text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
Example:
```py
>>> import PIL
>>> import requests
>>> import torch
>>> from io import BytesIO
>>> from diffusers import PaintByExamplePipeline
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.open(BytesI... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
>>> pipe = PaintByExamplePipeline.from_pretrained(
... "Fantasy-Studio/Paint-by-Example",
... torch_dtype=torch.float16,
... )
>>> pipe = pipe.to("cuda")
>>> image = pipe(image=init_image, mask_image=mask_image, example_image=example_image).images[0]
>>> image
... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.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... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0 | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# 2. Preprocess mask and image
mask, masked_image = prepare_mask_and_masked_image(image, mask_image)
height, width = masked_image.shape[-2:]
# 3. Check inputs
self.check_inputs(example_image, height, width, callback_steps)
# 4. Encode input image
image_embeddings = self... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# 7. Prepare mask latent variables
mask, masked_image_latents = self.prepare_mask_latents(
mask,
masked_image,
batch_size * num_images_per_prompt,
height,
width,
image_embeddings.dtype,
device,
generator,
... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# 8. Check that sizes of mask, masked image and latents match
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:
raise ValueError(
... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# 9. 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)
# 10. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(tota... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
# predict the noise residual
noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=image_embeddings).sample
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
n... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
if XLA_AVAILABLE:
xm.mark_step()
self.maybe_free_model_hooks()
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, image_embe... | 361 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/pipeline_paint_by_example.py |
class PaintByExampleImageEncoder(CLIPPreTrainedModel):
def __init__(self, config, proj_size=None):
super().__init__(config)
self.proj_size = proj_size or getattr(config, "projection_dim", 768)
self.model = CLIPVisionModel(config)
self.mapper = PaintByExampleMapper(config)
se... | 362 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py |
class PaintByExampleMapper(nn.Module):
def __init__(self, config):
super().__init__()
num_layers = (config.num_hidden_layers + 1) // 5
hid_size = config.hidden_size
num_heads = 1
self.blocks = nn.ModuleList(
[
BasicTransformerBlock(hid_size, num_he... | 363 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/paint_by_example/image_encoder.py |
class RePaintPipeline(DiffusionPipeline):
r"""
Pipeline for image inpainting using RePaint.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Paramete... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
@torch.no_grad()
def __call__(
self,
image: Union[torch.Tensor, PIL.Image.Image],
mask_image: Union[torch.Tensor, PIL.Image.Image],
num_inference_steps: int = 250,
eta: float = 0.0,
jump_length: int = 10,
jump_n_sample: int = 10,
generator: Optional[Un... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
Args:
image (`torch.Tensor` or `PIL.Image.Image`):
The original image to inpaint on.
mask_image (`torch.Tensor` or `PIL.Image.Image`):
The mask_image where 0.0 define which part of the original image to inpaint.
num_inference_steps (`int`, *optional*, ... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
jump_n_sample (`int`, *optional*, defaults to 10):
The number of times to make a forward time jump for a given chosen time sample. Take a look at Figure 9
and 10 in the [paper](https://arxiv.org/pdf/2201.09865.pdf).
generator (`torch.Generator`, *optional*):
A... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
Example:
```py
>>> from io import BytesIO
>>> import torch
>>> import PIL
>>> import requests
>>> from diffusers import RePaintPipeline, RePaintScheduler
>>> def download_image(url):
... response = requests.get(url)
... return PIL.Image.... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
>>> # Load the RePaint scheduler and pipeline based on a pretrained DDPM model
>>> scheduler = RePaintScheduler.from_pretrained("google/ddpm-ema-celebahq-256")
>>> pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
>>> pipe = pipe.to("cuda")
>>> ... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
original_image = image
original_image = _preprocess_image(original_image)
original_image = original_image.to(device=self._execution_device, dtype=self.unet.dtype)
mask_image = _preprocess_mask(mask_image)
mask_image = mask_image.to(device=self._execution_device, dtype=self.unet.dtype)
... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
# set step values
self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self._execution_device)
self.scheduler.eta = eta
t_last = self.scheduler.timesteps[0] + 1
generator = generator[0] if isinstance(generator, list) else generator
for i, t in enumerate(... | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 364 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/repaint/pipeline_repaint.py |
class KarrasVePipeline(DiffusionPipeline):
r"""
Pipeline for unconditional image generation.
Parameters:
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image.
scheduler ([`KarrasVeScheduler`]):
A scheduler to be used in combination with `unet` to deno... | 365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py |
Args:
batch_size (`int`, *optional*, defaults to 1):
The number of images to generate.
generator (`torch.Generator`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic... | 365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.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.
