text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
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|---|---|---|
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
bs_embed, 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_embed... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance and negative_prompt_embeds is None:
negative_prompt = negative_prompt or ""
uncond_tokens = [negative_prompt] * batch_size if isinstance(negative_prompt, str) else negative_prompt
max_l... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device)
negative_prompt_embeds... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_latents
def prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
return latents
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.upcast_vae
def upcast_vae(self):
dtype = self.vae.dtype
self.vae.to(dtype=torch.flo... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
negative_prompt: Union[str, List[str]] = None,
num_inference_steps: int = 50,
sigmas: List[float] = None,
guidance_scale: float = 3.5,
... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
Function invoked when calling the pipeline for generation. | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.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... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
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.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the times... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
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)
... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
provided, text embeddings will be generated from `prompt` input argument.
prompt_attention_mask (`torch.Tensor`, *optional*):
Pre-generated attention mask for text embeddings.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text em... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
of a plain tuple.
max_sequence_length (`int` defaults to 256): Maximum sequence length to use with the `prompt`. | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.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.
"""
# 1. Check inputs. Raise error i... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# 2. Determine batch size.
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
... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guidance... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
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_la... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# aura use timestep value between 0 and 1, with t=1 as noise and t=0 as the image
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = torch.tensor([t / 1000]).expand(latent_model_input.shape[0])
timestep = timestep.to(latents.device, dty... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
if output_type == "latent":
image = latents
else:
# make sure the VAE is in float32 mode, as it overflows in float16
needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
if needs_upcasting:
self.upcast_vae()
... | 218 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py |
class WuerstchenDiffNeXt(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
c_in=4,
c_out=4,
c_r=64,
patch_size=2,
c_cond=1024,
c_hidden=[320, 640, 1280, 1280],
nhead=[-1, 10, 20, 20],
blocks=[4, 4, 14, 4],
level_conf... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
# CONDITIONING
self.clip_mapper = nn.Linear(clip_embd, c_cond)
self.effnet_mappers = nn.ModuleList(
[
nn.Conv2d(effnet_embd, c_cond, kernel_size=1) if inject else None
for inject in inject_effnet + list(reversed(inject_effnet))
]
)
... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0):
if block_type == "C":
return ResBlockStageB(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout)
elif block_type == "A":
return AttnBlock(c_hidden, c_cond, nhead, self_attn=True, dropout=dropo... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
# BLOCKS
# -- down blocks
self.down_blocks = nn.ModuleList()
for i in range(len(c_hidden)):
down_block = nn.ModuleList()
if i > 0:
down_block.append(
nn.Sequential(
WuerstchenLayerNorm(c_hidden[i - 1], elementwis... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
# -- up blocks
self.up_blocks = nn.ModuleList()
for i in reversed(range(len(c_hidden))):
up_block = nn.ModuleList()
for j in range(blocks[i]):
for k, block_type in enumerate(level_config[i]):
c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j ... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
# OUTPUT
self.clf = nn.Sequential(
WuerstchenLayerNorm(c_hidden[0], elementwise_affine=False, eps=1e-6),
nn.Conv2d(c_hidden[0], 2 * c_out * (patch_size**2), kernel_size=1),
nn.PixelShuffle(patch_size),
)
# --- WEIGHT INIT ---
self.apply(self._init_wei... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
# blocks
for level_block in self.down_blocks + self.up_blocks:
for block in level_block:
if isinstance(block, ResBlockStageB):
block.channelwise[-1].weight.data *= np.sqrt(1 / sum(self.config.blocks))
elif isinstance(block, TimestepBlock):
... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
def _down_encode(self, x, r_embed, effnet, clip=None):
level_outputs = []
for i, down_block in enumerate(self.down_blocks):
effnet_c = None
for block in down_block:
if isinstance(block, ResBlockStageB):
if effnet_c is None and self.effnet_mappe... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
x = block(x)
level_outputs.insert(0, x)
return level_outputs | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
def _up_decode(self, level_outputs, r_embed, effnet, clip=None):
x = level_outputs[0]
for i, up_block in enumerate(self.up_blocks):
effnet_c = None
for j, block in enumerate(up_block):
if isinstance(block, ResBlockStageB):
if effnet_c is None a... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
skip = effnet_c
x = block(x, skip)
elif isinstance(block, AttnBlock):
x = block(x, clip)
elif isinstance(block, TimestepBlock):
x = block(x, r_embed)
else:
x = block(x)
return x | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
def forward(self, x, r, effnet, clip=None, x_cat=None, eps=1e-3, return_noise=True):
if x_cat is not None:
x = torch.cat([x, x_cat], dim=1)
# Process the conditioning embeddings
r_embed = self.gen_r_embedding(r)
if clip is not None:
clip = self.gen_c_embeddings(cl... | 219 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
class ResBlockStageB(nn.Module):
def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):
super().__init__()
self.depthwise = nn.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)
self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)
self.c... | 220 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_diffnext.py |
class MixingResidualBlock(nn.Module):
"""
Residual block with mixing used by Paella's VQ-VAE.
