cleanup
This commit is contained in:
kijai
@@ -1,487 +0,0 @@
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import argparse
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import builtins
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import json
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import math
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import multiprocessing as mp
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import os
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import random
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import socket
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import traceback
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import gradio as gr
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import numpy as np
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from safetensors.torch import load_file
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import torch
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import torch.distributed as dist
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from torchvision.transforms.functional import to_pil_image
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import models
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from transport import ODE
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class ModelFailure:
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pass
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# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
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def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
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captions = []
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for caption in prompt_batch:
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if random.random() < proportion_empty_prompts:
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captions.append("")
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elif isinstance(caption, str):
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captions.append(caption)
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elif isinstance(caption, (list, np.ndarray)):
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# take a random caption if there are multiple
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captions.append(random.choice(caption) if is_train else caption[0])
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with torch.no_grad():
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text_inputs = tokenizer(
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captions,
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padding=True,
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pad_to_multiple_of=8,
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max_length=256,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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prompt_masks = text_inputs.attention_mask
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prompt_embeds = text_encoder(
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input_ids=text_input_ids.cuda(),
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attention_mask=prompt_masks.cuda(),
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output_hidden_states=True,
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).hidden_states[-2]
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return prompt_embeds, prompt_masks
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@torch.no_grad()
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def model_main(args, master_port, rank, request_queue, response_queue, mp_barrier):
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# import here to avoid huggingface Tokenizer parallelism warnings
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from diffusers.models import AutoencoderKL
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from transformers import AutoModel, AutoTokenizer
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# override the default print function since the delay can be large for child process
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original_print = builtins.print
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# Redefine the print function with flush=True by default
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def print(*args, **kwargs):
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kwargs.setdefault("flush", True)
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original_print(*args, **kwargs)
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# Override the built-in print with the new version
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builtins.print = print
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os.environ["MASTER_PORT"] = str(master_port)
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os.environ["MASTER_ADDR"] = "127.0.0.1"
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os.environ["RANK"] = str(rank)
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os.environ["WORLD_SIZE"] = str(args.num_gpus)
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dist.init_process_group("nccl")
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# set up fairscale environment because some methods of the Lumina model need it,
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# though for single-GPU inference fairscale actually has no effect
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torch.cuda.set_device(rank)
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train_args = torch.load(os.path.join(args.ckpt, "model_args.pth"))
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if dist.get_rank() == 0:
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print("Loaded model arguments:", json.dumps(train_args.__dict__, indent=2))
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if dist.get_rank() == 0:
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print(f"Creating lm: Gemma-2B")
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dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.precision]
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text_encoder = AutoModel.from_pretrained(
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"google/gemma-2b", torch_dtype=dtype, device_map="cuda", token=args.hf_token
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).eval()
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cap_feat_dim = text_encoder.config.hidden_size
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if args.num_gpus > 1:
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raise NotImplementedError("Inference with >1 GPUs not yet supported")
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tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b", token=args.hf_token)
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tokenizer.padding_side = "right"
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if dist.get_rank() == 0:
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print(f"Creating vae: {train_args.vae}")
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vae = AutoencoderKL.from_pretrained(
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(f"stabilityai/sd-vae-ft-{train_args.vae}" if train_args.vae != "sdxl" else "stabilityai/sdxl-vae"),
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torch_dtype=torch.float32,
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).cuda()
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if dist.get_rank() == 0:
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print(f"Creating DiT: {train_args.model}")
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# latent_size = train_args.image_size // 8
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model = models.__dict__[train_args.model](
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qk_norm=train_args.qk_norm,
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cap_feat_dim=cap_feat_dim,
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)
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model.eval().to("cuda", dtype=dtype)
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if args.ema:
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print("Loading ema model.")
