neverbiasu
6747549500
debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug debug
1334 lines
55 KiB
Python
1334 lines
55 KiB
Python
import os
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from typing import List
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import torch
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from typing import Optional, Union, Any, Dict, Tuple, List, Callable
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from diffusers.utils.torch_utils import (
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is_compiled_module,
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is_torch_version,
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randn_tensor,
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)
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from diffusers.utils import (
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USE_PEFT_BACKEND,
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deprecate,
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logging,
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replace_example_docstring,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from diffusers.pipelines.controlnet.pipeline_controlnet import retrieve_timesteps
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from diffusers import StableDiffusionPipeline
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from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import (
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StableDiffusionPipelineOutput,
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)
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from diffusers.pipelines.controlnet.pipeline_controlnet import (
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StableDiffusionControlNetPipeline,
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)
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from diffusers.models.controlnet import ControlNetModel
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from diffusers.image_processor import PipelineImageInput
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from diffusers.pipelines.controlnet import MultiControlNetModel
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from PIL import Image
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from safetensors import safe_open
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from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
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from torchvision import transforms
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from .style_encoder import Style_Aware_Encoder
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from .tools import pre_processing
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from .utils import is_torch2_available
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if is_torch2_available():
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from .attention_processor import (
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AttnProcessor2_0 as AttnProcessor,
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)
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from .attention_processor import (
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CNAttnProcessor2_0 as CNAttnProcessor,
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)
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from .attention_processor import (
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IPAttnProcessor2_0 as IPAttnProcessor,
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)
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else:
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from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
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from .resampler import Resampler
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class ImageProjModel(torch.nn.Module):
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"""Projection Model"""
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def __init__(
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self,
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cross_attention_dim=1024,
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clip_embeddings_dim=1024,
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clip_extra_context_tokens=4,
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):
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super().__init__()
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self.cross_attention_dim = cross_attention_dim
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self.clip_extra_context_tokens = clip_extra_context_tokens
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self.proj = torch.nn.Linear(
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clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim
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)
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self.norm = torch.nn.LayerNorm(cross_attention_dim)
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def forward(self, image_embeds):
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embeds = image_embeds
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clip_extra_context_tokens = self.proj(embeds).reshape(
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-1, self.clip_extra_context_tokens, self.cross_attention_dim
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)
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clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
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return clip_extra_context_tokens
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class MLPProjModel(torch.nn.Module):
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"""SD model with image prompt"""
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def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
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super().__init__()
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self.proj = torch.nn.Sequential(
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torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
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torch.nn.GELU(),
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torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
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torch.nn.LayerNorm(cross_attention_dim),
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)
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def forward(self, image_embeds):
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clip_extra_context_tokens = self.proj(image_embeds)
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return clip_extra_context_tokens
