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# Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
def prepare_latents(
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
):
shape = (
batch_size,
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def prepare_masked_condition(
self,
image,
batch_size,
num_channels_latents,
num_frames,
height,
w... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
if isinstance(generator, list):
image_latent = [
self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size)
]
image_latent = torch.cat(image_latent, dim=0)
else:
image_latent = self.vae.encode(image).latent_dist.s... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
mask = torch.zeros((batch_size, 1, num_frames, scaled_height, scaled_width)).to(device=device, dtype=dtype)
mask_coef = prepare_mask_coef_by_statistics(num_frames, 0, motion_scale)
masked_image = torch.zeros(batch_size, 4, num_frames, scaled_height, scaled_width).to(
device=device, dtype=sel... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), n... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# 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 = 1`
# corresponds to doing no classifier free guidance.
@property
def do_classifier_free_guidance(self):
return self._guidance_scale... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PipelineImageInput,
prompt: Union[str, List[str]] = None,
strength: float = 1.0,
num_frames: Optional[int] = 16,
height: Optional[int] = None,
width: Optional[int] =... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
clip_skip: Optional[int] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
):
r"""
The call f... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
Args:
image (`PipelineImageInput`):
The input image to be used for video generation.
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
strength (`float`, *optio... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance sc... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
provided, text embeddings are generated from the `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are gen... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
motion_scale: (`int`, *optional*, defaults to 0):
Parameter that controls the amount and type of motion that is added to the image. Increasing the value
increases the amount of motion, while specific ranges of values control the type of motion that is
added. Must be betwe... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped ... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
Examples:
Returns:
[`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated frames.
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
self._guidance_scale = guidance_scale
self._clip_skip = clip_skip
self._cross_attention_kwargs = cross_attention_kwargs
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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_videos_per_pr... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.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_videos_per_prompt,
self.do_classifi... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# 5. Prepare latent variables
latents = self.prepare_latents(
batch_size * num_videos_per_prompt,
4,
num_frames,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents=latents,
)
m... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7. Add image embeds for IP-Adapter
added_cond_kwargs = (
{"image_embeds": image_embeds}
if ip_adapter_image i... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
with self.progress_bar(total=self._num_timesteps) as progress_bar:
for i, t in enumerate(timesteps):
# 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
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
# perform guidance
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy s... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
... | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
return PIAPipelineOutput(frames=video) | 48 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/pia/pipeline_pia.py |
class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
r"""
The mochi pipeline for text-to-video generation.
Reference: https://github.com/genmoai/models | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
Args:
transformer ([`MochiTransformer3DModel`]):
Conditional Transformer architecture to denoise the encoded video latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
[T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast).
""" | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
model_cpu_offload_seq = "text_encoder->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKLMochi,
text_encoder: T5Encode... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_spatial_scale_factor)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 256
)
self.default_height = 480
self.default_width = 848
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_attention_mask = tex... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
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(untruncated_ids[:, max_sequence_length - 1 : -1])
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
return prompt_embeds, prompt_attention_mask
# Adapted from diffusers.pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipeline.encode_prompt
def encod... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ign... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds wil... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
pro... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
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)}."
)
elif batch_size != len(negative_pro... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask
def check_inputs(
self,
prompt,
height,
width,
callback_on_step_end_tensor_inputs=None,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attenti... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.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 is None and prompt_embeds is None:
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
if negative_prompt_embeds is not None and negative_prompt_attention_mask is None:
raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.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... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def ... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
def prepare_latents(
self,
batch_size,
num_channels... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
if latents is not None:
return latents.to(device=device, dtype=dtype)
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"... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_frames: int = 19,
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 256,
):
r"""
Function invoked when calling the pipeline for generation. | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.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.
height (`int`, *optional*, defaults to `self.default_height`):
The height in pixels of t... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
passed will be used. Must be in descending order.
guidance_scale (`float`... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
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.
latents (`torch.Tensor`, *optional*):
Pre-generated no... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. For PixArt-Sigma this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from `negative_prompt` input argument.
negative_prompt_attention_mas... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the infer... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
`._callback_tensor_inputs` attribute of your pipeline class.
max_sequence_length (`int` defaults to `256`):
Maximum sequence length to use with the `prompt`. | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
Examples:
Returns:
[`~pipelines.mochi.MochiPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.mochi.MochiPipelineOutput`] is returned, otherwise a `tuple`
is returned where the first element is a list with the generated images.