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ckpt = load_file(
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os.path.join(
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args.ckpt,
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f"consolidated{'_ema' if args.ema else ''}.{rank:02d}-of-{args.num_gpus:02d}.safetensors",
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)
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)
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model.load_state_dict(ckpt, strict=True)
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mp_barrier.wait()
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with torch.autocast("cuda", dtype):
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while True:
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(
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cap,
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neg_cap,
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resolution,
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num_sampling_steps,
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cfg_scale,
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solver,
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t_shift,
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seed,
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scaling_method,
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scaling_watershed,
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proportional_attn,
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) = request_queue.get()
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metadata = dict(
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cap=cap,
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neg_cap=neg_cap,
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resolution=resolution,
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num_sampling_steps=num_sampling_steps,
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cfg_scale=cfg_scale,
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solver=solver,
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t_shift=t_shift,
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seed=seed,
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scaling_method=scaling_method,
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scaling_watershed=scaling_watershed,
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proportional_attn=proportional_attn,
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)
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print("> params:", json.dumps(metadata, indent=2))
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try:
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do_extrapolation = "Extrapolation" in resolution
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resolution = resolution.split(" ")[-1]
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w, h = resolution.split("x")
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w, h = int(w), int(h)
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latent_w, latent_h = w // 8, h // 8
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if int(seed) != 0:
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torch.random.manual_seed(int(seed))
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z = torch.randn([1, 4, latent_h, latent_w], device="cuda").to(dtype)
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z = z.repeat(2, 1, 1, 1)
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with torch.no_grad():
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if neg_cap != "":
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cap_feats, cap_mask = encode_prompt([cap] + [neg_cap], text_encoder, tokenizer, 0.0)
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else:
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cap_feats, cap_mask = encode_prompt([cap] + [""], text_encoder, tokenizer, 0.0)
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cap_mask = cap_mask.to(cap_feats.device)
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model_kwargs = dict(
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cap_feats=cap_feats,
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cap_mask=cap_mask,
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cfg_scale=cfg_scale,
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)
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if proportional_attn:
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model_kwargs["proportional_attn"] = True
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model_kwargs["base_seqlen"] = (train_args.image_size // 16) ** 2
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else:
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model_kwargs["proportional_attn"] = False
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model_kwargs["base_seqlen"] = None
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if do_extrapolation and scaling_method == "Time-aware":
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model_kwargs["scale_factor"] = math.sqrt(w * h / train_args.image_size**2)
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model_kwargs["scale_watershed"] = scaling_watershed
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else:
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model_kwargs["scale_factor"] = 1.0
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model_kwargs["scale_watershed"] = 1.0
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if dist.get_rank() == 0:
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print(f"> caption: {cap}")
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print(f"> num_sampling_steps: {num_sampling_steps}")
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print(f"> cfg_scale: {cfg_scale}")
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print("> start sample")
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samples = ODE(num_sampling_steps, solver, t_shift).sample(z, model.forward_with_cfg, **model_kwargs)[-1]
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samples = samples[:1]
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factor = 0.18215 if train_args.vae != "sdxl" else 0.13025
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print(f"> vae factor: {factor}")
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samples = vae.decode(samples / factor).sample
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samples = (samples + 1.0) / 2.0
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samples.clamp_(0.0, 1.0)
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img = to_pil_image(samples[0].float())
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print("> generated image, done.")
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if response_queue is not None:
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response_queue.put((img, metadata))
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except Exception:
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print(traceback.format_exc())
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response_queue.put(ModelFailure())
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def none_or_str(value):
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if value == "None":
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return None
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return value
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def find_free_port() -> int:
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.bind(("", 0))
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port = sock.getsockname()[1]
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sock.close()
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return port
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--num_gpus", type=int, default=1)
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parser.add_argument("--ckpt", type=str, required=True)
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parser.add_argument("--ema", action="store_true")
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parser.add_argument("--precision", default="bf16", choices=["bf16", "fp32"])
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parser.add_argument("--hf_token", type=str, default=None, help="huggingface read token for accessing gated repo.")
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args = parser.parse_known_args()[0]
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if args.num_gpus != 1:
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raise NotImplementedError("Multi-GPU Inference is not yet supported")
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master_port = find_free_port()
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processes = []
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request_queues = []
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response_queue = mp.Queue()
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mp_barrier = mp.Barrier(args.num_gpus + 1)
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for i in range(args.num_gpus):
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request_queues.append(mp.Queue())
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p = mp.Process(
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target=model_main,
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args=(
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args,
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master_port,
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i,
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request_queues[i],
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response_queue if i == 0 else None,
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mp_barrier,
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),
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)
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p.start()
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processes.append(p)
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description = """
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# Lumina Next Text-to-Image
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Lumina-Next-T2I is a 2B Next-DiT model with 2B text encoder.
