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| # Copyright 2024 The InstantX Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import math | |
| from typing import Any, Callable, Dict, List, Optional, Tuple, Union | |
| import cv2 | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| import torch.nn as nn | |
| from diffusers import StableDiffusionXLControlNetImg2ImgPipeline | |
| from diffusers.image_processor import PipelineImageInput | |
| from diffusers.models import ControlNetModel | |
| from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel | |
| from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput | |
| from diffusers.utils import ( | |
| deprecate, | |
| logging, | |
| replace_example_docstring, | |
| ) | |
| from diffusers.utils.import_utils import is_xformers_available | |
| from diffusers.utils.torch_utils import is_compiled_module, is_torch_version | |
| try: | |
| import xformers | |
| import xformers.ops | |
| xformers_available = True | |
| except Exception: | |
| xformers_available = False | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| def FeedForward(dim, mult=4): | |
| inner_dim = int(dim * mult) | |
| return nn.Sequential( | |
| nn.LayerNorm(dim), | |
| nn.Linear(dim, inner_dim, bias=False), | |
| nn.GELU(), | |
| nn.Linear(inner_dim, dim, bias=False), | |
| ) | |
| def reshape_tensor(x, heads): | |
| bs, length, width = x.shape | |
| # (bs, length, width) --> (bs, length, n_heads, dim_per_head) | |
| x = x.view(bs, length, heads, -1) | |
| # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) | |
| x = x.transpose(1, 2) | |
| # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) | |
| x = x.reshape(bs, heads, length, -1) | |
| return x | |
| class PerceiverAttention(nn.Module): | |
| def __init__(self, *, dim, dim_head=64, heads=8): | |
| super().__init__() | |
| self.scale = dim_head**-0.5 | |
| self.dim_head = dim_head | |
| self.heads = heads | |
| inner_dim = dim_head * heads | |
| self.norm1 = nn.LayerNorm(dim) | |
| self.norm2 = nn.LayerNorm(dim) | |
| self.to_q = nn.Linear(dim, inner_dim, bias=False) | |
| self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) | |
| self.to_out = nn.Linear(inner_dim, dim, bias=False) | |
| def forward(self, x, latents): | |
| """ | |
| Args: | |
| x (torch.Tensor): image features | |
| shape (b, n1, D) | |
| latent (torch.Tensor): latent features | |
| shape (b, n2, D) | |
| """ | |
| x = self.norm1(x) | |
| latents = self.norm2(latents) | |
| b, l, _ = latents.shape | |
| q = self.to_q(latents) | |
| kv_input = torch.cat((x, latents), dim=-2) | |
| k, v = self.to_kv(kv_input).chunk(2, dim=-1) | |
| q = reshape_tensor(q, self.heads) | |
| k = reshape_tensor(k, self.heads) | |
| v = reshape_tensor(v, self.heads) | |
| # attention | |
| scale = 1 / math.sqrt(math.sqrt(self.dim_head)) | |
| weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards | |
| weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) | |
| out = weight @ v | |
| out = out.permute(0, 2, 1, 3).reshape(b, l, -1) | |
| return self.to_out(out) | |
| class Resampler(nn.Module): | |
| def __init__( | |
| self, | |
| dim=1024, | |
| depth=8, | |
| dim_head=64, | |
| heads=16, | |
| num_queries=8, | |
| embedding_dim=768, | |
| output_dim=1024, | |
| ff_mult=4, | |
| ): | |
| super().__init__() | |
| self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) | |
| self.proj_in = nn.Linear(embedding_dim, dim) | |
| self.proj_out = nn.Linear(dim, output_dim) | |
| self.norm_out = nn.LayerNorm(output_dim) | |
| self.layers = nn.ModuleList([]) | |
| for _ in range(depth): | |
| self.layers.append( | |
| nn.ModuleList( | |
| [ | |
| PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), | |
| FeedForward(dim=dim, mult=ff_mult), | |
| ] | |
| ) | |
| ) | |
| def forward(self, x): | |
| latents = self.latents.repeat(x.size(0), 1, 1) | |
| x = self.proj_in(x) | |
| for attn, ff in self.layers: | |
| latents = attn(x, latents) + latents | |
| latents = ff(latents) + latents | |
| latents = self.proj_out(latents) | |
| return self.norm_out(latents) | |
| class AttnProcessor(nn.Module): | |
| r""" | |
| Default processor for performing attention-related computations. | |
| """ | |
| def __init__( | |
| self, | |
| hidden_size=None, | |
| cross_attention_dim=None, | |
| ): | |
| super().__init__() | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| elif attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| class IPAttnProcessor(nn.Module): | |
| r""" | |
| Attention processor for IP-Adapater. | |
| Args: | |
| hidden_size (`int`): | |
| The hidden size of the attention layer. | |
| cross_attention_dim (`int`): | |
| The number of channels in the `encoder_hidden_states`. | |
| scale (`float`, defaults to 1.0): | |
| the weight scale of image prompt. | |
| num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16): | |
| The context length of the image features. | |
| """ | |
