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408 lines
14 KiB
Python
408 lines
14 KiB
Python
# Minimum Inference Code for FLUX
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import argparse
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import datetime
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import math
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import os
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import random
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from typing import Callable, List, Optional, Tuple
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import einops
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import numpy as np
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import torch
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from tqdm import tqdm
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from PIL import Image
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import accelerate
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from library import device_utils
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from library.device_utils import init_ipex, get_preferred_device
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init_ipex()
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from library.utils import setup_logging, str_to_dtype
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setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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import networks.lora_flux as lora_flux
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from library import flux_models, flux_utils, sd3_utils, strategy_flux
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def time_shift(mu: float, sigma: float, t: torch.Tensor):
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return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
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def get_lin_function(x1: float = 256, y1: float = 0.5, x2: float = 4096, y2: float = 1.15) -> Callable[[float], float]:
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m = (y2 - y1) / (x2 - x1)
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b = y1 - m * x1
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return lambda x: m * x + b
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def get_schedule(
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num_steps: int,
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image_seq_len: int,
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base_shift: float = 0.5,
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max_shift: float = 1.15,
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shift: bool = True,
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) -> list[float]:
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# extra step for zero
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timesteps = torch.linspace(1, 0, num_steps + 1)
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# shifting the schedule to favor high timesteps for higher signal images
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if shift:
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# eastimate mu based on linear estimation between two points
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mu = get_lin_function(y1=base_shift, y2=max_shift)(image_seq_len)
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timesteps = time_shift(mu, 1.0, timesteps)
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return timesteps.tolist()
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def denoise(
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model: flux_models.Flux,
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img: torch.Tensor,
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img_ids: torch.Tensor,
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txt: torch.Tensor,
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txt_ids: torch.Tensor,
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vec: torch.Tensor,
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timesteps: list[float],
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guidance: float = 4.0,
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t5_attn_mask: Optional[torch.Tensor] = None,
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):
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# this is ignored for schnell
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guidance_vec = torch.full((img.shape[0],), guidance, device=img.device, dtype=img.dtype)
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for t_curr, t_prev in zip(tqdm(timesteps[:-1]), timesteps[1:]):
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t_vec = torch.full((img.shape[0],), t_curr, dtype=img.dtype, device=img.device)
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pred = model(
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img=img,
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img_ids=img_ids,
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txt=txt,
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txt_ids=txt_ids,
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y=vec,
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timesteps=t_vec,
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guidance=guidance_vec,
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txt_attention_mask=t5_attn_mask,
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)
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img = img + (t_prev - t_curr) * pred
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return img
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def do_sample(
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accelerator: Optional[accelerate.Accelerator],
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model: flux_models.Flux,
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img: torch.Tensor,
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img_ids: torch.Tensor,
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l_pooled: torch.Tensor,
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t5_out: torch.Tensor,
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txt_ids: torch.Tensor,
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num_steps: int,
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guidance: float,
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t5_attn_mask: Optional[torch.Tensor],
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is_schnell: bool,
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device: torch.device,
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flux_dtype: torch.dtype,
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):
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timesteps = get_schedule(num_steps, img.shape[1], shift=not is_schnell)
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# denoise initial noise
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if accelerator:
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with accelerator.autocast(), torch.no_grad():
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x = denoise(
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model, img, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=guidance, t5_attn_mask=t5_attn_mask
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)
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else:
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with torch.autocast(device_type=device.type, dtype=flux_dtype), torch.no_grad():
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x = denoise(
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model, img, img_ids, t5_out, txt_ids, l_pooled, timesteps=timesteps, guidance=guidance, t5_attn_mask=t5_attn_mask
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)
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return x
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def generate_image(
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model,
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clip_l,
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t5xxl,
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ae,
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prompt: str,
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seed: Optional[int],
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image_width: int,
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image_height: int,
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steps: Optional[int],
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guidance: float,
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):
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seed = seed if seed is not None else random.randint(0, 2**32 - 1)
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logger.info(f"Seed: {seed}")
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# make first noise with packed shape
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# original: b,16,2*h//16,2*w//16, packed: b,h//16*w//16,16*2*2
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packed_latent_height, packed_latent_width = math.ceil(image_height / 16), math.ceil(image_width / 16)
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noise = torch.randn(
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1,
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packed_latent_height * packed_latent_width,
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16 * 2 * 2,
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device=device,
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dtype=dtype,
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generator=torch.Generator(device=device).manual_seed(seed),
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)
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# prepare img and img ids
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# this is needed only for img2img
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# img = rearrange(img, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
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# if img.shape[0] == 1 and bs > 1:
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# img = repeat(img, "1 ... -> bs ...", bs=bs)
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# txt2img only needs img_ids
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img_ids = flux_utils.prepare_img_ids(1, packed_latent_height, packed_latent_width)
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# prepare embeddings
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logger.info("Encoding prompts...")