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Demo current model: `Lumina-Next-T2I`
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"""
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with gr.Blocks() as demo:
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with gr.Row():
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gr.Markdown(description)
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with gr.Row():
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with gr.Column():
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cap = gr.Textbox(
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lines=2,
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label="Caption",
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interactive=True,
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value="Miss Mexico portrait of the most beautiful mexican woman, Exquisite detail, 30-megapixel, 4k, 85-mm-lens, sharp-focus, f:8, "
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"ISO 100, shutter-speed 1:125, diffuse-back-lighting, award-winning photograph, small-catchlight, High-sharpness, facial-symmetry, 8k",
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placeholder="Enter a caption.",
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)
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neg_cap = gr.Textbox(
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lines=2,
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label="Negative Caption",
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interactive=True,
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value="",
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placeholder="Enter a negative caption.",
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)
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with gr.Row():
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res_choices = [
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"1024x1024",
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"512x2048",
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"2048x512",
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"(Extrapolation) 1536x1536",
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"(Extrapolation) 2048x1024",
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"(Extrapolation) 1024x2048",
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"(Extrapolation) 2048x2048",
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"(Extrapolation) 4096x1024",
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"(Extrapolation) 1024x4096",
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]
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resolution = gr.Dropdown(value=res_choices[0], choices=res_choices, label="Resolution")
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with gr.Row():
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num_sampling_steps = gr.Slider(
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minimum=1,
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maximum=70,
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value=30,
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step=1,
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interactive=True,
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label="Sampling steps",
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)
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seed = gr.Slider(
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minimum=0,
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maximum=int(1e5),
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value=1,
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step=1,
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interactive=True,
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label="Seed (0 for random)",
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)
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with gr.Row():
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solver = gr.Dropdown(
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value="midpoint",
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choices=["euler", "midpoint", "rk4"],
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label="solver",
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)
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t_shift = gr.Slider(
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minimum=1,
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maximum=20,
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value=4,
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step=1,
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interactive=True,
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label="Time shift",
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)
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cfg_scale = gr.Slider(
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minimum=1.0,
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maximum=20.0,
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value=4.0,
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interactive=True,
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label="CFG scale",
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)
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with gr.Accordion("Advanced Settings for Resolution Extrapolation", open=False):
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with gr.Row():
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scaling_method = gr.Dropdown(
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value="Time-aware",
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choices=["Time-aware", "None"],
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label="RoPE scaling method",
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)
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scaling_watershed = gr.Slider(
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minimum=0.0,
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maximum=1.0,
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value=0.3,
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interactive=True,
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label="Linear/NTK watershed",
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)
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with gr.Row():
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proportional_attn = gr.Checkbox(
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value=True,
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interactive=True,
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label="Proportional attention",
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)
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with gr.Row():
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submit_btn = gr.Button("Submit", variant="primary")
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with gr.Column():
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output_img = gr.Image(
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label="Generated image",
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interactive=False,
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format="png",
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)
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with gr.Accordion(label="Generation Parameters", open=True):
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gr_metadata = gr.JSON(label="metadata", show_label=False)
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with gr.Row():
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gr.Examples(
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[
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["👽🤖👹👻"],
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["孤舟蓑笠翁"],
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["两只黄鹂鸣翠柳"],
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["大漠孤烟直，长河落日圆"],
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["秋风起兮白云飞，草木黄落兮雁南归"],
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["도쿄 타워, 최고 품질의 우키요에, 에도 시대"],
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["味噌ラーメン, 最高品質の浮世絵、江戸時代。"],
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["東京タワー、最高品質の浮世絵、江戸時代。"],
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["Astronaut on Mars During sunset"],
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["Tour de Tokyo, estampes ukiyo-e de la plus haute qualité, période Edo"],
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["🐔 playing 🏀"],
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["☃️ with 🌹 in the ❄️"],
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["🐶 wearing 😎 flying on 🌈 "],
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["A small 🍎 and 🍊 with 😁 emoji in the Sahara desert"],
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["Токийская башня, лучшие укиё-э, период Эдо"],
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["Tokio-Turm, hochwertigste Ukiyo-e, Edo-Zeit"],
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["A scared cute rabbit in Happy Tree Friends style and punk vibe."], # noqa
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["A humanoid eagle soldier of the First World War."], # noqa
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[
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"A cute Christmas mockup on an old wooden industrial desk table with Christmas decorations and bokeh lights in the background."