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class IPAdapter:
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def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4):
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self.device = device
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self.image_encoder_path = image_encoder_path
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self.ip_ckpt = ip_ckpt
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self.num_tokens = num_tokens
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self.pipe = sd_pipe.to(self.device)
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self.set_ip_adapter()
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# load image encoder
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self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(
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self.image_encoder_path
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).to(self.device, dtype=torch.float16)
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self.clip_image_processor = CLIPImageProcessor()
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# image proj model
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self.image_proj_model = self.init_proj()
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self.load_ip_adapter()
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def init_proj(self):
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image_proj_model = ImageProjModel(
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cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
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clip_embeddings_dim=self.image_encoder.config.projection_dim,
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clip_extra_context_tokens=self.num_tokens,
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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def set_ip_adapter(self):
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unet = self.pipe.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 = (
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None
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if name.endswith("attn1.processor")
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else unet.config.cross_attention_dim
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)
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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()
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else:
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attn_procs[name] = IPAttnProcessor(
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hidden_size=hidden_size,
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cross_attention_dim=cross_attention_dim,
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scale=1.0,
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num_tokens=self.num_tokens,
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).to(self.device, dtype=torch.float16)
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unet.set_attn_processor(attn_procs)
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if hasattr(self.pipe, "controlnet"):
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if isinstance(self.pipe.controlnet, MultiControlNetModel):
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for controlnet in self.pipe.controlnet.nets:
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controlnet.set_attn_processor(
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CNAttnProcessor(num_tokens=self.num_tokens)
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)
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else:
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self.pipe.controlnet.set_attn_processor(
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CNAttnProcessor(num_tokens=self.num_tokens)
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)
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def load_ip_adapter(self):
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if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
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state_dict = {"image_proj": {}, "ip_adapter": {}}
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with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
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for key in f.keys():
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if key.startswith("image_proj."):
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state_dict["image_proj"][key.replace("image_proj.", "")] = (
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f.get_tensor(key)
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)
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elif key.startswith("ip_adapter."):
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state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = (
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f.get_tensor(key)
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)
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else:
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state_dict = torch.load(self.ip_ckpt, map_location="cpu")
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self.image_proj_model.load_state_dict(state_dict["image_proj"])
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ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
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ip_layers.load_state_dict(state_dict["ip_adapter"])
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@torch.inference_mode()
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def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
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if pil_image is not None:
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if isinstance(pil_image, Image.Image):
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pil_image = [pil_image]
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clip_image = self.clip_image_processor(
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images=pil_image, return_tensors="pt"
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).pixel_values
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clip_image_embeds = self.image_encoder(
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clip_image.to(self.device, dtype=torch.float16)
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).image_embeds
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else:
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clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
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image_prompt_embeds = self.image_proj_model(clip_image_embeds)
