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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 `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`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
height (`int`, *optional*, defaults to self.unet.config.sample_size):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size): | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.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 (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
attention_mask (`torch.Tensor`, *optional*):
Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
negative_attention_mask (`torch.Tensor`, *optional*):
Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[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.
callback (`Callable`, *optional*): | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.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 `callback` function will be called. If not specified, the callback will be
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 raw
prompt.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
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(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
) | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
cut_context = True
device = self._execution_device
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
attention_mask,
negative_attention_mask,
)
self._guidance_scale = guidance_scale | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.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]
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
prompt,
self.do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
_cut_context=cut_context,
attention_mask=attention_mask,
negative_attention_mask=negative_attention_mask,
) | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latents
height, width = downscale_height_and_width(height, width, 8)
latents = self.prepare_latents(
(batch_size * num_images_per_prompt, 4, height, width),
prompt_embeds.dtype,
device,
generator,
latents,
self.scheduler,
)
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload() | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
# 7. 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):
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=attention_mask,
return_dict=False,
)[0]
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond
# noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.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)
attention_mask = callback_outputs.pop("attention_mask", attention_mask)
negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)
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.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step() | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
# post-processing
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}"
)
if not output_type == "latent":
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
else:
image = latents
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 276 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3.py |
class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
model_cpu_offload_seq = "text_encoder->movq->unet->movq"
_callback_tensor_inputs = [
"latents",
"prompt_embeds",
"negative_prompt_embeds",
"negative_attention_mask",
"attention_mask",
]
def __init__(
self,
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
unet: Kandinsky3UNet,
scheduler: DDPMScheduler,
movq: VQModel,
):
super().__init__()
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq
)
def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
t_start = max(num_inference_steps - init_timestep, 0)
timesteps = self.scheduler.timesteps[t_start:] | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
return timesteps, num_inference_steps - t_start
def _process_embeds(self, embeddings, attention_mask, cut_context):
# return embeddings, attention_mask
if cut_context:
embeddings[attention_mask == 0] = torch.zeros_like(embeddings[attention_mask == 0])
max_seq_length = attention_mask.sum(-1).max() + 1
embeddings = embeddings[:, :max_seq_length]
attention_mask = attention_mask[:, :max_seq_length]
return embeddings, attention_mask | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
@torch.no_grad()
def encode_prompt(
self,
prompt,
do_classifier_free_guidance=True,
num_images_per_prompt=1,
device=None,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
_cut_context=False,
attention_mask: Optional[torch.Tensor] = None,
negative_attention_mask: Optional[torch.Tensor] = None,
):
r"""
Encodes the prompt into text encoder hidden states. | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
Args:
prompt (`str` or `List[str]`, *optional*):
prompt to be encoded
device: (`torch.device`, *optional*):
torch device to place the resulting embeddings on
num_images_per_prompt (`int`, *optional*, defaults to 1):
number of images that should be generated per prompt
do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
whether to use classifier free guidance or not
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. 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`).
prompt_embeds (`torch.Tensor`, *optional*): | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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 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.
attention_mask (`torch.Tensor`, *optional*):
Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
negative_attention_mask (`torch.Tensor`, *optional*):
Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
"""
if prompt is not None and negative_prompt is not None:
if type(prompt) is not type(negative_prompt): | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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]
max_length = 128 | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
if prompt_embeds is None:
text_inputs = self.tokenizer(
prompt,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
attention_mask = text_inputs.attention_mask.to(device)
prompt_embeds = self.text_encoder(
text_input_ids,
attention_mask=attention_mask,
)
prompt_embeds = prompt_embeds[0]
prompt_embeds, attention_mask = self._process_embeds(prompt_embeds, attention_mask, _cut_context)
prompt_embeds = prompt_embeds * attention_mask.unsqueeze(2)
if self.text_encoder is not None:
dtype = self.text_encoder.dtype
else:
dtype = None
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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)
attention_mask = attention_mask.repeat(num_images_per_prompt, 1)
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
uncond_tokens: List[str] | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
elif batch_size != len(negative_prompt):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
" the batch size of `prompt`."