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],
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[
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"A front view of a romantic flower shop in France filled with various blooming flowers including lavenders and roses."
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],
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["An old man, portrayed as a retro superhero, stands in the streets of New York City at night"],
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[
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"many trees are surrounded by a lake in autumn colors, in the style of nature-inspired imagery, havencore, brightly colored, dark white and dark orange, bright primary colors, environmental activism, forestpunk --ar 64:51"
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],
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[
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"A fluffy mouse holding a watermelon, in a magical and colorful setting, illustrated in the style of Hayao Miyazaki anime by Studio Ghibli."
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],
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[
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"Inka warrior with a war make up, medium shot, natural light, Award winning wildlife photography, hyperrealistic, 8k resolution, --ar 9:16"
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],
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[
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"Character of lion in style of saiyan, mafia, gangsta, citylights background, Hyper detailed, hyper realistic, unreal engine ue5, cgi 3d, cinematic shot, 8k"
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],
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[
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"In the sky above, a giant, whimsical cloud shaped like the 😊 emoji casts a soft, golden light over the scene"
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],
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[
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"Cyberpunk eagle, neon ambiance, abstract black oil, gear mecha, detailed acrylic, grunge, intricate complexity, rendered in unreal engine 5, photorealistic, 8k"
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],
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[
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"close-up photo of a beautiful red rose breaking through a cube made of ice , splintered cracked ice surface, frosted colors, blood dripping from rose, melting ice, Valentine’s Day vibes, cinematic, sharp focus, intricate, cinematic, dramatic light"
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],
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[
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"3D cartoon Fox Head with Human Body, Wearing Iridescent Holographic Liquid Texture & Translucent Material Sun Protective Shirt, Boss Feel, Nike or Addidas Sun Protective Shirt, WitchPunk, Y2K Style, Green and blue, Blue, Metallic Feel, Strong Reflection, plain background, no background, pure single color background, Digital Fashion, Surreal Futurism, Supreme Kong NFT Artwork Style, disney style, headshot photography for portrait studio shoot, fashion editorial aesthetic, high resolution in the style of HAPE PRIME NFT, NFT 3D IP Feel, Bored Ape Yacht Club NFT project Feel, high detail, fine luster, 3D render, oc render, best quality, 8K, bright, front lighting, Face Shot, fine luster, ultra detailed"
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],
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],
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[cap],
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label="Examples",
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)
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def on_submit(*args):
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for q in request_queues:
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q.put(args)
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result = response_queue.get()
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if isinstance(result, ModelFailure):
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raise RuntimeError
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img, metadata = result
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return img, metadata