| def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4): | |
| super().__init__() | |
| self.hidden_size = hidden_size | |
| self.cross_attention_dim = cross_attention_dim | |
| self.scale = scale | |
| self.num_tokens = num_tokens | |
| self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False) | |
| def __call__( | |
| self, | |
| attn, | |
| hidden_states, | |
| encoder_hidden_states=None, | |
| attention_mask=None, | |
| temb=None, | |
| ): | |
| residual = hidden_states | |
| if attn.spatial_norm is not None: | |
| hidden_states = attn.spatial_norm(hidden_states, temb) | |
| input_ndim = hidden_states.ndim | |
| if input_ndim == 4: | |
| batch_size, channel, height, width = hidden_states.shape | |
| hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) | |
| batch_size, sequence_length, _ = ( | |
| hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape | |
| ) | |
| attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) | |
| if attn.group_norm is not None: | |
| hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) | |
| query = attn.to_q(hidden_states) | |
| if encoder_hidden_states is None: | |
| encoder_hidden_states = hidden_states | |
| else: | |
| # get encoder_hidden_states, ip_hidden_states | |
| end_pos = encoder_hidden_states.shape[1] - self.num_tokens | |
| encoder_hidden_states, ip_hidden_states = ( | |
| encoder_hidden_states[:, :end_pos, :], | |
| encoder_hidden_states[:, end_pos:, :], | |
| ) | |
| if attn.norm_cross: | |
| encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) | |
| key = attn.to_k(encoder_hidden_states) | |
| value = attn.to_v(encoder_hidden_states) | |
| query = attn.head_to_batch_dim(query) | |
| key = attn.head_to_batch_dim(key) | |
| value = attn.head_to_batch_dim(value) | |
| if xformers_available: | |
| hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) | |
| else: | |
| attention_probs = attn.get_attention_scores(query, key, attention_mask) | |
| hidden_states = torch.bmm(attention_probs, value) | |
| hidden_states = attn.batch_to_head_dim(hidden_states) | |
| # for ip-adapter | |
| ip_key = self.to_k_ip(ip_hidden_states) | |
| ip_value = self.to_v_ip(ip_hidden_states) | |
| ip_key = attn.head_to_batch_dim(ip_key) | |
| ip_value = attn.head_to_batch_dim(ip_value) | |
| if xformers_available: | |
| ip_hidden_states = self._memory_efficient_attention_xformers(query, ip_key, ip_value, None) | |
| else: | |
| ip_attention_probs = attn.get_attention_scores(query, ip_key, None) | |
| ip_hidden_states = torch.bmm(ip_attention_probs, ip_value) | |
| ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states) | |
| hidden_states = hidden_states + self.scale * ip_hidden_states | |
| # linear proj | |
| hidden_states = attn.to_out[0](hidden_states) | |
| # dropout | |
| hidden_states = attn.to_out[1](hidden_states) | |
| if input_ndim == 4: | |
| hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width) | |
| if attn.residual_connection: | |
| hidden_states = hidden_states + residual | |
| hidden_states = hidden_states / attn.rescale_output_factor | |
| return hidden_states | |
| def _memory_efficient_attention_xformers(self, query, key, value, attention_mask): | |
| # TODO attention_mask | |
| query = query.contiguous() | |
| key = key.contiguous() | |
| value = value.contiguous() | |
| hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask) | |
| return hidden_states | |
| EXAMPLE_DOC_STRING = """ | |
| Examples: | |
| ```py | |
| >>> # !pip install opencv-python transformers accelerate insightface | |
| >>> import diffusers | |
| >>> from diffusers.utils import load_image | |
| >>> from diffusers.models import ControlNetModel | |
| >>> import cv2 | |
| >>> import torch | |
| >>> import numpy as np | |
| >>> from PIL import Image | |
| >>> from insightface.app import FaceAnalysis | |
| >>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps | |
| >>> # download 'antelopev2' under ./models | |
| >>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) | |
| >>> app.prepare(ctx_id=0, det_size=(640, 640)) | |
| >>> # download models under ./checkpoints | |
| >>> face_adapter = f'./checkpoints/ip-adapter.bin' | |
| >>> controlnet_path = f'./checkpoints/ControlNetModel' | |
| >>> # load IdentityNet | |
| >>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) | |
| >>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( | |
| ... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16 | |
| ... ) | |
| >>> pipe.cuda() | |
| >>> # load adapter | |
| >>> pipe.load_ip_adapter_instantid(face_adapter) | |
| >>> prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality" | |
| >>> negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured" | |
| >>> # load an image | |
| >>> image = load_image("your-example.jpg") | |
| >>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1] | |
| >>> face_emb = face_info['embedding'] | |
| >>> face_kps = draw_kps(face_image, face_info['kps']) | |
| >>> pipe.set_ip_adapter_scale(0.8) | |