"""
img_size = self.unet.config.sa... | 365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py |
# 1. Select temporarily increased noise level sigma_hat
# 2. Add new noise to move from sample_i to sample_hat
sample_hat, sigma_hat = self.scheduler.add_noise_to_input(sample, sigma, generator=generator)
# 3. Predict the noise residual given the noise magnitude `sigma_hat`
... | 365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py |
if sigma_prev != 0:
# 6. Apply 2nd order correction
# The model inputs and output are adjusted by following eq. (213) in [1].
model_output = (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2).sample
step_output = self.scheduler.ste... | 365 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/stochastic_karras_ve/pipeline_stochastic_karras_ve.py |
class AltDiffusionImg2ImgPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
StableDiffusionLoraLoaderMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-guided image-to-image generation using Alt Diffusion.
This model inherits from [`Di... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.StableDiffusionLoraLoaderMixi... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/c... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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 Alt Diffusion license and do not expose unfiltered"
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = N... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
prompt_embeds_tuple = self.encode_prompt(
prompt=prompt,
device=device,
num_images_per_prompt=num_images_per_prompt,
do_classifier_free_guidance=do_classifier_free_guidance,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = No... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_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... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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.
lora... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# dynamically adjust the LoRA scale
if not USE_PEFT_BACKEND:
adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
else:
scale_lora_layers(self.text_encoder, lora_scale)
if prompt is not None and isinstance(prompt, str):
batch_size = 1... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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 = sel... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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 | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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_... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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 prompt is not None and type(prompt) is no... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# textual inversion: process multi-vector tokens if necessary
if isinstance(self, TextualInversionLoaderMixin):
uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
max_length = prompt_embeds.shape[1]
uncond_input = self.tokenizer(
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if 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)
negative... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_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... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
else:
feat... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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,... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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,
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
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
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if negative_prompt is not None and negative_prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
)
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
return timesteps, num_inference_steps - t_start
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
else:
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
" images (`image`). In... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
raise ValueError(
f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
)
else:
init_latents = torch.cat([init_latents], dim=0) | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
shape = init_latents.shape
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
# get latents
init_latents = self.scheduler.add_noise(init_latents, noise, timestep)
latents = init_latents
return latents
def get_guidance_scale_embedding(self, w, embe... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
half_dim = embedding_dim // 2
emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb)
emb = w.to(dtype)[:, None] * emb[None, :]
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
if embedding_dim % 2 == 1: # zero ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
@property
def cross_attention_kwargs(self):
return self._cross_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: PipelineImageInput = None,
strength: float = 0.8,
num_inference_steps: Optional[int] = 50,
timesteps: List[int] = None,
sigmas:... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
**kwargs,
):
r"""
The call function to the pipeline for generation. | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a
starting point and more noise is added the higher the `strength`. The number of denoising steps depends
on the amount of noise initially added. When `strength` is 1, added noise is maximum ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
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`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images c... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https:/... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, *optional*):
A function that calls at the end of each denoising steps during the inference. The function is called
with the following arguments: `callback_on_... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
Returns:
[`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.AltDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if callback is not None:
deprecate(
"callback",
"1.0.0",
"Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
)
if callback_steps is not None:
deprecate(
"ca... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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 | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# 3. Encode input prompt
text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
)
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
device,
num_images_per_pr... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if ip_adapter_image is not None:
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
)
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# 6. Prepare latent variables
latents = self.prepare_latents(
image,
latent_timestep,
batch_size,
num_images_per_prompt,
prompt_embeds.dtype,
device,
generator,
)
# 7. Prepare extra step kwargs. TODO: Logic shou... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
timestep_cond = self.get_guidance_scale_embedding(
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# 8. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
self._num_timesteps = len(timesteps)
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are ... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
# perform guidance
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 -... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.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
... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[
0
]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = laten... | 366 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion_img2img.py |
class AltDiffusionPipelineOutput(BaseOutput):
"""
Output class for Alt Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channels)`.
... | 367 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py |
class AltDiffusionPipeline(
DiffusionPipeline,
StableDiffusionMixin,
TextualInversionLoaderMixin,
StableDiffusionLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
):
r"""
Pipeline for text-to-image generation using Alt Diffusion.
This model inherits from [`DiffusionPipeline`]. C... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.RobertaSeriesModelWithTransformation`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/c... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_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... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__(
self,
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "steps_offset", 1) != 1:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
if scheduler is not None and getattr(scheduler.config, "clip_sample", False) is True:
deprecation_message = (
f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
" `clip_sample` should be set to False in the configuration file... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.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 Alt Diffusion license and do not expose unfiltered"
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.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... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
is_unet_version_less_0_9_0 = (
unet is not None
and hasattr(unet.config, "_diffusers_version")
and version.parse(version.parse(unet.config._diffusers_version).base_version) < version.parse("0.9.0.dev0")
)
is_unet_sample_size_less_64 = (
unet is not None an... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
)
... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
lora_scale: Optional[float] = N... | 368 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_alt_diffusion.py |
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