diff --git a/CHANGELOG.md b/CHANGELOG.md
index c24484113..35d3fcbe9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,6 @@
# Change Log for SD.Next
-## Update for 2024-12-13
+## Update for 2024-12-15
### New models and integrations
@@ -32,13 +32,19 @@
enter multiple prompts in prompt field separated by new line
style-aligned applies selected attention layers uniformly to all images to achive consistency
can be used with or without input image in which case first prompt is used to establish baseline
- *note:* all prompts are processes as a single batch, so vram is limiting factor
+ *note:* all prompts are processes as a single batch, so vram is limiting factor
+- [FreeScale](https://github.com/ali-vilab/FreeScale)
+ enable in scripts, compatible with sd-xl for text and img2img
+ run iterative generation of images at different scales to achieve better results
+ can render 4k sdxl images
+ *note*: disable live preview to avoid memory issues when generating large images
- **ControlNet**
- - improved support for `Union` controlnets with granular control mode type
+ - improved support for **Union** controlnets with granular control mode type
- added support for latest [Xinsir ProMax](https://huggingface.co/xinsir/controlnet-union-sdxl-1.0) all-in-one controlnet
- added support for multiple **Tiling** controlnets, for example [Xinsir Tile](https://huggingface.co/xinsir/controlnet-tile-sdxl-1.0)
*note*: when selecting tiles in control settings, you can also specify non-square ratios
- in which case it will use context-aware image resize to maintain overall composition
+ in which case it will use context-aware image resize to maintain overall composition
+ *note*: available tiling options can be set in settings -> control
### UI and workflow improvements
@@ -118,6 +124,8 @@
- fix cogvideox-i2v
- lora auto-apply tags remove duplicates
- control load model on-demand if not already loaded
+- taesd limit render to 2024px
+- taesd downscale preview to 1024px max
## Update for 2024-11-21
diff --git a/modules/control/tile.py b/modules/control/tile.py
index 5dc104e47..de9df1131 100644
--- a/modules/control/tile.py
+++ b/modules/control/tile.py
@@ -1,3 +1,4 @@
+import time
from PIL import Image
from modules import shared, processing, images, sd_models
@@ -17,30 +18,25 @@ def set_tile(image: Image.Image, x: int, y: int, tiled: Image.Image):
def run_tiling(p: processing.StableDiffusionProcessing, input_image: Image.Image) -> processing.Processed:
+ t0 = time.time()
# prepare images
sx, sy = p.control_tile.split('x')
sx = int(sx)
sy = int(sy)
if sx <= 0 or sy <= 0:
- raise ValueError('Control: invalid tile size')
+ raise ValueError('Control Tile: invalid tile size')
control_image = p.task_args.get('control_image', None) or p.task_args.get('image', None)
control_upscaled = None
if isinstance(control_image, list) and len(control_image) > 0:
- control_upscaled = images.resize_image(resize_mode=1 if sx==sy else 5,
- im=control_image[0],
- width=8 * int(sx * control_image[0].width) // 8,
- height=8 * int(sy * control_image[0].height) // 8,
- context='add with forward'
- )
+ w, h = 8 * int(sx * control_image[0].width) // 8, 8 * int(sy * control_image[0].height) // 8
+ control_upscaled = images.resize_image(resize_mode=1 if sx==sy else 5, im=control_image[0], width=w, height=h, context='add with forward')
init_image = p.override or input_image
init_upscaled = None
if init_image is not None:
- init_upscaled = images.resize_image(resize_mode=1 if sx==sy else 5,
- im=init_image,
- width=8 * int(sx * init_image.width) // 8,
- height=8 * int(sy * init_image.height) // 8,
- context='add with forward'
- )
+ w, h = 8 * int(sx * init_image.width) // 8, 8 * int(sy * init_image.height) // 8
+ init_upscaled = images.resize_image(resize_mode=1 if sx==sy else 5, im=init_image, width=w, height=h, context='add with forward')
+ t1 = time.time()
+ shared.log.debug(f'Control Tile: scale={sx}x{sy} resize={"fixed" if sx==sy else "context"} control={control_upscaled} init={init_upscaled} time={t1-t0:.3f}')
# stop processing from restoring pipeline on each iteration
orig_restore_pipeline = getattr(shared.sd_model, 'restore_pipeline', None)
@@ -72,4 +68,6 @@ def run_tiling(p: processing.StableDiffusionProcessing, input_image: Image.Image
shared.sd_model.restore_pipeline = orig_restore_pipeline
if hasattr(shared.sd_model, 'restore_pipeline') and shared.sd_model.restore_pipeline is not None:
shared.sd_model.restore_pipeline()
+ t2 = time.time()
+ shared.log.debug(f'Control Tile: image={control_upscaled} time={t2-t0:.3f}')
return processed
diff --git a/modules/control/units/controlnet.py b/modules/control/units/controlnet.py
index 7361638c6..c887aca8f 100644
--- a/modules/control/units/controlnet.py
+++ b/modules/control/units/controlnet.py
@@ -101,6 +101,14 @@ predefined_sd3 = {
"Alimama Inpainting": 'alimama-creative/SD3-Controlnet-Inpainting',
"Alimama SoftEdge": 'alimama-creative/SD3-Controlnet-Softedge',
}
+variants = {
+ 'NoobAI Canny XL': 'fp16',
+ 'NoobAI Lineart Anime XL': 'fp16',
+ 'NoobAI Depth XL': 'fp16',
+ 'NoobAI Normal XL': 'fp16',
+ 'NoobAI SoftEdge XL': 'fp16',
+ 'TTPlanet Tile Realistic XL': 'fp16',
+}
models = {}
all_models = {}
all_models.update(predefined_sd15)
@@ -261,8 +269,8 @@ class ControlNet():
if cls is None:
log.error(f'Control {what} model load failed: id="{model_id}" unknown base model')
return
- if 'Eugeoter' in model_path:
- kwargs['variant'] = 'fp16'
+ if variants.get(model_id, None) is not None:
+ kwargs['variant'] = variants[model_id]
self.model = cls.from_pretrained(model_path, **self.load_config, **kwargs)
if self.model is None:
return
diff --git a/modules/freescale/__init__.py b/modules/freescale/__init__.py
new file mode 100644
index 000000000..7b9c17f5d
--- /dev/null
+++ b/modules/freescale/__init__.py
@@ -0,0 +1,4 @@
+# Credits: https://github.com/ali-vilab/FreeScale
+
+from .freescale_pipeline import StableDiffusionXLFreeScale
+from .freescale_pipeline_img2img import StableDiffusionXLFreeScaleImg2Img
diff --git a/modules/freescale/free_lunch_utils.py b/modules/freescale/free_lunch_utils.py
new file mode 100644
index 000000000..be26b732a
--- /dev/null
+++ b/modules/freescale/free_lunch_utils.py
@@ -0,0 +1,305 @@
+from typing import Any, Dict, Optional, Tuple
+import torch
+import torch.fft as fft
+from diffusers.utils import is_torch_version
+
+""" Borrowed from https://github.com/ChenyangSi/FreeU/blob/main/demo/free_lunch_utils.py
+"""
+
+def isinstance_str(x: object, cls_name: str):
+ """
+ Checks whether x has any class *named* cls_name in its ancestry.
+ Doesn't require access to the class's implementation.
+
+ Useful for patching!
+ """
+
+ for _cls in x.__class__.__mro__:
+ if _cls.__name__ == cls_name:
+ return True
+
+ return False
+
+
+def Fourier_filter(x, threshold, scale):
+ dtype = x.dtype
+ x = x.type(torch.float32)
+ # FFT
+ x_freq = fft.fftn(x, dim=(-2, -1))
+ x_freq = fft.fftshift(x_freq, dim=(-2, -1))
+
+ B, C, H, W = x_freq.shape
+ mask = torch.ones((B, C, H, W)).cuda()
+
+ crow, ccol = H // 2, W //2
+ mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale
+ x_freq = x_freq * mask
+
+ # IFFT
+ x_freq = fft.ifftshift(x_freq, dim=(-2, -1))
+ x_filtered = fft.ifftn(x_freq, dim=(-2, -1)).real
+
+ x_filtered = x_filtered.type(dtype)
+ return x_filtered
+
+
+def register_upblock2d(model):
+ def up_forward(self):
+ def forward(hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+ for resnet in self.resnets:
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+ #print(f"in upblock2d, hidden states shape: {hidden_states.shape}")
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ if is_torch_version(">=", "1.11.0"):
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
+ )
+ else:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb)
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
+
+ return forward
+
+ for i, upsample_block in enumerate(model.unet.up_blocks):
+ if isinstance_str(upsample_block, "UpBlock2D"):
+ upsample_block.forward = up_forward(upsample_block)
+
+
+def register_free_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2):
+ def up_forward(self):
+ def forward(hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+ for resnet in self.resnets:
+ # pop res hidden states
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+ #print(f"in free upblock2d, hidden states shape: {hidden_states.shape}")
+
+ # --------------- FreeU code -----------------------
+ # Only operate on the first two stages
+ if hidden_states.shape[1] == 1280:
+ hidden_states[:,:640] = hidden_states[:,:640] * self.b1
+ res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s1)
+ if hidden_states.shape[1] == 640:
+ hidden_states[:,:320] = hidden_states[:,:320] * self.b2
+ res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s2)
+ # ---------------------------------------------------------
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ return module(*inputs)
+
+ return custom_forward
+
+ if is_torch_version(">=", "1.11.0"):
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
+ )
+ else:
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet), hidden_states, temb
+ )
+ else:
+ hidden_states = resnet(hidden_states, temb)
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
+
+ return forward
+
+ for i, upsample_block in enumerate(model.unet.up_blocks):
+ if isinstance_str(upsample_block, "UpBlock2D"):
+ upsample_block.forward = up_forward(upsample_block)
+ setattr(upsample_block, 'b1', b1)
+ setattr(upsample_block, 'b2', b2)
+ setattr(upsample_block, 's1', s1)
+ setattr(upsample_block, 's2', s2)
+
+
+def register_crossattn_upblock2d(model):
+ def up_forward(self):
+ def forward(
+ hidden_states: torch.FloatTensor,
+ res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+ temb: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ upsample_size: Optional[int] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ ):
+ for resnet, attn in zip(self.resnets, self.attentions):
+ # pop res hidden states
+ #print(f"in crossatten upblock2d, hidden states shape: {hidden_states.shape}")
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ **ckpt_kwargs,
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(attn, return_dict=False),
+ hidden_states,
+ encoder_hidden_states,
+ None, # timestep
+ None, # class_labels
+ cross_attention_kwargs,
+ attention_mask,
+ encoder_attention_mask,
+ **ckpt_kwargs,
+ )[0]
+ else:
+ hidden_states = resnet(hidden_states, temb)
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ attention_mask=attention_mask,
+ encoder_attention_mask=encoder_attention_mask,
+ return_dict=False,
+ )[0]
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
+
+ return forward
+
+ for i, upsample_block in enumerate(model.unet.up_blocks):
+ if isinstance_str(upsample_block, "CrossAttnUpBlock2D"):
+ upsample_block.forward = up_forward(upsample_block)
+
+
+def register_free_crossattn_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2):
+ def up_forward(self):
+ def forward(
+ hidden_states: torch.FloatTensor,
+ res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+ temb: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ upsample_size: Optional[int] = None,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ ):
+ for resnet, attn in zip(self.resnets, self.attentions):
+ # pop res hidden states
+ #print(f"in free crossatten upblock2d, hidden states shape: {hidden_states.shape}")
+ res_hidden_states = res_hidden_states_tuple[-1]
+ res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+
+ # --------------- FreeU code -----------------------
+ # Only operate on the first two stages
+ if hidden_states.shape[1] == 1280:
+ hidden_states[:,:640] = hidden_states[:,:640] * self.b1
+ res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s1)
+ if hidden_states.shape[1] == 640:
+ hidden_states[:,:320] = hidden_states[:,:320] * self.b2
+ res_hidden_states = Fourier_filter(res_hidden_states, threshold=1, scale=self.s2)
+ # ---------------------------------------------------------
+
+ hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+ if self.training and self.gradient_checkpointing:
+
+ def create_custom_forward(module, return_dict=None):