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uncond_image_prompt_embeds = self.image_proj_model(
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torch.zeros_like(clip_image_embeds)
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)
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return image_prompt_embeds, uncond_image_prompt_embeds
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def set_scale(self, scale):
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for attn_processor in self.pipe.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 generate(
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self,
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pil_image=None,
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clip_image_embeds=None,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=None,
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guidance_scale=7.5,
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num_inference_steps=30,
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**kwargs,
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):
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self.set_scale(scale)
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if pil_image is not None:
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num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
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else:
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num_prompts = clip_image_embeds.size(0)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = (
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"monochrome, lowres, bad anatomy, worst quality, low quality"
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)
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
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pil_image=pil_image, clip_image_embeds=clip_image_embeds
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)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(
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bs_embed * num_samples, seq_len, -1
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)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(
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1, num_samples, 1
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)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(
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bs_embed * num_samples, seq_len, -1
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)
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with torch.inference_mode():
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prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
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prompt,
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device=self.device,
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num_images_per_prompt=num_samples,
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do_classifier_free_guidance=True,
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negative_prompt=negative_prompt,
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)
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prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat(
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[negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1
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)
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generator = (
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torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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)
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images = self.pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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guidance_scale=guidance_scale,
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num_inference_steps=num_inference_steps,
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generator=generator,
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**kwargs,
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).images
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return images
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class IPAdapterXL(IPAdapter):
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"""SDXL"""
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def generate(
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self,
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pil_image,
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prompt=None,
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negative_prompt=None,
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scale=1.0,
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num_samples=4,
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seed=None,
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num_inference_steps=30,
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**kwargs,
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):
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self.set_scale(scale)
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num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
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if prompt is None:
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prompt = "best quality, high quality"
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if negative_prompt is None:
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negative_prompt = (
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"monochrome, lowres, bad anatomy, worst quality, low quality"
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)
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if not isinstance(prompt, List):
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prompt = [prompt] * num_prompts
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if not isinstance(negative_prompt, List):
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negative_prompt = [negative_prompt] * num_prompts
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
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pil_image
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)
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bs_embed, seq_len, _ = image_prompt_embeds.shape
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
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image_prompt_embeds = image_prompt_embeds.view(
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bs_embed * num_samples, seq_len, -1
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)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(
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1, num_samples, 1
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)