)
else:
uncond_tokens = negative_prompt
if negative_prompt is not None:
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=128,
truncation=True,
return_attention_mask=True,
return_tensors="pt",
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
text_input_ids = uncond_input.input_ids.to(device)
negative_attention_mask = uncond_input.attention_mask.to(device) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
negative_prompt_embeds = self.text_encoder(
text_input_ids,
attention_mask=negative_attention_mask,
)
negative_prompt_embeds = negative_prompt_embeds[0]
negative_prompt_embeds = negative_prompt_embeds[:, : prompt_embeds.shape[1]]
negative_attention_mask = negative_attention_mask[:, : prompt_embeds.shape[1]]
negative_prompt_embeds = negative_prompt_embeds * negative_attention_mask.unsqueeze(2)
else:
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_attention_mask = torch.zeros_like(attention_mask)
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1] | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
if negative_prompt_embeds.shape != prompt_embeds.shape:
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)
negative_attention_mask = negative_attention_mask.repeat(num_images_per_prompt, 1)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
else:
negative_prompt_embeds = None
negative_attention_mask = None
return prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, device, generator=None):
if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
raise ValueError(
f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(image)}"
)
image = image.to(device=device, dtype=dtype)
batch_size = batch_size * num_images_per_prompt
if image.shape[1] == 4:
init_latents = image
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 length of the generators."
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
elif isinstance(generator, list):
init_latents = [
self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(batch_size)
]
init_latents = torch.cat(init_latents, dim=0)
else:
init_latents = self.movq.encode(image).latent_dist.sample(generator)
init_latents = self.movq.config.scaling_factor * init_latents
init_latents = torch.cat([init_latents], dim=0)
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 | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
def check_inputs(
self,
prompt,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
negative_prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
attention_mask=None,
negative_attention_mask=None,
):
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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:
raise ValueError(
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
)
elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
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."
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
f" {negative_prompt_embeds.shape}."
)
if negative_prompt_embeds is not None and negative_attention_mask is None:
raise ValueError("Please provide `negative_attention_mask` along with `negative_prompt_embeds`") | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
if negative_prompt_embeds is not None and negative_attention_mask is not None:
if negative_prompt_embeds.shape[:2] != negative_attention_mask.shape:
raise ValueError(
"`negative_prompt_embeds` and `negative_attention_mask` must have the same batch_size and token length when passed directly, but"
f" got: `negative_prompt_embeds` {negative_prompt_embeds.shape[:2]} != `negative_attention_mask`"
f" {negative_attention_mask.shape}."
)
if prompt_embeds is not None and attention_mask is None:
raise ValueError("Please provide `attention_mask` along with `prompt_embeds`") | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
if prompt_embeds is not None and attention_mask is not None:
if prompt_embeds.shape[:2] != attention_mask.shape:
raise ValueError(
"`prompt_embeds` and `attention_mask` must have the same batch_size and token length when passed directly, but"
f" got: `prompt_embeds` {prompt_embeds.shape[:2]} != `attention_mask`"
f" {attention_mask.shape}."
)
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def num_timesteps(self):
return self._num_timesteps | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
image: Union[torch.Tensor, PIL.Image.Image, List[torch.Tensor], List[PIL.Image.Image]] = None,
strength: float = 0.3,
num_inference_steps: int = 25,
guidance_scale: float = 3.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
negative_attention_mask: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"], | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
**kwargs,
):
"""
Function invoked when calling the pipeline for generation. | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image`, or tensor representing an image batch, that will be used as the starting point for the
process.
strength (`float`, *optional*, defaults to 0.8):
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 and the denoising
process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
essentially ignores `image`.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 3.0):
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
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 `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
attention_mask (`torch.Tensor`, *optional*):
Pre-generated attention mask. Must provide if passing `prompt_embeds` directly.
negative_attention_mask (`torch.Tensor`, *optional*):
Pre-generated negative attention mask. Must provide if passing `negative_prompt_embeds` directly.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[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.
