769 lines
39 KiB
Python
769 lines
39 KiB
Python
# Copyright 2024 The InstantX Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import cv2
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import math
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import numpy as np
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import PIL.Image
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import torch
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import torch.nn.functional as F
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from diffusers.image_processor import PipelineImageInput
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from diffusers.models import ControlNetModel
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from diffusers.utils import (
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deprecate,
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logging,
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replace_example_docstring,
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)
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from diffusers.utils.torch_utils import is_compiled_module, is_torch_version
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from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
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from diffusers import StableDiffusionXLControlNetPipeline
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from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
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from diffusers.utils.import_utils import is_xformers_available
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from ip_adapter.resampler import Resampler
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from ip_adapter.utils import is_torch2_available
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if is_torch2_available():
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from ip_adapter.attention_processor import IPAttnProcessor2_0 as IPAttnProcessor, AttnProcessor2_0 as AttnProcessor
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else:
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from ip_adapter.attention_processor import IPAttnProcessor, AttnProcessor
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> # !pip install opencv-python transformers accelerate insightface
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>>> import diffusers
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>>> from diffusers.utils import load_image
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>>> from diffusers.models import ControlNetModel
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>>> import cv2
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>>> import torch
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>>> import numpy as np
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>>> from PIL import Image
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>>> from insightface.app import FaceAnalysis
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>>> from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps
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>>> # download 'antelopev2' under ./models
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>>> app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
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>>> app.prepare(ctx_id=0, det_size=(640, 640))
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>>> # download models under ./checkpoints
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>>> face_adapter = f'./checkpoints/ip-adapter.bin'
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>>> controlnet_path = f'./checkpoints/ControlNetModel'
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>>> # load IdentityNet
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>>> controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
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>>> pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
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... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
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... )
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>>> pipe.cuda()
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>>> # load adapter
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>>> pipe.load_ip_adapter_instantid(face_adapter)
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>>> 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"
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>>> 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"
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>>> # load an image
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>>> image = load_image("your-example.jpg")
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>>> face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))[-1]
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>>> face_emb = face_info['embedding']
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>>> face_kps = draw_kps(face_image, face_info['kps'])
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>>> pipe.set_ip_adapter_scale(0.8)
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>>> # generate image
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>>> image = pipe(
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... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8
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... ).images[0]
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```
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"""
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def draw_kps(image_pil, kps, color_list=[(255,0,0), (0,255,0), (0,0,255), (255,255,0), (255,0,255)]):
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stickwidth = 4
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limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]])
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kps = np.array(kps)
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w, h = image_pil.size
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out_img = np.zeros([h, w, 3])
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for i in range(len(limbSeq)):
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index = limbSeq[i]
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color = color_list[index[0]]
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x = kps[index][:, 0]
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y = kps[index][:, 1]
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length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5
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angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1]))
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polygon = cv2.ellipse2Poly((int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
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out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color)
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out_img = (out_img * 0.6).astype(np.uint8)
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for idx_kp, kp in enumerate(kps):
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color = color_list[idx_kp]
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x, y = kp
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out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1)
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out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8))
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return out_img_pil
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class StableDiffusionXLInstantIDPipeline(StableDiffusionXLControlNetPipeline):
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def cuda(self, dtype=torch.float16, use_xformers=False):
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self.to('cuda', dtype)
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if hasattr(self, 'image_proj_model'):
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self.image_proj_model.to(self.unet.device).to(self.unet.dtype)
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if use_xformers:
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if is_xformers_available():
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import xformers
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from packaging import version
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xformers_version = version.parse(xformers.__version__)
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if xformers_version == version.parse("0.0.16"):
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logger.warn(
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"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."
