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Added support for FLUX.1-Kontext-dev

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Jaret Burkett
2025-06-26 15:24:37 -06:00
parent 8d9c47316a
commit 60ef2f1df7
12 changed files with 570 additions and 4 deletions
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3
extensions_built_in/diffusion_models/__init__.py
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@@ -2,8 +2,9 @@ from .chroma import ChromaModel
from .hidream import HidreamModel
from .f_light import FLiteModel
from .omnigen2 import OmniGen2Model
from .flux_kontext import FluxKontextModel
AI_TOOLKIT_MODELS = [
# put a list of models here
ChromaModel, HidreamModel, FLiteModel, OmniGen2Model
ChromaModel, HidreamModel, FLiteModel, OmniGen2Model, FluxKontextModel
]

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extensions_built_in/diffusion_models/flux_kontext/__init__.py Normal file
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@@ -0,0 +1 @@
from .flux_kontext import FluxKontextModel

400
extensions_built_in/diffusion_models/flux_kontext/flux_kontext.py Normal file
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import os
from typing import TYPE_CHECKING, List
import torch
import torchvision
import yaml
from toolkit import train_tools
from toolkit.config_modules import GenerateImageConfig, ModelConfig
from PIL import Image
from toolkit.models.base_model import BaseModel
from diffusers import FluxTransformer2DModel, AutoencoderKL, FluxKontextPipeline
from toolkit.basic import flush
from toolkit.prompt_utils import PromptEmbeds
from toolkit.samplers.custom_flowmatch_sampler import CustomFlowMatchEulerDiscreteScheduler
from toolkit.models.flux import add_model_gpu_splitter_to_flux, bypass_flux_guidance, restore_flux_guidance
from toolkit.dequantize import patch_dequantization_on_save
from toolkit.accelerator import get_accelerator, unwrap_model
from optimum.quanto import freeze, QTensor
from toolkit.util.mask import generate_random_mask, random_dialate_mask
from toolkit.util.quantize import quantize, get_qtype
from transformers import T5TokenizerFast, T5EncoderModel, CLIPTextModel, CLIPTokenizer
from einops import rearrange, repeat
import random
import torch.nn.functional as F
if TYPE_CHECKING:
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
scheduler_config = {
"base_image_seq_len": 256,
"base_shift": 0.5,
"max_image_seq_len": 4096,
"max_shift": 1.15,
"num_train_timesteps": 1000,
"shift": 3.0,
"use_dynamic_shifting": True
}
class FluxKontextModel(BaseModel):
arch = "flux_kontext"
def __init__(
self,
device,
model_config: ModelConfig,
dtype='bf16',
custom_pipeline=None,
noise_scheduler=None,
**kwargs
):
super().__init__(
device,
model_config,
dtype,
custom_pipeline,
noise_scheduler,
**kwargs
)
self.is_flow_matching = True
self.is_transformer = True
self.target_lora_modules = ['FluxTransformer2DModel']
# static method to get the noise scheduler
@staticmethod
def get_train_scheduler():
return CustomFlowMatchEulerDiscreteScheduler(**scheduler_config)
def get_bucket_divisibility(self):
return 16
def load_model(self):
dtype = self.torch_dtype
self.print_and_status_update("Loading Flux Kontext model")
# will be updated if we detect a existing checkpoint in training folder
model_path = self.model_config.name_or_path
# this is the original path put in the model directory
# it is here because for finetuning we only save the transformer usually
# so we need this for the VAE, te, etc
base_model_path = self.model_config.extras_name_or_path
transformer_path = model_path
transformer_subfolder = 'transformer'
if os.path.exists(transformer_path):
transformer_subfolder = None
transformer_path = os.path.join(transformer_path, 'transformer')