+ def custom_forward(*inputs):
+ if return_dict is not None:
+ return module(*inputs, return_dict=return_dict)
+ else:
+ return module(*inputs)
+
+ return custom_forward
+
+ ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(resnet),
+ hidden_states,
+ temb,
+ **ckpt_kwargs,
+ )
+ hidden_states = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(attn, return_dict=False),
+ hidden_states,
+ encoder_hidden_states,
+ None, # timestep
+ None, # class_labels
+ cross_attention_kwargs,
+ attention_mask,
+ encoder_attention_mask,
+ **ckpt_kwargs,
+ )[0]
+ else:
+ hidden_states = resnet(hidden_states, temb)
+ # hidden_states = attn(
+ # hidden_states,
+ # encoder_hidden_states=encoder_hidden_states,
+ # cross_attention_kwargs=cross_attention_kwargs,
+ # encoder_attention_mask=encoder_attention_mask,
+ # return_dict=False,
+ # )[0]
+ hidden_states = attn(
+ hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ cross_attention_kwargs=cross_attention_kwargs,
+ )[0]
+
+ if self.upsamplers is not None:
+ for upsampler in self.upsamplers:
+ hidden_states = upsampler(hidden_states, upsample_size)
+
+ return hidden_states
+
+ return forward
+
+ for i, upsample_block in enumerate(model.unet.up_blocks):
+ if isinstance_str(upsample_block, "CrossAttnUpBlock2D"):
+ upsample_block.forward = up_forward(upsample_block)
+ setattr(upsample_block, 'b1', b1)
+ setattr(upsample_block, 'b2', b2)
+ setattr(upsample_block, 's1', s1)
+ setattr(upsample_block, 's2', s2)
diff --git a/modules/freescale/freescale_pipeline.py b/modules/freescale/freescale_pipeline.py
new file mode 100644
index 000000000..9b7a68b68
--- /dev/null
+++ b/modules/freescale/freescale_pipeline.py
@@ -0,0 +1,1189 @@
+from inspect import isfunction
+from functools import partial
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import inspect
+import os
+import random
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.utils import is_accelerate_available, is_accelerate_version, logging, replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.models.attention import BasicTransformerBlock
+
+from .scale_attention import ori_forward, scale_forward
+
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> import torch
+ >>> from diffusers import StableDiffusionXLPipeline
+
+ >>> pipe = StableDiffusionXLPipeline.from_pretrained(
+ ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+ ... )
+ >>> pipe = pipe.to("cuda")
+
+ >>> prompt = "a photo of an astronaut riding a horse on mars"
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+def exists(val):
+ return val is not None
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+to_torch = partial(torch.tensor, dtype=torch.float16)
+betas = make_beta_schedule("linear", 1000, linear_start=0.00085, linear_end=0.012)
+alphas = 1. - betas
+alphas_cumprod = np.cumprod(alphas, axis=0)
+sqrt_alphas_cumprod = to_torch(np.sqrt(alphas_cumprod))
+sqrt_one_minus_alphas_cumprod = to_torch(np.sqrt(1. - alphas_cumprod))
+
+def q_sample(x_start, t, init_noise_sigma = 1.0, noise=None, device=None):
+ noise = default(noise, lambda: torch.randn_like(x_start)).to(device) * init_noise_sigma
+ return (extract_into_tensor(sqrt_alphas_cumprod.to(device), t, x_start.shape) * x_start +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod.to(device), t, x_start.shape) * noise)
+
+def get_views(height, width, h_window_size=128, w_window_size=128, h_window_stride=64, w_window_stride=64, vae_scale_factor=8):
+ height //= vae_scale_factor
+ width //= vae_scale_factor
+ num_blocks_height = int((height - h_window_size) / h_window_stride - 1e-6) + 2 if height > h_window_size else 1
+ num_blocks_width = int((width - w_window_size) / w_window_stride - 1e-6) + 2 if width > w_window_size else 1
+ total_num_blocks = int(num_blocks_height * num_blocks_width)
+ views = []
+ for i in range(total_num_blocks):
+ h_start = int((i // num_blocks_width) * h_window_stride)
+ h_end = h_start + h_window_size
+ w_start = int((i % num_blocks_width) * w_window_stride)
+ w_end = w_start + w_window_size
+
+ if h_end > height:
+ h_start = int(h_start + height - h_end)
+ h_end = int(height)
+ if w_end > width:
+ w_start = int(w_start + width - w_end)
+ w_end = int(width)
+ if h_start < 0:
+ h_end = int(h_end - h_start)
+ h_start = 0
+ if w_start < 0:
+ w_end = int(w_end - w_start)
+ w_start = 0
+
+ random_jitter = True
+ if random_jitter:
+ h_jitter_range = (h_window_size - h_window_stride) // 4
+ w_jitter_range = (w_window_size - w_window_stride) // 4
+ h_jitter = 0
+ w_jitter = 0
+
+ if (w_start != 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, w_jitter_range)
+ elif (w_start == 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, 0)
+ elif (w_start != 0) and (w_end == width):
+ w_jitter = random.randint(0, w_jitter_range)
+ if (h_start != 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, h_jitter_range)
+ elif (h_start == 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, 0)
+ elif (h_start != 0) and (h_end == height):
+ h_jitter = random.randint(0, h_jitter_range)
+ h_start += (h_jitter + h_jitter_range)
+ h_end += (h_jitter + h_jitter_range)
+ w_start += (w_jitter + w_jitter_range)
+ w_end += (w_jitter + w_jitter_range)
+
+ views.append((h_start, h_end, w_start, w_end))
+ return views
+
+def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):
+ x_coord = torch.arange(kernel_size)
+ gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2))
+ gaussian_1d = gaussian_1d / gaussian_1d.sum()
+ gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]
+ kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)
+
+ return kernel
+
+def gaussian_filter(latents, kernel_size=3, sigma=1.0):
+ channels = latents.shape[1]
+ kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)
+ if len(latents.shape) == 5:
+ b = latents.shape[0]
+ latents = rearrange(latents, 'b c t i j -> (b t) c i j')
+ blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
+ blurred_latents = rearrange(blurred_latents, '(b t) c i j -> b c t i j', b=b)
+ else:
+ blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
+
+ return blurred_latents
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+class StableDiffusionXLFreeScale(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+ r"""
+ Pipeline for text-to-image generation using Stable Diffusion XL.
+
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+ library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+ In addition the pipeline inherits the following loading methods:
+ - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
+ - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
+
+ as well as the following saving methods:
+ - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
+
+ Args:
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModel`]):
+ Frozen text-encoder. Stable Diffusion XL uses the text portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+ the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+ text_encoder_2 ([` CLIPTextModelWithProjection`]):
+ Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ tokenizer_2 (`CLIPTokenizer`):
+ Second Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`].
+ """
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ tokenizer_2: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ force_zeros_for_empty_prompt: bool = True,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ unet=unet,
+ scheduler=scheduler,
+ )
+ self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.default_sample_size = self.unet.config.sample_size
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+ def enable_vae_slicing(self):
+ r"""
+ Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+ compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+ """
+ self.vae.enable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+ def disable_vae_slicing(self):
+ r"""
+ Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+ def enable_vae_tiling(self):
+ r"""
+ Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+ compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+ processing larger images.
+ """
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+ def disable_vae_tiling(self):
+ r"""
+ Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_tiling()
+
+ def enable_model_cpu_offload(self, gpu_id=0):
+ r"""
+ Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+ to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+ method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+ `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+ """
+ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+ from accelerate import cpu_offload_with_hook
+ else:
+ raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ if self.device.type != "cpu":
+ self.to("cpu", silence_dtype_warnings=True)
+ torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
+
+ model_sequence = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+ model_sequence.extend([self.unet, self.vae])
+
+ hook = None
+ for cpu_offloaded_model in model_sequence:
+ _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+ # We'll offload the last model manually.
+ self.final_offload_hook = hook
+
+ def encode_prompt(
+ self,
+ prompt: str,
+ prompt_2: Optional[str] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ lora_scale: Optional[float] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ device: (`torch.device`):
+ torch device
+ num_images_per_prompt (`int`):
+ number of images that should be generated per prompt
+ do_classifier_free_guidance (`bool`):
+ 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ """
+ device = device or self._execution_device
+
+ # set lora scale so that monkey patched LoRA
+ # function of text encoder can correctly access it
+ if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ 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]
+
+ # Define tokenizers and text encoders
+ tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+ text_encoders = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ # textual inversion: procecss multi-vector tokens if necessary
+ prompt_embeds_list = []
+ prompts = [prompt, prompt_2]
+ for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = text_encoder(
+ text_input_ids.to(device),
+ output_hidden_states=True,
+ )
+
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ pooled_prompt_embeds = prompt_embeds[0]
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+
+ prompt_embeds_list.append(prompt_embeds)
+
+ prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+ # get unconditional embeddings for classifier free guidance
+ zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+ if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+ negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+ negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+ elif do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+ uncond_tokens: List[str]
+ if prompt is not None and type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+ 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, negative_prompt_2]
+
+ negative_prompt_embeds_list = []
+ for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+ max_length = prompt_embeds.shape[1]
+ uncond_input = tokenizer(
+ negative_prompt,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ negative_prompt_embeds = text_encoder(
+ uncond_input.input_ids.to(device),
+ output_hidden_states=True,
+ )
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+ negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+ negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+ negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+ prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ 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)
+
+ 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_2.dtype, device=device)
+ negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+ if do_classifier_free_guidance:
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+ # 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
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=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}.")
+
+ 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)}."
