551 lines
26 KiB
Python
551 lines
26 KiB
Python
# Copyright 2025 Tencent InstantX Team. All rights reserved.
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#
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from PIL import Image
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from einops import rearrange
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import torch
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from diffusers.pipelines.flux.pipeline_flux import *
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from transformers import SiglipVisionModel, SiglipImageProcessor, AutoModel, AutoImageProcessor
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from .models.attn_processor import FluxIPAttnProcessor
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from .models.resampler import CrossLayerCrossScaleProjector
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from .models.utils import flux_load_lora
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# TODO
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# EXAMPLE_DOC_STRING = """
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# Examples:
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# ```py
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# >>> import torch
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# >>> from diffusers import FluxPipeline
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# >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
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# >>> pipe.to("cuda")
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# >>> prompt = "A cat holding a sign that says hello world"
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# >>> # Depending on the variant being used, the pipeline call will slightly vary.
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# >>> # Refer to the pipeline documentation for more details.
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# >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
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# >>> image.save("flux.png")
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# ```
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# """
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class InstantCharacterFluxPipeline(FluxPipeline):
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@torch.inference_mode()
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def encode_siglip_image_emb(self, siglip_image, device, dtype):
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siglip_image = siglip_image.to(device, dtype=dtype)
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res = self.siglip_image_encoder(siglip_image, output_hidden_states=True)
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siglip_image_embeds = res.last_hidden_state
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siglip_image_shallow_embeds = torch.cat([res.hidden_states[i] for i in [7, 13, 26]], dim=1)
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return siglip_image_embeds, siglip_image_shallow_embeds
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@torch.inference_mode()
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def encode_dinov2_image_emb(self, dinov2_image, device, dtype):
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dinov2_image = dinov2_image.to(device, dtype=dtype)
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res = self.dino_image_encoder_2(dinov2_image, output_hidden_states=True)
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dinov2_image_embeds = res.last_hidden_state[:, 1:]
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dinov2_image_shallow_embeds = torch.cat([res.hidden_states[i][:, 1:] for i in [9, 19, 29]], dim=1)
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return dinov2_image_embeds, dinov2_image_shallow_embeds
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@torch.inference_mode()
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def encode_image_emb(self, siglip_image, device, dtype):
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object_image_pil = siglip_image
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object_image_pil_low_res = [object_image_pil.resize((384, 384))]
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object_image_pil_high_res = object_image_pil.resize((768, 768))
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object_image_pil_high_res = [
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object_image_pil_high_res.crop((0, 0, 384, 384)),
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object_image_pil_high_res.crop((384, 0, 768, 384)),
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object_image_pil_high_res.crop((0, 384, 384, 768)),
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object_image_pil_high_res.crop((384, 384, 768, 768)),
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]
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nb_split_image = len(object_image_pil_high_res)
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siglip_image_embeds = self.encode_siglip_image_emb(
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self.siglip_image_processor(images=object_image_pil_low_res, return_tensors="pt").pixel_values,
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device,
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dtype
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)
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dinov2_image_embeds = self.encode_dinov2_image_emb(
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self.dino_image_processor_2(images=object_image_pil_low_res, return_tensors="pt").pixel_values,
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device,
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dtype
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)
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image_embeds_low_res_deep = torch.cat([siglip_image_embeds[0], dinov2_image_embeds[0]], dim=2)
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image_embeds_low_res_shallow = torch.cat([siglip_image_embeds[1], dinov2_image_embeds[1]], dim=2)
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siglip_image_high_res = self.siglip_image_processor(images=object_image_pil_high_res, return_tensors="pt").pixel_values
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siglip_image_high_res = siglip_image_high_res[None]
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siglip_image_high_res = rearrange(siglip_image_high_res, 'b n c h w -> (b n) c h w')
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siglip_image_high_res_embeds = self.encode_siglip_image_emb(siglip_image_high_res, device, dtype)
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siglip_image_high_res_deep = rearrange(siglip_image_high_res_embeds[0], '(b n) l c -> b (n l) c', n=nb_split_image)
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dinov2_image_high_res = self.dino_image_processor_2(images=object_image_pil_high_res, return_tensors="pt").pixel_values
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dinov2_image_high_res = dinov2_image_high_res[None]
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dinov2_image_high_res = rearrange(dinov2_image_high_res, 'b n c h w -> (b n) c h w')
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dinov2_image_high_res_embeds = self.encode_dinov2_image_emb(dinov2_image_high_res, device, dtype)
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dinov2_image_high_res_deep = rearrange(dinov2_image_high_res_embeds[0], '(b n) l c -> b (n l) c', n=nb_split_image)
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image_embeds_high_res_deep = torch.cat([siglip_image_high_res_deep, dinov2_image_high_res_deep], dim=2)
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image_embeds_dict = dict(
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image_embeds_low_res_shallow=image_embeds_low_res_shallow,
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image_embeds_low_res_deep=image_embeds_low_res_deep,
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image_embeds_high_res_deep=image_embeds_high_res_deep,
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)
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return image_embeds_dict
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@torch.inference_mode()