callback_on_step_end (`Callable`, *optional*): | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
A function that calls at the end of each denoising steps during the inference. The function is called
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`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class. | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`
"""
callback = kwargs.pop("callback", None)
callback_steps = kwargs.pop("callback_steps", None)
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(
"callback_steps",
"1.0.0",
"Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`",
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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 callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
)
cut_context = True
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
callback_on_step_end_tensor_inputs,
attention_mask,
negative_attention_mask,
)
self._guidance_scale = guidance_scale | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.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
# 3. Encode input prompt
prompt_embeds, negative_prompt_embeds, attention_mask, negative_attention_mask = self.encode_prompt(
prompt,
self.do_classifier_free_guidance,
num_images_per_prompt=num_images_per_prompt,
device=device,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
_cut_context=cut_context,
attention_mask=attention_mask,
negative_attention_mask=negative_attention_mask,
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
attention_mask = torch.cat([negative_attention_mask, attention_mask]).bool()
if not isinstance(image, list):
image = [image]
if not all(isinstance(i, (PIL.Image.Image, torch.Tensor)) for i in image):
raise ValueError(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
image = torch.cat([prepare_image(i) for i in image], dim=0)
image = image.to(dtype=prompt_embeds.dtype, device=device)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
# 5. Prepare latents
latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
latents = self.prepare_latents(
latents, latent_timestep, batch_size, num_images_per_prompt, prompt_embeds.dtype, device, generator
)
if hasattr(self, "text_encoder_offload_hook") and self.text_encoder_offload_hook is not None:
self.text_encoder_offload_hook.offload() | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
# 7. 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):
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=attention_mask,
)[0]
if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = (guidance_scale + 1.0) * noise_pred_text - guidance_scale * noise_pred_uncond | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(
noise_pred,
t,
latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_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)
attention_mask = callback_outputs.pop("attention_mask", attention_mask)
negative_attention_mask = callback_outputs.pop("negative_attention_mask", negative_attention_mask)
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.scheduler, "order", 1)
callback(step_idx, t, latents)
if XLA_AVAILABLE:
xm.mark_step() | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
# post-processing
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}"
)
if not output_type == "latent":
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()
if output_type == "pil":
image = self.numpy_to_pil(image)
else:
image = latents
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ImagePipelineOutput(images=image) | 277 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py |
class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline to generate image variations from an input image using Stable Diffusion.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
image_encoder ([`~transformers.CLIPVisionModelWithProjection`]):
Frozen CLIP image-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/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 `safety_checker`.
""" | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# TODO: feature_extractor is required to encode images (if they are in PIL format),
# we should give a descriptive message if the pipeline doesn't have one.
_optional_components = ["safety_checker"]
model_cpu_offload_seq = "image_encoder->unet->vae"
_exclude_from_cpu_offload = ["safety_checker"]
def __init__(
self,
vae: AutoencoderKL,
image_encoder: CLIPVisionModelWithProjection,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__() | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.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 Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.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'` instead."
) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.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 and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
)
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
" \n- stable-diffusion-v1-5/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"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
self.register_modules(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=scheduler,
safety_checker=safety_checker,
feature_extractor=feature_extractor,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)
def _encode_image(self, image, device, num_images_per_prompt, do_classifier_free_guidance):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(images=image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeddings = self.image_encoder(image).image_embeds
image_embeddings = image_embeddings.unsqueeze(1) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# duplicate image embeddings for each generation per prompt, using mps friendly method
bs_embed, seq_len, _ = image_embeddings.shape
image_embeddings = image_embeddings.repeat(1, num_images_per_prompt, 1)
image_embeddings = image_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
if do_classifier_free_guidance:
negative_prompt_embeds = torch.zeros_like(image_embeddings)
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeddings = torch.cat([negative_prompt_embeds, image_embeddings])
return image_embeddings | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
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:
feature_extractor_input = self.image_processor.numpy_to_pil(image)
safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
image, has_nsfw_concept = self.safety_checker(
images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
)
return image, has_nsfw_concept | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False)
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, 1).float().numpy()
return image | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.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 with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
# check if the scheduler accepts generator
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
if accepts_generator:
extra_step_kwargs["generator"] = generator
return extra_step_kwargs | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
def check_inputs(self, image, height, width, callback_steps):
if (
not isinstance(image, torch.Tensor)
and not isinstance(image, PIL.Image.Image)
and not isinstance(image, list)
):
raise ValueError(
"`image` has to be of type `torch.Tensor` or `PIL.Image.Image` or `List[PIL.Image.Image]` but is"
f" {type(image)}"
)
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
int(height) // self.vae_scale_factor,
int(width) // self.vae_scale_factor,
)
if isinstance(generator, list) and len(generator) != batch_size:
raise ValueError(
f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
f" size of {batch_size}. Make sure the batch size matches the length of the generators."
)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents
@torch.no_grad()
def __call__(
self,
image: Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.Tensor], None]] = None,
callback_steps: int = 1,
):
r"""
The call function to the pipeline for generation. | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
Args:
image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torch.Tensor`):
Image or images to guide image generation. If you provide a tensor, it needs to be compatible with
[`CLIPImageProcessor`](https://huggingface.co/lambdalabs/sd-image-variations-diffusers/blob/main/feature_extractor/preprocessor_config.json).