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submit_btn.click(
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on_submit,
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[
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cap,
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neg_cap,
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resolution,
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||||
num_sampling_steps,
|
||||
cfg_scale,
|
||||
solver,
|
||||
t_shift,
|
||||
seed,
|
||||
scaling_method,
|
||||
scaling_watershed,
|
||||
proportional_attn,
|
||||
],
|
||||
[output_img, gr_metadata],
|
||||
)
|
||||
|
||||
def show_scaling_watershed(scaling_m):
|
||||
return gr.update(visible=scaling_m == "Time-aware")
|
||||
|
||||
scaling_method.change(show_scaling_watershed, scaling_method, scaling_watershed)
|
||||
|
||||
mp_barrier.wait()
|
||||
demo.queue().launch(
|
||||
server_name="0.0.0.0",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
mp.set_start_method("spawn")
|
||||
main()
|
||||
@@ -256,8 +256,8 @@ class LuminaT2ISampler:
|
||||
model.to(offload_device)
|
||||
samples = samples[:len(samples) // 2]
|
||||
|
||||
factor = 0.13025
|
||||
samples = samples / factor
|
||||
vae_scaling_factor = 0.13025
|
||||
samples = samples / vae_scaling_factor
|
||||
|
||||
return ({'samples': samples},)
|
||||
|
||||
|
||||
-89
@@ -1,89 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
import os
|
||||
import subprocess
|
||||
from time import sleep
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
|
||||
|
||||
def _setup_dist_env_from_slurm(args):
|
||||
while not os.environ.get("MASTER_ADDR", ""):
|
||||
os.environ["MASTER_ADDR"] = (
|
||||
subprocess.check_output(
|
||||
"sinfo -Nh -n %s | head -n 1 | awk '{print $1}'" % os.environ["SLURM_NODELIST"],
|
||||
shell=True,
|
||||
)
|
||||
.decode()
|
||||
.strip()
|
||||
)
|
||||
sleep(1)
|
||||
os.environ["MASTER_PORT"] = str(args.master_port)
|
||||
os.environ["RANK"] = os.environ["SLURM_PROCID"]
|
||||
os.environ["WORLD_SIZE"] = os.environ["SLURM_NPROCS"]
|
||||
os.environ["LOCAL_RANK"] = os.environ["SLURM_LOCALID"]
|
||||
os.environ["LOCAL_WORLD_SIZE"] = os.environ["SLURM_NTASKS_PER_NODE"]
|
||||
|
||||
|
||||
_INTRA_NODE_PROCESS_GROUP, _INTER_NODE_PROCESS_GROUP = None, None
|
||||
_LOCAL_RANK, _LOCAL_WORLD_SIZE = -1, -1
|
||||
|
||||
|
||||
def get_local_rank() -> int:
|
||||
return _LOCAL_RANK
|
||||
|
||||
|
||||
def get_local_world_size() -> int:
|
||||
return _LOCAL_WORLD_SIZE
|
||||
|
||||
|
||||
def distributed_init(args):
|
||||
if any([x not in os.environ for x in ["RANK", "WORLD_SIZE", "MASTER_PORT", "MASTER_ADDR"]]):
|
||||
_setup_dist_env_from_slurm(args)
|
||||
|
||||
dist.init_process_group("nccl")
|
||||
torch.cuda.set_device(dist.get_rank() % torch.cuda.device_count())
|
||||
|
||||
global _LOCAL_RANK, _LOCAL_WORLD_SIZE
|
||||
_LOCAL_RANK = int(os.environ["LOCAL_RANK"])
|
||||
_LOCAL_WORLD_SIZE = int(os.environ["LOCAL_WORLD_SIZE"])
|
||||
|
||||
global _INTRA_NODE_PROCESS_GROUP, _INTER_NODE_PROCESS_GROUP
|
||||
local_ranks, local_world_sizes = [
|
||||
torch.empty([dist.get_world_size()], dtype=torch.long, device="cuda") for _ in (0, 1)
|
||||
]
|
||||
dist.all_gather_into_tensor(local_ranks, torch.tensor(get_local_rank(), device="cuda"))
|
||||
dist.all_gather_into_tensor(local_world_sizes, torch.tensor(get_local_world_size(), device="cuda"))
|
||||
local_ranks, local_world_sizes = local_ranks.tolist(), local_world_sizes.tolist()
|
||||
node_ranks = [[0]]
|
||||
for i in range(1, dist.get_world_size()):
|
||||
if len(node_ranks[-1]) == local_world_sizes[i - 1]:
|
||||
node_ranks.append([])
|
||||
else:
|
||||
assert local_world_sizes[i] == local_world_sizes[i - 1]
|
||||
node_ranks[-1].append(i)
|
||||
for ranks in node_ranks:
|
||||
group = dist.new_group(ranks)
|
||||
if dist.get_rank() in ranks:
|
||||
assert _INTRA_NODE_PROCESS_GROUP is None
|
||||
_INTRA_NODE_PROCESS_GROUP = group
|
||||
assert _INTRA_NODE_PROCESS_GROUP is not None
|
||||
|
||||
if min(local_world_sizes) == max(local_world_sizes):
|
||||
for i in range(get_local_world_size()):
|
||||
group = dist.new_group(list(range(i, dist.get_world_size(), get_local_world_size())))
|
||||
if i == get_local_rank():
|
||||
assert _INTER_NODE_PROCESS_GROUP is None
|
||||
_INTER_NODE_PROCESS_GROUP = group
|
||||
assert _INTER_NODE_PROCESS_GROUP is not None
|
||||
|
||||
|
||||
def get_intra_node_process_group():
|
||||
assert _INTRA_NODE_PROCESS_GROUP is not None, "Intra-node process group is not initialized."