+ )
+
+ 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_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} 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)}")
+ elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+ raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+ 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."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+
+ 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 prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+ )
+
+ # 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, height // self.vae_scale_factor, 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)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
+ add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+ passed_add_embed_dim = (
+ self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
+ )
+ expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+ if expected_add_embed_dim != passed_add_embed_dim:
+ raise ValueError(
+ f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+ )
+
+ add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+ return add_time_ids
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+ def upcast_vae(self):
+ dtype = self.vae.dtype
+ self.vae.to(dtype=torch.float32)
+ use_torch_2_0_or_xformers = isinstance(
+ self.vae.decoder.mid_block.attentions[0].processor,
+ (
+ AttnProcessor2_0,
+ XFormersAttnProcessor,
+ LoRAXFormersAttnProcessor,
+ LoRAAttnProcessor2_0,
+ ),
+ )
+ # if xformers or torch_2_0 is used attention block does not need
+ # to be in float32 which can save lots of memory
+ if use_torch_2_0_or_xformers:
+ self.vae.post_quant_conv.to(dtype)
+ self.vae.decoder.conv_in.to(dtype)
+ self.vae.decoder.mid_block.to(dtype)
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Union[str, List[str]] = None,
+ prompt_2: Optional[Union[str, List[str]]] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 50,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ resolutions_list: Optional[Union[int, List[int]]] = None,
+ restart_steps: Optional[Union[int, List[int]]] = None,
+ cosine_scale: float = 2.0,
+ dilate_tau: int = 35,
+ ):
+ r"""
+ Function invoked when calling the pipeline for generation.
+
+ 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.
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ 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.
+ denoising_end (`float`, *optional*):
+ When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+ completed before it is intentionally prematurely terminated. As a result, the returned sample will
+ still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+ scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+ "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+ Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+ guidance_scale (`float`, *optional*, defaults to 5.0):
+ 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
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ 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) 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.
+ latents (`torch.FloatTensor`, *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
+ tensor will ge generated by sampling using the supplied random `generator`.
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generate image. Choose between
+ [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+ of a plain tuple.
+ callback (`Callable`, *optional*):
+ 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.FloatTensor)`.
+ 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.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.7):
+ Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+ [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+ Guidance rescale factor should fix overexposure when using zero terminal SNR.
+ original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+ `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
+ explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+ `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+ `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ For most cases, `target_size` should be set to the desired height and width of the generated image. If
+ not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
+ section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+
+ Examples:
+
+ Returns:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
+ """
+
+ # 0. Default height and width to unet
+ if resolutions_list:
+ height, width = resolutions_list[0]
+ target_sizes = resolutions_list[1:]
+ if not restart_steps:
+ restart_steps = [15] * len(target_sizes)
+ else:
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt,
+ negative_prompt_2,
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ )
+
+ # 2. Define call parameters
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ # 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 prompt
+ text_encoder_lora_scale = (
+ cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+ )
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = self.encode_prompt(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ lora_scale=text_encoder_lora_scale,
+ )
+
+ # 4. Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+
+ timesteps = self.scheduler.timesteps
+
+ # 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,
+ prompt_embeds.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)
+
+ # 7. Prepare added time ids & embeddings
+ add_text_embeds = pooled_prompt_embeds
+ add_time_ids = self._get_add_time_ids(
+ original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+ )
+
+ if do_classifier_free_guidance:
+ prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+ add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+ prompt_embeds = prompt_embeds.to(device)
+ add_text_embeds = add_text_embeds.to(device)
+ add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+ # 8. Denoising loop
+ num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+ # 9.1 Apply denoising_end
+ if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (denoising_end * self.scheduler.config.num_train_timesteps)
+ )
+ )
+ num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+ timesteps = timesteps[:num_inference_steps]
+
+ results_list = []
+
+ for block in self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks:
+ for module in block.modules():
+ if isinstance(module, BasicTransformerBlock):
+ module.forward = ori_forward.__get__(module, BasicTransformerBlock)
+
+ 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
+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ cross_attention_kwargs=cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # 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)
+
+ if do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, 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):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ callback(i, t, latents)
+ results_list.append(latents)
+
+ for restart_index, target_size in enumerate(target_sizes):
+ restart_step = restart_steps[restart_index]
+ target_size_ = [target_size[0]//8, target_size[1]//8]
+
+ for block in self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks:
+ for module in block.modules():
+ if isinstance(module, BasicTransformerBlock):
+ module.forward = scale_forward.__get__(module, BasicTransformerBlock)
+ module.current_hw = target_size
+
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ latents = latents / self.vae.config.scaling_factor
+ image = self.vae.decode(latents, return_dict=False)[0]
+ image = torch.nn.functional.interpolate(
+ image,
+ size=target_size,
+ mode='bicubic',
+ )
+ latents = self.vae.encode(image).latent_dist.sample().to(self.vae.dtype)
+ latents = latents * self.vae.config.scaling_factor
+
+ noise_latents = []
+ noise = torch.randn_like(latents)
+ for timestep in self.scheduler.timesteps:
+ noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))
+ noise_latents.append(noise_latent)
+ latents = noise_latents[restart_step]
+
+ self.scheduler._step_index = 0
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+
+ if i < restart_step:
+ self.scheduler._step_index += 1
+ progress_bar.update()
+ continue
+
+ cosine_factor = 0.5 * (1 + torch.cos(torch.pi * (self.scheduler.config.num_train_timesteps - t) / self.scheduler.config.num_train_timesteps)).cpu()
+ c1 = cosine_factor ** cosine_scale
+ latents = latents * (1 - c1) + noise_latents[i] * c1
+
+ dilate_coef=target_size[1]//1024
+
+ dilate_layers = [
+ # "down_blocks.1.resnets.0.conv1",
+ # "down_blocks.1.resnets.0.conv2",
+ # "down_blocks.1.resnets.1.conv1",
+ # "down_blocks.1.resnets.1.conv2",
+ "down_blocks.1.downsamplers.0.conv",
+ "down_blocks.2.resnets.0.conv1",
+ "down_blocks.2.resnets.0.conv2",
+ "down_blocks.2.resnets.1.conv1",
+ "down_blocks.2.resnets.1.conv2",
+ # "up_blocks.0.resnets.0.conv1",
+ # "up_blocks.0.resnets.0.conv2",
+ # "up_blocks.0.resnets.1.conv1",
+ # "up_blocks.0.resnets.1.conv2",
+ # "up_blocks.0.resnets.2.conv1",
+ # "up_blocks.0.resnets.2.conv2",
+ # "up_blocks.0.upsamplers.0.conv",
+ # "up_blocks.1.resnets.0.conv1",
+ # "up_blocks.1.resnets.0.conv2",
+ # "up_blocks.1.resnets.1.conv1",
+ # "up_blocks.1.resnets.1.conv2",
+ # "up_blocks.1.resnets.2.conv1",
+ # "up_blocks.1.resnets.2.conv2",
+ # "up_blocks.1.upsamplers.0.conv",
+ # "up_blocks.2.resnets.0.conv1",
+ # "up_blocks.2.resnets.0.conv2",
+ # "up_blocks.2.resnets.1.conv1",
+ # "up_blocks.2.resnets.1.conv2",
+ # "up_blocks.2.resnets.2.conv1",
+ # "up_blocks.2.resnets.2.conv2",
+ "mid_block.resnets.0.conv1",
+ "mid_block.resnets.0.conv2",
+ "mid_block.resnets.1.conv1",
+ "mid_block.resnets.1.conv2"
+ ]
+
+ for name, module in self.unet.named_modules():
+ if name in dilate_layers:
+ if i < dilate_tau:
+ module.dilation = (dilate_coef, dilate_coef)
+ module.padding = (dilate_coef, dilate_coef)
+ else:
+ module.dilation = (1, 1)
+ module.padding = (1, 1)
+
+ # 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
+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ cross_attention_kwargs=cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # 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)
+
+ if do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents_dtype = latents.dtype
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+ if latents.dtype != latents_dtype:
+ if torch.backends.mps.is_available():
+ # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+ latents = latents.to(latents_dtype)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ callback(i, t, latents)
+
+ for name, module in self.unet.named_modules():
+ # if ('.conv' in name) and ('.conv_' not in name):
+ if name in dilate_layers:
+ module.dilation = (1, 1)
+ module.padding = (1, 1)
+
+ results_list.append(latents)
+
+ """
+ final_results = []
+ for latents in results_list:
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ if not output_type == "latent":
+ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+ else:
+ image = latents
+ return StableDiffusionXLPipelineOutput(images=image)
+
+ image = self.image_processor.postprocess(image, output_type=output_type)
+
+ if not return_dict:
+ final_results += [(image,)]
+ else:
+ final_results += [StableDiffusionXLPipelineOutput(images=image)]
+
+ # Offload last model to CPU
+ if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+ self.final_offload_hook.offload()
+
+ return final_results
+ """
+ return StableDiffusionXLPipelineOutput(images=results_list)
+
+ # Overrride to properly handle the loading and unloading of the additional text encoder.
+ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+ # We could have accessed the unet config from `lora_state_dict()` too. We pass
+ # it here explicitly to be able to tell that it's coming from an SDXL
+ # pipeline.
