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Zero
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import torch
from PIL import Image
import time
import numpy as np
from einops import rearrange
from transformers import pipeline
from concept_attention.flux.src.flux.cli import SamplingOptions
from concept_attention.flux.src.flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
from concept_attention.flux.src.flux.util import configs, embed_watermark, load_ae, load_clip, load_t5
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file as load_sft
from concept_attention.modified_double_stream_block import ModifiedDoubleStreamBlock
from concept_attention.modified_flux_dit import ModifiedFluxDiT
from concept_attention.utils import embed_concepts
def load_flow_model(
name: str,
device: str | torch.device = "cuda",
hf_download: bool = True,
attention_block_class=ModifiedDoubleStreamBlock,
dit_class=ModifiedFluxDiT
):
# Loading Flux
print("Init model")
ckpt_path = configs[name].ckpt_path
if (
ckpt_path is None
and configs[name].repo_id is not None
and configs[name].repo_flow is not None
and hf_download
):
ckpt_path = hf_hub_download(configs[name].repo_id, configs[name].repo_flow)
with torch.device("meta" if ckpt_path is not None else device):
model = dit_class(configs[name].params, attention_block_class=attention_block_class).to(torch.bfloat16)
if ckpt_path is not None:
print("Loading checkpoint")
# load_sft doesn't support torch.device
sd = load_sft(ckpt_path, device=str(device))
missing, unexpected = model.load_state_dict(sd, strict=False, assign=True)
# print_load_warning(missing, unexpected)
return model
def get_models(
name: str,
device: torch.device,
offload: bool,
is_schnell: bool,
attention_block_class=ModifiedDoubleStreamBlock,
dit_class=ModifiedFluxDiT
):
t5 = load_t5(device, max_length=256 if is_schnell else 512)
clip = load_clip(device)
model = load_flow_model(name, device="cpu" if offload else device, attention_block_class=attention_block_class, dit_class=dit_class)
ae = load_ae(name, device="cpu" if offload else device)
return model, ae, t5, clip, None
class FluxGenerator():
def __init__(
self,
model_name: str,
device: str,
offload: bool,
attention_block_class=ModifiedDoubleStreamBlock,
dit_class=ModifiedFluxDiT
):
self.device = torch.device(device)
self.offload = offload
self.model_name = model_name
self.is_schnell = model_name == "flux-schnell"
self.model, self.ae, self.t5, self.clip, self.nsfw_classifier = get_models(
model_name,
device=self.device,
offload=self.offload,
is_schnell=self.is_schnell,
attention_block_class=attention_block_class,
dit_class=dit_class
)
@torch.inference_mode()
def generate_image(
self,
width,
height,
num_steps,
guidance,
seed,
prompt,
concepts,
init_image=None,
image2image_strength=0.0,
add_sampling_metadata=True,
restrict_clip_guidance=False,
joint_attention_kwargs=None,
):
seed = int(seed)
if seed == -1:
seed = None
opts = SamplingOptions(
prompt=prompt,
width=width,
height=height,
num_steps=num_steps,
guidance=guidance,
seed=seed,
)
if opts.seed is None:
opts.seed = torch.Generator(device="cpu").seed()
print(f"Generating '{opts.prompt}' with seed {opts.seed}")
t0 = time.perf_counter()
if init_image is not None:
if isinstance(init_image, np.ndarray):
init_image = torch.from_numpy(init_image).permute(2, 0, 1).float() / 255.0
init_image = init_image.unsqueeze(0)
init_image = init_image.to(self.device)
init_image = torch.nn.functional.interpolate(init_image, (opts.height, opts.width))
if self.offload:
self.ae.encoder.to(self.device)
init_image = self.ae.encode(init_image.to())
if self.offload:
self.ae = self.ae.cpu()
torch.cuda.empty_cache()
# prepare input
x = get_noise(
1,
opts.height,
opts.width,
device=self.device,
dtype=torch.bfloat16,
seed=opts.seed,
)
timesteps = get_schedule(
opts.num_steps,
x.shape[-1] * x.shape[-2] // 4,
shift=(not self.is_schnell),
)
if init_image is not None:
t_idx = int((1 - image2image_strength) * num_steps)
t = timesteps[t_idx]
timesteps = timesteps[t_idx:]
x = t * x + (1.0 - t) * init_image.to(x.dtype)
if self.offload:
self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device)
inp = prepare(t5=self.t5, clip=self.clip, img=x, prompt=opts.prompt, restrict_clip_guidance=restrict_clip_guidance)
############ Encode the concept ############
concept_embeddings, concept_ids, concept_vec = embed_concepts(
self.clip,
self.t5,
concepts,
)
inp["concepts"] = concept_embeddings.to(x.device)
inp["concept_ids"] = concept_ids.to(x.device)
inp["concept_vec"] = concept_vec.to(x.device)
###########################################
# offload TEs to CPU, load model to gpu
if self.offload:
self.t5, self.clip = self.t5.cpu(), self.clip.cpu()
torch.cuda.empty_cache()
self.model = self.model.to(self.device)
# denoise initial noise
x, intermediate_images, cross_attention_maps, concept_attention_maps = denoise(
self.model,
**inp,
timesteps=timesteps,
guidance=opts.guidance,
joint_attention_kwargs=joint_attention_kwargs
)
# offload model, load autoencoder to gpu
if self.offload:
self.model.cpu()
torch.cuda.empty_cache()
self.ae.decoder.to(x.device)
# decode latents to pixel space
x = unpack(x.float(), opts.height, opts.width)
with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16):
x = self.ae.decode(x)
if self.offload:
self.ae.decoder.cpu()
torch.cuda.empty_cache()
t1 = time.perf_counter()
print(f"Done in {t1 - t0:.1f}s.")
# bring into PIL format
x = x.clamp(-1, 1)
x = embed_watermark(x.float())
x = rearrange(x[0], "c h w -> h w c")
img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
return img, cross_attention_maps, concept_attention_maps |