text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
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 = ... | 302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.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... | 302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py |
if not output_type == "latent":
condition_kwargs = {}
if isinstance(self.vae, AsymmetricAutoencoderKL):
init_image = init_image.to(device=device, dtype=masked_image_latents.dtype)
init_image_condition = init_image.clone()
init_image = self._encode_... | 302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py |
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
if padding_mask_crop is n... | 302 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py |
class I2VGenXLPipelineOutput(BaseOutput):
r"""
Output class for image-to-video pipeline.
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
den... | 303 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
class I2VGenXLPipeline(
DiffusionPipeline,
StableDiffusionMixin,
):
r"""
Pipeline for image-to-video generation as proposed in [I2VGenXL](https://i2vgen-xl.github.io/).
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_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 ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
toke... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
image_encoder: CLIPVisionModelWithProjection,
feature_extractor: CLIPImageProcessor,
unet: I2VGenXLUNet,
scheduler: DDIMScheduler,
):
super().__init_... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.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... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`):
torch device
num_videos_per_prompt (`int`):
number of images that should be generated per prompt
do_classifier_free_guidance (`b... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
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 will be generated from `negative_prompt` input
argument.
clip... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = text_inputs.attention_mask.to(device)
else:
attention_mask = None | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if clip_skip is None:
prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
prompt_embeds = prompt_embeds[0]
else:
prompt_embeds = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if self.text_encoder is not None:
prompt_embeds_dtype = self.text_encoder.dtype
elif self.unet is not None:
prompt_embeds_dtype = self.unet.dtype
else:
prompt_embeds_dtype = prompt_embeds.dtype
prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, devic... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# get unconditional embeddings for classifier free guidance
if self.do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif prompt is not None and type(prompt) ... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
if hasattr(self.text_encoder.config,... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# Apply clip_skip to negative prompt embeds
if clip_skip is None:
negative_prompt_embeds = self.text_encoder(
uncond_input.input_ids.to(device),
attention_mask=attention_mask,
)
negative_prompt_embeds = negative_prompt_e... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# representations. The `last_hidden_states` that we typically use for
# obtaining the final prompt representations passes through the LayerNorm
# layer.
negative_prompt_embeds = self.text_encoder.text_model.final_layer_norm(negative_prompt_embeds) | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if self.do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
neg... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# Normalize the image with CLIP training stats.
image = self.feature_extractor(
images=image,
do_normalize=True,
do_center_crop=False,
do_resize=False,
do_rescale=False,
return_tensors="pt",
).pixel_v... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
if self.do_classifier_free_guidance:
negative_image_embeddings = torch.zeros_like(image_embeddings)
image_embeddings = torch.cat([negative_image_embeddings, image_embeddings])
return image_embeddings
def decode_latents(self, latents, decode_chunk_size=None):
latents = 1 / s... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
decode_shape = (batch_size, num_frames, -1) + image.shape[2:]
video = image[None, :].reshape(decode_shape).permute(0, 2, 1, 3, 4)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
video = video.float()
return video
# Copied from... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs
def check_inputs(
self,
prompt,
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_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 is None and prompt_embeds is None:
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_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... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
def prepare_image_latents(
self,
image,
device,
num_frames,
num_videos_per_prompt,
):
image = image.to(device=device)
image_latents = self.vae.encode(image).latent_dist.sample()
image_latents = image_latents * self.vae.config.scaling_factor
# ... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# duplicate image_latents for each generation per prompt, using mps friendly method
image_latents = image_latents.repeat(num_videos_per_prompt, 1, 1, 1, 1)
if self.do_classifier_free_guidance:
image_latents = torch.cat([image_latents] * 2)
return image_latents | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 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,
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
height: Optional[int] = 704,
width: Optional[int] = 1280,
target_fps: Optional[int] = 16,
num_frames... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
clip_skip: Optional[int] = 1,
):
r"""
The call function to the pipeline for image-to-video generation with [`I2VGenXLPipeline`]. | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.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 (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
Image or images to guide image generation. I... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
Frames per second. The rate at which the generated images shall be exported to a video after
generation. This is also used as a "micro-condition" while generation.
num_frames (`int`, *optional*):
The number of video frames to generate.
num_inference_steps (`int`, ... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
num_videos_per_prompt (`int`, *optional*):
The number of images to generate per prompt.