+ state_dict, network_alphas = self.lora_state_dict(
+ pretrained_model_name_or_path_or_dict,
+ unet_config=self.unet.config,
+ **kwargs,
+ )
+ self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
+
+ text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
+ if len(text_encoder_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder,
+ prefix="text_encoder",
+ lora_scale=self.lora_scale,
+ )
+
+ text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
+ if len(text_encoder_2_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_2_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder_2,
+ prefix="text_encoder_2",
+ lora_scale=self.lora_scale,
+ )
+
+ @classmethod
+ def save_lora_weights(
+ self,
+ save_directory: Union[str, os.PathLike],
+ unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ is_main_process: bool = True,
+ weight_name: str = None,
+ save_function: Callable = None,
+ safe_serialization: bool = True,
+ ):
+ state_dict = {}
+
+ def pack_weights(layers, prefix):
+ layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+ layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+ return layers_state_dict
+
+ state_dict.update(pack_weights(unet_lora_layers, "unet"))
+
+ if text_encoder_lora_layers and text_encoder_2_lora_layers:
+ state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
+ state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+
+ self.write_lora_layers(
+ state_dict=state_dict,
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ weight_name=weight_name,
+ save_function=save_function,
+ safe_serialization=safe_serialization,
+ )
+
+ def _remove_text_encoder_monkey_patch(self):
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
diff --git a/modules/freescale/freescale_pipeline_img2img.py b/modules/freescale/freescale_pipeline_img2img.py
new file mode 100644
index 000000000..df4c3f0c1
--- /dev/null
+++ b/modules/freescale/freescale_pipeline_img2img.py
@@ -0,0 +1,1245 @@
+from inspect import isfunction
+from functools import partial
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+import inspect
+import os
+import random
+
+from PIL import Image
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+import torchvision.transforms as transforms
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.models.attention_processor import AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
+from diffusers.schedulers import KarrasDiffusionSchedulers
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.utils import is_accelerate_available, is_accelerate_version, logging, replace_example_docstring
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput
+from diffusers.models.attention import BasicTransformerBlock
+
+from .scale_attention import ori_forward, scale_forward
+
+
+logger = logging.get_logger(__name__) # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+ Examples:
+ ```py
+ >>> import torch
+ >>> from diffusers import StableDiffusionXLPipeline
+
+ >>> pipe = StableDiffusionXLPipeline.from_pretrained(
+ ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
+ ... )
+ >>> pipe = pipe.to("cuda")
+
+ >>> prompt = "a photo of an astronaut riding a horse on mars"
+ >>> image = pipe(prompt).images[0]
+ ```
+"""
+
+def process_image_to_tensor(image):
+ image = image.convert("RGB")
+ # image = Image.open(image_path).convert("RGB")
+ transform = transforms.Compose(
+ [
+ # transforms.Resize((1024, 1024)),
+ transforms.ToTensor(),
+ transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+ ]
+ )
+ image_tensor = transform(image)
+ return image_tensor
+
+def process_image_to_bitensor(image):
+ # image = Image.open(image_path).convert("L")
+ image = image.convert("L")
+ transform = transforms.ToTensor()
+ image_tensor = transform(image)
+ binary_tensor = torch.where(image_tensor != 0, torch.tensor(1.0), torch.tensor(0.0))
+ return binary_tensor
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+def exists(val):
+ return val is not None
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+to_torch = partial(torch.tensor, dtype=torch.float16)
+betas = make_beta_schedule("linear", 1000, linear_start=0.00085, linear_end=0.012)
+alphas = 1. - betas
+alphas_cumprod = np.cumprod(alphas, axis=0)
+sqrt_alphas_cumprod = to_torch(np.sqrt(alphas_cumprod))
+sqrt_one_minus_alphas_cumprod = to_torch(np.sqrt(1. - alphas_cumprod))
+
+def q_sample(x_start, t, init_noise_sigma = 1.0, noise=None, device=None):
+ noise = default(noise, lambda: torch.randn_like(x_start)).to(device) * init_noise_sigma
+ return (extract_into_tensor(sqrt_alphas_cumprod.to(device), t, x_start.shape) * x_start +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod.to(device), t, x_start.shape) * noise)
+
+def get_views(height, width, h_window_size=128, w_window_size=128, h_window_stride=64, w_window_stride=64, vae_scale_factor=8):
+ height //= vae_scale_factor
+ width //= vae_scale_factor
+ num_blocks_height = int((height - h_window_size) / h_window_stride - 1e-6) + 2 if height > h_window_size else 1
+ num_blocks_width = int((width - w_window_size) / w_window_stride - 1e-6) + 2 if width > w_window_size else 1
+ total_num_blocks = int(num_blocks_height * num_blocks_width)
+ views = []
+ for i in range(total_num_blocks):
+ h_start = int((i // num_blocks_width) * h_window_stride)
+ h_end = h_start + h_window_size
+ w_start = int((i % num_blocks_width) * w_window_stride)
+ w_end = w_start + w_window_size
+
+ if h_end > height:
+ h_start = int(h_start + height - h_end)
+ h_end = int(height)
+ if w_end > width:
+ w_start = int(w_start + width - w_end)
+ w_end = int(width)
+ if h_start < 0:
+ h_end = int(h_end - h_start)
+ h_start = 0
+ if w_start < 0:
+ w_end = int(w_end - w_start)
+ w_start = 0
+
+ random_jitter = True
+ if random_jitter:
+ h_jitter_range = (h_window_size - h_window_stride) // 4
+ w_jitter_range = (w_window_size - w_window_stride) // 4
+ h_jitter = 0
+ w_jitter = 0
+
+ if (w_start != 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, w_jitter_range)
+ elif (w_start == 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, 0)
+ elif (w_start != 0) and (w_end == width):
+ w_jitter = random.randint(0, w_jitter_range)
+ if (h_start != 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, h_jitter_range)
+ elif (h_start == 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, 0)
+ elif (h_start != 0) and (h_end == height):
+ h_jitter = random.randint(0, h_jitter_range)
+ h_start += (h_jitter + h_jitter_range)
+ h_end += (h_jitter + h_jitter_range)
+ w_start += (w_jitter + w_jitter_range)
+ w_end += (w_jitter + w_jitter_range)
+
+ views.append((h_start, h_end, w_start, w_end))
+ return views
+
+def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):
+ x_coord = torch.arange(kernel_size)
+ gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2))
+ gaussian_1d = gaussian_1d / gaussian_1d.sum()
+ gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]
+ kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)
+
+ return kernel
+
+def gaussian_filter(latents, kernel_size=3, sigma=1.0):
+ channels = latents.shape[1]
+ kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)
+ if len(latents.shape) == 5:
+ b = latents.shape[0]
+ latents = rearrange(latents, 'b c t i j -> (b t) c i j')
+ blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
+ blurred_latents = rearrange(blurred_latents, '(b t) c i j -> b c t i j', b=b)
+ else:
+ blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
+
+ return blurred_latents
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+ """
+ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+ Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+ """
+ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+ std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+ # rescale the results from guidance (fixes overexposure)
+ noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+ # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+ noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+ return noise_cfg
+
+
+class StableDiffusionXLFreeScaleImg2Img(DiffusionPipeline, FromSingleFileMixin, LoraLoaderMixin):
+ r"""
+ Pipeline for text-to-image generation using Stable Diffusion XL.
+
+ This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+ library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+ In addition the pipeline inherits the following loading methods:
+ - *LoRA*: [`StableDiffusionXLPipeline.load_lora_weights`]
+ - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`]
+
+ as well as the following saving methods:
+ - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`]
+
+ Args:
+ vae ([`AutoencoderKL`]):
+ Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+ text_encoder ([`CLIPTextModel`]):
+ Frozen text-encoder. Stable Diffusion XL uses the text portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+ the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+ text_encoder_2 ([` CLIPTextModelWithProjection`]):
+ Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
+ [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+ specifically the
+ [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+ variant.
+ tokenizer (`CLIPTokenizer`):
+ Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ tokenizer_2 (`CLIPTokenizer`):
+ Second Tokenizer of class
+ [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+ unet ([`UNet2DConditionModel`]): Conditional U-Net architecture 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`].
+ """
+
+ def __init__(
+ self,
+ vae: AutoencoderKL,
+ text_encoder: CLIPTextModel,
+ text_encoder_2: CLIPTextModelWithProjection,
+ tokenizer: CLIPTokenizer,
+ tokenizer_2: CLIPTokenizer,
+ unet: UNet2DConditionModel,
+ scheduler: KarrasDiffusionSchedulers,
+ force_zeros_for_empty_prompt: bool = True,
+ add_watermarker: Optional[bool] = None,
+ ):
+ super().__init__()
+
+ self.register_modules(
+ vae=vae,
+ text_encoder=text_encoder,
+ text_encoder_2=text_encoder_2,
+ tokenizer=tokenizer,
+ tokenizer_2=tokenizer_2,
+ unet=unet,
+ scheduler=scheduler,
+ )
+ self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
+ self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+ self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+ self.default_sample_size = self.unet.config.sample_size
+
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
+ def enable_vae_slicing(self):
+ r"""
+ Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+ compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+ """
+ self.vae.enable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing
+ def disable_vae_slicing(self):
+ r"""
+ Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_slicing()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling
+ def enable_vae_tiling(self):
+ r"""
+ Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+ compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+ processing larger images.
+ """
+ self.vae.enable_tiling()
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling
+ def disable_vae_tiling(self):
+ r"""
+ Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
+ computing decoding in one step.
+ """
+ self.vae.disable_tiling()
+
+ def enable_model_cpu_offload(self, gpu_id=0):
+ r"""
+ Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
+ to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward`
+ method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
+ `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`.
+ """
+ if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"):
+ from accelerate import cpu_offload_with_hook
+ else:
+ raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")
+
+ device = torch.device(f"cuda:{gpu_id}")
+
+ if self.device.type != "cpu":
+ self.to("cpu", silence_dtype_warnings=True)
+ torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
+
+ model_sequence = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+ model_sequence.extend([self.unet, self.vae])
+
+ hook = None
+ for cpu_offloaded_model in model_sequence:
+ _, hook = cpu_offload_with_hook(cpu_offloaded_model, device, prev_module_hook=hook)
+
+ # We'll offload the last model manually.
+ self.final_offload_hook = hook
+
+ def encode_prompt(
+ self,
+ prompt: str,
+ prompt_2: Optional[str] = None,
+ device: Optional[torch.device] = None,
+ num_images_per_prompt: int = 1,
+ do_classifier_free_guidance: bool = True,
+ negative_prompt: Optional[str] = None,
+ negative_prompt_2: Optional[str] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ lora_scale: Optional[float] = None,
+ ):
+ r"""
+ Encodes the prompt into text encoder hidden states.
+
+ Args:
+ prompt (`str` or `List[str]`, *optional*):
+ prompt to be encoded
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ device: (`torch.device`):
+ torch device
+ num_images_per_prompt (`int`):
+ number of images that should be generated per prompt
+ do_classifier_free_guidance (`bool`):
+ 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ lora_scale (`float`, *optional*):
+ A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+ """
+ device = device or self._execution_device
+
+ # set lora scale so that monkey patched LoRA
+ # function of text encoder can correctly access it
+ if lora_scale is not None and isinstance(self, LoraLoaderMixin):
+ self._lora_scale = lora_scale
+
+ 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]
+
+ # Define tokenizers and text encoders
+ tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
+ text_encoders = (
+ [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
+ )
+
+ if prompt_embeds is None:
+ prompt_2 = prompt_2 or prompt
+ # textual inversion: procecss multi-vector tokens if necessary
+ prompt_embeds_list = []
+ prompts = [prompt, prompt_2]
+ for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ prompt = self.maybe_convert_prompt(prompt, tokenizer)
+
+ text_inputs = tokenizer(
+ prompt,
+ padding="max_length",
+ max_length=tokenizer.model_max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ text_input_ids = text_inputs.input_ids
+ untruncated_ids = 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 = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
+ logger.warning(
+ "The following part of your input was truncated because CLIP can only handle sequences up to"
+ f" {tokenizer.model_max_length} tokens: {removed_text}"
+ )
+
+ prompt_embeds = text_encoder(
+ text_input_ids.to(device),
+ output_hidden_states=True,
+ )
+
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ pooled_prompt_embeds = prompt_embeds[0]
+ prompt_embeds = prompt_embeds.hidden_states[-2]
+
+ prompt_embeds_list.append(prompt_embeds)
+
+ prompt_embeds = torch.concat(prompt_embeds_list, dim=-1)
+
+ # get unconditional embeddings for classifier free guidance
+ zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt
+ if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
+ negative_prompt_embeds = torch.zeros_like(prompt_embeds)
+ negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
+ elif do_classifier_free_guidance and negative_prompt_embeds is None:
+ negative_prompt = negative_prompt or ""
+ negative_prompt_2 = negative_prompt_2 or negative_prompt
+
+ uncond_tokens: List[str]
+ if prompt is not None and type(prompt) is not type(negative_prompt):
+ raise TypeError(
+ f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+ f" {type(prompt)}."
+ )
+ elif isinstance(negative_prompt, str):
+ uncond_tokens = [negative_prompt, negative_prompt_2]
+ 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, negative_prompt_2]
+
+ negative_prompt_embeds_list = []
+ for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders):
+ if isinstance(self, TextualInversionLoaderMixin):
+ negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer)
+
+ max_length = prompt_embeds.shape[1]
+ uncond_input = tokenizer(
+ negative_prompt,
+ padding="max_length",
+ max_length=max_length,
+ truncation=True,
+ return_tensors="pt",
+ )
+
+ negative_prompt_embeds = text_encoder(
+ uncond_input.input_ids.to(device),
+ output_hidden_states=True,
+ )
+ # We are only ALWAYS interested in the pooled output of the final text encoder
+ negative_pooled_prompt_embeds = negative_prompt_embeds[0]
+ negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2]
+
+ negative_prompt_embeds_list.append(negative_prompt_embeds)
+
+ negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1)
+
+ prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device)
+ 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)
+
+ 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_2.dtype, device=device)
+ negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
+ negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+ pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+ if do_classifier_free_guidance:
+ negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view(
+ bs_embed * num_images_per_prompt, -1
+ )
+
+ return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
+
+ # 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
+
+ def check_inputs(
+ self,
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt=None,
+ negative_prompt_2=None,
+ prompt_embeds=None,
+ negative_prompt_embeds=None,
+ pooled_prompt_embeds=None,
+ negative_pooled_prompt_embeds=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}.")
