Datasets:

Kain1999
/

change_ckpt

# Copyright (c) 2022 PaddlePaddle Authors. 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 tempfile
import paddle

paddle.set_device("cpu")
import argparse
import torch
from collections import OrderedDict
from diffusers import StableDiffusionPipeline as DiffusersStableDiffusionPipeline
from ppdiffusers.configuration_utils import FrozenDict
from ppdiffusers import StableDiffusionPipeline as PPDiffusersStableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler
from ppdiffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
from paddlenlp.transformers import CLIPTextModel, CLIPVisionModel, CLIPTokenizer, CLIPFeatureExtractor


def convert_to_ppdiffusers(vae_or_unet, dtype="float32"):
    need_transpose = []
    for k, v in vae_or_unet.named_modules():
        if isinstance(v, torch.nn.Linear):
            need_transpose.append(k + ".weight")
    new_vae_or_unet = OrderedDict()
    for k, v in vae_or_unet.state_dict().items():
        if k not in need_transpose:
            new_vae_or_unet[k] = v.cpu().numpy().astype(dtype)
        else:
            new_vae_or_unet[k] = v.t().cpu().numpy().astype(dtype)
    return new_vae_or_unet


def convert_hf_clip_to_ppnlp_clip(clip, dtype="float32", is_text_encoder=True):
    new_model_state = {}
    transformers2ppnlp = {
        ".encoder.": ".transformer.",
        ".layer_norm": ".norm",
        ".mlp.": ".",
        ".fc1.": ".linear1.",
        ".fc2.": ".linear2.",
        ".final_layer_norm.": ".ln_final.",
        ".embeddings.": ".",
        ".position_embedding.": ".positional_embedding.",
        ".patch_embedding.": ".conv1.",
        "visual_projection.weight": "vision_projection",
        "text_projection.weight": "text_projection",
        ".pre_layrnorm.": ".ln_pre.",
        ".post_layernorm.": ".ln_post.",
        ".vision_model.": "."
    }
    ignore_value = ["position_ids"]
    donot_transpose = [
        "embeddings", "norm", "concept_embeds", "special_care_embeds"
    ]

    for name, value in clip.state_dict().items():
        # step1: ignore position_ids
        if any(i in name for i in ignore_value):
            continue
        # step2: transpose nn.Linear weight
        if value.ndim == 2 and not any(i in name for i in donot_transpose):
            value = value.t()
        # step3: hf_name -> ppnlp_name mapping
        for hf_name, ppnlp_name in transformers2ppnlp.items():
            name = name.replace(hf_name, ppnlp_name)
        # step4: 0d tensor -> 1d tensor
        if name == "logit_scale": value = value.reshape((1, ))
        # step5: safety_checker need prefix "clip."
        if "vision_model" in name: name = "clip." + name
        new_model_state[name] = value.cpu().numpy().astype(dtype)

    if is_text_encoder:
        new_config = {
            'max_text_length': clip.config.max_position_embeddings,
            'vocab_size': clip.config.vocab_size,
            'text_embed_dim': clip.config.hidden_size,
            'text_heads': clip.config.num_attention_heads,
            'text_layers': clip.config.num_hidden_layers,
            'text_hidden_act': clip.config.hidden_act,
            'projection_dim': clip.config.projection_dim,
            'initializer_range': clip.config.initializer_range,
            'initializer_factor': clip.config.initializer_factor,
        }
    else:
        new_config = {
            'image_resolution':
            clip.config.vision_config.image_size,
            'vision_layers':
            clip.config.vision_config.num_hidden_layers,
            'vision_heads':
            clip.config.vision_config.num_attention_heads,
            'vision_embed_dim':
            clip.config.vision_config.hidden_size,
            'vision_patch_size':
            clip.config.vision_config.patch_size,
            'vision_mlp_ratio':
            clip.config.vision_config.intermediate_size //
            clip.config.vision_config.hidden_size,
            'vision_hidden_act':
            clip.config.vision_config.hidden_act,
            'projection_dim':
            clip.config.projection_dim,
        }
    return new_model_state, new_config


def convert_diffusers_stable_diffusion_to_ppdiffusers(
        pretrained_model_name_or_path, output_path=None):
    # 0. load diffusers pipe and convert to ppdiffusers weights format
    diffusers_pipe = DiffusersStableDiffusionPipeline.from_pretrained(
        pretrained_model_name_or_path, use_auth_token=True)
    vae_state_dict = convert_to_ppdiffusers(diffusers_pipe.vae)
    unet_state_dict = convert_to_ppdiffusers(diffusers_pipe.unet)
    text_encoder_state_dict, text_encoder_config = convert_hf_clip_to_ppnlp_clip(
        diffusers_pipe.text_encoder, is_text_encoder=True)
    safety_checker_state_dict, safety_checker_config = convert_hf_clip_to_ppnlp_clip(
        diffusers_pipe.safety_checker, is_text_encoder=False)