"""
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt=prompt,
height=height,
width=width,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
device = self._execution_device
# 3. Prepare text embeddings
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_pro... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
height,
width,
num_frames,
prompt_embeds.dtype,
device,
generator,
latents,
) | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0)
# 5. Prepare timestep
# from https://github.com/genmoai/models/blob/07... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
# 6. Denoising loop
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents_dty... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.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 = ... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
if output_type == "latent":
video = latents
else:
# 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
h... | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
video = self.vae.decode(latents, return_dict=False)[0]
video = self.video_processor.postprocess_video(video, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return MochiPipelineOutput(frames=video) | 49 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_mochi.py |
class MochiPipelineOutput(BaseOutput):
r"""
Output class for Mochi pipelines.
Args:
frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
denoised PIL image... | 50 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/mochi/pipeline_output.py |
class KolorsImg2ImgPipeline(DiffusionPipeline, StableDiffusionMixin, StableDiffusionXLLoraLoaderMixin, IPAdapterMixin):
r"""
Pipeline for text-to-image generation using Kolors.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library impleme... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`ChatGLMModel`]):
Frozen text-encoder. Kolors uses [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b).
tokenizer (`ChatG... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
`Kwai-Kolors/Kolors-diffusers`.
""" | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
model_cpu_offload_seq = "text_encoder->image_encoder-unet->vae"
_optional_components = [
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"add_text_embeds",
"add_time_ids",
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
)
self.register_to_config(force_zeros_for_... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# Copied from diffusers.pipelines.kolors.pipeline_kolors.KolorsPipeline.encode_prompt
def encode_prompt(
self,
prompt,
device: Optional[torch.device] = None,
num_images_per_prompt: int = 1,
do_classifier_free_guidance: bool = True,
negative_prompt=None,
prompt... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_images_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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_prompt_embeds (`torch.Fl... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`.
"""
# from IPython import embed; embed(); exit()
device = device or self._execution_device | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
# Define tokenizers and text encoders
tokenizers = [self.tokeniz... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
if prompt_embeds is None:
prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# [max_sequence_length, batch, hidden_size] -> [batch, max_sequence_length, hidden_size]
# clone to have a contiguous tensor
prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
# [max_sequence_length, batch, hidden_size] -> [batch, hidden_size]
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
elif do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
negative_prompt_embeds_list = []
for tokenizer, text_encoder in zip(tokenizers, text_encoders):
uncond_input = tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# [max_sequence_length, batch, hidden_size] -> [batch, max_sequence_length, hidden_size]
# clone to have a contiguous tensor
negative_prompt_embeds = output.hidden_states[-2].permute(1, 0, 2).clone()
# [max_sequence_length, batch, hidden_size] -> [batch, hidden_size]
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(
batch_size * num_images_per_prompt, seq_len, -1
)
negative_prompt_embeds_list.append(negative_prompt_... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# 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):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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 = ... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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]] * ... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
def check_inputs(
self,
prompt,
strength,
num_inference_steps,
height,
width,
negative_prompt=None,
prompt_embeds=None,
pooled_prompt_embeds=None,
negative_prompt_embeds=None,
negative_pooled_prompt_embeds=None,
ip_adapter_i... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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 is None and prompt_embeds is None:
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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 ... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device, denoising_start=None):
# get the original timestep using init_timestep
if denoising_start is None:
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
else:
# Strength is irrelevant if we directly request a timestep to start at;
# that is, strength is determined by the denoising_start instead.
discrete_timestep_cutoff = int(
round(
self.scheduler.config.num_train_timesteps
- (... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
num_inference_steps = (self.scheduler.timesteps < discrete_timestep_cutoff).sum().item()
if self.scheduler.order == 2 and num_inference_steps % 2 == 0:
# if the scheduler is a 2nd order scheduler we might have to do +1
# because `num_inference_steps` might be even given that ... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
# because t_n+1 >= t_n, we slice the timesteps starting from the end
t_start = len(self.scheduler.timesteps) - num_inference_steps
timesteps = self.scheduler.timesteps[t_start:]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start)
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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:
... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_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... | 51 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py |
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