Update Stable Cascade Conversion Scripts (#7271)

* update

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* update

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* update

---------
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

scripts/convert_stable_cascade.py

 # Run this script to convert the Stable Cascade model weights to a diffusers pipeline.
 import argparse
+from contextlib import nullcontext
 
-import accelerate
 import torch
 from safetensors.torch import load_file
 from transformers import (
@@ -18,23 +18,56 @@ from diffusers import (
     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.modeling_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, default="../StableCascade", help="Location of Stable Cascade weights")
+parser.add_argument("--model_path", type=str, help="Location of Stable Cascade weights")
 parser.add_argument("--stage_c_name", type=str, default="stage_c.safetensors", help="Name of stage c checkpoint file")
 parser.add_argument("--stage_b_name", type=str, default="stage_b.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("--save_org", type=str, default="diffusers", help="Hub organization to save the pipelines to")
+parser.add_argument(
+    "--prior_output_path", default="stable-cascade-prior", type=str, help="Hub organization to save the pipelines to"
+)
+parser.add_argument(
+    "--decoder_output_path",
+    type=str,
+    default="stable-cascade-decoder",
+    help="Hub organization to save the pipelines to",
+)
+parser.add_argument(
+    "--combined_output_path",
+    type=str,
+    default="stable-cascade-combined",
+    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}"
@@ -52,164 +85,134 @@ tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b1
 feature_extractor = CLIPImageProcessor()
 image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")
 
-# Prior
-if args.use_safetensors:
-    orig_state_dict = load_file(prior_checkpoint_path, device=device)
-else:
-    orig_state_dict = torch.load(prior_checkpoint_path, map_location=device)
-
-state_dict = {}
-for key in orig_state_dict.keys():
-    if key.endswith("in_proj_weight"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
-        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
-        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
-    elif key.endswith("in_proj_bias"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
-        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
-        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
-    elif key.endswith("out_proj.weight"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
-    elif key.endswith("out_proj.bias"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
-    else:
-        state_dict[key] = orig_state_dict[key]
-
-
-with accelerate.init_empty_weights():
-    prior_model = StableCascadeUNet(
-        in_channels=16,
-        out_channels=16,
-        timestep_ratio_embedding_dim=64,
-        patch_size=1,
-        conditioning_dim=2048,
-        block_out_channels=[2048, 2048],
-        num_attention_heads=[32, 32],
-        down_num_layers_per_block=[8, 24],
-        up_num_layers_per_block=[24, 8],
-        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],
-    )
-load_model_dict_into_meta(prior_model, state_dict)
-
 # scheduler for prior and decoder
 scheduler = DDPMWuerstchenScheduler()
+ctx = init_empty_weights if is_accelerate_available() else nullcontext
 
-# 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.save_pretrained(f"{args.save_org}/StableCascade-prior", push_to_hub=args.push_to_hub)
-
-# Decoder
-if args.use_safetensors:
-    orig_state_dict = load_file(decoder_checkpoint_path, device=device)
-else:
-    orig_state_dict = torch.load(decoder_checkpoint_path, map_location=device)
-
-state_dict = {}
-for key in orig_state_dict.keys():
-    if key.endswith("in_proj_weight"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
-        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
-        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
-    elif key.endswith("in_proj_bias"):
-        weights = orig_state_dict[key].chunk(3, 0)
-        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
-        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
-        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
-    elif key.endswith("out_proj.weight"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
-    elif key.endswith("out_proj.bias"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
-    # rename clip_mapper to clip_txt_pooled_mapper
-    elif key.endswith("clip_mapper.weight"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("clip_mapper.weight", "clip_txt_pooled_mapper.weight")] = weights
-    elif key.endswith("clip_mapper.bias"):
-        weights = orig_state_dict[key]
-        state_dict[key.replace("clip_mapper.bias", "clip_txt_pooled_mapper.bias")] = weights
+if not args.skip_stage_c:
+    # Prior
+    if args.use_safetensors:
+        prior_orig_state_dict = load_file(prior_checkpoint_path, device=device)
     else:
-        state_dict[key] = orig_state_dict[key]
-
-with accelerate.init_empty_weights():
-    decoder = StableCascadeUNet(
-        in_channels=4,
-        out_channels=4,
-        timestep_ratio_embedding_dim=64,
-        patch_size=2,
-        conditioning_dim=1280,
-        block_out_channels=[320, 640, 1280, 1280],
-        down_num_layers_per_block=[2, 6, 28, 6],
-        up_num_layers_per_block=[6, 28, 6, 2],
-        down_blocks_repeat_mappers=[1, 1, 1, 1],
-        up_blocks_repeat_mappers=[3, 3, 2, 2],
-        num_attention_heads=[0, 0, 20, 20],
-        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"],
+        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=2048,
+            block_out_channels=[2048, 2048],
+            num_attention_heads=[32, 32],
+            down_num_layers_per_block=[8, 24],
+            up_num_layers_per_block=[24, 8],
+            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
     )
-load_model_dict_into_meta(decoder, state_dict)
 
-# VQGAN from Wuerstchen-V2
-vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan")
+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, 640, 1280, 1280],
+            down_num_layers_per_block=[2, 6, 28, 6],
+            up_num_layers_per_block=[6, 28, 6, 2],
+            down_blocks_repeat_mappers=[1, 1, 1, 1],
+            up_blocks_repeat_mappers=[3, 3, 2, 2],
+            num_attention_heads=[0, 0, 20, 20],
+            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)
 
-# Decoder pipeline
-decoder_pipeline = StableCascadeDecoderPipeline(
-    decoder=decoder, text_encoder=text_encoder, tokenizer=tokenizer, vqgan=vqmodel, scheduler=scheduler
-)
-decoder_pipeline.save_pretrained(f"{args.save_org}/StableCascade-decoder", push_to_hub=args.push_to_hub)
+    # VQGAN from Wuerstchen-V2
+    vqmodel = PaellaVQModel.from_pretrained("warp-ai/wuerstchen", subfolder="vqgan")
 
-# 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.save_pretrained(f"{args.save_org}/StableCascade", push_to_hub=args.push_to_hub)
+    # 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
+    )

scripts/convert_stable_cascade_lite.py

+# 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.modeling_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
+    )