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. This parameter is modulated by `strength`. | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
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://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*): | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
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.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
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): | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated images and the
second element is a list of `bool`s indicating whether the corresponding generated image contains
"not-safe-for-work" (nsfw) content.
Examples:
```py
from diffusers import StableDiffusionImageVariationPipeline
from PIL import Image
from io import BytesIO
import requests
pipe = StableDiffusionImageVariationPipeline.from_pretrained(
"lambdalabs/sd-image-variations-diffusers", revision="v2.0"
)
pipe = pipe.to("cuda")
url = "https://lh3.googleusercontent.com/y-iFOHfLTwkuQSUegpwDdgKmOjRSTvPxat63dQLB25xkTs4lhIbRUFeNBWZzYf370g=s1200" | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
response = requests.get(url)
image = Image.open(BytesIO(response.content)).convert("RGB")
out = pipe(image, num_images_per_prompt=3, guidance_scale=15)
out["images"][0].save("result.jpg")
```
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(image, height, width, callback_steps) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# 2. Define call parameters
if isinstance(image, PIL.Image.Image):
batch_size = 1
elif isinstance(image, list):
batch_size = len(image)
else:
batch_size = image.shape[0]
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.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input image
image_embeddings = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
image_embeddings.dtype,
device,
generator,
latents,
)
# 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) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.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):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# 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)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
# 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.scheduler, "order", 1)
callback(step_idx, t, latents)
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_embeddings.dtype)
else:
image = latents
has_nsfw_concept = None | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
self.maybe_free_model_hooks()
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) | 278 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py |
class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
r"""
Flax-based pipeline for text-to-image generation using Stable Diffusion.
This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.). | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
Args:
vae ([`FlaxAutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.FlaxCLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`FlaxUNet2DConditionModel`]):
A `FlaxUNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]): | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/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 `safety_checker`.
""" | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
def __init__(
self,
vae: FlaxAutoencoderKL,
text_encoder: FlaxCLIPTextModel,
tokenizer: CLIPTokenizer,
unet: FlaxUNet2DConditionModel,
scheduler: Union[
FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
],
safety_checker: FlaxStableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor,
dtype: jnp.dtype = jnp.float32,
):
super().__init__()
self.dtype = dtype | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
if safety_checker is None:
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 Stable Diffusion license and do not expose unfiltered"
" results in services or applications open to the public. Both the diffusers team and Hugging Face"
" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
" it only for use-cases that involve analyzing network behavior or auditing its results. For more"
" information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_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 and hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
)
if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
deprecation_message = (
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5" | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
" \n- stable-diffusion-v1-5/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"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
" the `unet/config.json` file"
)
deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
new_config = dict(unet.config)
new_config["sample_size"] = 64
unet._internal_dict = FrozenDict(new_config) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_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,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
def prepare_inputs(self, prompt: Union[str, List[str]]):
if not isinstance(prompt, (str, list)):
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
text_input = self.tokenizer(
prompt,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="np",
)
return text_input.input_ids
def _get_has_nsfw_concepts(self, features, params):
has_nsfw_concepts = self.safety_checker(features, params)
return has_nsfw_concepts | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
def _run_safety_checker(self, images, safety_model_params, jit=False):
# safety_model_params should already be replicated when jit is True
pil_images = [Image.fromarray(image) for image in images]
features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
if jit:
features = shard(features)
has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
has_nsfw_concepts = unshard(has_nsfw_concepts)
safety_model_params = unreplicate(safety_model_params)
else:
has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
images_was_copied = False
for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
if has_nsfw_concept:
if not images_was_copied:
images_was_copied = True
images = images.copy() | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
images[idx] = np.zeros(images[idx].shape, dtype=np.uint8) # black image
if any(has_nsfw_concepts):
warnings.warn(
"Potential NSFW content was detected in one or more images. A black image will be returned"
" instead. Try again with a different prompt and/or seed."