+
+ 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)}."
+ )
+
+ 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_2 is not None and prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `prompt_2`: {prompt_2} 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)}")
+ elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
+ raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
+
+ 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."
+ )
+ elif negative_prompt_2 is not None and negative_prompt_embeds is not None:
+ raise ValueError(
+ f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:"
+ f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+ )
+
+ 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 prompt_embeds is not None and pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+ )
+
+ if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None:
+ raise ValueError(
+ "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`."
+ )
+
+ # 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, height // self.vae_scale_factor, 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)
+
+ # scale the initial noise by the standard deviation required by the scheduler
+ latents = latents * self.scheduler.init_noise_sigma
+ return latents
+
+ def _get_add_time_ids(self, original_size, crops_coords_top_left, target_size, dtype):
+ add_time_ids = list(original_size + crops_coords_top_left + target_size)
+
+ passed_add_embed_dim = (
+ self.unet.config.addition_time_embed_dim * len(add_time_ids) + self.text_encoder_2.config.projection_dim
+ )
+ expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features
+
+ if expected_add_embed_dim != passed_add_embed_dim:
+ raise ValueError(
+ f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`."
+ )
+
+ add_time_ids = torch.tensor([add_time_ids], dtype=dtype)
+ return add_time_ids
+
+ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae
+ def upcast_vae(self):
+ dtype = self.vae.dtype
+ self.vae.to(dtype=torch.float32)
+ use_torch_2_0_or_xformers = isinstance(
+ self.vae.decoder.mid_block.attentions[0].processor,
+ (
+ AttnProcessor2_0,
+ XFormersAttnProcessor,
+ LoRAXFormersAttnProcessor,
+ LoRAAttnProcessor2_0,
+ ),
+ )
+ # if xformers or torch_2_0 is used attention block does not need
+ # to be in float32 which can save lots of memory
+ if use_torch_2_0_or_xformers:
+ self.vae.post_quant_conv.to(dtype)
+ self.vae.decoder.conv_in.to(dtype)
+ self.vae.decoder.mid_block.to(dtype)
+
+ @torch.no_grad()
+ @replace_example_docstring(EXAMPLE_DOC_STRING)
+ def __call__(
+ self,
+ prompt: Union[str, List[str]] = None,
+ prompt_2: Optional[Union[str, List[str]]] = None,
+ height: Optional[int] = None,
+ width: Optional[int] = None,
+ num_inference_steps: int = 50,
+ denoising_end: Optional[float] = None,
+ guidance_scale: float = 5.0,
+ negative_prompt: Optional[Union[str, List[str]]] = None,
+ negative_prompt_2: Optional[Union[str, List[str]]] = None,
+ num_images_per_prompt: Optional[int] = 1,
+ eta: float = 0.0,
+ generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+ latents: Optional[torch.FloatTensor] = None,
+ prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+ pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+ output_type: Optional[str] = "pil",
+ return_dict: bool = True,
+ callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+ callback_steps: int = 1,
+ cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+ guidance_rescale: float = 0.0,
+ original_size: Optional[Tuple[int, int]] = None,
+ crops_coords_top_left: Tuple[int, int] = (0, 0),
+ target_size: Optional[Tuple[int, int]] = None,
+ resolutions_list: Optional[Union[int, List[int]]] = None,
+ restart_steps: Optional[Union[int, List[int]]] = None,
+ cosine_scale: float = 2.0,
+ cosine_scale_bg: float = 1.0,
+ dilate_tau: int = 35,
+ img_path: Optional[str] = "",
+ mask_path: Optional[str] = "",
+ ):
+ r"""
+ Function invoked when calling the pipeline for generation.
+
+ 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.
+ prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+ used in both text-encoders
+ 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.
+ denoising_end (`float`, *optional*):
+ When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
+ completed before it is intentionally prematurely terminated. As a result, the returned sample will
+ still retain a substantial amount of noise as determined by the discrete timesteps selected by the
+ scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
+ "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
+ Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
+ guidance_scale (`float`, *optional*, defaults to 5.0):
+ 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
+ `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+ less than `1`).
+ negative_prompt_2 (`str` or `List[str]`, *optional*):
+ The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
+ `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
+ 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) 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.
+ latents (`torch.FloatTensor`, *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
+ tensor will ge generated by sampling using the supplied random `generator`.
+ prompt_embeds (`torch.FloatTensor`, *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.FloatTensor`, *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.
+ pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
+ If not provided, pooled text embeddings will be generated from `prompt` input argument.
+ negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
+ Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+ weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
+ input argument.
+ output_type (`str`, *optional*, defaults to `"pil"`):
+ The output format of the generate image. Choose between
+ [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+ return_dict (`bool`, *optional*, defaults to `True`):
+ Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
+ of a plain tuple.
+ callback (`Callable`, *optional*):
+ 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.FloatTensor)`.
+ 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.
+ cross_attention_kwargs (`dict`, *optional*):
+ A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+ `self.processor` in
+ [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+ guidance_rescale (`float`, *optional*, defaults to 0.7):
+ Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
+ Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
+ [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
+ Guidance rescale factor should fix overexposure when using zero terminal SNR.
+ original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
+ `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
+ explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
+ `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
+ `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
+ `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
+ [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+ target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
+ For most cases, `target_size` should be set to the desired height and width of the generated image. If
+ not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
+ section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
+
+ Examples:
+
+ Returns:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
+ [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
+ `tuple`. When returning a tuple, the first element is a list with the generated images.
+ """
+
+
+ # 0. Default height and width to unet
+ if resolutions_list:
+ height, width = resolutions_list[0]
+ target_sizes = resolutions_list[1:]
+ if not restart_steps:
+ restart_steps = [15] * len(target_sizes)
+ else:
+ height = height or self.default_sample_size * self.vae_scale_factor
+ width = width or self.default_sample_size * self.vae_scale_factor
+
+ original_size = original_size or (height, width)
+ target_size = target_size or (height, width)
+
+ # 1. Check inputs. Raise error if not correct
+ self.check_inputs(
+ prompt,
+ prompt_2,
+ height,
+ width,
+ callback_steps,
+ negative_prompt,
+ negative_prompt_2,
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ )
+
+ # 2. Define call parameters
+ if prompt is not None and isinstance(prompt, str):
+ batch_size = 1
+ elif prompt is not None and isinstance(prompt, list):
+ batch_size = len(prompt)
+ else:
+ batch_size = prompt_embeds.shape[0]
+
+ device = self._execution_device
+
+ # 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 prompt
+ text_encoder_lora_scale = (
+ cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+ )
+ (
+ prompt_embeds,
+ negative_prompt_embeds,
+ pooled_prompt_embeds,
+ negative_pooled_prompt_embeds,
+ ) = self.encode_prompt(
+ prompt=prompt,
+ prompt_2=prompt_2,
+ device=device,
+ num_images_per_prompt=num_images_per_prompt,
+ do_classifier_free_guidance=do_classifier_free_guidance,
+ negative_prompt=negative_prompt,
+ negative_prompt_2=negative_prompt_2,
+ prompt_embeds=prompt_embeds,
+ negative_prompt_embeds=negative_prompt_embeds,
+ pooled_prompt_embeds=pooled_prompt_embeds,
+ negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+ lora_scale=text_encoder_lora_scale,
+ )
+
+ # 4. Prepare timesteps
+ self.scheduler.set_timesteps(num_inference_steps, device=device)
+
+ timesteps = self.scheduler.timesteps
+
+ # 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,
+ prompt_embeds.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)
+
+ # 7. Prepare added time ids & embeddings
+ add_text_embeds = pooled_prompt_embeds
+ add_time_ids = self._get_add_time_ids(
+ original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype
+ )
+
+ if do_classifier_free_guidance:
+ prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+ add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
+ add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0)
+
+ prompt_embeds = prompt_embeds.to(device)
+ add_text_embeds = add_text_embeds.to(device)
+ add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
+
+ # 8. Denoising loop
+ num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
+
+ # 9.1 Apply denoising_end
+ if denoising_end is not None and type(denoising_end) == float and denoising_end > 0 and denoising_end < 1:
+ discrete_timestep_cutoff = int(
+ round(
+ self.scheduler.config.num_train_timesteps
+ - (denoising_end * self.scheduler.config.num_train_timesteps)
+ )
+ )
+ num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
+ timesteps = timesteps[:num_inference_steps]
+
+ results_list = []
+
+ for block in self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks:
+ for module in block.modules():
+ if isinstance(module, BasicTransformerBlock):
+ module.forward = ori_forward.__get__(module, BasicTransformerBlock)
+
+ if img_path != '':
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+ input_image = process_image_to_tensor(img_path).unsqueeze(0).to(dtype=self.vae.dtype, device=device)
+ latents = self.vae.encode(input_image).latent_dist.sample().to(self.vae.dtype)
+ latents = latents * self.vae.config.scaling_factor
+ else:
+ 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
+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ cross_attention_kwargs=cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # 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)
+
+ if do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, 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):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ callback(i, t, latents)