| >>> # generate image | |
| >>> image = pipe( | |
| ... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8 | |
| ... ).images[0] | |
| ``` | |
| """ | |
| def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]): | |
| stickwidth = 4 | |
| limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) | |
| kps = np.array(kps) | |
| w, h = image_pil.size | |
| out_img = np.zeros([h, w, 3]) | |
| for i in range(len(limbSeq)): | |
| index = limbSeq[i] | |
| color = color_list[index[0]] | |
| x = kps[index][:, 0] | |
| y = kps[index][:, 1] | |
| length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 | |
| angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) | |
| polygon = cv2.ellipse2Poly( | |
| (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1 | |
| ) | |
| out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) | |
| out_img = (out_img * 0.6).astype(np.uint8) | |
| for idx_kp, kp in enumerate(kps): | |
| color = color_list[idx_kp] | |
| x, y = kp | |
| out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) | |
| out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8)) | |
| return out_img_pil | |
| class StableDiffusionXLInstantIDImg2ImgPipeline(StableDiffusionXLControlNetImg2ImgPipeline): | |
| def cuda(self, dtype=torch.float16, use_xformers=False): | |
| self.to("cuda", dtype) | |
| if hasattr(self, "image_proj_model"): | |
| self.image_proj_model.to(self.unet.device).to(self.unet.dtype) | |
| if use_xformers: | |
| if is_xformers_available(): | |
| import xformers | |
| from packaging import version | |
| xformers_version = version.parse(xformers.__version__) | |
| if xformers_version == version.parse("0.0.16"): | |
| logger.warning( | |
| "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details." | |
| ) | |
| self.enable_xformers_memory_efficient_attention() | |
| else: | |
| raise ValueError("xformers is not available. Make sure it is installed correctly") | |
| def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5): | |
| self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens) | |
| self.set_ip_adapter(model_ckpt, num_tokens, scale) | |
| def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16): | |
| image_proj_model = Resampler( | |
| dim=1280, | |
| depth=4, | |
| dim_head=64, | |
| heads=20, | |
| num_queries=num_tokens, | |
| embedding_dim=image_emb_dim, | |
| output_dim=self.unet.config.cross_attention_dim, | |
| ff_mult=4, | |
| ) | |
| image_proj_model.eval() | |
| self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype) | |
| state_dict = torch.load(model_ckpt, map_location="cpu") | |
| if "image_proj" in state_dict: | |
| state_dict = state_dict["image_proj"] | |
| self.image_proj_model.load_state_dict(state_dict) | |
| self.image_proj_model_in_features = image_emb_dim | |
| def set_ip_adapter(self, model_ckpt, num_tokens, scale): | |
| unet = self.unet | |
| attn_procs = {} | |
| for name in unet.attn_processors.keys(): | |
| cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim | |
| if name.startswith("mid_block"): | |
| hidden_size = unet.config.block_out_channels[-1] | |
| elif name.startswith("up_blocks"): | |
| block_id = int(name[len("up_blocks.")]) | |
| hidden_size = list(reversed(unet.config.block_out_channels))[block_id] | |
| elif name.startswith("down_blocks"): | |
| block_id = int(name[len("down_blocks.")]) | |
| hidden_size = unet.config.block_out_channels[block_id] | |
| if cross_attention_dim is None: | |
| attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype) | |
| else: | |
| attn_procs[name] = IPAttnProcessor( | |
| hidden_size=hidden_size, | |
| cross_attention_dim=cross_attention_dim, | |
| scale=scale, | |
| num_tokens=num_tokens, | |
| ).to(unet.device, dtype=unet.dtype) | |
| unet.set_attn_processor(attn_procs) | |
| state_dict = torch.load(model_ckpt, map_location="cpu") | |
| ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values()) | |
| if "ip_adapter" in state_dict: | |
| state_dict = state_dict["ip_adapter"] | |
| ip_layers.load_state_dict(state_dict) | |
| def set_ip_adapter_scale(self, scale): | |
| unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet | |
| for attn_processor in unet.attn_processors.values(): | |
| if isinstance(attn_processor, IPAttnProcessor): | |
| attn_processor.scale = scale | |
| def _encode_prompt_image_emb(self, prompt_image_emb, device, dtype, do_classifier_free_guidance): | |
| if isinstance(prompt_image_emb, torch.Tensor): | |
| prompt_image_emb = prompt_image_emb.clone().detach() | |
| else: | |
| prompt_image_emb = torch.tensor(prompt_image_emb) | |
| prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype) | |
| prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features]) | |
| if do_classifier_free_guidance: | |
| prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0) | |
| else: | |
| prompt_image_emb = torch.cat([prompt_image_emb], dim=0) | |
| image_proj_model_device = self.image_proj_model.to(device) | |