"""
def __init__(self, inp_channels, embed_dim):
super().__init__()
# depthwise
self.norm1 = nn.LayerNorm(inp_channels, elementwise_affine=False, eps=1e-6)
self.depthwise = nn.S... | 221 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
def forward(self, x):
mods = self.gammas
x_temp = self.norm1(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[0]) + mods[1]
x = x + self.depthwise(x_temp) * mods[2]
x_temp = self.norm2(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * (1 + mods[3]) + mods[4]
x = x + self.channel... | 221 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
class PaellaVQModel(ModelMixin, ConfigMixin):
r"""VQ-VAE model from Paella model.
This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
implements for all the model (such as downloading or saving, etc.) | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
Parameters:
in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
out_channels (int, *optional*, defaults to 3): Number of channels in the output.
up_down_scale_factor (int, *optional*, defaults to 2): Up and Downscale factor of the input image.
levels (i... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
@register_to_config
def __init__(
self,
in_channels: int = 3,
out_channels: int = 3,
up_down_scale_factor: int = 2,
levels: int = 2,
bottleneck_blocks: int = 12,
embed_dim: int = 384,
latent_channels: int = 4,
num_vq_embeddings: int = 8192,
... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
c_levels = [embed_dim // (2**i) for i in reversed(range(levels))]
# Encoder blocks
self.in_block = nn.Sequential(
nn.PixelUnshuffle(up_down_scale_factor),
nn.Conv2d(in_channels * up_down_scale_factor**2, c_levels[0], kernel_size=1),
)
down_blocks = []
for ... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
# Vector Quantizer
self.vquantizer = VectorQuantizer(num_vq_embeddings, vq_embed_dim=latent_channels, legacy=False, beta=0.25)
# Decoder blocks
up_blocks = [nn.Sequential(nn.Conv2d(latent_channels, c_levels[-1], kernel_size=1))]
for i in range(levels):
for j in range(bottlen... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
@apply_forward_hook
def encode(self, x: torch.Tensor, return_dict: bool = True) -> VQEncoderOutput:
h = self.in_block(x)
h = self.down_blocks(h)
if not return_dict:
return (h,)
return VQEncoderOutput(latents=h)
@apply_forward_hook
def decode(
self, h: t... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
def forward(self, sample: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
r"""
Args:
sample (`torch.Tensor`): Input sample.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`DecoderOutput`] instead of... | 222 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_paella_vq_model.py |
class WuerstchenLayerNorm(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def forward(self, x):
x = x.permute(0, 2, 3, 1)
x = super().forward(x)
return x.permute(0, 3, 1, 2) | 223 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py |
class TimestepBlock(nn.Module):
def __init__(self, c, c_timestep):
super().__init__()
self.mapper = nn.Linear(c_timestep, c * 2)
def forward(self, x, t):
a, b = self.mapper(t)[:, :, None, None].chunk(2, dim=1)
return x * (1 + a) + b | 224 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py |
class ResBlock(nn.Module):
def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0):
super().__init__()
self.depthwise = nn.Conv2d(c + c_skip, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c)
self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)
se... | 225 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py |
class GlobalResponseNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim))
self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim))
def forward(self, x):
agg_norm = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
stand_d... | 226 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py |
class AttnBlock(nn.Module):
def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0):
super().__init__()
self.self_attn = self_attn
self.norm = WuerstchenLayerNorm(c, elementwise_affine=False, eps=1e-6)
self.attention = Attention(query_dim=c, heads=nhead, dim_head=c // nhea... | 227 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_common.py |
class WuerstchenPrior(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin):
unet_name = "prior"
_supports_gradient_checkpointing = True
@register_to_config
def __init__(self, c_in=16, c=1280, c_cond=1024, c_r=64, depth=16, nhead=16, dropout=0.1):
super().__init__()
s... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
self.gradient_checkpointing = False
self.set_default_attn_processor()
@property
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
def attn_processors(self) -> Dict[str, AttentionProcessor]:
r"""
Returns:
`dict` of attention proce... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
for name, module in self.named_children():
fn_recursive_add_processors(name, module, processors)
return processors