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uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(
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bs_embed * num_samples, seq_len, -1
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)
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with torch.inference_mode():
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(
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prompt_embeds,
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negative_prompt_embeds,
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pooled_prompt_embeds,
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negative_pooled_prompt_embeds,
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) = self.pipe.encode_prompt(
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prompt,
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num_images_per_prompt=num_samples,
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do_classifier_free_guidance=True,
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negative_prompt=negative_prompt,
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)
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prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
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negative_prompt_embeds = torch.cat(
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[negative_prompt_embeds, uncond_image_prompt_embeds], dim=1
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)
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generator = (
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torch.Generator(self.device).manual_seed(seed) if seed is not None else None
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)
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images = self.pipe(
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prompt_embeds=prompt_embeds,
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negative_prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
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num_inference_steps=num_inference_steps,
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generator=generator,
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**kwargs,
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).images
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return images
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class IPAdapterPlus(IPAdapter):
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"""IP-Adapter with fine-grained features"""
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def init_proj(self):
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image_proj_model = Resampler(
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dim=self.pipe.unet.config.cross_attention_dim,
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depth=4,
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dim_head=64,
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heads=12,
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num_queries=self.num_tokens,
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embedding_dim=self.image_encoder.config.hidden_size,
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output_dim=self.pipe.unet.config.cross_attention_dim,
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ff_mult=4,
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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@torch.inference_mode()
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def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
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if isinstance(pil_image, Image.Image):
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pil_image = [pil_image]
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clip_image = self.clip_image_processor(
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images=pil_image, return_tensors="pt"
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).pixel_values
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clip_image = clip_image.to(self.device, dtype=torch.float16)
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clip_image_embeds = self.image_encoder(
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clip_image, output_hidden_states=True
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).hidden_states[-2]
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image_prompt_embeds = self.image_proj_model(clip_image_embeds)
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uncond_clip_image_embeds = self.image_encoder(
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torch.zeros_like(clip_image), output_hidden_states=True
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).hidden_states[-2]
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uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
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return image_prompt_embeds, uncond_image_prompt_embeds
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|
|
|
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class IPAdapterFull(IPAdapterPlus):
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"""IP-Adapter with full features"""
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|
|
|
def init_proj(self):
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image_proj_model = MLPProjModel(
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cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
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clip_embeddings_dim=self.image_encoder.config.hidden_size,
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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|
|
|
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class IPAdapterPlusXL(IPAdapter):
|
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"""SDXL"""
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|
|
|
def init_proj(self):
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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=self.num_tokens,
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embedding_dim=self.image_encoder.config.hidden_size,
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output_dim=self.pipe.unet.config.cross_attention_dim,
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ff_mult=4,
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).to(self.device, dtype=torch.float16)
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return image_proj_model
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|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, pil_image):
|
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if isinstance(pil_image, Image.Image):
|
|
pil_image = [pil_image]
|
|
clip_image = self.clip_image_processor(
|
|
images=pil_image, return_tensors="pt"