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tokens_and_masks = tokenize_strategy.tokenize(prompt)
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clip_l = clip_l.to(device)
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t5xxl = t5xxl.to(device)
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with torch.no_grad():
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if is_fp8(clip_l_dtype) or is_fp8(t5xxl_dtype):
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clip_l.to(clip_l_dtype)
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t5xxl.to(t5xxl_dtype)
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with accelerator.autocast():
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_, t5_out, txt_ids, t5_attn_mask = encoding_strategy.encode_tokens(
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tokenize_strategy, [clip_l, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
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)
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else:
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with torch.autocast(device_type=device.type, dtype=clip_l_dtype):
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l_pooled, _, _, _ = encoding_strategy.encode_tokens(tokenize_strategy, [clip_l, None], tokens_and_masks)
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with torch.autocast(device_type=device.type, dtype=t5xxl_dtype):
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_, t5_out, txt_ids, t5_attn_mask = encoding_strategy.encode_tokens(
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tokenize_strategy, [None, t5xxl], tokens_and_masks, args.apply_t5_attn_mask
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)
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# NaN check
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if torch.isnan(l_pooled).any():
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raise ValueError("NaN in l_pooled")
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if torch.isnan(t5_out).any():
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raise ValueError("NaN in t5_out")
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if args.offload:
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clip_l = clip_l.cpu()
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t5xxl = t5xxl.cpu()
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# del clip_l, t5xxl
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device_utils.clean_memory()
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# generate image
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logger.info("Generating image...")
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model = model.to(device)
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if steps is None:
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steps = 4 if is_schnell else 50
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img_ids = img_ids.to(device)
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t5_attn_mask = t5_attn_mask.to(device) if args.apply_t5_attn_mask else None
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x = do_sample(
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accelerator, model, noise, img_ids, l_pooled, t5_out, txt_ids, steps, guidance, t5_attn_mask, is_schnell, device, flux_dtype
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)
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if args.offload:
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model = model.cpu()
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# del model
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device_utils.clean_memory()
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# unpack
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x = x.float()
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x = einops.rearrange(x, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=packed_latent_height, w=packed_latent_width, ph=2, pw=2)
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# decode
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logger.info("Decoding image...")
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ae = ae.to(device)
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with torch.no_grad():
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if is_fp8(ae_dtype):
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with accelerator.autocast():
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x = ae.decode(x)
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else:
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with torch.autocast(device_type=device.type, dtype=ae_dtype):
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x = ae.decode(x)
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if args.offload:
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ae = ae.cpu()
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x = x.clamp(-1, 1)
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x = x.permute(0, 2, 3, 1)
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img = Image.fromarray((127.5 * (x + 1.0)).float().cpu().numpy().astype(np.uint8)[0])
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# save image
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output_dir = args.output_dir
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os.makedirs(output_dir, exist_ok=True)
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output_path = os.path.join(output_dir, f"{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}.png")
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img.save(output_path)
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logger.info(f"Saved image to {output_path}")
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if __name__ == "__main__":
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target_height = 768 # 1024
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target_width = 1360 # 1024
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# steps = 50 # 28 # 50
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# guidance_scale = 5
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# seed = 1 # None # 1
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device = get_preferred_device()
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parser = argparse.ArgumentParser()
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parser.add_argument("--ckpt_path", type=str, required=True)
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parser.add_argument("--clip_l", type=str, required=False)
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parser.add_argument("--t5xxl", type=str, required=False)
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parser.add_argument("--ae", type=str, required=False)
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parser.add_argument("--apply_t5_attn_mask", action="store_true")
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parser.add_argument("--prompt", type=str, default="A photo of a cat")
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parser.add_argument("--output_dir", type=str, default=".")
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parser.add_argument("--dtype", type=str, default="bfloat16", help="base dtype")
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parser.add_argument("--clip_l_dtype", type=str, default=None, help="dtype for clip_l")
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parser.add_argument("--ae_dtype", type=str, default=None, help="dtype for ae")
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parser.add_argument("--t5xxl_dtype", type=str, default=None, help="dtype for t5xxl")
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parser.add_argument("--flux_dtype", type=str, default=None, help="dtype for flux")
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parser.add_argument("--seed", type=int, default=None)
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parser.add_argument("--steps", type=int, default=None, help="Number of steps. Default is 4 for schnell, 50 for dev")
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parser.add_argument("--guidance", type=float, default=3.5)
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parser.add_argument("--offload", action="store_true", help="Offload to CPU")
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parser.add_argument(
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"--lora_weights",
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type=str,
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nargs="*",
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default=[],
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help="LoRA weights, only supports networks.lora_flux, each argument is a `path;multiplier` (semi-colon separated)",
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)
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parser.add_argument("--merge_lora_weights", action="store_true", help="Merge LoRA weights to model")
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parser.add_argument("--width", type=int, default=target_width)
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parser.add_argument("--height", type=int, default=target_height)
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parser.add_argument("--interactive", action="store_true")
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args = parser.parse_args()
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seed = args.seed
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steps = args.steps
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guidance_scale = args.guidance
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name = "schnell" if "schnell" in args.ckpt_path else "dev" # TODO change this to a more robust way
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is_schnell = name == "schnell"
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def is_fp8(dt):
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return dt in [torch.float8_e4m3fn, torch.float8_e4m3fnuz, torch.float8_e5m2, torch.float8_e5m2fnuz]
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dtype = str_to_dtype(args.dtype)
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clip_l_dtype = str_to_dtype(args.clip_l_dtype, dtype)
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t5xxl_dtype = str_to_dtype(args.t5xxl_dtype, dtype)
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ae_dtype = str_to_dtype(args.ae_dtype, dtype)
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flux_dtype = str_to_dtype(args.flux_dtype, dtype)
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logger.info(f"Dtypes for clip_l, t5xxl, ae, flux: {clip_l_dtype}, {t5xxl_dtype}, {ae_dtype}, {flux_dtype}")
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loading_device = "cpu" if args.offload else device
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use_fp8 = [is_fp8(d) for d in [dtype, clip_l_dtype, t5xxl_dtype, ae_dtype, flux_dtype]]
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if any(use_fp8):
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accelerator = accelerate.Accelerator(mixed_precision="bf16")
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else:
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accelerator = None
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# load clip_l
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logger.info(f"Loading clip_l from {args.clip_l}...")