| prompt_image_emb = image_proj_model_device(prompt_image_emb) | |
| return prompt_image_emb | |
| def __call__( | |
| self, | |
| prompt: Union[str, List[str]] = None, | |
| prompt_2: Optional[Union[str, List[str]]] = None, | |
| image: PipelineImageInput = None, | |
| control_image: PipelineImageInput = None, | |
| strength: float = 0.8, | |
| height: Optional[int] = None, | |
| width: Optional[int] = None, | |
| num_inference_steps: int = 50, | |
| 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, | |
| image_embeds: Optional[torch.FloatTensor] = None, | |
| output_type: Optional[str] = "pil", | |
| return_dict: bool = True, | |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| controlnet_conditioning_scale: Union[float, List[float]] = 1.0, | |
| guess_mode: bool = False, | |
| control_guidance_start: Union[float, List[float]] = 0.0, | |
| control_guidance_end: Union[float, List[float]] = 1.0, | |
| original_size: Tuple[int, int] = None, | |
| crops_coords_top_left: Tuple[int, int] = (0, 0), | |
| target_size: Tuple[int, int] = None, | |
| negative_original_size: Optional[Tuple[int, int]] = None, | |
| negative_crops_coords_top_left: Tuple[int, int] = (0, 0), | |
| negative_target_size: Optional[Tuple[int, int]] = None, | |
| aesthetic_score: float = 6.0, | |
| negative_aesthetic_score: float = 2.5, | |
| clip_skip: Optional[int] = None, | |
| callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, | |
| callback_on_step_end_tensor_inputs: List[str] = ["latents"], | |
| **kwargs, | |
| ): | |
| r""" | |
| The call function to the pipeline for generation. | |
| Args: | |
| prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. | |
| prompt_2 (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is | |
| used in both text-encoders. | |
| image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: | |
| `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): | |
| The ControlNet input condition to provide guidance to the `unet` for generation. If the type is | |
| specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be | |
| accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height | |
| and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in | |
| `init`, images must be passed as a list such that each element of the list can be correctly batched for | |
| input to a single ControlNet. | |
| height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): | |
| The height in pixels of the generated image. Anything below 512 pixels won't work well for | |
| [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | |
| and checkpoints that are not specifically fine-tuned on low resolutions. | |
| width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): | |
| The width in pixels of the generated image. Anything below 512 pixels won't work well for | |
| [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) | |
| and checkpoints that are not specifically fine-tuned on low resolutions. | |
| num_inference_steps (`int`, *optional*, defaults to 50): | |
| The number of denoising steps. More denoising steps usually lead to a higher quality image at the | |
| expense of slower inference. | |
| guidance_scale (`float`, *optional*, defaults to 5.0): | |
| A higher guidance scale value encourages the model to generate images closely linked to the text | |
| `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. | |
| negative_prompt (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide what to not include in image generation. If not defined, you need to | |
| pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). | |
| negative_prompt_2 (`str` or `List[str]`, *optional*): | |
| The prompt or prompts to guide what to not include in image generation. This is 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) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies | |
| to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. | |
| generator (`torch.Generator` or `List[torch.Generator]`, *optional*): | |
| A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make | |
| generation deterministic. | |
| latents (`torch.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 is 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 (prompt weighting). If not | |
| provided, text embeddings are generated from the `prompt` input argument. | |
| negative_prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If | |
| not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. | |
| pooled_prompt_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If | |
| not provided, pooled text embeddings are 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 (prompt | |
| weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input | |
| argument. | |
| image_embeds (`torch.FloatTensor`, *optional*): | |
| Pre-generated image embeddings. | |
| output_type (`str`, *optional*, defaults to `"pil"`): | |
| The output format of the generated image. Choose between `PIL.Image` or `np.array`. | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a | |