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def init_ccp_and_attn_processor(self, *args, **kwargs):
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subject_ip_adapter_path = kwargs['subject_ip_adapter_path']
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nb_token = kwargs['nb_token']
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state_dict = torch.load(subject_ip_adapter_path, map_location="cpu")
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device, dtype = self.transformer.device, self.transformer.dtype
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print(f"=> init attn processor")
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attn_procs = {}
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for idx_attn, (name, v) in enumerate(self.transformer.attn_processors.items()):
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attn_procs[name] = FluxIPAttnProcessor(
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hidden_size=self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads,
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ip_hidden_states_dim=self.text_encoder_2.config.d_model,
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).to(device, dtype=dtype)
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self.transformer.set_attn_processor(attn_procs)
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tmp_ip_layers = torch.nn.ModuleList(self.transformer.attn_processors.values())
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key_name = tmp_ip_layers.load_state_dict(state_dict["ip_adapter"], strict=False)
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print(f"=> load attn processor: {key_name}")
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print(f"=> init project")
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image_proj_model = CrossLayerCrossScaleProjector(
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inner_dim=1152 + 1536,
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num_attention_heads=42,
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attention_head_dim=64,
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cross_attention_dim=1152 + 1536,
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num_layers=4,
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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=nb_token,
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embedding_dim=1152 + 1536,
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output_dim=4096,
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ff_mult=4,
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timestep_in_dim=320,
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timestep_flip_sin_to_cos=True,
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timestep_freq_shift=0,
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)
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image_proj_model.eval()
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image_proj_model.to(device, dtype=dtype)
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key_name = image_proj_model.load_state_dict(state_dict["image_proj"], strict=False)
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print(f"=> load project: {key_name}")
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self.subject_image_proj_model = image_proj_model
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@torch.inference_mode()
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def init_adapter(
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self,
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image_encoder_path=None,
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image_encoder_2_path=None,
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subject_ipadapter_cfg=None,
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):
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device, dtype = self.transformer.device, self.transformer.dtype
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# image encoder
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print(f"=> loading image_encoder_1: {image_encoder_path}")
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image_encoder = SiglipVisionModel.from_pretrained(image_encoder_path)
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image_processor = SiglipImageProcessor.from_pretrained(image_encoder_path)
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image_encoder.eval()
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image_encoder.to(device, dtype=dtype)
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self.siglip_image_encoder = image_encoder
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self.siglip_image_processor = image_processor
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# image encoder 2
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print(f"=> loading image_encoder_2: {image_encoder_2_path}")
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image_encoder_2 = AutoModel.from_pretrained(image_encoder_2_path)
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image_processor_2 = AutoImageProcessor.from_pretrained(image_encoder_2_path)
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image_encoder_2.eval()
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image_encoder_2.to(device, dtype=dtype)
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image_processor_2.crop_size = dict(height=384, width=384)
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image_processor_2.size = dict(shortest_edge=384)
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self.dino_image_encoder_2 = image_encoder_2
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self.dino_image_processor_2 = image_processor_2
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# ccp and adapter
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self.init_ccp_and_attn_processor(**subject_ipadapter_cfg)
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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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negative_prompt: Union[str, List[str]] = None,
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negative_prompt_2: Optional[Union[str, List[str]]] = None,
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true_cfg_scale: float = 1.0,
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height: Optional[int] = None,
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width: Optional[int] = None,
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num_inference_steps: int = 28,
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sigmas: Optional[List[float]] = None,
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guidance_scale: float = 3.5,
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num_images_per_prompt: Optional[int] = 1,
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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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pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
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ip_adapter_image: Optional[PipelineImageInput] = None,
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ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
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negative_ip_adapter_image: Optional[PipelineImageInput] = None,
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negative_ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
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negative_prompt_embeds: Optional[torch.FloatTensor] = None,
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negative_pooled_prompt_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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joint_attention_kwargs: Optional[Dict[str, Any]] = 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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max_sequence_length: int = 512,
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subject_image: Image.Image = None,
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subject_scale: float = 0.8,
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):
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r"""
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Function invoked when calling 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 the image generation. If not defined, one has to pass `prompt_embeds`.