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, Atte... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
if isinstance(processor, dict) and len(processor) != count:
raise ValueError(
f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESS... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
def gen_r_embedding(self, r, max_positions=10000):
r = r * max_positions
half_dim = self.c_r // 2
emb = math.log(max_positions) / (half_dim - 1)
emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
emb = r[:, None] * emb[None, :]
emb = torch.cat([emb.sin(... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
if is_torch_version(">=", "1.11.0"):
for block in self.blocks:
if isinstance(block, AttnBlock):
x = torch.utils.checkpoint.checkpoint(
create_custom_forward(block), x, c_embed, use_reentrant=False
)
... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x, r_embed)
else:
x = torch.utils.checkpoint.checkpoint(create_custom_forward(block), x)
else:
for block in self.blocks:
if isinstance(block, AttnBlock):
... | 228 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/modeling_wuerstchen_prior.py |
class WuerstchenDecoderPipeline(DiffusionPipeline):
"""
Pipeline for generating images from the Wuerstchen model.
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, runni... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
Args:
tokenizer (`CLIPTokenizer`):
The CLIP tokenizer.
text_encoder (`CLIPTextModel`):
The CLIP text encoder.
decoder ([`WuerstchenDiffNeXt`]):
The WuerstchenDiffNeXt unet decoder.
vqgan ([`PaellaVQModel`]):
The VQGAN model.
schedul... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
model_cpu_offload_seq = "text_encoder->decoder->vqgan"
_callback_tensor_inputs = [
"latents",
"text_encoder_hidden_states",
"negative_prompt_embeds",
"image_embeddings",
]
def __init__(
self,
tokenizer: CLIPTokenizer,
text_encoder: CLIPTextModel,
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if ... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
def encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt=None,
):
batch_size = len(prompt) if isinstance(prompt, list) else 1
# get prompt text embeddings
text_inputs = self.tokenizer(
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.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.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated ... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
uncond_text_encoder_hidden_states = None
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
uncond_tokens = negative_prompt | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds_text_encoder_output = self.text_encoder(
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = uncond_text_encoder_hidden_states.shape[1]
uncond_text_encoder_hidden_states = uncond_text_encoder_hidden_states.repeat(1, num_images_per_prompt, 1)
uncond_text_encoder_hidden_sta... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
@property
def num_timesteps(self):
return self._num_timesteps
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image_embeddings: Union[torch.Tensor, List[torch.Tensor]],
prompt: Union[str, List[str]] = None,
num_inference_steps:... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
Args:
image_embedding (`torch.Tensor` or `List[torch.Tensor]`):
Image Embeddings either extracted from an image or generated by a Prior Model.
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_inference_steps (`int... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
`decoder_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 (`... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.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 will ge generated by sampling using the supplied random `generator`.
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
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 specifie... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
Examples:
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple` [`~pipelines.ImagePipelineOutput`] if `return_dict` is True,
otherwise a `tuple`. When returning a tuple, the first element is a list with the generated image
embeddings.
"""
callback = kwargs.... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
if callback_on_step_end_tensor_inputs is not None and not all(
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
):
raise ValueError(
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in cal... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
if self.do_classifier_free_guidance:
if negative_prompt is not None and not isinstance(negative_prompt, list):
if isinstance(negative_prompt, str):
negative_prompt = [negative_prompt]
else:
raise TypeError(
f"'ne... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
if not isinstance(num_inference_steps, int):
raise TypeError(
f"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\
In Case you want to provide explicit timesteps, please use the 'timesteps' argument."