# check if the path is a full checkpoint.
te_folder_path = os.path.join(model_path, 'text_encoder')
# if we have the te, this folder is a full checkpoint, use it as the base
if os.path.exists(te_folder_path):
base_model_path = model_path
self.print_and_status_update("Loading transformer")
transformer = FluxTransformer2DModel.from_pretrained(
transformer_path,
subfolder=transformer_subfolder,
torch_dtype=dtype,
revision="7610c9af"
)
transformer.to(self.quantize_device, dtype=dtype)
if self.model_config.quantize:
# patch the state dict method
patch_dequantization_on_save(transformer)
quantization_type = get_qtype(self.model_config.qtype)
self.print_and_status_update("Quantizing transformer")
quantize(transformer, weights=quantization_type,
**self.model_config.quantize_kwargs)
freeze(transformer)
transformer.to(self.device_torch)
else:
transformer.to(self.device_torch, dtype=dtype)
flush()
self.print_and_status_update("Loading T5")
tokenizer_2 = T5TokenizerFast.from_pretrained(
base_model_path, subfolder="tokenizer_2", torch_dtype=dtype
)
text_encoder_2 = T5EncoderModel.from_pretrained(
base_model_path, subfolder="text_encoder_2", torch_dtype=dtype
)
text_encoder_2.to(self.device_torch, dtype=dtype)
flush()
if self.model_config.quantize_te:
self.print_and_status_update("Quantizing T5")
quantize(text_encoder_2, weights=get_qtype(
self.model_config.qtype))
freeze(text_encoder_2)
flush()
self.print_and_status_update("Loading CLIP")
text_encoder = CLIPTextModel.from_pretrained(
base_model_path, subfolder="text_encoder", torch_dtype=dtype)
tokenizer = CLIPTokenizer.from_pretrained(
base_model_path, subfolder="tokenizer", torch_dtype=dtype)
text_encoder.to(self.device_torch, dtype=dtype)
self.print_and_status_update("Loading VAE")
vae = AutoencoderKL.from_pretrained(
base_model_path, subfolder="vae", torch_dtype=dtype)
self.noise_scheduler = FluxKontextModel.get_train_scheduler()
self.print_and_status_update("Making pipe")
pipe: FluxKontextPipeline = FluxKontextPipeline(
scheduler=self.noise_scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=None,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=None,
)
# for quantization, it works best to do these after making the pipe
pipe.text_encoder_2 = text_encoder_2
pipe.transformer = transformer
self.print_and_status_update("Preparing Model")
text_encoder = [pipe.text_encoder, pipe.text_encoder_2]
tokenizer = [pipe.tokenizer, pipe.tokenizer_2]
pipe.transformer = pipe.transformer.to(self.device_torch)
flush()
# just to make sure everything is on the right device and dtype
text_encoder[0].to(self.device_torch)
text_encoder[0].requires_grad_(False)
text_encoder[0].eval()
text_encoder[1].to(self.device_torch)
text_encoder[1].requires_grad_(False)
text_encoder[1].eval()
pipe.transformer = pipe.transformer.to(self.device_torch)
flush()
# save it to the model class
self.vae = vae
self.text_encoder = text_encoder # list of text encoders
self.tokenizer = tokenizer # list of tokenizers
self.model = pipe.transformer
self.pipeline = pipe
self.print_and_status_update("Model Loaded")
def get_generation_pipeline(self):
scheduler = FluxKontextModel.get_train_scheduler()
pipeline: FluxKontextPipeline = FluxKontextPipeline(
scheduler=scheduler,
text_encoder=unwrap_model(self.text_encoder[0]),
tokenizer=self.tokenizer[0],
text_encoder_2=unwrap_model(self.text_encoder[1]),
tokenizer_2=self.tokenizer[1],
vae=unwrap_model(self.vae),
transformer=unwrap_model(self.transformer)
)
pipeline = pipeline.to(self.device_torch)
return pipeline
def generate_single_image(
self,
pipeline: FluxKontextPipeline,
gen_config: GenerateImageConfig,
conditional_embeds: PromptEmbeds,
unconditional_embeds: PromptEmbeds,
generator: torch.Generator,
extra: dict,
):
if gen_config.ctrl_img is None:
raise ValueError(
"Control image is required for Flux Kontext model generation."
)
else:
control_img = Image.open(gen_config.ctrl_img)
control_img = control_img.convert("RGB")
img = pipeline(
image=control_img,
prompt_embeds=conditional_embeds.text_embeds,
pooled_prompt_embeds=conditional_embeds.pooled_embeds,
height=gen_config.height,
width=gen_config.width,
num_inference_steps=gen_config.num_inference_steps,
guidance_scale=gen_config.guidance_scale,
latents=gen_config.latents,
generator=generator,
**extra
).images[0]
return img
def get_noise_prediction(
self,
latent_model_input: torch.Tensor,
timestep: torch.Tensor, # 0 to 1000 scale
text_embeddings: PromptEmbeds,
guidance_embedding_scale: float,
bypass_guidance_embedding: bool,
**kwargs
):
with torch.no_grad():
bs, c, h, w = latent_model_input.shape