|
||||
return _INTRA_NODE_PROCESS_GROUP
|
||||
|
||||
|
||||
def get_inter_node_process_group():
|
||||
assert _INTRA_NODE_PROCESS_GROUP is not None, "Intra- and inter-node process groups are not initialized."
|
||||
return _INTER_NODE_PROCESS_GROUP
|
||||
@@ -1,280 +0,0 @@
|
||||
import os
|
||||
import argparse
|
||||
import random
|
||||
import socket
|
||||
import json
|
||||
import time
|
||||
import math
|
||||
from tqdm import tqdm
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from torchvision.transforms.functional import to_pil_image
|
||||
from diffusers.models import AutoencoderKL
|
||||
from transformers import AutoTokenizer, AutoModel
|
||||
|
||||
import models
|
||||
from transport import ODE
|
||||
|
||||
|
||||
# Adapted from pipelines.StableDiffusionXLPipeline.encode_prompt
|
||||
def encode_prompt(prompt_batch, text_encoder, tokenizer, proportion_empty_prompts, is_train=True):
|
||||
captions = []
|
||||
for caption in prompt_batch:
|
||||
if random.random() < proportion_empty_prompts:
|
||||
captions.append("")
|
||||
elif isinstance(caption, str):
|
||||
captions.append(caption)
|
||||
elif isinstance(caption, (list, np.ndarray)):
|
||||
# take a random caption if there are multiple
|
||||
captions.append(random.choice(caption) if is_train else caption[0])
|
||||
|
||||
with torch.no_grad():
|
||||
text_inputs = tokenizer(
|
||||
captions,
|
||||
padding=True,
|
||||
pad_to_multiple_of=8,
|
||||
max_length=256,
|
||||
truncation=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
|
||||
text_input_ids = text_inputs.input_ids
|
||||
prompt_masks = text_inputs.attention_mask
|
||||
|
||||
prompt_embeds = text_encoder(input_ids=text_input_ids.cuda(),attention_mask=prompt_masks.cuda(),output_hidden_states=True,).hidden_states[-2]
|
||||
|
||||
return prompt_embeds, prompt_masks
|
||||
|
||||
def none_or_str(value):
|
||||
if value == 'None':
|
||||
return None
|
||||
return value
|
||||
|
||||
|
||||
def main(args, rank, master_port):
|
||||
# Setup PyTorch:
|
||||
torch.set_grad_enabled(False)
|
||||
|
||||
os.environ["RANK"] = str(rank)
|
||||
os.environ["WORLD_SIZE"] = str(args.num_gpus)
|
||||
os.environ["MASTER_PORT"] = str(master_port)
|
||||
os.environ["MASTER_ADDR"] = "127.0.0.1"
|
||||
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
||||
|
||||
dist.init_process_group("nccl")
|
||||
torch.cuda.set_device(rank)
|
||||
|
||||
train_args = torch.load(os.path.join(args.ckpt, "model_args.pth"))
|
||||
if dist.get_rank() == 0:
|
||||
print("Loaded model arguments:",
|
||||
json.dumps(train_args.__dict__, indent=2))
|
||||
|
||||
if dist.get_rank() == 0:
|
||||
print(f"Creating lm: Gemma-2B")
|
||||
|
||||
dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[args.precision]
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
|
||||
tokenizer.padding_side = 'right'
|
||||
|
||||
text_encoder = AutoModel.from_pretrained(
|
||||
"google/gemma-2b",
|
||||
torch_dtype=dtype,
|
||||
).eval().cuda()
|
||||
# Load scheduler and models
|
||||
cap_feat_dim = text_encoder.config.hidden_size
|
||||
|
||||
if dist.get_rank() == 0:
|
||||
print(f"Creating vae: {train_args.vae}")
|
||||
vae = AutoencoderKL.from_pretrained(
|
||||
(f"stabilityai/sd-vae-ft-{train_args.vae}" if train_args.vae != "sdxl" else "stabilityai/sdxl-vae"),
|
||||
torch_dtype=torch.float32,
|
||||
).cuda()
|
||||
|
||||
if dist.get_rank() == 0:
|
||||
print(f"Creating DiT: {train_args.model}")
|
||||
# latent_size = train_args.image_size // 8
|
||||
model = models.__dict__[train_args.model](
|
||||
qk_norm=train_args.qk_norm,
|
||||
cap_feat_dim=cap_feat_dim,
|
||||
)
|
||||
model.eval().to("cuda", dtype=dtype)
|
||||
|
||||
if args.debug == False:
|
||||
# assert train_args.model_parallel_size == args.num_gpus
|
||||
if args.ema:
|
||||
print("Loading ema model.")