|
|
).pixel_values
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clip_image = clip_image.to(self.device, dtype=torch.float16)
|
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clip_image_embeds = self.image_encoder(
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clip_image, output_hidden_states=True
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|
).hidden_states[-2]
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image_prompt_embeds = self.image_proj_model(clip_image_embeds)
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uncond_clip_image_embeds = self.image_encoder(
|
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torch.zeros_like(clip_image), output_hidden_states=True
|
|
).hidden_states[-2]
|
|
uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
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|
return image_prompt_embeds, uncond_image_prompt_embeds
|
|
|
|
def generate(
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self,
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pil_image,
|
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prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=4,
|
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seed=None,
|
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num_inference_steps=30,
|
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**kwargs,
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):
|
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self.set_scale(scale)
|
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|
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num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
|
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|
|
if prompt is None:
|
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prompt = "best quality, high quality"
|
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if negative_prompt is None:
|
|
negative_prompt = (
|
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"monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
)
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
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negative_prompt = [negative_prompt] * num_prompts
|
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|
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image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
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pil_image
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)
|
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bs_embed, seq_len, _ = image_prompt_embeds.shape
|
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image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(
|
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bs_embed * num_samples, seq_len, -1
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)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(
|
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1, num_samples, 1
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)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(
|
|
bs_embed * num_samples, seq_len, -1
|
|
)
|
|
|
|
with torch.inference_mode():
|
|
(
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds,
|
|
) = self.pipe.encode_prompt(
|
|
prompt,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat(
|
|
[negative_prompt_embeds, uncond_image_prompt_embeds], dim=1
|
|
)
|
|
|
|
generator = (
|
|
torch.Generator(self.device).manual_seed(seed) if seed is not None else None
|
|
)
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
pooled_prompt_embeds=pooled_prompt_embeds,
|
|
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
**kwargs,
|
|
).images
|
|
|
|
return images
|
|
|
|
|
|
def StyleProcessor(style_image, device):
|
|
print("Processing style image")
|
|
print("device", device)
|
|
transform = transforms.Compose(
|
|
[
|
|
transforms.ToTensor(),
|
|
transforms.Normalize([0.5], [0.5]),
|
|
]
|
|
)
|
|
print("Transformed style image")
|
|
# centercrop for style condition
|
|
crop = transforms.Compose(
|
|
[
|
|
transforms.Resize(512, interpolation=transforms.InterpolationMode.BILINEAR),
|
|
transforms.CenterCrop(512),
|
|
]
|
|
)
|
|
style_image = crop(style_image)
|
|
print("Cropped style image")
|
|
high_style_patch, middle_style_patch, low_style_patch = pre_processing(
|
|
style_image.convert("RGB"), transform
|
|
)
|
|
print("Preprocessed style image")
|
|
# shuffling
|
|
high_style_patch, middle_style_patch, low_style_patch = (
|
|
high_style_patch[torch.randperm(high_style_patch.shape[0])],
|
|
middle_style_patch[torch.randperm(middle_style_patch.shape[0])],
|
|
low_style_patch[torch.randperm(low_style_patch.shape[0])],
|
|
)
|
|
print("Shuffled style image")
|
|
return (
|
|
high_style_patch.to(device, dtype=torch.float32),
|
|
middle_style_patch.to(device, dtype=torch.float32),
|
|
low_style_patch.to(device, dtype=torch.float32),
|
|
)
|
|
|
|
|
|
class StyleShot(torch.nn.Module):
|
|
"""StyleShot generation"""
|
|
|
|
def __init__(
|
|
self, device, pipe, ip_ckpt, style_aware_encoder_ckpt, transformer_patch
|
|
):
|
|
super().__init__()
|
|
self.num_tokens = 6
|
|
self.device = device
|
|
self.pipe = pipe
|
|
|
|
self.set_ip_adapter(device)
|
|
self.ip_ckpt = ip_ckpt
|
|
|
|
self.style_aware_encoder = Style_Aware_Encoder(
|
|
CLIPVisionModelWithProjection.from_pretrained(transformer_patch)
|
|
).to(self.device, dtype=torch.float32)
|
|
self.style_aware_encoder.load_state_dict(torch.load(style_aware_encoder_ckpt))
|
|
|
|
self.style_image_proj_modules = self.init_proj()
|
|
|
|
self.load_ip_adapter()
|
|
self.pipe = self.pipe.to(self.device, dtype=torch.float32)
|
|
|
|
self.results = []
|
|
|
|
def __len__(self):
|
|
return len(self.results)
|
|
|
|
def __getitem__(self, index):
|
|
return self.results[index]
|
|
|
|
def init_proj(self):
|
|
style_image_proj_modules = torch.nn.ModuleList(
|
|
[
|
|
ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.style_aware_encoder.projection_dim,
|
|
clip_extra_context_tokens=2,
|
|
),
|
|
ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.style_aware_encoder.projection_dim,
|
|
clip_extra_context_tokens=2,
|
|
),
|
|
ImageProjModel(
|
|
cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
|
|
clip_embeddings_dim=self.style_aware_encoder.projection_dim,
|
|
clip_extra_context_tokens=2,
|
|
),
|
|
]
|
|
)
|
|
return style_image_proj_modules.to(self.device, dtype=torch.float32)
|
|
|
|
def load_ip_adapter(self):
|
|
sd = torch.load(self.ip_ckpt, map_location="cpu")
|
|
style_image_proj_sd = {}
|
|
ip_sd = {}
|
|
controlnet_sd = {}
|
|
for k in sd:
|
|
if k.startswith("unet"):
|
|
pass
|
|
elif k.startswith("style_image_proj_modules"):
|
|
style_image_proj_sd[k.replace("style_image_proj_modules.", "")] = sd[k]
|
|
elif k.startswith("adapter_modules"):
|
|
ip_sd[k.replace("adapter_modules.", "")] = sd[k]
|
|
