text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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 is None and prompt_embeds is None:
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.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... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._or... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# When `image` is a nested list:
# (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]])
elif any(isinstance(i, list) for i in image):
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
elif len(image) != len(self... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError(... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
raise ValueError(
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
" the same length as the number of controlnets"
)
else:
assert False | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
for start, end in zip(control_guidance_start, control_guidance_end):
if start >= end:
raise ValueError(
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
)
if start < 0.0:
raise ValueEr... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if ip_adapter_image_embeds is not None:
if not isinstance(ip_adapter_image_embeds, list):
raise ValueError(
f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
)
elif ip_adapter_image_embeds[0].ndim not ... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image
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(ima... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.p... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
image = image.repeat_interleave(repeat_by, dim=0)
image = image.to(device=device, dtype=dtype)
if do_classifier_free_guidance and not guess_mode:
image = torch.cat([image] * 2)
return image
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.S... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.prepare_latents
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
else:
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 ... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
init_latents = [
retrieve_latents(self.vae.encode(image[i : i + 1]), generator=generator[i])
for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = retrieve_latents(self.vae.encod... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). In... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0) | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
@property
def guidance_scale(self):
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
control_image: PipelineImageInput = None,
height: Optional[int] = None,
width: Optional[int] = None,
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
controlnet_conditioning_scale: Union[float, List[float]] = 0.8,
guess_mode: bool = False,
control_guidance_start: Union[float, List[float]] = 0.0,
control_guidance_end: Union[float, List[float]] = ... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
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 dimensions. If height and/or
width are passed, `image` is resized accordingly. If multiple ControlNets are specified... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising
process runs for the full number of iterations specified in `num_inference_steps`... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily twea... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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 n... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
[`self.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_conditioning_scale` before th... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_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.
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 mean... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
callback_on_step_end_tensor_inputs (`List`, *optional*):
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... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with ... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# align format for control guidance
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,... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
control_image,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
ip_adapter_image,
ip_adapter_image_embeds,
c... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
global_pool_conditions = (
controlnet.config.global_pool_conditions
if isinstance(cont... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# 3. 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 = self.encode_prompt(
prompt,
device,
num_images_per_pr... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
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_prompt,
self.do_classifi... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# 5. Prepare controlnet_conditioning_image
if isinstance(controlnet, ControlNetModel):
control_image = self.prepare_control_image(
image=control_image,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
for control_image_ in control_image:
control_image_ = self.prepare_control_image(
image=control_image_,
width=width,
height=height,
batch_size=batch_size * num_images_per_prompt,
num_images_per_prompt=num... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
self._num_timesteps = len(timestep... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
else None
)
# 7.2 Create tensor stating which controlnets to keep
controlnet_keep = []
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# controlnet(s) inference
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_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):
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_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.
... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_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... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_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 = ... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
if XLA_AVAILABLE:
xm.mark_step()
# If we do sequential model offloading, let's offload unet and controlnet
# manually for max memory savings
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.unet.to("cpu")
self.contr... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_d... | 90 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py |
class StableDiffusionXLControlNetUnionImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
):
r"""
Pipeline for image-to-image generation using Stable Diffusion XL with ControlNet gui... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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 uses the text portion of
[CLIP](https://huggingface.co/docs/tran... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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).
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
config of `stabilityai/stable-diffusion-xl-refiner-1-0`.
force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`):
Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of
`stabilityai/stable-diffusion-xl-base-1-0`.
add_w... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
model_cpu_offload_seq = "text_encoder->text_encoder_2->image_encoder->unet->vae"
_optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"feature_extractor",
"image_encoder",
]
_callback_tensor_inputs = [
"latents",
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
text_encoder_2: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel,
controlnet: ControlNetUnionModel,
scheduler: KarrasD... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if add_watermarker:
self.watermark = StableDiffusionXLWatermarker()
else:
self.watermark = None
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.register_to_config(requires_aesthetics_score=requires_aesthetics_score) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 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*):
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
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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, StableDiffusionXLLoraLoaderMixin):
self._lora_scale = lora_scale
# dynamically adjust the LoRA scale
if self.text_encoder is... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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]
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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)}."
)
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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 ... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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 = ... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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]] * ... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
def check_inputs(
self,
prompt,
prompt_2,
image,
strength,
num_inference_steps,
callback_steps,
negative_prompt=None,
negative_prompt_2=None,
prompt_embeds=None,
negative_prompt_embeds=None,
pooled_prompt_embeds=None,
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
f"`num_inference_steps` has to be a positive integer but is {num_inference_steps} of type"
f" {type(num_inference_steps)}."
) | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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 ... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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)}") | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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."
)
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
raise ValueError(
"If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# Check `image`
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
)
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._or... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# Check `controlnet_conditioning_scale`
if (
isinstance(self.controlnet, ControlNetModel)
or is_compiled
and isinstance(self.controlnet._orig_mod, ControlNetModel)
):
if not isinstance(controlnet_conditioning_scale, float):
raise TypeError(... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if not isinstance(control_guidance_end, (tuple, list)):
control_guidance_end = [control_guidance_end]
if len(control_guidance_start) != len(control_guidance_end):
raise ValueError(
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidan... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if ip_adapter_image is not None and ip_adapter_image_embeds is not None:
raise ValueError(
"Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
)
if ip_adapter_image_embeds is not No... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# Copied from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl.StableDiffusionXLControlNetPipeline.check_image
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 = isinst... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if (
not image_is_pil
and not image_is_tensor
and not image_is_np
and not image_is_pil_list
and not image_is_tensor_list
and not image_is_np_list
):
raise TypeError(
f"image must be passed and be one of PIL image... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if image_batch_size != 1 and image_batch_size != prompt_batch_size:
raise ValueError(
f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}"
)
# Copied from diffusers.p... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_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... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
latents_mean = latents_std = None
if hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None:
latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1)
if hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None:
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
else:
# make sure the VAE is in float32 mode, as it overflows in float16
if self.vae.config.force_upcast:
image = image.float()
self.vae.to(dtype=torch.float32)
if isinstance(generator, list) and len(generator) != batch_size:
raise Val... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
elif isinstance(generator, list):
if image.shape[0] < batch_size and batch_size % image.shape[0] == 0:
image = torch.cat([image] * (batch_size // image.shape[0]), dim=0)
elif image.shape[0] < batch_size and batch_size % image.shape[0] != 0:
raise V... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
init_latents = init_latents.to(dtype)
if latents_mean is not None and latents_std is not None:
latents_mean = latents_mean.to(device=device, dtype=dtype)
latents_std = latents_std.to(device=device, dtype=dtype)
init_latents = (init_latents - latents_mean) * se... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
additional_image_per_prompt = batch_size // init_latents.shape[0]
init_latents = torch.cat([init_latents] * additional_image_per_prompt, dim=0)
elif batch_s... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline._get_add_time_ids
def _get_add_time_ids(
self,
original_size,
crops_coords_top_left,
target_size,
aesthetic_score,
negative_aesthetic_score,
... | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
passed_add_embed_dim = (
self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim
)
expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features | 91 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/controlnet/pipeline_controlnet_union_sd_xl_img2img.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.