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class VersatileDiffusionDualGuidedPipeline(DiffusionPipeline):
r"""
Pipeline for image-text dual-guided generation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, ru... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokeni... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
tokenizer: CLIPTokenizer
image_feature_extractor: CLIPImageProcessor
text_encoder: CLIPTextModelWithProjection
image_encoder: CLIPVisionModelWithProjection
image_unet: UNet2DConditionModel
text_unet: UNetFlatConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
_optiona... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
def __init__(
self,
tokenizer: CLIPTokenizer,
image_feature_extractor: CLIPImageProcessor,
text_encoder: CLIPTextModelWithProjection,
image_encoder: CLIPVisionModelWithProjection,
image_unet: UNet2DConditionModel,
text_unet: UNetFlatConditionModel,
vae: Au... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
if self.text_unet is not None and (
"dual_cross_attention" not in self.image_unet.config or not self.image_unet.config.dual_cross_attention
):
# if loading from a universal checkpoint rather than a saved dual-guided pipeline
self._convert_to_dual_attention()
def remove_u... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
config = image_transformer.config
dual_transformer = DualTransformer2DModel(
num_attention_heads=config.num_attention_heads,
attention_head_dim=config.attention_head_dim,
in_channels=config.in_channels,
num_layers=config.num... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
self.image_unet.get_submodule(parent_name)[index] = dual_transformer
self.image_unet.register_to_config(dual_cross_attention=True)
def _revert_dual_attention(self):
"""
Revert the image_unet `DualTransformer2DModel` blocks back to `Transformer2DModel` with image_unet weights Call
... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
Args:
prompt (`str` or `List[str]`):
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 (`bool`):
... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
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_ids
untruncated_ids = self.tokenizer(prompt, padding="max... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = normalize_embeddings(prompt_embeds)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
attention_mask = uncond_input.attention_mask.to(device)
else:
attention_mask = None
negative_prompt_embeds = self.text_encoder(
uncond_inpu... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
return prompt_embeds
... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
def normalize_embeddings(encoder_output):
embeds = self.image_encoder.vision_model.post_layernorm(encoder_output.last_hidden_state)
embeds = self.image_encoder.visual_projection(embeds)
embeds_pooled = embeds[:, 0:1]
embeds = embeds / torch.norm(embeds_pooled, dim=-1, kee... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# 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.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.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... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
def check_inputs(self, prompt, image, height, width, callback_steps):
if not isinstance(prompt, str) and not isinstance(prompt, PIL.Image.Image) and not isinstance(prompt, list):
raise ValueError(f"`prompt` has to be of type `str` `PIL.Image` or `list` but is {type(prompt)}")
if not isinstan... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
def set_transformer_params(self, mix_ratio: float = 0.5, condition_types: Tuple = ("text", "image")):
for name, module in self.image_unet.named_mo... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
image: Union[str, List[str]],
text_to_image_strength: float = 0.5,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
gui... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *opti... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
negative_prompt (`str` or `List[str]`, *optional*):
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`,... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.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`.
outp... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionDualGuidedPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diff... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
>>> image = pipe(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `return_dict` i... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# 2. Define call parameters
prompt = [prompt] if not isinstance(prompt, list) else prompt
image = [image] if not isinstance(image, list) else image
batch_size = len(prompt)
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equ... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_image... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# 8. Denoising loop
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_inp... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scal... | 412 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_dual_guided.py |
class VersatileDiffusionImageVariationPipeline(DiffusionPipeline):
r"""
Pipeline for image variation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a par... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokeni... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
def __init__(
self,
image_feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection,
image_unet: UNet2DConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):
super().__init__()
self.register_modules(
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
Args:
prompt (`str` or `List[str]`):
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 (`bool`):
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4:
prompt = list(prompt)
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
image_input = self.image_feature_extractor(images=prompt, return_tensors="pt")
pixel_values = image... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_images: List[str]
if negative_prompt is None:
uncond_images = [np.zeros((512, 512, 3)) + 0.5] * batch_size
elif type(prompt) is not type(negative_prompt):
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
)
else:
uncond_images = negative_prompt | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
uncond_images = self.image_feature_extractor(images=uncond_images, return_tensors="pt")
pixel_values = uncond_images.pixel_values.to(device).to(self.image_encoder.dtype)
negative_prompt_embeds = self.image_encoder(pixel_values)
negative_prompt_embeds = normalize_embeddings(negative_p... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and conditional embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.decode(latents, return_dict=False)[0]
image = (image / 2 + 0.5).clamp(0, 1)
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
image = image.cpu().permute(0, 2, 3,... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step)... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline.check_inputs
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.I... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
if (callback_steps is None) or (
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)}."