elif k.startswith("controlnet"):
|
|
controlnet_sd[k.replace("controlnet.", "")] = sd[k]
|
|
# Load state dict for image_proj_model and adapter_modules
|
|
self.style_image_proj_modules.load_state_dict(style_image_proj_sd, strict=True)
|
|
ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
|
|
if hasattr(self.pipe, "controlnet") and isinstance(
|
|
self.pipe, StyleContentStableDiffusionControlNetPipeline
|
|
):
|
|
self.pipe.controlnet.load_state_dict(controlnet_sd, strict=True)
|
|
ip_layers.load_state_dict(ip_sd, strict=True)
|
|
|
|
def set_ip_adapter(self, device):
|
|
unet = self.pipe.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()
|
|
else:
|
|
attn_procs[name] = IPAttnProcessor(
|
|
hidden_size=hidden_size,
|
|
cross_attention_dim=cross_attention_dim,
|
|
scale=1.0,
|
|
num_tokens=self.num_tokens,
|
|
).to(device, dtype=torch.float16)
|
|
if hasattr(self.pipe, "controlnet") and not isinstance(
|
|
self.pipe, StyleContentStableDiffusionControlNetPipeline
|
|
):
|
|
if isinstance(self.pipe.controlnet, MultiControlNetModel):
|
|
for controlnet in self.pipe.controlnet.nets:
|
|
controlnet.set_attn_processor(
|
|
CNAttnProcessor(num_tokens=self.num_tokens)
|
|
)
|
|
else:
|
|
self.pipe.controlnet.set_attn_processor(
|
|
CNAttnProcessor(num_tokens=self.num_tokens)
|
|
)
|
|
unet.set_attn_processor(attn_procs)
|
|
|
|
@torch.inference_mode()
|
|
def get_image_embeds(self, style_image=None):
|
|
print("Getting image embeds")
|
|
style_image = StyleProcessor(style_image, self.device)
|
|
print("Got style image")
|
|
style_embeds = self.style_aware_encoder(style_image).to(
|
|
self.device, dtype=torch.float32
|
|
)
|
|
print("Got style embeds")
|
|
style_ip_tokens = []
|
|
uncond_style_ip_tokens = []
|
|
print("Starting loop")
|
|
for idx, style_embed in enumerate(
|
|
[style_embeds[:, 0, :], style_embeds[:, 1, :], style_embeds[:, 2, :]]
|
|
):
|
|
style_ip_tokens.append(self.style_image_proj_modules[idx](style_embed))
|
|
uncond_style_ip_tokens.append(
|
|
self.style_image_proj_modules[idx](torch.zeros_like(style_embed))
|
|
)
|
|
print("Finished loop")
|
|
style_ip_tokens = torch.cat(style_ip_tokens, dim=1)
|
|
print("Got style ip tokens")
|
|
uncond_style_ip_tokens = torch.cat(uncond_style_ip_tokens, dim=1)
|
|
print("Got image embeds")
|
|
return style_ip_tokens, uncond_style_ip_tokens
|
|
|
|
def set_scale(self, scale):
|
|
for attn_processor in self.pipe.unet.attn_processors.values():
|
|
if isinstance(attn_processor, IPAttnProcessor):
|
|
attn_processor.scale = scale
|
|
|
|
def samples(
|
|
self,
|
|
image_prompt_embeds,
|
|
uncond_image_prompt_embeds,
|
|
num_samples,
|
|
device,
|
|
prompt,
|
|
negative_prompt,
|
|
seed,
|
|
guidance_scale,
|
|
num_inference_steps,
|
|
content_image,
|
|
**kwargs,
|
|
):
|
|
print("Get prompt embeds")
|
|
bs_embed, seq_len, _ = image_prompt_embeds.shape
|
|
image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
|
|
image_prompt_embeds = image_prompt_embeds.view(
|
|
bs_embed * num_samples, seq_len, -1
|
|
)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(
|
|
1, num_samples, 1
|
|
)
|
|
uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(
|
|
bs_embed * num_samples, seq_len, -1
|
|
)
|
|
with torch.inference_mode():
|
|
prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
|
|
prompt,
|
|
device=device,
|
|
num_images_per_prompt=num_samples,
|
|
do_classifier_free_guidance=True,
|
|
negative_prompt=negative_prompt,
|
|
)
|
|
prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
|
|
negative_prompt_embeds = torch.cat(
|
|
[negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1
|
|
)
|
|
print("Got prompt embeds")
|
|
generator = (
|
|
torch.Generator(device).manual_seed(seed) if seed is not None else None
|
|
)
|
|
print("Start generating")
|
|
if content_image is None:
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
**kwargs,
|
|
).images
|
|
else:
|
|
images = self.pipe(
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
guidance_scale=guidance_scale,
|
|
num_inference_steps=num_inference_steps,
|
|
generator=generator,
|
|
image=content_image,
|
|
style_embeddings=image_prompt_embeds,
|
|
negative_style_embeddings=uncond_image_prompt_embeds,
|
|
**kwargs,
|
|
).images
|
|
return images
|
|
|
|
def generate(
|
|
self,
|
|
style_image=None,
|
|
prompt=None,
|
|
negative_prompt=None,
|
|
scale=1.0,
|
|
num_samples=1,
|
|
seed=42,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=50,
|
|
content_image=None,
|
|
**kwargs,
|
|
):
|
|
print("Setting scale")
|
|
self.set_scale(scale)
|
|
|
|
num_prompts = 1
|
|
|
|
if prompt is None:
|
|
prompt = "best quality, high quality"
|
|
if negative_prompt is None:
|
|
negative_prompt = (
|
|
"monochrome, lowres, bad anatomy, worst quality, low quality"
|
|
)
|
|
|
|
if not isinstance(prompt, List):
|
|
prompt = [prompt] * num_prompts
|
|
if not isinstance(negative_prompt, List):
|
|
negative_prompt = [negative_prompt] * num_prompts
|
|
print("Getting image embeds")
|
|
style_ip_tokens, uncond_style_ip_tokens = self.get_image_embeds(style_image)
|
|
print("Getting image embeds")
|
|
generate_images = []
|
|
for p in prompt:
|
|
print("Generating images")
|
|
images = self.samples(
|
|
style_ip_tokens,
|
|
uncond_style_ip_tokens,
|
|
num_samples,
|
|
self.device,
|
|
p * num_prompts,
|
|
negative_prompt,
|
|
seed,
|
|
guidance_scale,
|
|
num_inference_steps,
|
|
content_image,
|
|
**kwargs,
|
|
)
|
|
generate_images.append(images)
|
|
print("Images generated")
|
|
return generate_images
|
|
|
|
|
|
class StyleContentStableDiffusionControlNetPipeline(StableDiffusionControlNetPipeline):
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
image: PipelineImageInput = None,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
timesteps: List[int] = None,
|
|
guidance_scale: float = 7.5,
|
|
negative_prompt: 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,
|
|
ip_adapter_image: Optional[PipelineImageInput] = None,
|
|
ip_adapter_image_embeds: Optional[List[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,
|
|
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"],
|
|
style_embeddings: Optional[torch.FloatTensor] = None,
|
|
negative_style_embeddings: Optional[torch.FloatTensor] = None,
|
|
**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`.
|
|
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. When `prompt` is a list, and if a list of images is passed for a single ControlNet,
|
|
each will be paired with each prompt in the `prompt` list. This also applies to multiple ControlNets,
|
|
where a list of image lists can be passed to batch for each prompt and each ControlNet.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated image.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated image.
|
|
num_inference_steps (`int`, *optional*, defaults to 50):
|
|
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
|
|
expense of slower inference.