+
+ results_list.append(latents)
+
+ if mask_path != '':
+ mask = process_image_to_bitensor(mask_path).unsqueeze(0)
+
+ for restart_index, target_size in enumerate(target_sizes):
+ restart_step = restart_steps[restart_index]
+ target_size_ = [target_size[0]//8, target_size[1]//8]
+
+ for block in self.unet.down_blocks + [self.unet.mid_block] + self.unet.up_blocks:
+ for module in block.modules():
+ if isinstance(module, BasicTransformerBlock):
+ module.forward = scale_forward.__get__(module, BasicTransformerBlock)
+ module.current_hw = target_size
+
+ needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
+ if needs_upcasting:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ latents = latents / self.vae.config.scaling_factor
+ image = self.vae.decode(latents, return_dict=False)[0]
+ image = torch.nn.functional.interpolate(
+ image,
+ size=target_size,
+ mode='bicubic',
+ )
+ latents = self.vae.encode(image).latent_dist.sample().to(self.vae.dtype)
+ latents = latents * self.vae.config.scaling_factor
+
+ if mask_path != '':
+ mask_ = torch.nn.functional.interpolate(
+ mask,
+ size=target_size_,
+ mode="nearest",
+ ).to(device)
+
+ noise_latents = []
+ noise = torch.randn_like(latents)
+ for timestep in self.scheduler.timesteps:
+ noise_latent = self.scheduler.add_noise(latents, noise, timestep.unsqueeze(0))
+ noise_latents.append(noise_latent)
+ latents = noise_latents[restart_step]
+
+ self.scheduler._step_index = 0
+ with self.progress_bar(total=num_inference_steps) as progress_bar:
+ for i, t in enumerate(timesteps):
+
+ if i < restart_step:
+ self.scheduler._step_index += 1
+ progress_bar.update()
+ continue
+
+ cosine_factor = 0.5 * (1 + torch.cos(torch.pi * (self.scheduler.config.num_train_timesteps - t) / self.scheduler.config.num_train_timesteps)).cpu()
+ if mask_path != '':
+ c1 = (cosine_factor ** (mask_ * cosine_scale + (1-mask_) * cosine_scale_bg)).to(dtype=torch.float16)
+ else:
+ c1 = cosine_factor ** cosine_scale
+ latents = latents * (1 - c1) + noise_latents[i] * c1
+
+ dilate_coef=target_size[1]//1024
+
+ dilate_layers = [
+ # "down_blocks.1.resnets.0.conv1",
+ # "down_blocks.1.resnets.0.conv2",
+ # "down_blocks.1.resnets.1.conv1",
+ # "down_blocks.1.resnets.1.conv2",
+ "down_blocks.1.downsamplers.0.conv",
+ "down_blocks.2.resnets.0.conv1",
+ "down_blocks.2.resnets.0.conv2",
+ "down_blocks.2.resnets.1.conv1",
+ "down_blocks.2.resnets.1.conv2",
+ # "up_blocks.0.resnets.0.conv1",
+ # "up_blocks.0.resnets.0.conv2",
+ # "up_blocks.0.resnets.1.conv1",
+ # "up_blocks.0.resnets.1.conv2",
+ # "up_blocks.0.resnets.2.conv1",
+ # "up_blocks.0.resnets.2.conv2",
+ # "up_blocks.0.upsamplers.0.conv",
+ # "up_blocks.1.resnets.0.conv1",
+ # "up_blocks.1.resnets.0.conv2",
+ # "up_blocks.1.resnets.1.conv1",
+ # "up_blocks.1.resnets.1.conv2",
+ # "up_blocks.1.resnets.2.conv1",
+ # "up_blocks.1.resnets.2.conv2",
+ # "up_blocks.1.upsamplers.0.conv",
+ # "up_blocks.2.resnets.0.conv1",
+ # "up_blocks.2.resnets.0.conv2",
+ # "up_blocks.2.resnets.1.conv1",
+ # "up_blocks.2.resnets.1.conv2",
+ # "up_blocks.2.resnets.2.conv1",
+ # "up_blocks.2.resnets.2.conv2",
+ "mid_block.resnets.0.conv1",
+ "mid_block.resnets.0.conv2",
+ "mid_block.resnets.1.conv1",
+ "mid_block.resnets.1.conv2"
+ ]
+
+ for name, module in self.unet.named_modules():
+ if name in dilate_layers:
+ if i < dilate_tau:
+ module.dilation = (dilate_coef, dilate_coef)
+ module.padding = (dilate_coef, dilate_coef)
+ else:
+ module.dilation = (1, 1)
+ module.padding = (1, 1)
+
+ # 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
+ added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
+ noise_pred = self.unet(
+ latent_model_input,
+ t,
+ encoder_hidden_states=prompt_embeds,
+ cross_attention_kwargs=cross_attention_kwargs,
+ added_cond_kwargs=added_cond_kwargs,
+ return_dict=False,
+ )[0]
+
+ # 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)
+
+ if do_classifier_free_guidance and guidance_rescale > 0.0:
+ # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+ noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
+
+ # compute the previous noisy sample x_t -> x_t-1
+ latents_dtype = latents.dtype
+ latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+ if latents.dtype != latents_dtype:
+ if torch.backends.mps.is_available():
+ # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
+ latents = latents.to(latents_dtype)
+
+ # call the callback, if provided
+ if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+ progress_bar.update()
+ if callback is not None and i % callback_steps == 0:
+ callback(i, t, latents)
+
+ for name, module in self.unet.named_modules():
+ # if ('.conv' in name) and ('.conv_' not in name):
+ if name in dilate_layers:
+ module.dilation = (1, 1)
+ module.padding = (1, 1)
+
+ results_list.append(latents)
+
+ """
+ final_results = []
+ for latents in results_list:
+ # make sure the VAE is in float32 mode, as it overflows in float16
+ if self.vae.dtype == torch.float16 and self.vae.config.force_upcast:
+ self.upcast_vae()
+ latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
+
+ if not output_type == "latent":
+ image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+ else:
+ image = latents
+ return StableDiffusionXLPipelineOutput(images=image)
+
+ # apply watermark if available
+ if self.watermark is not None:
+ image = self.watermark.apply_watermark(image)
+
+ image = self.image_processor.postprocess(image, output_type=output_type)
+
+ if not return_dict:
+ final_results += [(image,)]
+ else:
+ final_results += [StableDiffusionXLPipelineOutput(images=image)]
+
+ # Offload last model to CPU
+ if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+ self.final_offload_hook.offload()
+
+ return final_results
+ """
+ return StableDiffusionXLPipelineOutput(images=results_list)
+
+ # Overrride to properly handle the loading and unloading of the additional text encoder.
+ def load_lora_weights(self, pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]], **kwargs):
+ # We could have accessed the unet config from `lora_state_dict()` too. We pass
+ # it here explicitly to be able to tell that it's coming from an SDXL
+ # pipeline.
+ state_dict, network_alphas = self.lora_state_dict(
+ pretrained_model_name_or_path_or_dict,
+ unet_config=self.unet.config,
+ **kwargs,
+ )
+ self.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=self.unet)
+
+ text_encoder_state_dict = {k: v for k, v in state_dict.items() if "text_encoder." in k}
+ if len(text_encoder_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder,
+ prefix="text_encoder",
+ lora_scale=self.lora_scale,
+ )
+
+ text_encoder_2_state_dict = {k: v for k, v in state_dict.items() if "text_encoder_2." in k}
+ if len(text_encoder_2_state_dict) > 0:
+ self.load_lora_into_text_encoder(
+ text_encoder_2_state_dict,
+ network_alphas=network_alphas,
+ text_encoder=self.text_encoder_2,
+ prefix="text_encoder_2",
+ lora_scale=self.lora_scale,
+ )
+
+ @classmethod
+ def save_lora_weights(
+ self,
+ save_directory: Union[str, os.PathLike],
+ unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
+ is_main_process: bool = True,
+ weight_name: str = None,
+ save_function: Callable = None,
+ safe_serialization: bool = True,
+ ):
+ state_dict = {}
+
+ def pack_weights(layers, prefix):
+ layers_weights = layers.state_dict() if isinstance(layers, torch.nn.Module) else layers
+ layers_state_dict = {f"{prefix}.{module_name}": param for module_name, param in layers_weights.items()}
+ return layers_state_dict
+
+ state_dict.update(pack_weights(unet_lora_layers, "unet"))
+
+ if text_encoder_lora_layers and text_encoder_2_lora_layers:
+ state_dict.update(pack_weights(text_encoder_lora_layers, "text_encoder"))
+ state_dict.update(pack_weights(text_encoder_2_lora_layers, "text_encoder_2"))
+
+ self.write_lora_layers(
+ state_dict=state_dict,
+ save_directory=save_directory,
+ is_main_process=is_main_process,
+ weight_name=weight_name,
+ save_function=save_function,
+ safe_serialization=safe_serialization,
+ )
+
+ def _remove_text_encoder_monkey_patch(self):
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder)
+ self._remove_text_encoder_monkey_patch_classmethod(self.text_encoder_2)
diff --git a/modules/freescale/scale_attention.py b/modules/freescale/scale_attention.py
new file mode 100644
index 000000000..9e83d5067
--- /dev/null
+++ b/modules/freescale/scale_attention.py
@@ -0,0 +1,367 @@
+from typing import Any, Dict, Optional
+import random
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+
+
+def gaussian_kernel(kernel_size=3, sigma=1.0, channels=3):
+ x_coord = torch.arange(kernel_size)
+ gaussian_1d = torch.exp(-(x_coord - (kernel_size - 1) / 2) ** 2 / (2 * sigma ** 2))
+ gaussian_1d = gaussian_1d / gaussian_1d.sum()
+ gaussian_2d = gaussian_1d[:, None] * gaussian_1d[None, :]
+ kernel = gaussian_2d[None, None, :, :].repeat(channels, 1, 1, 1)
+
+ return kernel
+
+def gaussian_filter(latents, kernel_size=3, sigma=1.0):
+ channels = latents.shape[1]
+ kernel = gaussian_kernel(kernel_size, sigma, channels).to(latents.device, latents.dtype)
+ blurred_latents = F.conv2d(latents, kernel, padding=kernel_size//2, groups=channels)
+
+ return blurred_latents
+
+def get_views(height, width, h_window_size=128, w_window_size=128, scale_factor=8):
+ height = int(height)
+ width = int(width)
+ h_window_stride = h_window_size // 2
+ w_window_stride = w_window_size // 2
+ h_window_size = int(h_window_size / scale_factor)
+ w_window_size = int(w_window_size / scale_factor)
+ h_window_stride = int(h_window_stride / scale_factor)
+ w_window_stride = int(w_window_stride / scale_factor)
+ num_blocks_height = int((height - h_window_size) / h_window_stride - 1e-6) + 2 if height > h_window_size else 1
+ num_blocks_width = int((width - w_window_size) / w_window_stride - 1e-6) + 2 if width > w_window_size else 1
+ total_num_blocks = int(num_blocks_height * num_blocks_width)
+ views = []
+ for i in range(total_num_blocks):
+ h_start = int((i // num_blocks_width) * h_window_stride)
+ h_end = h_start + h_window_size
+ w_start = int((i % num_blocks_width) * w_window_stride)
+ w_end = w_start + w_window_size
+
+ if h_end > height:
+ h_start = int(h_start + height - h_end)
+ h_end = int(height)
+ if w_end > width:
+ w_start = int(w_start + width - w_end)
+ w_end = int(width)
+ if h_start < 0:
+ h_end = int(h_end - h_start)
+ h_start = 0
+ if w_start < 0:
+ w_end = int(w_end - w_start)
+ w_start = 0
+
+ random_jitter = True
+ if random_jitter:
+ h_jitter_range = h_window_size // 8
+ w_jitter_range = w_window_size // 8
+ h_jitter = 0
+ w_jitter = 0
+
+ if (w_start != 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, w_jitter_range)
+ elif (w_start == 0) and (w_end != width):
+ w_jitter = random.randint(-w_jitter_range, 0)
+ elif (w_start != 0) and (w_end == width):
+ w_jitter = random.randint(0, w_jitter_range)
+ if (h_start != 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, h_jitter_range)
+ elif (h_start == 0) and (h_end != height):
+ h_jitter = random.randint(-h_jitter_range, 0)
+ elif (h_start != 0) and (h_end == height):
+ h_jitter = random.randint(0, h_jitter_range)
+ h_start += (h_jitter + h_jitter_range)
+ h_end += (h_jitter + h_jitter_range)
+ w_start += (w_jitter + w_jitter_range)
+ w_end += (w_jitter + w_jitter_range)
+
+ views.append((h_start, h_end, w_start, w_end))
+ return views
+
+def scale_forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+):
+ # Notice that normalization is always applied before the real computation in the following blocks.
