text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
if (
expected_add_embed_dim > passed_add_embed_dim
and (expected_add_embed_dim - passed_add_embed_dim) == self.unet.config.addition_time_embed_dim
):
raise ValueError(
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vec... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. Please make sure to disable `requires_aesthetics_score` with `pipe.register_to_config(requires_aesthetics_score=False)` to make sure `target_size` {target_size} is correctly used by the... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
add_neg_time_ids = torch.tensor([add_neg_time_ids], dtype=dtype)
return add_time_ids, add_neg_time_ids
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
def upc... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
@property
def guidance_scale(self):
return self._guidance_scale
@property
def clip_skip(self):
return self._clip_skip
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale =... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[s... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
aesthetic_score: float = 6.0,
negative_aesthetic_score: float = 2.5,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[
Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
] = None,
callback_on_step_end_tensor_inputs: List[s... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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 the `to... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
the type is specified as `torch.Tensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also
be accepted as an image. The dimensions of the output image defaults to `image`'s di... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
and checkpoints that are not specifically fine-tuned on low resolutions.
width (`int`, *optional*, defaults to the size of control_image):
The width in pixels of the generated image. Anything below 512 pixels won't work well for
[stabilityai/stable-diffusion-xl-base-1.0](http... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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.
guidance_scale (`float`, *optional*, defaults to 7.... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
to make generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`t... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should
contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not
provided, embeddings are computed from the `ip_adapter_image` input argument.
ou... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
The outputs of the controlnet are multiplied by `controlnet_condition... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
The percentage of total steps at which the controlnet stops applying.
original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
If `original_size` is not the same as `target_size` the imag... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
For most cases, `target_... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
To nega... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/42... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
simulate an aesthetic score of the generated image by influencing the negative text condition.
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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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. | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple`
containing the output images.
"""
callback = kwargs.pop("... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
# align format for control guidance
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
num_control_type = controlnet.config.num_control_type
# 1. Check inputs
control_type = [0 for _ in range(num_control_type)]
for _image, control_idx in zip(control_image, control_mode):
control_type[control_idx] = 1
self.check_inputs(
prompt,
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_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 No... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 3.1. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 3.2 Encode ip_adapter_image
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,
batch_size * num_images_per_pr... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
for idx, _ in enumerate(control_image):
control_image[idx] = self.prepare_control_image(
image=control_image[idx],
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num_images_per_p... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 6. Prepare latent variables
if latents is None:
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# 7.2 Prepare added time ids & embeddings
original_size = original_size or (height, width)
target_size = target_size or (height, width)
for _image in control_image:
if isinstance(_image, torch.Tensor):
original_size = original_size or _image.shape[-2:]
if neg... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
add_time_ids, add_neg_time_ids = self._get_add_time_ids(
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
negative_original_size,
negative_crops_coords_top_left,
negative_target_s... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
prompt_embeds = prompt_embeds.to(device)
add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device)
control_type = (
control_type.reshape(1, -1)
.to(device, dtype=prompt_embeds.dtype)
.repeat(batch_size * num_images_per_prompt * 2, 1)
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
added_cond_kwargs = {
"text_embeds": add_text_embeds,
"time_ids": add_time_ids,
}
# controlnet(s) inference
if guess_mode and self.do_classifier_free_guidance:
# Infer ControlNet only for the conditional batch.
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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):
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
return_dict=False,
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if guess_mode and self.do_classifier_free_guidance:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=self.cross_attention_kwargs,
down_block_additional_residuals=down_bl... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
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, callback_kwargs)
latents = ... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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 callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if needs_upcasting:
self.upcast_vae()
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# unscale/denormalize the latents
# denormalize with the mean and std if available and not None
has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.laten... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# cast back to fp16 if needed
if needs_upcasting:
self.vae.to(dtype=torch.float16)
else:
image = latents
return StableDiffusionXLPipelineOutput(images=image)
# apply watermark if available
if self.watermark is not None:
image = sel... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
class BlipDiffusionControlNetPipeline(DiffusionPipeline):
"""
Pipeline for Canny Edge based Controlled subject-driven generation using Blip Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines ... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Args:
tokenizer ([`CLIPTokenizer`]):
Tokenizer for the text encoder
text_encoder ([`ContextCLIPTextModel`]):
Text encoder to encode the text prompt
vae ([`AutoencoderKL`]):
VAE model to map the latents to the image
unet ([`UNet2DConditionModel`]):
... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
model_cpu_offload_seq = "qformer->text_encoder->unet->vae"
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: ContextCLIPTextModel,
vae: AutoencoderKL,
unet: UNet2DConditionModel,
scheduler: PNDMScheduler,
qformer: Blip2QFormerModel,
controln... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
def get_query_embeddings(self, input_image, src_subject):
return self.qformer(image_input=input_image, text_input=src_subject, return_dict=False)
# from the original Blip Diffusion code, speciefies the target subject and augments the prompt by repeating it
def _build_prompt(self, prompts, tgt_subjects,... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Copied from diffusers.pipelines.consistency_models.pipeline_consistency_models.ConsistencyModelPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels, height, width, dtype, device, generator, latents=None):
shape = (batch_size, num_channels, height, width)
if isinstance(genera... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
def encode_prompt(self, query_embeds, prompt, device=None):
device = device or self._execution_device
# embeddings for prompt, with query_embeds as context
max_len = self.text_encoder.text_model.config.max_position_embeddings
max_len -= self.qformer.config.num_query_tokens
toke... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Adapted from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image
def prepare_control_image(
self,
image,
width,
height,
batch_size,
num_images_per_prompt,
device,
dtype,
do_classifier_free_guidan... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
if do_classifier_free_guidance:
image = torch.cat([image] * 2)
return image
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: List[str],
reference_image: PIL.Image.Image,
condtioning_image: PIL.Image.Image,
... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Args:
prompt (`List[str]`):
The prompt or prompts to guide the image generation.