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)
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self.enable_xformers_memory_efficient_attention()
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else:
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raise ValueError("xformers is not available. Make sure it is installed correctly")
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def load_ip_adapter_instantid(self, model_ckpt, image_emb_dim=512, num_tokens=16, scale=0.5):
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self.set_image_proj_model(model_ckpt, image_emb_dim, num_tokens)
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self.set_ip_adapter(model_ckpt, num_tokens, scale)
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def set_image_proj_model(self, model_ckpt, image_emb_dim=512, num_tokens=16):
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image_proj_model = Resampler(
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dim=1280,
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depth=4,
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dim_head=64,
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heads=20,
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num_queries=num_tokens,
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embedding_dim=image_emb_dim,
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output_dim=self.unet.config.cross_attention_dim,
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ff_mult=4,
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)
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image_proj_model.eval()
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self.image_proj_model = image_proj_model.to(self.device, dtype=self.dtype)
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state_dict = torch.load(model_ckpt, map_location="cpu")
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if 'image_proj' in state_dict:
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state_dict = state_dict["image_proj"]
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self.image_proj_model.load_state_dict(state_dict)
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self.image_proj_model_in_features = image_emb_dim
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def set_ip_adapter(self, model_ckpt, num_tokens, scale):
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unet = self.unet
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attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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if cross_attention_dim is None:
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attn_procs[name] = AttnProcessor().to(unet.device, dtype=unet.dtype)
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else:
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attn_procs[name] = IPAttnProcessor(hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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scale=scale,
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num_tokens=num_tokens).to(unet.device, dtype=unet.dtype)
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unet.set_attn_processor(attn_procs)
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state_dict = torch.load(model_ckpt, map_location="cpu")
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ip_layers = torch.nn.ModuleList(self.unet.attn_processors.values())
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if 'ip_adapter' in state_dict:
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state_dict = state_dict['ip_adapter']
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ip_layers.load_state_dict(state_dict)
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def set_ip_adapter_scale(self, scale):
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unet = getattr(self, self.unet_name) if not hasattr(self, "unet") else self.unet
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for attn_processor in unet.attn_processors.values():
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if isinstance(attn_processor, IPAttnProcessor):
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attn_processor.scale = scale
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def _encode_prompt_image_emb(self, prompt_image_emb, device, num_images_per_prompt, dtype, do_classifier_free_guidance):
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if isinstance(prompt_image_emb, torch.Tensor):
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prompt_image_emb = prompt_image_emb.clone().detach()
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else:
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prompt_image_emb = torch.tensor(prompt_image_emb)
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prompt_image_emb = prompt_image_emb.to(device=device, dtype=dtype)
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prompt_image_emb = prompt_image_emb.reshape([1, -1, self.image_proj_model_in_features])
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if do_classifier_free_guidance:
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prompt_image_emb = torch.cat([torch.zeros_like(prompt_image_emb), prompt_image_emb], dim=0)
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else:
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prompt_image_emb = torch.cat([prompt_image_emb], dim=0)
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prompt_image_emb = self.image_proj_model(prompt_image_emb)
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bs_embed, seq_len, _ = prompt_image_emb.shape
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prompt_image_emb = prompt_image_emb.repeat(1, num_images_per_prompt, 1)
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prompt_image_emb = prompt_image_emb.view(bs_embed * num_images_per_prompt, seq_len, -1)
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return prompt_image_emb
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@torch.no_grad()
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@replace_example_docstring(EXAMPLE_DOC_STRING)
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def __call__(
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self,
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prompt: Union[str, List[str]] = None,
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prompt_2: Optional[Union[str, List[str]]] = None,
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image: PipelineImageInput = None,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 50,
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guidance_scale: float = 5.0,
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negative_prompt: Optional[Union[str, List[str]]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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num_images_per_prompt: Optional[int] = 1,
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eta: float = 0.0,
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generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
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latents: Optional[torch.FloatTensor] = None,
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prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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image_embeds: Optional[torch.FloatTensor] = None,
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output_type: Optional[str] = "pil",
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return_dict: bool = True,
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cross_attention_kwargs: Optional[Dict[str, Any]] = None,
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controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
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guess_mode: bool = False,
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control_guidance_start: Union[float, List[float]] = 0.0,
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control_guidance_end: Union[float, List[float]] = 1.0,
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original_size: Tuple[int, int] = None,
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crops_coords_top_left: Tuple[int, int] = (0, 0),
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target_size: Tuple[int, int] = None,
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negative_original_size: Optional[Tuple[int, int]] = None,
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negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
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negative_target_size: Optional[Tuple[int, int]] = None,
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clip_skip: Optional[int] = None,
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callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
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callback_on_step_end_tensor_inputs: List[str] = ["latents"],
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# IP adapter
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ip_adapter_scale=None,
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**kwargs,
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):
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r"""
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The call function to the pipeline for generation.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
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prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
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used in both text-encoders.