# if we have a control on the channel dimension, put it on the batch for packing
has_control = False
if latent_model_input.shape[1] == 32:
# chunk it and stack it on batch dimension
# dont update batch size for img_its
lat, control = torch.chunk(latent_model_input, 2, dim=1)
latent_model_input = torch.cat([lat, control], dim=0)
has_control = True
latent_model_input_packed = rearrange(
latent_model_input,
"b c (h ph) (w pw) -> b (h w) (c ph pw)",
ph=2,
pw=2
)
img_ids = torch.zeros(h // 2, w // 2, 3)
img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2)[:, None]
img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2)[None, :]
img_ids = repeat(img_ids, "h w c -> b (h w) c",
b=bs).to(self.device_torch)
# handle control image ids
if has_control:
ctrl_ids = img_ids.clone()
ctrl_ids[..., 0] = 1
img_ids = torch.cat([img_ids, ctrl_ids], dim=1)
txt_ids = torch.zeros(
bs, text_embeddings.text_embeds.shape[1], 3).to(self.device_torch)
# # handle guidance
if self.unet_unwrapped.config.guidance_embeds:
if isinstance(guidance_embedding_scale, list):
guidance = torch.tensor(
guidance_embedding_scale, device=self.device_torch)
else:
guidance = torch.tensor(
[guidance_embedding_scale], device=self.device_torch)
guidance = guidance.expand(latent_model_input.shape[0])
else:
guidance = None
if bypass_guidance_embedding:
bypass_flux_guidance(self.unet)
cast_dtype = self.unet.dtype
# changes from orig implementation
if txt_ids.ndim == 3:
txt_ids = txt_ids[0]
if img_ids.ndim == 3:
img_ids = img_ids[0]
latent_size = latent_model_input_packed.shape[1]
# move the kontext channels. We have them on batch dimension to here, but need to put them on the latent dimension
if has_control:
latent, control = torch.chunk(latent_model_input_packed, 2, dim=0)
latent_model_input_packed = torch.cat(
[latent, control], dim=1
)
latent_size = latent.shape[1]
noise_pred = self.unet(
hidden_states=latent_model_input_packed.to(
self.device_torch, cast_dtype),
timestep=timestep / 1000,
encoder_hidden_states=text_embeddings.text_embeds.to(
self.device_torch, cast_dtype),
pooled_projections=text_embeddings.pooled_embeds.to(
self.device_torch, cast_dtype),
txt_ids=txt_ids,
img_ids=img_ids,
guidance=guidance,
return_dict=False,
**kwargs,
)[0]
# remove kontext image conditioning
noise_pred = noise_pred[:, :latent_size]
if isinstance(noise_pred, QTensor):
noise_pred = noise_pred.dequantize()
noise_pred = rearrange(
noise_pred,
"b (h w) (c ph pw) -> b c (h ph) (w pw)",
h=latent_model_input.shape[2] // 2,
w=latent_model_input.shape[3] // 2,
ph=2,
pw=2,
c=self.vae.config.latent_channels
)
if bypass_guidance_embedding:
restore_flux_guidance(self.unet)
return noise_pred
def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:
if self.pipeline.text_encoder.device != self.device_torch:
self.pipeline.text_encoder.to(self.device_torch)
prompt_embeds, pooled_prompt_embeds = train_tools.encode_prompts_flux(
self.tokenizer,
self.text_encoder,
prompt,
max_length=512,
)
pe = PromptEmbeds(
prompt_embeds
)
pe.pooled_embeds = pooled_prompt_embeds
return pe
def get_model_has_grad(self):
# return from a weight if it has grad
return self.model.proj_out.weight.requires_grad
def get_te_has_grad(self):
# return from a weight if it has grad
return self.text_encoder[1].encoder.block[0].layer[0].SelfAttention.q.weight.requires_grad
def save_model(self, output_path, meta, save_dtype):
# only save the unet
transformer: FluxTransformer2DModel = unwrap_model(self.model)
transformer.save_pretrained(
save_directory=os.path.join(output_path, 'transformer'),
safe_serialization=True,
)
meta_path = os.path.join(output_path, 'aitk_meta.yaml')
with open(meta_path, 'w') as f:
yaml.dump(meta, f)
def get_loss_target(self, *args, **kwargs):
noise = kwargs.get('noise')
batch = kwargs.get('batch')
return (noise - batch.latents).detach()
def condition_noisy_latents(self, latents: torch.Tensor, batch:'DataLoaderBatchDTO'):
with torch.no_grad():
control_tensor = batch.control_tensor
if control_tensor is not None:
# we are not packed here, so we just need to pass them so we can pack them later
control_tensor = control_tensor * 2 - 1
control_tensor = control_tensor.to(self.vae_device_torch, dtype=self.torch_dtype)
# if it is not the size of batch.tensor, (bs,ch,h,w) then we need to resize it
if control_tensor.shape[2] != batch.tensor.shape[2] or control_tensor.shape[3] != batch.tensor.shape[3]:
control_tensor = F.interpolate(control_tensor, size=(batch.tensor.shape[2], batch.tensor.shape[3]), mode='bilinear')
control_latent = self.encode_images(control_tensor).to(latents.device, latents.dtype)
latents = torch.cat((latents, control_latent), dim=1)
return latents.detach()