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
height: Optional[int]... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
Args:
image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
The image prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_e... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling u... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionImageVariationPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
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 the generated image... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# 2. Define call parameters
batch_size = 1 if isinstance(image, PIL.Image.Image) else len(image)
device = self._execution_device
# 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_sc... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
# perform guidance
if 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 sample x_t -> x_t-1
latent... | 413 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py |
class VersatileDiffusionTextToImagePipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Versatile Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokeni... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
_optional_components = ["text_unet"]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModelWithProjection,
image_unet: UNet2DConditionModel,
text_unet: UNetFlatConditionModel,
vae: AutoencoderKL,
scheduler: KarrasDiffusionSchedulers,
):... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
def _swap_unet_attention_blocks(self):
"""
Swap the `Transformer2DModel` blocks between the image and text UNets
"""
for name, module in self.image_unet.named_modules():
if isinstance(module, Transformer2DModel):
parent_name, index = name.rsplit(".", 1)
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
Args:
prompt (`str` or `List[str]`):
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 (`bool`):
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
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_ids
untruncated_ids = self.tokenizer(prompt, padding="max... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
prompt_embeds = self.text_encoder(
text_input_ids.to(device),
attention_mask=attention_mask,
)
prompt_embeds = normalize_embeddings(prompt_embeds)
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = prompt_... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
uncond_tokens = negative_prompt | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
max_length = text_input_ids.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.confi... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# 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.repeat(1, num_images_per_prompt, 1)
negative_prompt_embeds = negative_prompt_embeds.view(batch_size... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.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... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
):
if height % 8 != 0 or width % 8 != 0:
raise Va... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.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_... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.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:
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.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... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int]... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *opti... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
negative_prompt (`str` or `List[str]`, *optional*):
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`,... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.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`.
outp... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionTextToImagePipeline
>>> import torch
>>> pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe.remove_unused_weig... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
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 the generated image... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# 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`
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# 5. Prepare latent variables
num_channels_latents = self.image_unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
# perform guidance
if 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 sample x_t -> x_t-1
latent... | 414 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion_text_to_image.py |
class VersatileDiffusionPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular ... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `saf... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
tokenizer: CLIPTokenizer
image_feature_extractor: CLIPImageProcessor
text_encoder: CLIPTextModel
image_encoder: CLIPVisionModel
image_unet: UNet2DConditionModel
text_unet: UNet2DConditionModel
vae: AutoencoderKL
scheduler: KarrasDiffusionSchedulers
def __init__(
self,
to... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
self.register_modules(
tokenizer=tokenizer,
image_feature_extractor=image_feature_extractor,
text_encoder=text_encoder,
image_encoder=image_encoder,
image_unet=image_unet,
text_unet=text_unet,
vae=vae,
scheduler=scheduler,
... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
@torch.no_grad()
def image_variation(
self,
image: Union[torch.Tensor, PIL.Image.Image],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = N... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Args:
image (`PIL.Image.Image`, `List[PIL.Image.Image]` or `torch.Tensor`):
The image prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_e... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling u... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/imag... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
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 the generated image... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,
callback_steps=callback_steps,... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
@torch.no_grad()
def text_to_image(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *opti... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
negative_prompt (`str` or `List[str]`, *optional*):
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`,... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.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`.
outp... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
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