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image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
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`List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
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The ControlNet input condition to provide guidance to the `unet` for generation. If the type is
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specified as `torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can also be
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accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If height
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and/or width are passed, `image` is resized accordingly. If multiple ControlNets are specified in
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`init`, images must be passed as a list such that each element of the list can be correctly batched for
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input to a single ControlNet.
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height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
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The height in pixels of the generated image. Anything below 512 pixels won't work well for
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[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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and checkpoints that are not specifically fine-tuned on low resolutions.
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width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
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The width in pixels of the generated image. Anything below 512 pixels won't work well for
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[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
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and checkpoints that are not specifically fine-tuned on low resolutions.
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num_inference_steps (`int`, *optional*, defaults to 50):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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guidance_scale (`float`, *optional*, defaults to 5.0):
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A higher guidance scale value encourages the model to generate images closely linked to the text
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`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. If not defined, you need to
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pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
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negative_prompt_2 (`str` or `List[str]`, *optional*):
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The prompt or prompts to guide what to not include in image generation. This is sent to `tokenizer_2`
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and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders.
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num_images_per_prompt (`int`, *optional*, defaults to 1):
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The number of images to generate per prompt.
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eta (`float`, *optional*, defaults to 0.0):
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Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
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to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
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generation deterministic.
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latents (`torch.FloatTensor`, *optional*):
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Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
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generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
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tensor is generated by sampling using the supplied random `generator`.
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prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
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provided, text embeddings are generated from the `prompt` input argument.
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negative_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
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not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
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pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated pooled text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
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not provided, pooled text embeddings are generated from `prompt` input argument.
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negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
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weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
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argument.
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image_embeds (`torch.FloatTensor`, *optional*):
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Pre-generated image embeddings.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generated image. Choose between `PIL.Image` or `np.array`.
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return_dict (`bool`, *optional*, defaults to `True`):
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Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
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plain tuple.
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cross_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
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[`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
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controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
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The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
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to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
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the corresponding scale as a list.
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guess_mode (`bool`, *optional*, defaults to `False`):
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The ControlNet encoder tries to recognize the content of the input image even if you remove all
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prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
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control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
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The percentage of total steps at which the ControlNet starts applying.
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control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
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The percentage of total steps at which the ControlNet stops applying.
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original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
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If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
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`original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as
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explained in section 2.2 of
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[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
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crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
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`crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
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`crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
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`crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
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[https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
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|
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 pipeine 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],
|
|
)
|
|
|
|
# 0. set ip_adapter_scale
|
|
if ip_adapter_scale is not None:
|
|
self.set_ip_adapter_scale(ip_adapter_scale)
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt,
|
|
prompt_2,
|
|
image,
|
|
callback_steps,
|
|
negative_prompt,
|
|
negative_prompt_2,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
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,
|
|
num_images_per_prompt,
|
|
self.unet.dtype,
|
|
self.do_classifier_free_guidance)
|
|
|
|
# 4. Prepare image
|
|
if isinstance(controlnet, ControlNetModel):
|
|
image = self.prepare_image(
|
|
image=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 = image.shape[-2:]
|
|
elif isinstance(controlnet, MultiControlNetModel):
|
|
images = []
|
|
|
|
for image_ in image:
|
|
image_ = self.prepare_image(
|
|
image=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,
|
|
)
|
|
|
|
images.append(image_)
|
|
|
|
image = images
|
|
height, width = image[0].shape[-2:]
|
|
else:
|
|
assert False
|
|
|
|
# 5. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 6. 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,
|
|
)
|
|
|
|
# 6.5 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(image, list):
|
|
original_size = original_size or image[0].shape[-2:]
|
|
else:
|
|
original_size = original_size or image.shape[-2:]
|
|
target_size = target_size or (height, width)
|
|
|
|
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 = self._get_add_time_ids(
|
|
original_size,
|
|
crops_coords_top_left,
|
|
target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
|
|
if negative_original_size is not None and negative_target_size is not None:
|
|
negative_add_time_ids = self._get_add_time_ids(
|
|
negative_original_size,
|
|
negative_crops_coords_top_left,
|
|
negative_target_size,
|
|
dtype=prompt_embeds.dtype,
|
|
text_encoder_projection_dim=text_encoder_projection_dim,
|
|
)
|
|
else:
|
|
negative_add_time_ids = add_time_ids
|
|
|
|
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_time_ids = torch.cat([negative_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)
|
|
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=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) |