| plain tuple. | |
| cross_attention_kwargs (`dict`, *optional*): | |
| A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in | |
| [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
| controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): | |
| The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added | |
| to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set | |
| the corresponding scale as a list. | |
| guess_mode (`bool`, *optional*, defaults to `False`): | |
| The ControlNet encoder tries to recognize the content of the input image even if you remove all | |
| prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. | |
| control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): | |
| The percentage of total steps at which the ControlNet starts applying. | |
| control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): | |
| The percentage of total steps at which the ControlNet stops applying. | |
| 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 `(height, width)` 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 `(height, width)`. 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). | |
| negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): | |
| To negatively condition the generation process based on a specific image resolution. 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). For more | |
| information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. | |
| negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): | |
| To negatively condition the generation process based on a specific crop coordinates. 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). For more | |
| information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. | |
| negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): | |
| To negatively condition the generation process based on a target image resolution. It should be as same | |
| as the `target_size` for most cases. 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). For more | |
| information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. | |
| clip_skip (`int`, *optional*): | |
| Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that | |
| the output of the pre-final layer will be used for computing the prompt embeddings. | |
| callback_on_step_end (`Callable`, *optional*): | |
| A function that calls at the end of each denoising steps during the inference. The function is called | |
| with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, | |
| callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by | |
| `callback_on_step_end_tensor_inputs`. | |
| callback_on_step_end_tensor_inputs (`List`, *optional*): | |
| The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list | |
| will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the | |
| `._callback_tensor_inputs` attribute of your pipeline class. | |
| Examples: | |
| Returns: | |
| [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: | |
| If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, | |
| otherwise a `tuple` is returned containing the output images. | |
| """ | |
| callback = kwargs.pop("callback", None) | |
| callback_steps = kwargs.pop("callback_steps", None) | |
| if callback is not None: | |
| deprecate( | |
| "callback", | |
| "1.0.0", | |
| "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", | |
| ) | |
| if callback_steps is not None: | |
| deprecate( | |
| "callback_steps", | |
| "1.0.0", | |
| "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", | |
| ) | |
| controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet | |
| # align format for control guidance | |
| if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): | |
| control_guidance_start = len(control_guidance_end) * [control_guidance_start] | |
| elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): | |
| control_guidance_end = len(control_guidance_start) * [control_guidance_end] | |
| elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): | |
| mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 | |
| control_guidance_start, control_guidance_end = ( | |
| mult * [control_guidance_start], | |
| mult * [control_guidance_end], | |
| ) | |
| # 1. Check inputs. Raise error if not correct | |
| self.check_inputs( | |
| prompt, | |
| prompt_2, | |
| control_image, | |
| strength, | |
| num_inference_steps, | |
| callback_steps, | |
| negative_prompt, | |
| negative_prompt_2, | |
| prompt_embeds, | |
| negative_prompt_embeds, | |
| pooled_prompt_embeds, | |
| negative_pooled_prompt_embeds, | |
| None, | |
| None, | |
| controlnet_conditioning_scale, | |
| control_guidance_start, | |
| control_guidance_end, | |
| callback_on_step_end_tensor_inputs, | |
| ) | |
| self._guidance_scale = guidance_scale | |
| self._clip_skip = clip_skip | |
| self._cross_attention_kwargs = cross_attention_kwargs | |