|
||||
ckpt = torch.load(os.path.join(
|
||||
args.ckpt,
|
||||
f"consolidated{'_ema' if args.ema else ''}."
|
||||
f"{rank:02d}-of-{args.num_gpus:02d}.pth",
|
||||
), map_location="cpu")
|
||||
model.load_state_dict(ckpt, strict=True)
|
||||
|
||||
sample_folder_dir = args.image_save_path
|
||||
|
||||
if rank == 0:
|
||||
os.makedirs(sample_folder_dir, exist_ok=True)
|
||||
os.makedirs(os.path.join(sample_folder_dir, 'images'), exist_ok=True)
|
||||
print(f"Saving .png samples at {sample_folder_dir}")
|
||||
dist.barrier()
|
||||
|
||||
info_path = os.path.join(args.image_save_path, 'data.json')
|
||||
if os.path.exists(info_path):
|
||||
with open(info_path, 'r') as f:
|
||||
info = json.loads(f.read())
|
||||
collected_id = []
|
||||
for i in info:
|
||||
collected_id.append(f'{id(i["caption"])}_{i["resolution"]}')
|
||||
else:
|
||||
info = []
|
||||
collected_id = []
|
||||
|
||||
captions = []
|
||||
|
||||
with open(args.caption_path, 'r', encoding='utf-8') as file:
|
||||
for line in file:
|
||||
text = line.strip()
|
||||
if text:
|
||||
captions.append(line.strip())
|
||||
|
||||
total = len(info)
|
||||
resolution = args.resolution
|
||||
with torch.autocast("cuda", dtype):
|
||||
for res in resolution:
|
||||
for idx, caption in tqdm(enumerate(captions)):
|
||||
|
||||
if int(args.seed) != 0:
|
||||
torch.random.manual_seed(int(args.seed))
|
||||
|
||||
sample_id = f'{idx}_{res.split(":")[-1]}'
|
||||
if sample_id in collected_id:
|
||||
continue
|
||||
caps_list = [caption]
|
||||
|
||||
res_cat, resolution = res.split(":")
|
||||
res_cat = int(res_cat)
|
||||
do_extrapolation = res_cat > 1024
|
||||
|
||||
n = len(caps_list)
|
||||
w, h = resolution.split("x")
|
||||
w, h = int(w), int(h)
|
||||
latent_w, latent_h = w // 8, h // 8
|
||||
z = torch.randn([1, 4, latent_w, latent_h], device="cuda").to(dtype)
|
||||
z = z.repeat(n * 2, 1, 1, 1)
|
||||
|
||||
|
||||
with torch.no_grad():
|
||||
cap_feats, cap_mask = encode_prompt([caps_list] + [""], text_encoder, tokenizer, 0.0)
|
||||
|
||||
cap_mask = cap_mask.to(cap_feats.device)
|
||||
|
||||
model_kwargs = dict(
|
||||
cap_feats=cap_feats, cap_mask=cap_mask, cfg_scale=args.cfg_scale,
|
||||
)
|
||||
|
||||
if args.proportional_attn:
|
||||
model_kwargs["proportional_attn"] = True
|
||||
model_kwargs["base_seqlen"] = (train_args.image_size // 16) ** 2
|
||||
else:
|
||||
model_kwargs["proportional_attn"] = False
|
||||
model_kwargs["base_seqlen"] = None
|
||||
|
||||
if do_extrapolation and args.scaling_method == "Time-aware":
|
||||
model_kwargs["scale_factor"] = math.sqrt(w * h / train_args.image_size**2)
|
||||
model_kwargs["scale_watershed"] = args.scaling_watershed
|
||||
else:
|
||||
model_kwargs["scale_factor"] = 1.0
|
||||
model_kwargs["scale_watershed"] = 1.0
|
||||
|
||||
samples = ODE(args.num_sampling_steps, args.solver, args.time_shifting_factor).sample(z, model.forward_with_cfg, **model_kwargs)[-1]
|
||||
samples = samples[:1]
|
||||
|
||||
factor = 0.18215 if train_args.vae != "sdxl" else 0.13025
|
||||
samples = vae.decode(samples / factor).sample
