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# 7. 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)
# 8. Denoising loop
latents = image_latents[0].clone()
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
).sample
# perform guidance
... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.sch... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_d... | 243 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py |
class AmusedInpaintPipeline(DiffusionPipeline):
image_processor: VaeImageProcessor
vqvae: VQModel
tokenizer: CLIPTokenizer
text_encoder: CLIPTextModelWithProjection
transformer: UVit2DModel
scheduler: AmusedScheduler
model_cpu_offload_seq = "text_encoder->transformer->vqvae"
# TODO - w... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
self.register_modules(
vqvae=vqvae,
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = (
2 ** (len(self.vqvae.config.block_out_channels) - 1) if getattr(self, "vqv... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[List[str], str]] = None,
image: PipelineImageInput = None,
mask_image: PipelineImageInput = None,
strength: float = 1.0,
num_inference_steps: int = 12,
... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
micro_conditioning_aesthetic_score: int = 6,
micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
):
"""
The call function to the pipeline for generation. | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.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]`):
... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask
are repainted while black pixels are preserved. If `mas... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
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 and the denoising
process runs for the full number of iterations specified in `num_inference_steps`... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
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`, *optional*, defaults to 1):
The number of images to... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
Pre-generated penultimate hidden states from the text encoder providing additional text conditioning.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `neg... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
A function that calls every `callback_steps` steps during inference. The function is 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 `callback` func... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
https://arxiv.org/abs/2307.01952.
micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)):
The targeted height, width crop coordinates. See the micro-conditioning section of
https://arxiv.org/abs/2307.01952.
temperature (`Union[int, Tuple[int,... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
Examples:
Returns:
[`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated images.
... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
raise ValueError("pass only one of `prompt` or `prompt_embeds`")
if isinstance(prompt, str):
prompt = [prompt]
if prompt is not None:
batch_size = len(prompt)
... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
if guidance_scale > 1.0:
if negative_prompt_embeds is None:
if negative_prompt is None:
negative_prompt = [""]... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
encoder_hidden_states = torch.co... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
# Note that the micro conditionings _do_ flip the order of width, height for the original size
# and the crop coordinates. This is how it was done in the original code base
micro_conds = torch.tensor(
[
width,
height,
micro_conditioning_crop_co... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
if needs_upcasting:
self.vqvae.float()
latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
latents_bsz, channels, latents_height, latents_width = ... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i in range(start_timestep_idx, len(self.scheduler.timesteps)):
timestep = self.scheduler.timesteps[i]
if guidance_scale > 1.0:
model_input = torch.cat([latents] * 2)
el... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
latents = self.scheduler.step(
model_output=model_output,
timestep=timestep,
sample=latents,
generator=generator,
starting_mask_ratio=starting_mask_ratio,
).prev_sample
if i == len(self.s... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
if output_type == "latent":
output = latents
else:
output = self.vqvae.decode(
latents,
force_not_quantize=True,
shape=(
batch_size,
height // self.vae_scale_factor,
width // self.... | 244 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py |
class AmusedImg2ImgPipeline(DiffusionPipeline):
image_processor: VaeImageProcessor
vqvae: VQModel
tokenizer: CLIPTokenizer
text_encoder: CLIPTextModelWithProjection
transformer: UVit2DModel
scheduler: AmusedScheduler
model_cpu_offload_seq = "text_encoder->transformer->vqvae"
# TODO - w... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
self.register_modules(
vqvae=vqvae,
tokenizer=tokenizer,
text_encoder=text_encoder,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = (
2 ** (len(self.vqvae.config.block_out_channels) - 1) if getattr(self, "vqv... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[List[str], str]] = None,
image: PipelineImageInput = None,
strength: float = 0.5,
num_inference_steps: int = 12,
guidance_scale: float = 10.0,
negati... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
):
"""
The call function to the pipeline for generation. | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_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]`):
... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_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 ... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.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... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
provided, text embeddings are generated from the `prompt` input argument. A single vector from the
pooled and projected final hidden states.
encoder_hidden_states (`torch.Tensor`, *optional*):
Pre-generated penultimate hidden states from the text encoder providing additional ... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that calls every `callback_steps` steps durin... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
The targeted aesthetic score according to the laion aesthetic classifier. See
https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of
https://arxiv.org/abs/2307.01952.
... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
Examples:
Returns:
[`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated images.
... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
raise ValueError("pass only one of `prompt` or `prompt_embeds`")
if isinstance(prompt, str):
prompt = [prompt]
if prompt is not None:
batch_size = len(prompt)
... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
encoder_hidden_states = encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
if guidance_scale > 1.0:
if negative_prompt_embeds is None:
if negative_prompt is None:
negative_prompt = [""]... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
negative_encoder_hidden_states = negative_encoder_hidden_states.repeat(num_images_per_prompt, 1, 1)
prompt_embeds = torch.concat([negative_prompt_embeds, prompt_embeds])
encoder_hidden_states = torch.co... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
# Note that the micro conditionings _do_ flip the order of width, height for the original size
# and the crop coordinates. This is how it was done in the original code base
micro_conds = torch.tensor(
[
width,
height,
micro_conditioning_crop_co... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
if needs_upcasting:
self.vqvae.float()
latents = self.vqvae.encode(image.to(dtype=self.vqvae.dtype, device=self._execution_device)).latents
latents_bsz, channels, latents_height, latents_width = ... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
if guidance_scale > 1.0:
model_input = torch.cat([latents] * 2)
else:
model_input = latents
model_output = self.transformer(
model_input,
micro_conds=micro_conds,
pooled_text_emb=prompt_e... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
if i == len(self.scheduler.timesteps) - 1 or ((i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
step_idx = i // getattr(self.scheduler, "order", 1)
callback(step_idx, ti... | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
self.maybe_free_model_hooks()
if not return_dict:
return (output,)
return ImagePipelineOutput(output) | 245 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused_img2img.py |
class AmusedPipeline(DiffusionPipeline):
image_processor: VaeImageProcessor
vqvae: VQModel
tokenizer: CLIPTokenizer
text_encoder: CLIPTextModelWithProjection
transformer: UVit2DModel
scheduler: AmusedScheduler
model_cpu_offload_seq = "text_encoder->transformer->vqvae"
def __init__(
... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[List[str], str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 12,
guidance_scale: float = 10.0,
nega... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
micro_conditioning_aesthetic_score: int = 6,
micro_conditioning_crop_coord: Tuple[int, int] = (0, 0),
temperature: Union[int, Tuple[int, int], List[int]] = (2, 0),
):
"""
The call function to the pipeline for generation. | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.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`.
height (`int`, *optional*, defaults to `self.transformer.config.sample_size * self.vae_scale_factor`):
The height in... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.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... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument. A single vector from the
pooled and projected final hidd... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
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 to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutp... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6):
The targeted aesthetic score according to the laion aesthetic classifier. See
https://lai... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
Examples:
Returns:
[`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a
`tuple` is returned where the first element is a list with the generated images.
... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
if (prompt is None and prompt_embeds is None) or (prompt is not None and prompt_embeds is not None):
raise ValueError("pass only one of `prompt` or `prompt_embeds`")
if isinstance(prompt, str):
prompt = [prompt]
if prompt is not None:
batch_size = len(prompt)
... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
prompt_embeds = outputs.text_embeds
encoder_hidden_states = outputs.hidden_states[-2]
prompt_embeds = prompt_embeds.repeat(num_images_per_prompt, 1)
encoder_hidden_states = encoder_hidden_states.... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
outputs = self.text_encoder(input_ids, return_dict=True, output_hidden_states=True)
negative_prompt_embeds = outputs.text_embeds
negative_encoder_hidden_states = outputs.hidden_states[-2]
negative_prompt_embeds = negative_prompt_embeds.repeat(num_images_per_prompt, 1)
... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
# Note that the micro conditionings _do_ flip the order of width, height for the original size
# and the crop coordinates. This is how it was done in the original code base
micro_conds = torch.tensor(
[
width,
height,
micro_conditioning_crop_co... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
self.scheduler.set_timesteps(num_inference_steps, temperature, self._execution_device)
num_warmup_steps = len(self.scheduler.timesteps) - num_inference_steps * self.scheduler.order
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, timestep in enumerate(self.scheduler... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
if guidance_scale > 1.0:
uncond_logits, cond_logits = model_output.chunk(2)
model_output = uncond_logits + guidance_scale * (cond_logits - uncond_logits)
latents = self.scheduler.step(
model_output=model_output,
timestep=ti... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
if output_type == "latent":
output = latents
else:
needs_upcasting = self.vqvae.dtype == torch.float16 and self.vqvae.config.force_upcast
if needs_upcasting:
self.vqvae.float()
output = self.vqvae.decode(
latents,
... | 246 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/amused/pipeline_amused.py |
class LuminaText2ImgPipeline(DiffusionPipeline):