    # 1. vae
    pp_vae = AutoencoderKL(**diffusers_pipe.vae.config)
    pp_vae.set_dict(vae_state_dict)

    # 2. unet
    pp_unet = UNet2DConditionModel(**diffusers_pipe.unet.config)
    pp_unet.set_dict(unet_state_dict)

    # 3. text_encoder
    pp_text_encoder = CLIPTextModel(**text_encoder_config)
    pp_text_encoder.set_dict(text_encoder_state_dict)

    # 4. safety_checker
    pp_safety_checker = StableDiffusionSafetyChecker(
        CLIPVisionModel(**safety_checker_config))
    pp_safety_checker.set_dict(safety_checker_state_dict)

    # 5. scheduler
    beta_start = diffusers_pipe.scheduler.beta_start
    beta_end = diffusers_pipe.scheduler.beta_end
    num_train_timesteps = diffusers_pipe.scheduler.num_train_timesteps
    scheduler_type = diffusers_pipe.scheduler._class_name.lower()
    if "pndm" in scheduler_type:
        pp_scheduler = PNDMScheduler(
            beta_end=beta_end,
            beta_schedule="scaled_linear",
            beta_start=beta_start,
            num_train_timesteps=num_train_timesteps,
            skip_prk_steps=True,
        )
    elif "lms" in scheduler_type:
        pp_scheduler = LMSDiscreteScheduler(beta_start=beta_start,
                                            beta_end=beta_end,
                                            beta_schedule="scaled_linear")
    elif "ddim" in scheduler_type:
        pp_scheduler = DDIMScheduler(
            beta_start=beta_start,
            beta_end=beta_end,
            beta_schedule="scaled_linear",
            clip_sample=False,
            set_alpha_to_one=False,
        )
    else:
        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")

    with tempfile.TemporaryDirectory() as tmpdirname:
        # 6. feature_extractor
        diffusers_pipe.feature_extractor.save_pretrained(tmpdirname)
        pp_feature_extractor = CLIPFeatureExtractor.from_pretrained(tmpdirname)

        # 7. tokenizer
        diffusers_pipe.tokenizer.save_pretrained(tmpdirname)
        pp_tokenizer = CLIPTokenizer.from_pretrained(tmpdirname)

        # 8. create ppdiffusers pipe
        paddle_pipe = PPDiffusersStableDiffusionPipeline(
            vae=pp_vae,
            text_encoder=pp_text_encoder,
            tokenizer=pp_tokenizer,
            unet=pp_unet,
            safety_checker=pp_safety_checker,
            feature_extractor=pp_feature_extractor,
            scheduler=pp_scheduler)
        if "runwayml/stable-diffusion-inpainting" in pretrained_model_name_or_path:
            _internal_dict = dict(paddle_pipe._internal_dict)
            if _internal_dict["_ppdiffusers_version"] == "0.0.0":
                _internal_dict.update({"_ppdiffusers_version": "0.6.0"})
            paddle_pipe._internal_dict = FrozenDict(_internal_dict)
        # 9. save_pretrained
        paddle_pipe.save_pretrained(output_path)
    return paddle_pipe


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Pytorch model weights to Paddle model weights.")
    parser.add_argument(
        "--pretrained_model_name_or_path",
        type=str,
        default="runwayml/stable-diffusion-v1-5",
        help=
        "Path to pretrained model or model identifier from huggingface.co/models.",
    )
    parser.add_argument(
        "--output_path",
        type=str,
        default="stable-diffusion-v1-5-ppdiffusers",
        help="The model output path.",
    )
    args = parser.parse_args()
    ppdiffusers_pipe = convert_diffusers_stable_diffusion_to_ppdiffusers(
        args.pretrained_model_name_or_path, args.output_path)