reference_image (`PIL.Image.Image`):
The reference image to condition the generation on.
condtioning_image (`PIL.Image.Image`):
The conditioning canny edge... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Highe... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
expense of slower inference.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
neg_prompt (`str`, *optional*... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
device = self._execution_device
reference_image = self.image_processor.preprocess(
reference_image, image_mean=self.config.mean, image_std=self.config.std, return_tensors="pt"
)["pixel_values"]
re... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
prompt = self._build_prompt(
prompts=prompt,
tgt_subjects=target_subject_category,
prompt_strength=prompt_strength,
prompt_reps=prompt_reps,
)
query_embeds = self.get_query_embeddings(reference_image, source_subject_category)
text_embeddings = self... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
uncond_input = self.tokenizer(
[neg_prompt] * batch_size,
padding="max_length",
max_length=max_length,
return_tensors="pt",
)
uncond_embeddings = self.text_encoder(
input_ids=uncond_input.input_ids.to(device),
... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
latents=latents,
dtype=self.unet.dtype,
device=device,
)
# set timesteps
extra_set_kwargs = {}
self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
cond_image = self.prepare_control_image(
image=condtioning_image,
width=width,
height=height,
batch_size=batch_size,
num_images_per_prompt=1,
device=device,
dtype=self.controlnet.dtype,
do_classifier_free_guidance=do_classif... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
noise_pred = self.unet(
latent_model_input,
timestep=t,
encoder_hidden_states=text_embeddings,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
)["sample"]
... | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 92 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_blip_diffusion.py |
class StableDiffusionXLControlNetInpaintPipeline(
DiffusionPipeline,
StableDiffusionMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
TextualInversionLoaderMixin,
):
r"""
Pipeline for text-to-image generation using Stable Diffusion XL.
This model inherits... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionXLLoraLoader... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`CLIPTextModel`]):
Frozen text-encoder. Stable Diffusion XL uses the text portion of
[CLIP](https://huggingface.co/docs/t... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
tokenizer_2 (`CLIPTokenizer`):
Second Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
"prompt_emb... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: Union[ControlNetModel, List[ControlNetModel], Tu... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
text_encoder_2=text_encoder_2,
tokenizer=tokenizer,
tokenizer_2=tokenizer_2,
unet=unet,
controlnet=controlnet,
scheduler=scheduler,
feature_extractor=fea... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available()
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt(
self,
prompt: str,
prompt_2: Optional[str] = None,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_c... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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 both text-encoders
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak tex... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_pooled_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Define tokenizers and text encoders
tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
text_encoders = (
[self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
)
if prompt_embe... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True)
# We are only ALWAYS interested in the pooled output of the final text encoder
if pooled_prompt_embeds is None and prompt_embeds[0].ndim == 2:
pooled_prompt_embeds = prompt_embeds[0]
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# get unconditional embeddings for classifier free guidance
zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_lik... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
uncond_tokens: List[str]
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
negative_prompt_embeds_list = []
for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
if isinstance(self, TextualInversionLoaderMixin):
negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
max_l... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# We are only ALWAYS interested in the pooled output of the final text encoder
if negative_pooled_prompt_embeds is None and negative_prompt_embeds[0].ndim == 2:
negative_pooled_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds.h... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_clas... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed * num_images_per_prompt, -1
)
if do_classifier_free_guidance:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
bs_embed ... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
dt... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
image = image.to(device=device, dtype=dtype)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_imag... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
def prepare_ip_adapter_image_embeds(
self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
):
image_embeds = ... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
for single_ip_adapter_image, image_proj_layer in zip(
ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
):
output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
single_image_embeds, single_negative_image_embeds = self.encod... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
ip_adapter_image_embeds = []
for i, single_image_embeds in enumerate(image_embeds):
single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
if do_classifier_free_guidance:
single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * ... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def check_image(self, image, prompt, prompt_embeds):
image_is_pil = isinstance(image, PIL.Image.Image)
image_is_tensor = isinstance(image, torch.Tensor)
image_is_np = isinstance(image, np.ndarray)
image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image)
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if image_is_pil:
image_batch_size = 1
else:
image_batch_size = len(image)
if prompt is not None and isinstance(prompt, str):
prompt_batch_size = 1
elif prompt is not None and isinstance(prompt, list):
prompt_batch_size = len(prompt)
elif p... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
def check_inputs(
self,
prompt,
prompt_2,
image,
mask_image,
strength,
num_inference_steps,
callback_steps,
output_type,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
raise ValueError(
f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
f" {type(num_inference_steps)}."
) | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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 ... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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)}") | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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."
)
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.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... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
if padding_mask_crop is not None:
if not isinstance(image, PIL.Image.Image):
raise ValueError(
f"The image should be a PIL image when inpainting mask crop, but is of type" f" {type(image)}."
)
if not isinstance(mask_image, PIL.Image.Image):
... | 93 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py |
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