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clip_l = flux_utils.load_clip_l(args.clip_l, clip_l_dtype, loading_device)
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clip_l.eval()
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logger.info(f"Loading t5xxl from {args.t5xxl}...")
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t5xxl = flux_utils.load_t5xxl(args.t5xxl, t5xxl_dtype, loading_device)
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t5xxl.eval()
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if is_fp8(clip_l_dtype):
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clip_l = accelerator.prepare(clip_l)
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if is_fp8(t5xxl_dtype):
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t5xxl = accelerator.prepare(t5xxl)
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t5xxl_max_length = 256 if is_schnell else 512
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tokenize_strategy = strategy_flux.FluxTokenizeStrategy(t5xxl_max_length)
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encoding_strategy = strategy_flux.FluxTextEncodingStrategy()
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# DiT
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model = flux_utils.load_flow_model(name, args.ckpt_path, None, loading_device)
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model.eval()
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logger.info(f"Casting model to {flux_dtype}")
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model.to(flux_dtype) # make sure model is dtype
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if is_fp8(flux_dtype):
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model = accelerator.prepare(model)
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# AE
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ae = flux_utils.load_ae(name, args.ae, ae_dtype, loading_device)
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ae.eval()
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if is_fp8(ae_dtype):
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ae = accelerator.prepare(ae)
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# LoRA
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lora_models: List[lora_flux.LoRANetwork] = []
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for weights_file in args.lora_weights:
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if ";" in weights_file:
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weights_file, multiplier = weights_file.split(";")
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multiplier = float(multiplier)
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else:
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multiplier = 1.0
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lora_model, weights_sd = lora_flux.create_network_from_weights(
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multiplier, weights_file, ae, [clip_l, t5xxl], model, None, True
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)
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if args.merge_lora_weights:
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lora_model.merge_to([clip_l, t5xxl], model, weights_sd)
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else:
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lora_model.apply_to([clip_l, t5xxl], model)
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info = lora_model.load_state_dict(weights_sd, strict=True)
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logger.info(f"Loaded LoRA weights from {weights_file}: {info}")
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lora_model.eval()
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lora_model.to(device)
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lora_models.append(lora_model)
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if not args.interactive:
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generate_image(model, clip_l, t5xxl, ae, args.prompt, args.seed, args.width, args.height, args.steps, args.guidance)
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else:
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# loop for interactive
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width = target_width
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height = target_height
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steps = None
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guidance = args.guidance
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while True:
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print(
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"Enter prompt (empty to exit). Options: --w <width> --h <height> --s <steps> --d <seed> --g <guidance> --m <multipliers for LoRA>"
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)
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prompt = input()
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if prompt == "":
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break
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# parse options
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options = prompt.split("--")
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prompt = options[0].strip()
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seed = None
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for opt in options[1:]:
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opt = opt.strip()
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if opt.startswith("w"):
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width = int(opt[1:].strip())
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elif opt.startswith("h"):
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height = int(opt[1:].strip())
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elif opt.startswith("s"):
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steps = int(opt[1:].strip())
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elif opt.startswith("d"):
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seed = int(opt[1:].strip())
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elif opt.startswith("g"):
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guidance = float(opt[1:].strip())
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elif opt.startswith("m"):
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mutipliers = opt[1:].strip().split(",")
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if len(mutipliers) != len(lora_models):
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logger.error(f"Invalid number of multipliers, expected {len(lora_models)}")
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continue
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for i, lora_model in enumerate(lora_models):
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lora_model.set_multiplier(float(mutipliers[i]))
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generate_image(model, clip_l, t5xxl, ae, prompt, seed, width, height, steps, guidance)
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logger.info("Done!")
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