	# Copyright (c) 2022 PaddlePaddle Authors. 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 tempfile
	import paddle

	paddle.set_device("cpu")
	import argparse
	import torch
	from collections import OrderedDict
	from diffusers import StableDiffusionPipeline as DiffusersStableDiffusionPipeline
	from ppdiffusers.configuration_utils import FrozenDict
	from ppdiffusers import StableDiffusionPipeline as PPDiffusersStableDiffusionPipeline, AutoencoderKL, UNet2DConditionModel, PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler
	from ppdiffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
	from paddlenlp.transformers import CLIPTextModel, CLIPVisionModel, CLIPTokenizer, CLIPFeatureExtractor


	def convert_to_ppdiffusers(vae_or_unet, dtype="float32"):
	need_transpose = []
	for k, v in vae_or_unet.named_modules():
	if isinstance(v, torch.nn.Linear):
	need_transpose.append(k + ".weight")
	new_vae_or_unet = OrderedDict()
	for k, v in vae_or_unet.state_dict().items():
	if k not in need_transpose:
	new_vae_or_unet[k] = v.cpu().numpy().astype(dtype)
	else:
	new_vae_or_unet[k] = v.t().cpu().numpy().astype(dtype)
	return new_vae_or_unet


	def convert_hf_clip_to_ppnlp_clip(clip, dtype="float32", is_text_encoder=True):
	new_model_state = {}
	transformers2ppnlp = {
	".encoder.": ".transformer.",
	".layer_norm": ".norm",
	".mlp.": ".",
	".fc1.": ".linear1.",
	".fc2.": ".linear2.",
	".final_layer_norm.": ".ln_final.",
	".embeddings.": ".",
	".position_embedding.": ".positional_embedding.",
	".patch_embedding.": ".conv1.",
	"visual_projection.weight": "vision_projection",
	"text_projection.weight": "text_projection",
	".pre_layrnorm.": ".ln_pre.",
	".post_layernorm.": ".ln_post.",
	".vision_model.": "."
	}
	ignore_value = ["position_ids"]
	donot_transpose = [
	"embeddings", "norm", "concept_embeds", "special_care_embeds"
	]

	for name, value in clip.state_dict().items():
	# step1: ignore position_ids
	if any(i in name for i in ignore_value):
	continue
	# step2: transpose nn.Linear weight
	if value.ndim == 2 and not any(i in name for i in donot_transpose):
	value = value.t()
	# step3: hf_name -> ppnlp_name mapping
	for hf_name, ppnlp_name in transformers2ppnlp.items():
	name = name.replace(hf_name, ppnlp_name)
	# step4: 0d tensor -> 1d tensor
	if name == "logit_scale": value = value.reshape((1, ))
	# step5: safety_checker need prefix "clip."
	if "vision_model" in name: name = "clip." + name
	new_model_state[name] = value.cpu().numpy().astype(dtype)

	if is_text_encoder:
	new_config = {
	'max_text_length': clip.config.max_position_embeddings,
	'vocab_size': clip.config.vocab_size,
	'text_embed_dim': clip.config.hidden_size,
	'text_heads': clip.config.num_attention_heads,
	'text_layers': clip.config.num_hidden_layers,
	'text_hidden_act': clip.config.hidden_act,
	'projection_dim': clip.config.projection_dim,
	'initializer_range': clip.config.initializer_range,
	'initializer_factor': clip.config.initializer_factor,
	}
	else:
	new_config = {
	'image_resolution':
	clip.config.vision_config.image_size,
	'vision_layers':
	clip.config.vision_config.num_hidden_layers,
	'vision_heads':
	clip.config.vision_config.num_attention_heads,
	'vision_embed_dim':
	clip.config.vision_config.hidden_size,
	'vision_patch_size':
	clip.config.vision_config.patch_size,
	'vision_mlp_ratio':
	clip.config.vision_config.intermediate_size //
	clip.config.vision_config.hidden_size,
	'vision_hidden_act':
	clip.config.vision_config.hidden_act,
	'projection_dim':
	clip.config.projection_dim,
	}
	return new_model_state, new_config


	def convert_diffusers_stable_diffusion_to_ppdiffusers(
	pretrained_model_name_or_path, output_path=None):
	# 0. load diffusers pipe and convert to ppdiffusers weights format
	diffusers_pipe = DiffusersStableDiffusionPipeline.from_pretrained(
	pretrained_model_name_or_path, use_auth_token=True)
	vae_state_dict = convert_to_ppdiffusers(diffusers_pipe.vae)
	unet_state_dict = convert_to_ppdiffusers(diffusers_pipe.unet)
	text_encoder_state_dict, text_encoder_config = convert_hf_clip_to_ppnlp_clip(
	diffusers_pipe.text_encoder, is_text_encoder=True)
	safety_checker_state_dict, safety_checker_config = convert_hf_clip_to_ppnlp_clip(
	diffusers_pipe.safety_checker, is_text_encoder=False)

