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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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 ... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
The width in pixels of the generated image.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torc... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/sta... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
called at every step.
clean_caption (`bool`, *optional*, defaults to `True`):
Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to
be installed. If the dependencies are not installed, the embeddings will be created from the r... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second elemen... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defi... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embe... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(0)
# 5. Prepare intermediate images
intermediate_images = self.prepare_intermediate_images(
batch_size * num_images_per_prompt,
self.unet.config.in_channels,
height,
... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
# 7. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
model_input = (
torch.cat([intermediate_images] * 2) if ... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1], dim=1)
noise_pred_text, predicted_variance = noise_pred_text.split(... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.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:
callback(i, t, intermediate_imag... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
# 11. Apply watermark
if self.watermarker is not None:
image = self.watermarker.apply_watermark(image, self.unet.config.sample_size)
elif output_type == "pt":
nsfw_detected = None
watermark_detected = None
if hasattr(self, "unet_offload_hook") and... | 311 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if.py |
class IFImg2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker: Optional[IFSafetyChecker]
watermarker: Option... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: UNet2DConditionModel,
scheduler: DDPMScheduler,
safety_checker: Optional[IFSafetyChecker],
feature_extractor: Optional[CLIPImageProcessor],
watermarker: Optional[IFWatermarker]... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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 IF license and do not expose unfiltered"
" re... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
@torch.no_grad()
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Op... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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):
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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]
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
bs_embed, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings for each generation per prompt, using mps friendly method
prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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 isinstance(negative_prompt, str):
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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,
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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 ... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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 ... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
def check_inputs(
self,
prompt,
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 (not isinstance(callback_st... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
if isinstance(image, list):
image_batch_size = len(image)
elif isinstance(image, torch.Tensor):
image_batch_size = image.shape[0]
elif isinstance(image, PIL.Image.Image):
image_batch_size = 1
elif isinstance(image, np.ndarray):
image_batch_size = i... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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]
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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:
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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)... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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 = ... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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()
def preprocess_image(self, image: PIL.Image.Image) -> t... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
image = new_image
image = np.stack(image, axis=0) # to np
image = numpy_to_pt(image) # to pt
elif isinstance(image[0], np.ndarray):
image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
image = numpy_to_pt(image)
eli... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :]
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(t_start * self.scheduler.order)
return timesteps, num_inference_steps - t_start
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
image = image.repeat_interleave(num_images_per_prompt, dim=0)
image = self.scheduler.add_noise(image, noise, timestep)
return image | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[
PIL.Image.Image, torch.Tensor, np.ndarray, List[PIL.Image.Image], List[torch.Tensor], List[np.ndarray]
] = None,
strength: f... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
):
"""
Function invoked when calling the pipeline for generation. | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
image (`torch.Tensor` or `PIL.Image.Image`):
`Image`, or tensor representing an image ba... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
num_inference_steps (`int`, *optional*, defaults to 80):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
timesteps (`List[int]`, *optional*):
Custom timesteps to use for the denoisi... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second elemen... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
# 2. Define call parameters
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`
# corresponds to doing no classifier free guidance.
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
# 4. Prepare timesteps
if timesteps is not None:
self.scheduler.set_timesteps(timesteps=timesteps, device=device)
timesteps = self.scheduler.timesteps
num_inference_steps = len(timesteps)
else:
self.scheduler.set_timesteps(num_inference_steps, device=devic... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.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)
# HACK: see comment in `enable_model_cpu_offload`
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is n... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
if self.scheduler.config.variance_type not in ["learned", "learned_range"]:
noise_pred, _ = noise_pred.split(model_input.shape[1], dim=1)
# compute the previous noisy sample x_t -> x_t-1
intermediate_images = self.scheduler.step(
noise_pred, t, in... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
if output_type == "pil":
# 8. Post-processing
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
# 9. Run safety checker
image, nsfw_detected, watermark_detected = self.run_safety_checker(image, device, prompt_embeds.dtype)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, nsfw_detected, watermark_detected)
return IFPipe... | 312 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_img2img.py |
class IFSuperResolutionPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
tokenizer: T5Tokenizer
text_encoder: T5EncoderModel
unet: UNet2DConditionModel
scheduler: DDPMScheduler
image_noising_scheduler: DDPMScheduler
feature_extractor: Optional[CLIPImageProcessor]
safety_checker:... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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]
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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:
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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)... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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 = ... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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.... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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):
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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]
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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) | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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):
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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,
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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 ... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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 ... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
def check_inputs(
self,
prompt,
image,
batch_size,
noise_level,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if (callback_steps is None) or (
callback_steps is not None and (not i... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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:
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline.prepare_intermediate_images
def prepare_intermediate_images(self, batch_size, num_channels, height, width, dtype, device, generator):
shape = (batch_size, num_channels, height, width)
if isinstance(generator, list) and len(generat... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
def preprocess_image(self, image, num_images_per_prompt, device):
if not isinstance(image, torch.Tensor) and not isinstance(image, list):
image = [image]
if isinstance(image[0], PIL.Image.Image):
image = [np.array(i).astype(np.float32) / 127.5 - 1.0 for i in image]
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_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)
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: int = None,
width: int = None,
image: Union[PIL.Image.Image, np.ndarray, torch.Tensor] = None,
num_inference_steps: int = 50,
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
clean_caption: bool = True,
):
"""
Function invoked when calling the pipeline for generation. | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
height (`int`, *optional*, defaults to None):
The height in pixels of the generated imag... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
timesteps are used. Must be in descending order.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`tor... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/sta... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diff... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
Examples:
Returns:
[`~pipelines.stable_diffusion.IFPipelineOutput`] or `tuple`:
[`~pipelines.stable_diffusion.IFPipelineOutput`] if `return_dict` is True, otherwise a `tuple. When
returning a tuple, the first element is a list with the generated images, and the second elemen... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
self.check_inputs(
prompt,
image,
batch_size,
noise_level,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
height = height or self.unet.config.s... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
prompt,
do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embe... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
if hasattr(self.scheduler, "set_begin_index"):
self.scheduler.set_begin_index(0)
# 5. Prepare intermediate images
num_channels = self.unet.config.in_channels // 2
intermediate_images = self.prepare_intermediate_images(
batch_size * num_images_per_prompt,
num_... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
noise_level = torch.tensor([noise_level] * upscaled.shape[0], device=upscaled.device)
noise = randn_tensor(upscaled.shape, generator=generator, device=upscaled.device, dtype=upscaled.dtype)
upscaled = self.image_noising_scheduler.add_noise(upscaled, noise, timesteps=noise_level)
if do_classifie... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
model_input = torch.cat([model_input] * 2) if do_classifier_free_guidance else model_input
model_input = self.scheduler.scale_model_input(model_input, t)
# predict the noise residual
noise_pred = self.unet(
model_input,
t,
... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred_uncond, _ = noise_pred_uncond.split(model_input.shape[1] // 2, dim=1)
noise_pred_text, predicted_variance = noise_pred_text.s... | 313 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deepfloyd_if/pipeline_if_superresolution.py |
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