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# Run this script to convert the Stable Cascade model weights to a diffusers pipeline.

import argparse

from contextlib import nullcontext

import torch

from safetensors.torch import load_file

from transformers import (

AutoTokenizer,

CLIPConfig,

CLIPImageProcessor,

CLIPTextModelWithProjection,

CLIPVisionModelWithProjection,

)

from diffusers import (

DDPMWuerstchenScheduler,

StableCascadeCombinedPipeline,

StableCascadeDecoderPipeline,

StableCascadePriorPipeline,

)

from diffusers.loaders.single_file_utils import convert_stable_cascade_unet_single_file_to_diffusers

from diffusers.models import StableCascadeUNet

from diffusers.models.model_loading_utils import load_model_dict_into_meta

from diffusers.pipelines.wuerstchen import PaellaVQModel

from diffusers.utils import is_accelerate_available

if is_accelerate_available():

from accelerate import init_empty_weights

parser = argparse.ArgumentParser(description="Convert Stable Cascade model weights to a diffusers pipeline")

parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights")

parser.add_argument(

"--stage_c_name", type=str, default="stage_c_lite.safetensors", help="Name of stage c checkpoint file"

)

parser.add_argument(

"--stage_b_name", type=str, default="stage_b_lite.safetensors", help="Name of stage b checkpoint file"

)

parser.add_argument("--skip_stage_c", action="store_true", help="Skip converting stage c")

parser.add_argument("--skip_stage_b", action="store_true", help="Skip converting stage b")

parser.add_argument("--use_safetensors", action="store_true", help="Use SafeTensors for conversion")

parser.add_argument(

"--prior_output_path",

default="stable-cascade-prior-lite",

type=str,

help="Hub organization to save the pipelines to",

)

parser.add_argument(

"--decoder_output_path",

type=str,

default="stable-cascade-decoder-lite",

help="Hub organization to save the pipelines to",

)

parser.add_argument(

"--combined_output_path",

type=str,

default="stable-cascade-combined-lite",

help="Hub organization to save the pipelines to",

)

parser.add_argument("--save_combined", action="store_true")

parser.add_argument("--push_to_hub", action="store_true", help="Push to hub")

parser.add_argument("--variant", type=str, help="Set to bf16 to save bfloat16 weights")

args = parser.parse_args()

if args.skip_stage_b and args.skip_stage_c:

raise ValueError("At least one stage should be converted")

if (args.skip_stage_b or args.skip_stage_c) and args.save_combined:

raise ValueError("Cannot skip stages when creating a combined pipeline")

model_path = args.model_path

device = "cpu"

if args.variant == "bf16":

dtype = torch.bfloat16

else:

dtype = torch.float32

# set paths to model weights

prior_checkpoint_path = f"{model_path}/{args.stage_c_name}"

decoder_checkpoint_path = f"{model_path}/{args.stage_b_name}"

# Clip Text encoder and tokenizer

config = CLIPConfig.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")

config.text_config.projection_dim = config.projection_dim

text_encoder = CLIPTextModelWithProjection.from_pretrained(

"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", config=config.text_config

)

tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")

# image processor

feature_extractor = CLIPImageProcessor()

image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

# scheduler for prior and decoder

scheduler = DDPMWuerstchenScheduler()

ctx = init_empty_weights if is_accelerate_available() else nullcontext

if not args.skip_stage_c:

# Prior

if args.use_safetensors:

prior_orig_state_dict = load_file(prior_checkpoint_path, device=device)

else:

prior_orig_state_dict = torch.load(prior_checkpoint_path, map_location=device)

prior_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(prior_orig_state_dict)

with ctx():

prior_model = StableCascadeUNet(

in_channels=16,

out_channels=16,

timestep_ratio_embedding_dim=64,

patch_size=1,

conditioning_dim=1536,

block_out_channels=[1536, 1536],

num_attention_heads=[24, 24],

down_num_layers_per_block=[4, 12],

up_num_layers_per_block=[12, 4],

down_blocks_repeat_mappers=[1, 1],

up_blocks_repeat_mappers=[1, 1],

block_types_per_layer=[

["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],

["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],

],

clip_text_in_channels=1280,

clip_text_pooled_in_channels=1280,

clip_image_in_channels=768,

clip_seq=4,

kernel_size=3,

dropout=[0.1, 0.1],

self_attn=True,

timestep_conditioning_type=["sca", "crp"],

switch_level=[False],

)

if is_accelerate_available():

load_model_dict_into_meta(prior_model, prior_state_dict)

else:

prior_model.load_state_dict(prior_state_dict)

# Prior pipeline

prior_pipeline = StableCascadePriorPipeline(

prior=prior_model,

tokenizer=tokenizer,

text_encoder=text_encoder,

image_encoder=image_encoder,

scheduler=scheduler,

feature_extractor=feature_extractor,

)

prior_pipeline.to(dtype).save_pretrained(

args.prior_output_path, push_to_hub=args.push_to_hub, variant=args.variant

)

if not args.skip_stage_b:

# Decoder

if args.use_safetensors:

decoder_orig_state_dict = load_file(decoder_checkpoint_path, device=device)

else:

decoder_orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device)

decoder_state_dict = convert_stable_cascade_unet_single_file_to_diffusers(decoder_orig_state_dict)

with ctx():

decoder = StableCascadeUNet(

in_channels=4,

out_channels=4,

timestep_ratio_embedding_dim=64,

patch_size=2,

conditioning_dim=1280,

block_out_channels=[320, 576, 1152, 1152],

down_num_layers_per_block=[2, 4, 14, 4],

up_num_layers_per_block=[4, 14, 4, 2],

down_blocks_repeat_mappers=[1, 1, 1, 1],

up_blocks_repeat_mappers=[2, 2, 2, 2],

num_attention_heads=[0, 9, 18, 18],

block_types_per_layer=[

["SDCascadeResBlock", "SDCascadeTimestepBlock"],

["SDCascadeResBlock", "SDCascadeTimestepBlock"],

["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],

["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],

],

clip_text_pooled_in_channels=1280,

clip_seq=4,

effnet_in_channels=16,

pixel_mapper_in_channels=3,

kernel_size=3,

dropout=[0, 0, 0.1, 0.1],

self_attn=True,

timestep_conditioning_type=["sca"],

)

if is_accelerate_available():

load_model_dict_into_meta(decoder, decoder_state_dict)

else:

decoder.load_state_dict(decoder_state_dict)

# VQGAN from Wuerstchen-V2

vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan")

# Decoder pipeline

decoder_pipeline = StableCascadeDecoderPipeline(

decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler

)

decoder_pipeline.to(dtype).save_pretrained(

args.decoder_output_path, push_to_hub=args.push_to_hub, variant=args.variant

)

if args.save_combined:

# Stable Cascade combined pipeline

stable_cascade_pipeline = StableCascadeCombinedPipeline(

# Decoder

text_encoder=text_encoder,

tokenizer=tokenizer,

decoder=decoder,

scheduler=scheduler,

vqgan=vqmodel,

# Prior

prior_text_encoder=text_encoder,

prior_tokenizer=tokenizer,

prior_prior=prior_model,

prior_scheduler=scheduler,

prior_image_encoder=image_encoder,

prior_feature_extractor=feature_extractor,

)

stable_cascade_pipeline.to(dtype).save_pretrained(

args.combined_output_path, push_to_hub=args.push_to_hub, variant=args.variant

)