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instead.
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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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will be used instead
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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. This is set to 1024 by default for the best results.
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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. This is set to 1024 by default for the best results.
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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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sigmas (`List[float]`, *optional*):
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Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
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their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
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will be used.
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guidance_scale (`float`, *optional*, defaults to 7.0):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
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1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
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usually at the expense of lower image quality.
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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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generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
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One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
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to make 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 will ge 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, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `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, *e.g.* prompt weighting.
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If not provided, pooled text embeddings will be generated from `prompt` input argument.
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ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
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ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
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Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
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provided, embeddings are computed from the `ip_adapter_image` input argument.
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negative_ip_adapter_image:
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(`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
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negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
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Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
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IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not
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provided, embeddings are computed from the `ip_adapter_image` input argument.
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output_type (`str`, *optional*, defaults to `"pil"`):
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The output format of the generate image. Choose between
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[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.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.flux.FluxPipelineOutput`] instead of a plain tuple.
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joint_attention_kwargs (`dict`, *optional*):
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A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
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`self.processor` in
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[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
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callback_on_step_end (`Callable`, *optional*):
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A function that calls at the end of each denoising steps during the inference. The function is called
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with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
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callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
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`callback_on_step_end_tensor_inputs`.
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callback_on_step_end_tensor_inputs (`List`, *optional*):
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The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
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will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
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`._callback_tensor_inputs` attribute of your pipeline class.
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max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
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Examples:
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Returns:
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[`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
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is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
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images.
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"""
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height = height or self.default_sample_size * self.vae_scale_factor
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width = width or self.default_sample_size * self.vae_scale_factor
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# 1. Check inputs. Raise error if not correct
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self.check_inputs(
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prompt,
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prompt_2,
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height,
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width,
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negative_prompt=negative_prompt,
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negative_prompt_2=negative_prompt_2,
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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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callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
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max_sequence_length=max_sequence_length,
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)
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self._guidance_scale = guidance_scale
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self._joint_attention_kwargs = joint_attention_kwargs
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self._interrupt = False
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# 2. Define call parameters
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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device = self._execution_device
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dtype = self.transformer.dtype
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lora_scale = (
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self.joint_attention_kwargs.get("scale", None) if self.joint_attention_kwargs is not None else None
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)
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do_true_cfg = true_cfg_scale > 1 and negative_prompt is not None
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(
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prompt_embeds,
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pooled_prompt_embeds,
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text_ids,
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) = self.encode_prompt(
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prompt=prompt,
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prompt_2=prompt_2,