+ if self.current_hw:
+ current_scale_num_h, current_scale_num_w = max(self.current_hw[0] // 1024, 1), max(self.current_hw[1] // 1024, 1)
+ else:
+ current_scale_num_h, current_scale_num_w = 1, 1
+
+ # 0. Self-Attention
+ if self.use_ada_layer_norm:
+ norm_hidden_states = self.norm1(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero:
+ norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+ hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+ )
+ else:
+ norm_hidden_states = self.norm1(hidden_states)
+
+ # 2. Prepare GLIGEN inputs
+ cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+ gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+ ratio_hw = current_scale_num_h / current_scale_num_w
+ latent_h = int((norm_hidden_states.shape[1] * ratio_hw) ** 0.5)
+ latent_w = int(latent_h / ratio_hw)
+ scale_factor = 128 * current_scale_num_h / latent_h
+ if ratio_hw > 1:
+ sub_h = 128
+ sub_w = int(128 / ratio_hw)
+ else:
+ sub_h = int(128 * ratio_hw)
+ sub_w = 128
+
+ h_jitter_range = int(sub_h / scale_factor // 8)
+ w_jitter_range = int(sub_w / scale_factor // 8)
+ views = get_views(latent_h, latent_w, sub_h, sub_w, scale_factor = scale_factor)
+
+ current_scale_num = max(current_scale_num_h, current_scale_num_w)
+ global_views = [[h, w] for h in range(current_scale_num_h) for w in range(current_scale_num_w)]
+
+ four_window = True
+ fourg_window = False
+
+ if four_window:
+ norm_hidden_states_ = rearrange(norm_hidden_states, 'bh (h w) d -> bh h w d', h = latent_h)
+ norm_hidden_states_ = F.pad(norm_hidden_states_, (0, 0, w_jitter_range, w_jitter_range, h_jitter_range, h_jitter_range), 'constant', 0)
+ value = torch.zeros_like(norm_hidden_states_)
+ count = torch.zeros_like(norm_hidden_states_)
+ for index, view in enumerate(views):
+ h_start, h_end, w_start, w_end = view
+ local_states = norm_hidden_states_[:, h_start:h_end, w_start:w_end, :]
+ local_states = rearrange(local_states, 'bh h w d -> bh (h w) d')
+ local_output = self.attn1(
+ local_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ local_output = rearrange(local_output, 'bh (h w) d -> bh h w d', h = int(sub_h / scale_factor))
+
+ value[:, h_start:h_end, w_start:w_end, :] += local_output * 1
+ count[:, h_start:h_end, w_start:w_end, :] += 1
+
+ value = value[:, h_jitter_range:-h_jitter_range, w_jitter_range:-w_jitter_range, :]
+ count = count[:, h_jitter_range:-h_jitter_range, w_jitter_range:-w_jitter_range, :]
+ attn_output = torch.where(count>0, value/count, value)
+
+ gaussian_local = gaussian_filter(attn_output, kernel_size=(2*current_scale_num-1), sigma=1.0)
+
+ attn_output_global = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ attn_output_global = rearrange(attn_output_global, 'bh (h w) d -> bh h w d', h = latent_h)
+
+ gaussian_global = gaussian_filter(attn_output_global, kernel_size=(2*current_scale_num-1), sigma=1.0)
+
+ attn_output = gaussian_local + (attn_output_global - gaussian_global)
+ attn_output = rearrange(attn_output, 'bh h w d -> bh (h w) d')
+
+ elif fourg_window:
+ norm_hidden_states = rearrange(norm_hidden_states, 'bh (h w) d -> bh h w d', h = latent_h)
+ norm_hidden_states_ = F.pad(norm_hidden_states, (0, 0, w_jitter_range, w_jitter_range, h_jitter_range, h_jitter_range), 'constant', 0)
+ value = torch.zeros_like(norm_hidden_states_)
+ count = torch.zeros_like(norm_hidden_states_)
+ for index, view in enumerate(views):
+ h_start, h_end, w_start, w_end = view
+ local_states = norm_hidden_states_[:, h_start:h_end, w_start:w_end, :]
+ local_states = rearrange(local_states, 'bh h w d -> bh (h w) d')
+ local_output = self.attn1(
+ local_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ local_output = rearrange(local_output, 'bh (h w) d -> bh h w d', h = int(sub_h / scale_factor))
+
+ value[:, h_start:h_end, w_start:w_end, :] += local_output * 1
+ count[:, h_start:h_end, w_start:w_end, :] += 1
+
+ value = value[:, h_jitter_range:-h_jitter_range, w_jitter_range:-w_jitter_range, :]
+ count = count[:, h_jitter_range:-h_jitter_range, w_jitter_range:-w_jitter_range, :]
+ attn_output = torch.where(count>0, value/count, value)
+
+ gaussian_local = gaussian_filter(attn_output, kernel_size=(2*current_scale_num-1), sigma=1.0)
+
+ value = torch.zeros_like(norm_hidden_states)
+ count = torch.zeros_like(norm_hidden_states)
+ for index, global_view in enumerate(global_views):
+ h, w = global_view
+ global_states = norm_hidden_states[:, h::current_scale_num_h, w::current_scale_num_w, :]
+ global_states = rearrange(global_states, 'bh h w d -> bh (h w) d')
+ global_output = self.attn1(
+ global_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+ global_output = rearrange(global_output, 'bh (h w) d -> bh h w d', h = int(global_output.shape[1] ** 0.5))
+
+ value[:, h::current_scale_num_h, w::current_scale_num_w, :] += global_output * 1
+ count[:, h::current_scale_num_h, w::current_scale_num_w, :] += 1
+
+ attn_output_global = torch.where(count>0, value/count, value)
+
+ gaussian_global = gaussian_filter(attn_output_global, kernel_size=(2*current_scale_num-1), sigma=1.0)
+
+ attn_output = gaussian_local + (attn_output_global - gaussian_global)
+ attn_output = rearrange(attn_output, 'bh h w d -> bh (h w) d')
+
+ else:
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+
+ if self.use_ada_layer_norm_zero:
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ hidden_states = attn_output + hidden_states
+
+ # 2.5 GLIGEN Control
+ if gligen_kwargs is not None:
+ hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+ # 2.5 ends
+
+ # 3. Cross-Attention
+ if self.attn2 is not None:
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+ )
+ attn_output = self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ **cross_attention_kwargs,
+ )
+ hidden_states = attn_output + hidden_states
+
+ # 4. Feed-forward
+ norm_hidden_states = self.norm3(hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+ if self._chunk_size is not None:
+ # "feed_forward_chunk_size" can be used to save memory
+ if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+ raise ValueError(
+ f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+ )
+
+ num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+ ff_output = torch.cat(
+ [
+ self.ff(hid_slice)
+ for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
+ ],
+ dim=self._chunk_dim,
+ )
+ else:
+ ff_output = self.ff(norm_hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+ hidden_states = ff_output + hidden_states
+
+ return hidden_states
+
+def ori_forward(
+ self,
+ hidden_states: torch.FloatTensor,
+ attention_mask: Optional[torch.FloatTensor] = None,
+ encoder_hidden_states: Optional[torch.FloatTensor] = None,
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
+ timestep: Optional[torch.LongTensor] = None,
+ cross_attention_kwargs: Dict[str, Any] = None,
+ class_labels: Optional[torch.LongTensor] = None,
+):
+ # Notice that normalization is always applied before the real computation in the following blocks.
+ # 0. Self-Attention
+ if self.use_ada_layer_norm:
+ norm_hidden_states = self.norm1(hidden_states, timestep)
+ elif self.use_ada_layer_norm_zero:
+ norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+ hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+ )
+ else:
+ norm_hidden_states = self.norm1(hidden_states)
+
+ # 2. Prepare GLIGEN inputs
+ cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}
+ gligen_kwargs = cross_attention_kwargs.pop("gligen", None)
+
+ attn_output = self.attn1(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+ attention_mask=attention_mask,
+ **cross_attention_kwargs,
+ )
+
+ if self.use_ada_layer_norm_zero:
+ attn_output = gate_msa.unsqueeze(1) * attn_output
+ hidden_states = attn_output + hidden_states
+
+ # 2.5 GLIGEN Control
+ if gligen_kwargs is not None:
+ hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"])
+ # 2.5 ends
+
+ # 3. Cross-Attention
+ if self.attn2 is not None:
+ norm_hidden_states = (
+ self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+ )
+ attn_output = self.attn2(
+ norm_hidden_states,
+ encoder_hidden_states=encoder_hidden_states,
+ attention_mask=encoder_attention_mask,
+ **cross_attention_kwargs,
+ )
+ hidden_states = attn_output + hidden_states
+
+ # 4. Feed-forward
+ norm_hidden_states = self.norm3(hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+ if self._chunk_size is not None:
+ # "feed_forward_chunk_size" can be used to save memory
+ if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
+ raise ValueError(
+ f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+ )
+
+ num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
+ ff_output = torch.cat(
+ [
+ self.ff(hid_slice)
+ for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
+ ],
+ dim=self._chunk_dim,
+ )
+ else:
+ ff_output = self.ff(norm_hidden_states)
+
+ if self.use_ada_layer_norm_zero:
+ ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+ hidden_states = ff_output + hidden_states
+
+ return hidden_states
diff --git a/modules/processing_diffusers.py b/modules/processing_diffusers.py
index 3b6f228ba..adb047511 100644
--- a/modules/processing_diffusers.py
+++ b/modules/processing_diffusers.py
@@ -365,13 +365,26 @@ def process_decode(p: processing.StableDiffusionProcessing, output):
else:
width = getattr(p, 'width', 0)
height = getattr(p, 'height', 0)
- results = processing_vae.vae_decode(
- latents = output.images,
- model = model,
- full_quality = p.full_quality,
- width = width,
- height = height,
- )
+ if isinstance(output.images, list):
+ results = []
+ for i in range(len(output.images)):
+ result_batch = processing_vae.vae_decode(
+ latents = output.images[i],
+ model = model,
+ full_quality = p.full_quality,
+ width = width,
+ height = height,
+ )
+ for result in list(result_batch):
+ results.append(result)
+ else:
+ results = processing_vae.vae_decode(
+ latents = output.images,
+ model = model,
+ full_quality = p.full_quality,
+ width = width,
+ height = height,
+ )
elif hasattr(output, 'images'):