)
# 2. Encod... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# 3. Determine latent shape of latents
latent_height = int(image_embeddings.size(2) * self.config.latent_dim_scale)
latent_width = int(image_embeddings.size(3) * self.config.latent_dim_scale)
latent_features_shape = (image_embeddings.size(0) * num_images_per_prompt, 4, latent_height, latent_widt... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# 6. Run denoising loop
self._num_timesteps = len(timesteps[:-1])
for i, t in enumerate(self.progress_bar(timesteps[:-1])):
ratio = t.expand(latents.size(0)).to(dtype)
# 7. Denoise latents
predicted_latents = self.decoder(
torch.cat([latents] * 2) if s... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# 9. Renoise latents to next timestep
latents = self.scheduler.step(
model_output=predicted_latents,
timestep=ratio,
sample=latents,
generator=generator,
).prev_sample
if callback_on_step_end is not None:
... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
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()
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError... | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return images
return ImagePipelineOutput(images) | 229 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen.py |
class WuerstchenPriorPipelineOutput(BaseOutput):
"""
Output class for WuerstchenPriorPipeline.
Args:
image_embeddings (`torch.Tensor` or `np.ndarray`)
Prior image embeddings for text prompt
"""
image_embeddings: Union[torch.Tensor, np.ndarray] | 230 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
class WuerstchenPriorPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
"""
Pipeline for generating image prior for Wuerstchen.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as down... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
Args:
prior ([`Prior`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder ([`CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](htt... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
unet_name = "prior"
text_encoder_name = "text_encoder"
model_cpu_offload_seq = "text_encoder->prior"
_callback_tensor_inputs = ["latents", "text_encoder_hidden_states", "negative_prompt_embeds"]
_lora_loadable_modules = ["prior", "text_encoder"]
def __init__(
self,
tokenizer: CLIPTo... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
# Copied from diffusers.pipelines.unclip.pipeline_unclip.UnCLIPPipeline.prepare_latents
def prepare_latents(self, shape, dtype, device, generator, latents, scheduler):
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if ... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
def encode_prompt(
self,
device,
num_images_per_prompt,
do_classifier_free_guidance,
prompt=None,
negative_prompt=None,
prompt_embeds: Optional[torch.Tensor] = None,
negative_prompt_embeds: Optional[torch.Tensor] = None,
):
if prompt is not Non... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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(
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
text_encoder_output = self.text_encoder(
text_input_ids.to(device), attention_mask=attention_mask.to(device)
)
prompt_embeds = text_encoder_output.last_hidden_state
prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
prompt_embeds = pro... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
if negative_prompt_embeds is None and do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_p... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=self.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
negative_prompt_embeds_text_encoder_output = self.text_encoder(
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
if do_classifier_free_guidance:
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
seq_len = negative_prompt_embeds.shape[1]
negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
negati... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
def check_inputs(
self,
prompt,
negative_prompt,
num_inference_steps,
do_classifier_free_guidance,
prompt_embeds=None,
negative_prompt_embeds=None,
):
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.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."
)
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
if not isinstance(num_inference_steps, int):
raise TypeError(
f"'num_inference_steps' must be of type 'int', but got {type(num_inference_steps)}\
In Case you want to provide explicit timesteps, please use the 'timesteps' argument."
)
@property
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: int = 1024,
width: int = 1024,
num_inference_steps: int = 60,
timesteps: List[float] = None,
guidance_scale: float =... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
Function invoked when calling the pipeline for generation. | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 1024):
The width ... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
`decoder_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
`decoder_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely
linked to the text `p... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
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.
num_images_per_prompt (`int`, *optional*, defaults to 1):
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between: `"pil"` (`PIL.Image.Image`), `"np"`
(`np.array`) or `"pt"` (`torch.Tensor`).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to ... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
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. | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
Examples:
Returns:
[`~pipelines.WuerstchenPriorPipelineOutput`] or `tuple` [`~pipelines.WuerstchenPriorPipelineOutput`] if
`return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
generated image embeddings.
"""
... | 231 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py |
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