| # 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 | |
| if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): | |
| controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) | |
| global_pool_conditions = ( | |
| controlnet.config.global_pool_conditions | |
| if isinstance(controlnet, ControlNetModel) | |
| else controlnet.nets[0].config.global_pool_conditions | |
| ) | |
| guess_mode = guess_mode or global_pool_conditions | |
| # 3.1 Encode input prompt | |
| text_encoder_lora_scale = ( | |
| self.cross_attention_kwargs.get("scale", None) if self.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_2, | |
| device, | |
| num_images_per_prompt, | |
| self.do_classifier_free_guidance, | |
| negative_prompt, | |
| 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, | |
| clip_skip=self.clip_skip, | |
| ) | |
| # 3.2 Encode image prompt | |
| prompt_image_emb = self._encode_prompt_image_emb( | |
| image_embeds, device, self.unet.dtype, self.do_classifier_free_guidance | |
| ) | |
| bs_embed, seq_len, _ = prompt_image_emb.shape | |
| prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1) | |
| prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1) | |
| # 4. Prepare image and controlnet_conditioning_image | |
| image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) | |
| if isinstance(controlnet, ControlNetModel): | |
| control_image = self.prepare_control_image( | |
| image=control_image, | |
| width=width, | |
| height=height, | |
| batch_size=batch_size * num_images_per_prompt, | |
| num_images_per_prompt=num_images_per_prompt, | |
| device=device, | |
| dtype=controlnet.dtype, | |
| do_classifier_free_guidance=self.do_classifier_free_guidance, | |
| guess_mode=guess_mode, | |
| ) | |
| height, width = control_image.shape[-2:] | |
| elif isinstance(controlnet, MultiControlNetModel): | |
| control_images = [] | |
| for control_image_ in control_image: | |
| control_image_ = self.prepare_control_image( | |
| image=control_image_, | |
| width=width, | |
| height=height, | |
| batch_size=batch_size * num_images_per_prompt, | |
| num_images_per_prompt=num_images_per_prompt, | |
| device=device, | |
| dtype=controlnet.dtype, | |
| do_classifier_free_guidance=self.do_classifier_free_guidance, | |
| guess_mode=guess_mode, | |
| ) | |
| control_images.append(control_image_) | |
| control_image = control_images | |
| height, width = control_image[0].shape[-2:] | |
| else: | |
| assert False | |
| # 5. Prepare timesteps | |
| self.scheduler.set_timesteps(num_inference_steps, device=device) | |
| timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device) | |
| latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) | |
| self._num_timesteps = len(timesteps) | |
| # 6. Prepare latent variables | |
| latents = self.prepare_latents( | |
| image, | |
| latent_timestep, | |
| batch_size, | |
| num_images_per_prompt, | |
| prompt_embeds.dtype, | |
| device, | |
| generator, | |
| True, | |
| ) | |
| # # 6.5 Optionally get Guidance Scale Embedding | |
| timestep_cond = None | |
| if self.unet.config.time_cond_proj_dim is not None: | |
| guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) | |
| timestep_cond = self.get_guidance_scale_embedding( | |
| guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim | |
| ).to(device=device, dtype=latents.dtype) | |
| # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline | |
| extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) | |
| # 7.1 Create tensor stating which controlnets to keep | |
| controlnet_keep = [] | |
| for i in range(len(timesteps)): | |
| keeps = [ | |
| 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) | |
| for s, e in zip(control_guidance_start, control_guidance_end) | |
| ] | |
| controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) | |
| # 7.2 Prepare added time ids & embeddings | |
| if isinstance(control_image, list): | |
| original_size = original_size or control_image[0].shape[-2:] | |
| else: | |
| original_size = original_size or control_image.shape[-2:] | |
| target_size = target_size or (height, width) | |
| if negative_original_size is None: | |
| negative_original_size = original_size | |
| if negative_target_size is None: | |
| negative_target_size = target_size | |
| add_text_embeds = pooled_prompt_embeds | |
| if self.text_encoder_2 is None: | |
| text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) | |
| else: | |
| text_encoder_projection_dim = self.text_encoder_2.config.projection_dim | |
| add_time_ids, add_neg_time_ids = self._get_add_time_ids( | |
| original_size, | |
| crops_coords_top_left, | |
| target_size, | |
| aesthetic_score, | |
| negative_aesthetic_score, | |
| negative_original_size, | |
| negative_crops_coords_top_left, | |
| negative_target_size, | |
| dtype=prompt_embeds.dtype, | |
| text_encoder_projection_dim=text_encoder_projection_dim, | |
| ) | |
| add_time_ids = add_time_ids.repeat(batch_size * num_images_per_prompt, 1) | |