results = output.images
else:
diff --git a/modules/sd_samplers_common.py b/modules/sd_samplers_common.py
index cd51043c7..723f7b181 100644
--- a/modules/sd_samplers_common.py
+++ b/modules/sd_samplers_common.py
@@ -40,7 +40,6 @@ def single_sample_to_image(sample, approximation=None):
if approximation is None:
warn_once('Unknown decode type')
approximation = 0
- # normal sample is [4,64,64]
try:
if sample.dtype == torch.bfloat16 and (approximation == 0 or approximation == 1):
sample = sample.to(torch.float16)
@@ -62,6 +61,9 @@ def single_sample_to_image(sample, approximation=None):
sample = sample * (5 / abs(sample_min))
"""
if approximation == 2: # TAESD
+ if sample.shape[-1] > 128 or sample.shape[-2] > 128:
+ scale = 128 / max(sample.shape[-1], sample.shape[-2])
+ sample = torch.nn.functional.interpolate(sample.unsqueeze(0), scale_factor=[scale, scale], mode='bilinear', align_corners=False)[0]
x_sample = sd_vae_taesd.decode(sample)
x_sample = (1.0 + x_sample) / 2.0 # preview requires smaller range
elif shared.sd_model_type == 'sc' and approximation != 3:
diff --git a/modules/sd_vae_taesd.py b/modules/sd_vae_taesd.py
index 4d213ad48..a1959817c 100644
--- a/modules/sd_vae_taesd.py
+++ b/modules/sd_vae_taesd.py
@@ -169,6 +169,9 @@ def decode(latents):
if vae is None:
return latents
try:
+ size = max(latents.shape[-1], latents.shape[-2])
+ if size > 256:
+ return latents
with devices.inference_context():
latents = latents.detach().clone().to(devices.device, dtype)
if len(latents.shape) == 3:
diff --git a/modules/shared.py b/modules/shared.py
index eaf4b361d..90dbe2647 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -873,6 +873,7 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
options_templates.update(options_section(('control', "Control Options"), {
"control_max_units": OptionInfo(4, "Maximum number of units", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
+ "control_tiles": OptionInfo("1x1, 1x2, 1x3, 1x4, 2x1, 2x1, 2x2, 2x3, 2x4, 3x1, 3x2, 3x3, 3x4, 4x1, 4x2, 4x3, 4x4", "Tiling options"),
"control_move_processor": OptionInfo(False, "Processor move to CPU after use"),
"control_unload_processor": OptionInfo(False, "Processor unload after use"),
}))
diff --git a/modules/shared_state.py b/modules/shared_state.py
index 3d3cb1ae6..a3312ec33 100644
--- a/modules/shared_state.py
+++ b/modules/shared_state.py
@@ -141,9 +141,9 @@ class State:
if self.job == 'VAE': # avoid generating preview while vae is running
return
from modules.shared import opts, cmd_opts
- if cmd_opts.lowvram or self.api:
+ if cmd_opts.lowvram or self.api or not opts.live_previews_enable or opts.show_progress_every_n_steps <= 0:
return
- if abs(self.sampling_step - self.current_image_sampling_step) >= opts.show_progress_every_n_steps and opts.live_previews_enable and opts.show_progress_every_n_steps > 0:
+ if abs(self.sampling_step - self.current_image_sampling_step) >= opts.show_progress_every_n_steps:
self.do_set_current_image()
def do_set_current_image(self):
diff --git a/modules/ui_control.py b/modules/ui_control.py
index 5a146a8fc..7baf74d75 100644
--- a/modules/ui_control.py
+++ b/modules/ui_control.py
@@ -254,7 +254,7 @@ def create_ui(_blocks: gr.Blocks=None):
control_start = gr.Slider(label="CN Start", minimum=0.0, maximum=1.0, step=0.05, value=0, elem_id=f'control_unit-{i}-start')
control_end = gr.Slider(label="CN End", minimum=0.0, maximum=1.0, step=0.05, value=1.0, elem_id=f'control_unit-{i}-end')
control_mode = gr.Dropdown(label="CN Mode", choices=['default'], value='default', visible=False, elem_id=f'control_unit-{i}-mode')
- control_tile = gr.Dropdown(label="CN Tiles", choices=['1x1', '1x2', '1x3', '1x4', '2x1', '2x1', '2x2', '2x3', '2x4', '3x1', '3x2', '3x3', '3x4', '4x1', '4x2', '4x3', '4x4'], value='1x1', visible=False, elem_id=f'control_unit-{i}-tile')
+ control_tile = gr.Dropdown(label="CN Tiles", choices=[x.strip() for x in shared.opts.control_tiles.split(',') if 'x' in x], value='1x1', visible=False, elem_id=f'control_unit-{i}-tile')
reset_btn = ui_components.ToolButton(value=ui_symbols.reset)
image_upload = gr.UploadButton(label=ui_symbols.upload, file_types=['image'], elem_classes=['form', 'gradio-button', 'tool'])
image_reuse= ui_components.ToolButton(value=ui_symbols.reuse)
diff --git a/scripts/freescale.py b/scripts/freescale.py
new file mode 100644
index 000000000..672ceea41
--- /dev/null
+++ b/scripts/freescale.py
@@ -0,0 +1,130 @@
+import gradio as gr
+from modules import scripts, processing, shared, sd_models
+
+
+registered = False
+
+
+class Script(scripts.Script):
+ def __init__(self):
+ super().__init__()
+ self.orig_pipe = None
+ self.orig_slice = None
+ self.orig_tile = None
+ self.is_img2img = False
+
+ def title(self):
+ return 'FreeScale: Tuning-Free Scale Fusion'
+
+ def show(self, is_img2img):
+ self.is_img2img = is_img2img
+ return shared.native
+
+ def ui(self, _is_img2img): # ui elements
+ with gr.Row():
+ gr.HTML(' FreeScale: Tuning-Free Scale Fusion
')
+ with gr.Row():
+ cosine_scale = gr.Slider(minimum=0.1, maximum=5.0, value=2.0, label='Cosine scale')
+ override_sampler = gr.Checkbox(value=True, label='Override sampler')
+ with gr.Row(visible=self.is_img2img):
+ cosine_scale_bg = gr.Slider(minimum=0.1, maximum=5.0, value=1.0, label='Cosine Background')
+ dilate_tau = gr.Slider(minimum=1, maximum=100, value=35, label='Dilate tau')
+ with gr.Row():
+ s1_enable = gr.Checkbox(value=True, label='1st Stage', interactive=False)
+ s1_scale = gr.Slider(minimum=1, maximum=8.0, value=1.0, label='Scale')
+ s1_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
+ with gr.Row():
+ s2_enable = gr.Checkbox(value=True, label='2nd Stage')
+ s2_scale = gr.Slider(minimum=1, maximum=8.0, value=2.0, label='Scale')
+ s2_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
+ with gr.Row():
+ s3_enable = gr.Checkbox(value=False, label='3rd Stage')
+ s3_scale = gr.Slider(minimum=1, maximum=8.0, value=3.0, label='Scale')
+ s3_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
+ with gr.Row():
+ s4_enable = gr.Checkbox(value=False, label='4th Stage')
+ s4_scale = gr.Slider(minimum=1, maximum=8.0, value=4.0, label='Scale')
+ s4_restart = gr.Slider(minimum=0, maximum=1.0, value=0.75, label='Restart step')
+ return [cosine_scale, override_sampler, cosine_scale_bg, dilate_tau, s1_enable, s1_scale, s1_restart, s2_enable, s2_scale, s2_restart, s3_enable, s3_scale, s3_restart, s4_enable, s4_scale, s4_restart]
+
+ def run(self, p: processing.StableDiffusionProcessing, cosine_scale, override_sampler, cosine_scale_bg, dilate_tau, s1_enable, s1_scale, s1_restart, s2_enable, s2_scale, s2_restart, s3_enable, s3_scale, s3_restart, s4_enable, s4_scale, s4_restart): # pylint: disable=arguments-differ
+ supported_model_list = ['sdxl']
+ if shared.sd_model_type not in supported_model_list:
+ shared.log.warning(f'FreeScale: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
+ return None
+
+ if self.is_img2img:
+ if p.init_images is None or len(p.init_images) == 0:
+ shared.log.warning('FreeScale: missing input image')
+ return None
+
+ from modules.freescale import StableDiffusionXLFreeScale, StableDiffusionXLFreeScaleImg2Img
+ self.orig_pipe = shared.sd_model
+ self.orig_slice = shared.opts.diffusers_vae_slicing
+ self.orig_tile = shared.opts.diffusers_vae_tiling
+
+ def scale(x):
+ if (p.width == 0 or p.height == 0) and p.init_images is not None:
+ p.width, p.height = p.init_images[0].width, p.init_images[0].height
+ resolution = [int(8 * p.width * x // 8), int(8 * p.height * x // 8)]
+ return resolution
+
+ scales = []
+ resolutions_list = []
+ restart_steps = []
+ if s1_enable:
+ scales.append(s1_scale)
+ resolutions_list.append(scale(s1_scale))
+ restart_steps.append(int(p.steps * s1_restart))
+ if s2_enable and s2_scale > s1_scale:
+ scales.append(s2_scale)
+ resolutions_list.append(scale(s2_scale))
+ restart_steps.append(int(p.steps * s2_restart))
+ if s3_enable and s3_scale > s2_scale:
+ scales.append(s3_scale)
+ resolutions_list.append(scale(s3_scale))
+ restart_steps.append(int(p.steps * s3_restart))
+ if s4_enable and s4_scale > s3_scale:
+ scales.append(s4_scale)
+ resolutions_list.append(scale(s4_scale))
+ restart_steps.append(int(p.steps * s4_restart))
+
+ p.task_args['resolutions_list'] = resolutions_list
+ p.task_args['cosine_scale'] = cosine_scale
+ p.task_args['restart_steps'] = [min(max(1, step), p.steps-1) for step in restart_steps]
+ if self.is_img2img:
+ p.task_args['cosine_scale_bg'] = cosine_scale_bg
+ p.task_args['dilate_tau'] = dilate_tau
+ p.task_args['img_path'] = p.init_images[0]
+ p.init_images = None
+ if override_sampler:
+ p.sampler_name = 'Euler a'
+
+ if p.width < 1024 or p.height < 1024:
+ shared.log.error(f'FreeScale: width={p.width} height={p.height} minimum=1024')
+ return None
+
+ if not self.is_img2img:
+ shared.sd_model = sd_models.switch_pipe(StableDiffusionXLFreeScale, shared.sd_model)
+ else:
+ shared.sd_model = sd_models.switch_pipe(StableDiffusionXLFreeScaleImg2Img, shared.sd_model)
+ shared.sd_model.enable_vae_slicing()
+ shared.sd_model.enable_vae_tiling()
+
+ shared.log.info(f'FreeScale: mode={"txt" if not self.is_img2img else "img"} cosine={cosine_scale} bg={cosine_scale_bg} tau={dilate_tau} scales={scales} resolutions={resolutions_list} steps={restart_steps} sampler={p.sampler_name}')
+ resolutions = ','.join([f'{x[0]}x{x[1]}' for x in resolutions_list])
+ steps = ','.join([str(x) for x in restart_steps])
+ p.extra_generation_params["FreeScale"] = f'cosine {cosine_scale} resolutions {resolutions} steps {steps}'
+
+ def after(self, p: processing.StableDiffusionProcessing, processed: processing.Processed, *args): # pylint: disable=arguments-differ, unused-argument
+ if self.orig_pipe is None:
+ return processed
+ # restore pipeline
+ if shared.sd_model_type == "sdxl":
+ shared.sd_model = self.orig_pipe
+ self.orig_pipe = None
+ if not self.orig_slice:
+ shared.sd_model.disable_vae_slicing()
+ if not self.orig_tile:
+ shared.sd_model.disable_vae_tiling()
+ return processed