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prompt_embeds=prompt_embeds,
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pooled_prompt_embeds=pooled_prompt_embeds,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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max_sequence_length=max_sequence_length,
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lora_scale=lora_scale,
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)
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if do_true_cfg:
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(
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negative_prompt_embeds,
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negative_pooled_prompt_embeds,
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_,
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) = self.encode_prompt(
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prompt=negative_prompt,
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prompt_2=negative_prompt_2,
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prompt_embeds=negative_prompt_embeds,
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pooled_prompt_embeds=negative_pooled_prompt_embeds,
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device=device,
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num_images_per_prompt=num_images_per_prompt,
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max_sequence_length=max_sequence_length,
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lora_scale=lora_scale,
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)
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# 3.1 Prepare subject emb
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if subject_image is not None:
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subject_image = subject_image.resize((max(subject_image.size), max(subject_image.size)))
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subject_image_embeds_dict = self.encode_image_emb(subject_image, device, dtype)
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# 4. Prepare latent variables
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num_channels_latents = self.transformer.config.in_channels // 4
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latents, latent_image_ids = self.prepare_latents(
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batch_size * num_images_per_prompt,
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num_channels_latents,
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height,
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width,
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prompt_embeds.dtype,
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device,
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generator,
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latents,
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)
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# 5. Prepare timesteps
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sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps) if sigmas is None else sigmas
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image_seq_len = latents.shape[1]
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mu = calculate_shift(
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image_seq_len,
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self.scheduler.config.base_image_seq_len,
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self.scheduler.config.max_image_seq_len,
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self.scheduler.config.base_shift,
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self.scheduler.config.max_shift,
|
|
)
|
|
timesteps, num_inference_steps = retrieve_timesteps(
|
|
self.scheduler,
|
|
num_inference_steps,
|
|
device,
|
|
sigmas=sigmas,
|
|
mu=mu,
|
|
)
|
|
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# handle guidance
|
|
if self.transformer.config.guidance_embeds:
|
|
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
|
|
guidance = guidance.expand(latents.shape[0])
|
|
else:
|
|
guidance = None
|
|
|
|
if (ip_adapter_image is not None or ip_adapter_image_embeds is not None) and (
|
|
negative_ip_adapter_image is None and negative_ip_adapter_image_embeds is None
|
|
):
|
|
negative_ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
|
|
elif (ip_adapter_image is None and ip_adapter_image_embeds is None) and (
|
|
negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None
|
|
):
|
|
ip_adapter_image = np.zeros((width, height, 3), dtype=np.uint8)
|
|
|
|
if self.joint_attention_kwargs is None:
|
|
self._joint_attention_kwargs = {}
|
|
|
|
image_embeds = None
|
|
negative_image_embeds = None
|
|
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,
|
|
)
|
|
if negative_ip_adapter_image is not None or negative_ip_adapter_image_embeds is not None:
|
|
negative_image_embeds = self.prepare_ip_adapter_image_embeds(
|
|
negative_ip_adapter_image,
|
|
negative_ip_adapter_image_embeds,
|
|
device,
|
|
batch_size * num_images_per_prompt,
|
|
)
|
|
|
|
# 6. Denoising loop
|
|
with self.progress_bar(total=num_inference_steps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
if self.interrupt:
|
|
continue
|
|
|
|
if image_embeds is not None:
|
|
self._joint_attention_kwargs["ip_adapter_image_embeds"] = image_embeds
|
|
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
|
|
timestep = t.expand(latents.shape[0]).to(latents.dtype)
|
|
|
|
|
|
# subject adapter
|
|
if subject_image is not None:
|
|
subject_image_prompt_embeds = self.subject_image_proj_model(
|
|
low_res_shallow=subject_image_embeds_dict['image_embeds_low_res_shallow'],
|
|
low_res_deep=subject_image_embeds_dict['image_embeds_low_res_deep'],
|
|
high_res_deep=subject_image_embeds_dict['image_embeds_high_res_deep'],
|
|
timesteps=timestep.to(dtype=latents.dtype),
|
|
need_temb=True
|
|
)[0]
|
|
self._joint_attention_kwargs['emb_dict'] = dict(
|
|
length_encoder_hidden_states=prompt_embeds.shape[1]
|
|
)
|
|
self._joint_attention_kwargs['subject_emb_dict'] = dict(
|
|
ip_hidden_states=subject_image_prompt_embeds,
|
|
scale=subject_scale,
|
|
)
|
|
|
|
noise_pred = self.transformer(
|
|
hidden_states=latents,
|
|
timestep=timestep / 1000,
|
|
guidance=guidance,
|
|
pooled_projections=pooled_prompt_embeds,
|
|
encoder_hidden_states=prompt_embeds,
|
|
txt_ids=text_ids,
|
|
img_ids=latent_image_ids,
|
|
joint_attention_kwargs=self.joint_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
|
|
if do_true_cfg:
|
|
if negative_image_embeds is not None:
|
|
self._joint_attention_kwargs["ip_adapter_image_embeds"] = negative_image_embeds
|
|
neg_noise_pred = self.transformer(
|
|
hidden_states=latents,
|
|
timestep=timestep / 1000,
|
|
guidance=guidance,
|
|
pooled_projections=negative_pooled_prompt_embeds,
|
|
encoder_hidden_states=negative_prompt_embeds,
|
|
txt_ids=text_ids,
|
|
img_ids=latent_image_ids,
|
|
joint_attention_kwargs=self.joint_attention_kwargs,
|
|
return_dict=False,
|
|
)[0]
|
|
noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
|
|
|
|
# compute the previous noisy sample x_t -> x_t-1
|
|
latents_dtype = latents.dtype
|
|
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
|
|
|
|
if latents.dtype != latents_dtype:
|
|
if torch.backends.mps.is_available():
|
|
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
|
|
latents = latents.to(latents_dtype)
|
|
|
|
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)
|
|
|
|
# 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 XLA_AVAILABLE:
|
|
xm.mark_step()
|
|
|
|
if output_type == "latent":
|
|
image = latents
|
|
|
|
else:
|
|
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
|
|
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
|
|
image = self.vae.decode(latents, return_dict=False)[0]
|
|
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 FluxPipelineOutput(images=image)
|
|
|
|
|
|
def with_style_lora(self, lora_file_path, lora_weight=1.0, trigger='', *args, **kwargs):
|
|
flux_load_lora(self, lora_file_path, lora_weight)
|
|
kwargs['prompt'] = f"{trigger}, {kwargs['prompt']}"
|
|
res = self.__call__(*args, **kwargs)
|
|
flux_load_lora(self, lora_file_path, -lora_weight)
|
|
return res
|
|
|