)
return images, has_nsfw_concepts
def _generate(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.Array,
num_inference_steps: int,
height: int,
width: int,
guidance_scale: float,
latents: Optional[jnp.ndarray] = None,
neg_prompt_ids: Optional[jnp.ndarray] = None,
):
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
# get prompt text embeddings
prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
# TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
# implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
batch_size = prompt_ids.shape[0]
max_length = prompt_ids.shape[-1]
if neg_prompt_ids is None:
uncond_input = self.tokenizer(
[""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
).input_ids
else:
uncond_input = neg_prompt_ids
negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
# Ensure model output will be `float32` before going into the scheduler
guidance_scale = jnp.array([guidance_scale], dtype=jnp.float32) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
latents_shape = (
batch_size,
self.unet.config.in_channels,
height // self.vae_scale_factor,
width // self.vae_scale_factor,
)
if latents is None:
latents = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
else:
if latents.shape != latents_shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
def loop_body(step, args):
latents, scheduler_state = args
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
latents_input = jnp.concatenate([latents] * 2)
t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
timestep = jnp.broadcast_to(t, latents_input.shape[0]) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
# predict the noise residual
noise_pred = self.unet.apply(
{"params": params["unet"]},
jnp.array(latents_input),
jnp.array(timestep, dtype=jnp.int32),
encoder_hidden_states=context,
).sample
# perform guidance
noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
return latents, scheduler_state
scheduler_state = self.scheduler.set_timesteps(
params["scheduler"], num_inference_steps=num_inference_steps, shape=latents.shape
) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * params["scheduler"].init_noise_sigma
if DEBUG:
# run with python for loop
for i in range(num_inference_steps):
latents, scheduler_state = loop_body(i, (latents, scheduler_state))
else:
latents, _ = jax.lax.fori_loop(0, num_inference_steps, loop_body, (latents, scheduler_state))
# scale and decode the image latents with vae
latents = 1 / self.vae.config.scaling_factor * latents
image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
return image | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt_ids: jnp.array,
params: Union[Dict, FrozenDict],
prng_seed: jax.Array,
num_inference_steps: int = 50,
height: Optional[int] = None,
width: Optional[int] = None,
guidance_scale: Union[float, jnp.ndarray] = 7.5,
latents: jnp.ndarray = None,
neg_prompt_ids: jnp.ndarray = None,
return_dict: bool = True,
jit: bool = False,
):
r"""
The call function to the pipeline for generation. | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
latents (`jnp.ndarray`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
array is generated by sampling using the supplied random `generator`.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions. | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
<Tip warning={true}>
This argument exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a
future release.
</Tip>
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
a plain tuple.
Examples: | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
Returns:
[`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] is
returned, otherwise a `tuple` is returned where the first element is a list with the generated images
and the second element is a list of `bool`s indicating whether the corresponding generated image
contains "not-safe-for-work" (nsfw) content.
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
if isinstance(guidance_scale, float):
# Convert to a tensor so each device gets a copy. Follow the prompt_ids for
# shape information, as they may be sharded (when `jit` is `True`), or not.
guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
if len(prompt_ids.shape) > 2:
# Assume sharded
guidance_scale = guidance_scale[:, None] | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
if jit:
images = _p_generate(
self,
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
else:
images = self._generate(
prompt_ids,
params,
prng_seed,
num_inference_steps,
height,
width,
guidance_scale,
latents,
neg_prompt_ids,
)
if self.safety_checker is not None:
safety_params = params["safety_checker"]
images_uint8_casted = (images * 255).round().astype("uint8")
num_devices, batch_size = images.shape[:2] | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
images = np.asarray(images).copy()
# block images
if any(has_nsfw_concept):
for i, is_nsfw in enumerate(has_nsfw_concept):
if is_nsfw:
images[i, 0] = np.asarray(images_uint8_casted[i])
images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False
if not return_dict:
return (images, has_nsfw_concept)
return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) | 279 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py |
class OnnxStableDiffusionPipeline(DiffusionPipeline):
vae_encoder: OnnxRuntimeModel
vae_decoder: OnnxRuntimeModel
text_encoder: OnnxRuntimeModel
tokenizer: CLIPTokenizer
unet: OnnxRuntimeModel
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
safety_checker: OnnxRuntimeModel
feature_extractor: CLIPImageProcessor
_optional_components = ["safety_checker", "feature_extractor"]
_is_onnx = True
def __init__(
self,
vae_encoder: OnnxRuntimeModel,
vae_decoder: OnnxRuntimeModel,
text_encoder: OnnxRuntimeModel,
tokenizer: CLIPTokenizer,
unet: OnnxRuntimeModel,
scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
safety_checker: OnnxRuntimeModel,
feature_extractor: CLIPImageProcessor,
requires_safety_checker: bool = True,
):
super().__init__() | 280 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py |
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