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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import torch
from nemo.collections.diffusion.models.flux.pipeline import FluxInferencePipeline
from nemo.collections.diffusion.utils.flux_pipeline_utils import configs
from nemo.collections.diffusion.utils.mcore_parallel_utils import Utils
def parse_args():
# pylint: disable=C0116
parser = argparse.ArgumentParser(
description="The flux inference pipeline is utilizing megatron core transformer.\n"
"Please prepare the necessary checkpoints for flux model on local disk in order to use this script"
)
parser.add_argument("--flux_ckpt", type=str, default="", help="Path to Flux transformer checkpoint(s)")
parser.add_argument("--vae_ckpt", type=str, default="/ckpts/ae.safetensors", help="Path to \'ae.safetensors\'")
parser.add_argument(
"--clip_version",
type=str,
default='/ckpts/text_encoder',
help="Clip version, provide either ckpt dir or clip version like openai/clip-vit-large-patch14",
)
parser.add_argument(
"--t5_version",
type=str,
default='/ckpts/text_encoder_2',
help="Clip version, provide either ckpt dir or clip version like google/t5-v1_1-xxl",
)
parser.add_argument(
"--t5_load_config_only",
action='store_true',
default=False,
help="randomly initialize T5 weights for testing purpose",
)
parser.add_argument(
"--do_convert_from_hf",
action='store_true',
default=False,
help="Must be true if provided checkpoint is not already converted to NeMo version",
)
parser.add_argument(
"--save_converted_model_to",
type=str,
default=None,
help="Whether to save the converted NeMo transformer checkpoint for Flux",
)
parser.add_argument(
"--version",
type=str,
default='dev',
help="Must align with the checkpoint provided.",
)
parser.add_argument("--height", type=int, default=1024, help="Image height.")
parser.add_argument("--width", type=int, default=1024, help="Image width.")
parser.add_argument("--inference_steps", type=int, default=10, help="Number of inference steps to run.")
parser.add_argument(
"--num_images_per_prompt", type=int, default=1, help="Number of images to generate for each prompt."
)
parser.add_argument(
"--num_joint_layers", type=int, default=19, help="Number of joint transformer layers in controlnet."
)
parser.add_argument(
"--num_single_layers", type=int, default=38, help="Number of single transformer layers in controlnet."
)
parser.add_argument("--guidance", type=float, default=0.0, help="Guidance scale.")
parser.add_argument(
"--offload", action='store_true', default=False, help="Offload modules to cpu after being called."
)
parser.add_argument(
"--prompts",
type=str,
default="A cat holding a sign that says hello world",
help="Inference prompts, use \',\' to separate if multiple prompts are provided.",
)
parser.add_argument("--output_path", type=str, default="/tmp/flux_output", help="Path to save inference output.")
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print('Initializing model parallel config')
Utils.initialize_distributed(1, 1, 1)
print('Initializing flux inference pipeline')
params = configs[args.version]
params.vae_config.ckpt = args.vae_ckpt if os.path.exists(args.vae_ckpt) else None
params.clip_params.version = (
args.clip_version if os.path.exists(args.clip_version) else "openai/clip-vit-large-patch14"
)
params.t5_params.version = args.t5_version if os.path.exists(args.t5_version) else "google/t5-v1_1-xxl"
params.t5_params.load_config_only = args.t5_load_config_only
params.flux_config.num_joint_layers = args.num_joint_layers
params.flux_config.num_single_layers = args.num_single_layers
pipe = FluxInferencePipeline(params)
if os.path.exists(args.flux_ckpt):
print('Loading transformer weights')
pipe.load_from_pretrained(
args.flux_ckpt,
do_convert_from_hf=args.do_convert_from_hf,
save_converted_model_to=args.save_converted_model_to,
)
dtype = torch.float32
text = args.prompts.split(',')
pipe(
text,
max_sequence_length=512,
height=args.height,
width=args.width,
num_inference_steps=args.inference_steps,
num_images_per_prompt=args.num_images_per_prompt,
offload=args.offload,
guidance_scale=args.guidance,
dtype=dtype,
output_path=args.output_path,
)
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