	# 1. vae
	pp_vae = AutoencoderKL(**diffusers_pipe.vae.config)
	pp_vae.set_dict(vae_state_dict)

	# 2. unet
	pp_unet = UNet2DConditionModel(**diffusers_pipe.unet.config)
	pp_unet.set_dict(unet_state_dict)

	# 3. text_encoder
	pp_text_encoder = CLIPTextModel(**text_encoder_config)
	pp_text_encoder.set_dict(text_encoder_state_dict)

	# 4. safety_checker
	pp_safety_checker = StableDiffusionSafetyChecker(
	CLIPVisionModel(**safety_checker_config))
	pp_safety_checker.set_dict(safety_checker_state_dict)

	# 5. scheduler
	beta_start = diffusers_pipe.scheduler.beta_start
	beta_end = diffusers_pipe.scheduler.beta_end
	num_train_timesteps = diffusers_pipe.scheduler.num_train_timesteps
	scheduler_type = diffusers_pipe.scheduler._class_name.lower()
	if "pndm" in scheduler_type:
	pp_scheduler = PNDMScheduler(
	beta_end=beta_end,
	beta_schedule="scaled_linear",
	beta_start=beta_start,
	num_train_timesteps=num_train_timesteps,
	skip_prk_steps=True,
	)
	elif "lms" in scheduler_type:
	pp_scheduler = LMSDiscreteScheduler(beta_start=beta_start,
	beta_end=beta_end,
	beta_schedule="scaled_linear")
	elif "ddim" in scheduler_type:
	pp_scheduler = DDIMScheduler(
	beta_start=beta_start,
	beta_end=beta_end,
	beta_schedule="scaled_linear",
	clip_sample=False,
	set_alpha_to_one=False,
	)
	else:
	raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")

	with tempfile.TemporaryDirectory() as tmpdirname:
	# 6. feature_extractor
	diffusers_pipe.feature_extractor.save_pretrained(tmpdirname)
	pp_feature_extractor = CLIPFeatureExtractor.from_pretrained(tmpdirname)

	# 7. tokenizer
	diffusers_pipe.tokenizer.save_pretrained(tmpdirname)
	pp_tokenizer = CLIPTokenizer.from_pretrained(tmpdirname)

	# 8. create ppdiffusers pipe
	paddle_pipe = PPDiffusersStableDiffusionPipeline(
	vae=pp_vae,
	text_encoder=pp_text_encoder,
	tokenizer=pp_tokenizer,
	unet=pp_unet,
	safety_checker=pp_safety_checker,
	feature_extractor=pp_feature_extractor,
	scheduler=pp_scheduler)
	if "runwayml/stable-diffusion-inpainting" in pretrained_model_name_or_path:
	_internal_dict = dict(paddle_pipe._internal_dict)
	if _internal_dict["_ppdiffusers_version"] == "0.0.0":
	_internal_dict.update({"_ppdiffusers_version": "0.6.0"})
	paddle_pipe._internal_dict = FrozenDict(_internal_dict)
	# 9. save_pretrained
	paddle_pipe.save_pretrained(output_path)
	return paddle_pipe


	if __name__ == "__main__":
	parser = argparse.ArgumentParser(
	description="Pytorch model weights to Paddle model weights.")
	parser.add_argument(
	"--pretrained_model_name_or_path",
	type=str,
	default="runwayml/stable-diffusion-v1-5",
	help=
	"Path to pretrained model or model identifier from huggingface.co/models.",
	)
	parser.add_argument(
	"--output_path",
	type=str,
	default="stable-diffusion-v1-5-ppdiffusers",
	help="The model output path.",
	)
	args = parser.parse_args()
	ppdiffusers_pipe = convert_diffusers_stable_diffusion_to_ppdiffusers(
	args.pretrained_model_name_or_path, args.output_path)