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prom... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
cross_atten... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
Examples:
Returns:
[`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`pipelines.i2vgen_xl.pipeline_i2vgen_xl.I2VGenXLPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a li... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 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):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 3.1 Encode input text prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_videos_per_prompt,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
clip_... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 3.2 Encode image prompt
# 3.2.1 Image encodings.
# https://github.com/ali-vilab/i2vgen-xl/blob/2539c9262ff8a2a22fa9daecbfd13f0a2dbc32d0/tools/inferences/inference_i2vgen_entrance.py#L114
cropped_image = _center_crop_wide(image, (width, width))
cropped_image = _resize_bilinear(
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 3.3 Prepare additional conditions for the UNet.
if self.do_classifier_free_guidance:
fps_tensor = torch.tensor([target_fps, target_fps]).to(device)
else:
fps_tensor = torch.tensor([target_fps]).to(device)
fps_tensor = fps_tensor.repeat(batch_size * num_videos_per_prompt... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.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. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
fps=fps_tensor,
image_latents=image_latents,
image_embeddings=image_emb... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# reshape latents
batch_size, channel, frames, width, height = latents.shape
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channel, width, height)
noise_pred = noise_pred.permute(0, 2, 1, 3, 4).reshape(batch_size * frames, channel, width, height)
... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
# 8. Post processing
if output_type == "latent":
video = latents
else:
video_tensor = self.decode_latents(latents, decode_chunk_size=decode_chunk_size)
video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
# 9. Offload al... | 304 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/i2vgen_xl/pipeline_i2vgen_xl.py |
class DiTPipeline(DiffusionPipeline):
r"""
Pipeline for image generation based on a Transformer backbone instead of a UNet.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particu... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
def __init__(
self,
transformer: DiTTransformer2DModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
id2label: Optional[Dict[int, str]] = None,
):
super().__init__()
self.register_modules(transformer=transformer, vae=vae, scheduler=scheduler)
... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
Returns:
`list` of `int`:
Class ids to be processed by pipeline.
"""
if not isinstance(label, list):
label = list(label)
for l in label:
if l not in self.labels:
raise ValueError(
f"{l} does not exist. Plea... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
Args:
class_labels (List[int]):
List of ImageNet class labels for the images to be generated.
guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`p... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`ImagePipelineOutput`] instead of a plain tuple. | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
Examples:
```py
>>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler
>>> import torch
>>> pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is
returned where the first element is a list with the generated images
"""
batch_size = len(class_labels)
... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
class_labels = torch.tensor(class_labels, device=self._execution_device).reshape(-1)
class_null = torch.tensor([1000] * batch_size, device=self._execution_device)
class_labels_input = torch.cat([class_labels, class_null], 0) if guidance_scale > 1 else class_labels
# set step values
self... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
timesteps = t
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
latent_model_input, timestep=timesteps, class_labels=class_labels_input
).sample | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
# perform guidance
if guidance_scale > 1:
eps, rest = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
if guidance_scale > 1:
latents, _ = latent_model_input.chunk(2, dim=0)
else:
latents = latent_model_input
latents = 1 / self.vae.config.scaling_factor * latents
samples = self.vae.decode(latents).sample
samples = (samples / 2 + 0.5).clamp(0, 1)
# we alw... | 305 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/dit/pipeline_dit.py |
class IFImg2ImgSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
image_noising_scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_c... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
image_noising_scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcesso... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the IF license and do not expose unfiltered"
" re... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` i... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
self.register_modules(
tokenizer=tokenizer,
text_encoder=text_encoder,
unet=unet,
scheduler=scheduler,
image_noising_scheduler=image_noising_scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
wate... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if clean_caption and not is_ftfy_available():
logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`"))
logger.warning("Setting `clean_caption` to False...")