| if self.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_neg_time_ids = add_neg_time_ids.repeat(batch_size * num_images_per_prompt, 1) | |
| add_time_ids = torch.cat([add_neg_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) | |
| encoder_hidden_states = torch.cat([prompt_embeds, prompt_image_emb], dim=1) | |
| # 8. Denoising loop | |
| num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order | |
| is_unet_compiled = is_compiled_module(self.unet) | |
| is_controlnet_compiled = is_compiled_module(self.controlnet) | |
| is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") | |
| with self.progress_bar(total=num_inference_steps) as progress_bar: | |
| for i, t in enumerate(timesteps): | |
| # Relevant thread: | |
| # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 | |
| if (is_unet_compiled and is_controlnet_compiled) and is_torch_higher_equal_2_1: | |
| torch._inductor.cudagraph_mark_step_begin() | |
| # expand the latents if we are doing classifier free guidance | |
| latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents | |
| latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) | |
| added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} | |
| # controlnet(s) inference | |
| if guess_mode and self.do_classifier_free_guidance: | |
| # Infer ControlNet only for the conditional batch. | |
| control_model_input = latents | |
| control_model_input = self.scheduler.scale_model_input(control_model_input, t) | |
| controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] | |
| controlnet_added_cond_kwargs = { | |
| "text_embeds": add_text_embeds.chunk(2)[1], | |
| "time_ids": add_time_ids.chunk(2)[1], | |
| } | |
| else: | |
| control_model_input = latent_model_input | |
| controlnet_prompt_embeds = prompt_embeds | |
| controlnet_added_cond_kwargs = added_cond_kwargs | |
| if isinstance(controlnet_keep[i], list): | |
| cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] | |
| else: | |
| controlnet_cond_scale = controlnet_conditioning_scale | |
| if isinstance(controlnet_cond_scale, list): | |
| controlnet_cond_scale = controlnet_cond_scale[0] | |
| cond_scale = controlnet_cond_scale * controlnet_keep[i] | |
| down_block_res_samples, mid_block_res_sample = self.controlnet( | |
| control_model_input, | |
| t, | |
| encoder_hidden_states=prompt_image_emb, | |
| controlnet_cond=control_image, | |
| conditioning_scale=cond_scale, | |
| guess_mode=guess_mode, | |
| added_cond_kwargs=controlnet_added_cond_kwargs, | |
| return_dict=False, | |
| ) | |
| if guess_mode and self.do_classifier_free_guidance: | |
| # Infered ControlNet only for the conditional batch. | |
| # To apply the output of ControlNet to both the unconditional and conditional batches, | |
| # add 0 to the unconditional batch to keep it unchanged. | |
| down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] | |
| mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) | |
| # predict the noise residual | |
| noise_pred = self.unet( | |
| latent_model_input, | |
| t, | |
| encoder_hidden_states=encoder_hidden_states, | |
| timestep_cond=timestep_cond, | |
| cross_attention_kwargs=self.cross_attention_kwargs, | |
| down_block_additional_residuals=down_block_res_samples, | |
| mid_block_additional_residual=mid_block_res_sample, | |
| added_cond_kwargs=added_cond_kwargs, | |
| return_dict=False, | |
| )[0] | |
| # perform guidance | |
| if self.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) | |
| # 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] | |
| if callback_on_step_end is not None: | |
| callback_kwargs = {} | |
| for k in callback_on_step_end_tensor_inputs: | |
| callback_kwargs[k] = locals()[k] | |
| callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) | |
| latents = callback_outputs.pop("latents", latents) | |
| prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) | |
| negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) | |
| # call the callback, if provided | |
| if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): | |
| progress_bar.update() | |
| if callback is not None and i % callback_steps == 0: | |
| step_idx = i // getattr(self.scheduler, "order", 1) | |
| callback(step_idx, t, latents) | |
| if not output_type == "latent": | |
| # make sure the VAE is in float32 mode, as it overflows in float16 | |
| needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast | |
| if needs_upcasting: | |
| self.upcast_vae() | |
| latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) | |
| image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] | |
| # cast back to fp16 if needed | |
| if needs_upcasting: | |
| self.vae.to(dtype=torch.float16) | |
| else: | |
| image = latents | |
| if not output_type == "latent": | |
| # 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) | |
| # Offload all models | |
| self.maybe_free_model_hooks() | |
| if not return_dict: | |
| return (image,) | |
| return StableDiffusionXLPipelineOutput(images=image) | |