|
|
timesteps (`List[int]`, *optional*):
|
|
Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
|
|
in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
|
|
passed will be used. Must be in descending order.
|
|
guidance_scale (`float`, *optional*, defaults to 7.5):
|
|
A higher guidance scale value encourages the model to generate images closely linked to the text
|
|
`prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
|
|
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`).
|
|
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.
|
|
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
|
|
ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*):
|
|
Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters.
|
|
Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding
|
|
if `do_classifier_free_guidance` is set to `True`.
|
|
If not provided, embeddings are computed from the `ip_adapter_image` input argument.
|
|
output_type (`str`, *optional*, defaults to `"pil"`):
|
|
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
|
|
return_dict (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
|
|
plain tuple.
|
|
callback (`Callable`, *optional*):
|
|
A function that calls every `callback_steps` steps during inference. The function is called with the
|
|
following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
|
|
callback_steps (`int`, *optional*, defaults to 1):
|
|
The frequency at which the `callback` function is called. If not specified, the callback is called at
|
|
every step.
|
|
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.
|
|
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 where the first element is a list with the generated images and the
|
|
second element is a list of `bool`s indicating whether the corresponding generated image contains
|
|
"not-safe-for-work" (nsfw) content.
|
|
"""
|
|
|
|
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,
|
|
image,
|
|
callback_steps,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
ip_adapter_image,
|
|
ip_adapter_image_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. 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 = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_images_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None:
|
|
image_embeds = self.prepare_ip_adapter_image_embeds(
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
device,
|
|
batch_size * num_images_per_prompt,
|
|
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 = []
|
|
|
|
# Nested lists as ControlNet condition
|
|
if isinstance(image[0], list):
|
|
# Transpose the nested image list
|
|
image = [list(t) for t in zip(*image)]
|
|
|
|
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
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler, num_inference_steps, device, 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 Add image embeds for IP-Adapter
|
|
added_cond_kwargs = (
|
|
{"image_embeds": image_embeds}
|
|
if ip_adapter_image is not None or ip_adapter_image_embeds is not None
|
|
else None
|
|
)
|
|
|
|
# 7.2 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
|
|
)
|
|
|
|
# 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
|
|
)
|
|
|
|
# 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]
|
|
else:
|
|
control_model_input = latent_model_input
|
|
controlnet_prompt_embeds = prompt_embeds
|
|
|
|
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]
|
|
|
|
if self.do_classifier_free_guidance:
|
|
style_embeddings_input = torch.cat(
|
|
[negative_style_embeddings, style_embeddings]
|
|
)
|
|
|
|
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
|
control_model_input,
|
|
t,
|
|
encoder_hidden_states=style_embeddings_input,
|
|
controlnet_cond=image,
|
|
conditioning_scale=cond_scale,
|
|
guess_mode=guess_mode,
|
|
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=prompt_embeds,
|
|
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 + self.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 we do sequential model offloading, let's offload unet and controlnet
|
|
# manually for max memory savings
|
|
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
|
|
self.unet.to("cpu")
|
|
self.controlnet.to("cpu")
|
|
torch.cuda.empty_cache()
|
|
|
|
if not output_type == "latent":
|
|
image = self.vae.decode(
|
|
latents / self.vae.config.scaling_factor,
|
|
return_dict=False,
|
|
generator=generator,
|
|
)[0]
|
|
image, has_nsfw_concept = self.run_safety_checker(
|
|
image, device, prompt_embeds.dtype
|
|
)
|
|
else:
|
|
image = latents
|
|
has_nsfw_concept = None
|
|
|
|
if has_nsfw_concept is None:
|
|
do_denormalize = [True] * image.shape[0]
|
|
else:
|
|
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
|
|
|
|
image = self.image_processor.postprocess(
|
|
image, output_type=output_type, do_denormalize=do_denormalize
|
|
)
|
|
|
|
# Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (image, has_nsfw_concept)
|
|
|
|
return StableDiffusionPipelineOutput(
|
|
images=image, nsfw_content_detected=has_nsfw_concept
|
|
)
|