r"""
Pipeline for text-to-image generation using Lumina-T2I.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running o... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
text_encoder ([`AutoModel`]):
Frozen text-encoder. Lumina-T2I uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transforme... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
bad_punct_regex = re.compile(
r"["
+ "#®•©™&@·º½¾¿¡§~"
+ r"\)"
+ r"\("
+ r"\]"
+ r"\["
+ r"\}"
+ r"\{"
+ r"\|"
+ "\\"
+ r"\/"
+ r"\*"
+ r"]{1,}"
) # noqa
_optional_components = []
model_cpu_offload_seq ... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
def _get_gemma_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_images_per_prompt: int = 1,
device: Optional[torch.device] = None,
clean_caption: Optional[bool] = False,
max_length: Optional[int] = None,
):
device = device or self._execution_device
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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[:, self.max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
_, seq_len, _ = prompt_embeds.shape
# duplicate text embeddings and attention mask 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(batch_size * num_images_per_prompt, seq_len, -1)
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt
def encode_prompt(
self,
prompt: Union[str, List[str]],
do_classifier_free_guidance: bool = True,
negative_prompt: Union[str, List[str]] = None,
num_images_per_prompt: int = 1,
device: Optional[... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
negative_prompt (`str` or `List[str]`, *optional*):
The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds`
instead. Ignored when n... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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. For L... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
prompt = [prompt] if isinstance(prompt, str) else prompt
if prompt is not None:
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
if prompt_embeds is None:
prompt_embeds, prompt_attention_mask = self._get_gemma_prompt_embeds(
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
if prompt is not None and type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, st... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
max_length=prompt_max_length,
truncation=True,
return_tensors="pt",
)
negative_text_input_ids = negative_text_inputs.input_ids.to(device)
negative_prompt_attention_mask = negative_text_inputs.attention_mask.to(device)
# Get the negative pro... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
negative_dtype = self.text_encoder.dtype
negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
_, seq_len, _ = negative_prompt_embeds.shape
negative_prompt_embeds = negative_prompt_embeds.to(dtype=negative_dtype, device=device)
# duplicate text embeddings and... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
def check_inputs(
self,
prompt,
height,
width,
negative_prompt,
prompt_embeds=None,
negative_prompt_embeds=None,
prompt_attention_mask=None,
negative_prompt_attention_mask=None,
):
if height % 8 != 0 or width % 8 != 0:
raise... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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:
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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."
)
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing
def _text_preprocessing(self, text, clean_caption=False):
if clean_caption and not is_bs4_available():
logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`"))
logger.w... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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:
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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)... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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 = ... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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 prepare_latents(self, batch_size, num_channels_late... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device)
return latents
@property
def guidance_scale(self):
return self._guidance_scale
# here `guidance_scale` is defined analog to... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
width: Optional[int] = None,
height: Optional[int] = None,
num_inference_steps: int = 30,
guidance_scale: float = 4.0,
negative_prompt... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
scaling_watershed: Optional[float] = 1.0,
proportional_attn: Optional[bool] = True,
) -> Union[ImagePipelineOutput, Tuple]:
"""
Function invoked when calling the pipeline for generation. | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide t... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
guidance_scale (`float`, *optional*, defaults to 4.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
width (`int`, *optional*, defaults to self.unet.config.sample_size):
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
[... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
prompt_embeds (`torch.Tensor`, *optional*):
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.
prompt_attention_mask (`torch.Tensor`, *optional*): Pr... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple.
clean_caption (`bool`, *optional*, default... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
`callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
Examples:
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
"""
height = heig... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.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]
if proportional_attn:
cr... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prom... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_m... | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
current_timestep = current_timestep.expand(latent_model_input.shape[0]) | 247 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/lumina/pipeline_lumina.py |
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