|
||||
samples = (samples + 1.0) / 2.0
|
||||
samples.clamp_(0.0, 1.0)
|
||||
|
||||
# Save samples to disk as individual .png files
|
||||
for i, (sample, cap) in enumerate(zip(samples, caps_list)):
|
||||
img = to_pil_image(sample.float())
|
||||
save_path = f"{args.image_save_path}/images/{args.solver}_{args.num_sampling_steps}_{sample_id}.png"
|
||||
img.save(save_path)
|
||||
info.append({
|
||||
'caption': cap,
|
||||
'image_url': f"{args.image_save_path}/images/{args.solver}_{args.num_sampling_steps}_{sample_id}.png",
|
||||
'resolution': f'res: {resolution}\ntime_shift: {args.time_shifting_factor}',
|
||||
'solver': args.solver,
|
||||
'num_sampling_steps': args.num_sampling_steps
|
||||
})
|
||||
|
||||
with open(info_path, 'w') as f:
|
||||
f.write(json.dumps(info))
|
||||
|
||||
total += len(samples)
|
||||
dist.barrier()
|
||||
|
||||
dist.barrier()
|
||||
dist.barrier()
|
||||
dist.destroy_process_group()
|
||||
|
||||
|
||||
def find_free_port() -> int:
|
||||
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
|
||||
sock.bind(("", 0))
|
||||
port = sock.getsockname()[1]
|
||||
sock.close()
|
||||
return port
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--cfg_scale", type=float, default=4.0)
|
||||
parser.add_argument("--num_sampling_steps", type=int, default=250)
|
||||
parser.add_argument("--seed", type=int, default=0)
|
||||
parser.add_argument("--ckpt", type=str, required=True)
|
||||
parser.add_argument("--solver", type=str, default="euler")
|
||||
parser.add_argument(
|
||||
"--precision", type=str, choices=["fp32", "bf16"],
|
||||
default="bf16",
|
||||
)
|
||||
parser.add_argument("--num_gpus", type=int, default=1)
|
||||
parser.add_argument("--ema", action="store_true", help="Use EMA models.")
|
||||
parser.set_defaults(ema=True)
|
||||
parser.add_argument(
|
||||
"--image_save_path", type=str, default='samples',
|
||||
help="If specified, overrides the default image save path "
|
||||
"(sample{_ema}.png in the model checkpoint directory)."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--time_shifting_factor", type=float, default=1.0,
|
||||
)
|
||||
parser.add_argument(
|
||||
"--caption_path", type=str, default='prompts.txt',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--resolution", type=str, default='', nargs="+",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tokenizer_path", type=str, default='',
|
||||
)
|
||||
parser.add_argument(
|
||||
"--proportional_attn", type=bool, default=True
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scaling_method", type=str, default="Time-aware",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scaling_watershed", type=float, default=0.3,
|
||||
)
|
||||
parser.add_argument("--debug", action="store_true")
|
||||
parser.add_argument("--batch_size", type=int, default=8)
|
||||
|
||||
args = parser.parse_known_args()[0]
|
||||
|
||||
master_port = find_free_port()
|
||||
assert args.num_gpus == 1, "Multi-GPU sampling is currently not supported."
|
||||
|
||||
main(args, 0, master_port)
|
||||
Reference in New Issue
Block a user