.flux.FluxPriorReduxPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When
returning a tuple, the first element is a list with the generated images.
"""
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
# 2. Define call parameters
if image is not None and isinstance(image, Image.Image):
batch_size = 1
elif image is not None and isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
if prompt is not None and isinstance(promp... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
# 3. Prepare (dummy) text embeddings
if hasattr(self, "text_encoder") and self.text_encoder is not None:
(
prompt_embeds,
pooled_prompt_embeds,
_,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
prompt_embeds = torch.zeros((batch_size, 512, 4096), device=device, dtype=image_embeds.dtype)
# pooled_prompt_embeds is 768, clip text encoder hidden size
pooled_prompt_embeds = torch.zeros((batch_size, 768), device=device, dtype=image_embeds.dtype) | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
# scale & concatenate image and text embeddings
prompt_embeds = torch.cat([prompt_embeds, image_embeds], dim=1)
prompt_embeds *= torch.tensor(prompt_embeds_scale, device=device, dtype=image_embeds.dtype)[:, None, None]
pooled_prompt_embeds *= torch.tensor(pooled_prompt_embeds_scale, device=devi... | 163 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_prior_redux.py |
class FluxControlImg2ImgPipeline(DiffusionPipeline, FluxLoraLoaderMixin, FromSingleFileMixin):
r"""
The Flux pipeline for image inpainting.
Reference: https://blackforestlabs.ai/announcing-black-forest-labs/ | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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.
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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,
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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:... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.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
... | 164 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/flux/pipeline_flux_control_img2img.py |
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