clean_caption = False
if not isinstance(text, (tuple, list)):
text = [text]
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption
def _clean_caption(self, caption):
caption = str(caption)
caption = ul.unquote_plus(caption)
caption = caption.strip().lower()
caption = re.sub("<person>", "person", caption)
# urls:
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# 31C0—31EF CJK Strokes
# 31F0—31FF Katakana Phonetic Extensions
# 3200—32FF Enclosed CJK Letters and Months
# 3300—33FF CJK Compatibility
# 3400—4DBF CJK Unified Ideographs Extension A
# 4DC0—4DFF Yijing Hexagram Symbols
# 4E00—9FFF CJK Unified Ideographs
caption... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# все виды тире / all types of dash --> "-"
caption = re.sub(
r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa
"-",
caption,
)
# кавычки к одному стандарту
caption... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# "#123"
caption = re.sub(r"#\d{1,3}\b", "", caption)
# "#12345.."
caption = re.sub(r"#\d{5,}\b", "", caption)
# "123456.."
caption = re.sub(r"\b\d{6,}\b", "", caption)
# filenames:
caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption)... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640
caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc
caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231
caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption)
caption = ... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption)
caption = re.sub(r"^[\'\_,\-\:;]", r"", caption)
caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption)
caption = re.sub(r"^\.\S+$", "", caption)
return caption.strip()
@torch.no_grad()
# Copied from diffusers.pipelines.... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
num_images_per_prompt (`int`, *optional*, defaults to 1):
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can b... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if device is None:
device = self._execution_device
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]
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
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_length - 1 : -1])
logger.warning(
"The following part of your... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.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) | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
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)
# For classifier ... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is not None:
safety_checker_input = self.feature_extractor(self.numpy_to_pil(image), return_tensors="pt").to(device)
ima... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.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 with the DDIMScheduler, it will ... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
def check_inputs(
self,
prompt,
image,
original_image,
batch_size,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (no... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.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:
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.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... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(check_image_type, np.ndarray)
):
raise ValueError(
"`image` has to be of type `torch.Tensor`, `PIL.Image.Image`, `np.ndarra... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if isinstance(original_image, list):
check_image_type = original_image[0]
else:
check_image_type = original_image
if (
not isinstance(check_image_type, torch.Tensor)
and not isinstance(check_image_type, PIL.Image.Image)
and not isinstance(chec... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if batch_size != image_batch_size:
raise ValueError(
f"original_image batch size: {image_batch_size} must be same as prompt batch size {batch_size}"
)
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_img2img.IFImg2ImgPipeline.preprocess_image with preprocess_image ... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
for image_ in image:
image_ = image_.convert("RGB")
image_ = resize(image_, self.unet.config.sample_size)
image_ = np.array(image_)
image_ = image_.astype(np.float32)
image_ = image_ / 127.5 - 1
new_image.append(image_)
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if_superresolution.IFSuperResolutionPipeline.preprocess_image
def preprocess_image(self, image: PIL.Image.Image, num_images_per_prompt, device) -> torch.Tensor:
if not isinstance(image, torch.Tensor) and not isinstance(image, list):
image =... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if dims == 3:
image = torch.stack(image, dim=0)
elif dims == 4:
image = torch.concat(image, dim=0)
else:
raise ValueError(f"Image must have 3 or 4 dimensions, instead got {dims}")
image = image.to(device=device, dtype=self.unet.dtype)
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.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
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: Union[PIL.Image.Image, np.ndarray, torch.Tensor],
original_image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
]... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
noise_level: int = 250,
clean_caption: bool = True,
):
"""
Function invoked when calling the pipeline for generation. | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
original_image (`torch.Tensor` or `PIL.Image.Image`):
The original image that `image` was va... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
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
... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, on... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The fun... | 306 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img_superresolution.py |
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