make blending happen once after conversion
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3EED7B957D362AF1
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@ -223,97 +223,100 @@ def convert_models(ctx: ConversionContext, args, models: Models):
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ctx, name, model["source"], format=model_format
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)
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converted = False
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if model_format in model_formats_original:
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convert_diffusion_original(
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converted, _dest = convert_diffusion_original(
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ctx,
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model,
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source,
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)
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else:
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convert_diffusion_diffusers(
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converted, _dest = convert_diffusion_diffusers(
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ctx,
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model,
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source,
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)
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# keep track of which models have been blended
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blend_models = {}
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# make sure blending only happens once, not every run
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if converted:
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# keep track of which models have been blended
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blend_models = {}
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inversion_dest = path.join(ctx.model_path, "inversion")
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lora_dest = path.join(ctx.model_path, "lora")
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inversion_dest = path.join(ctx.model_path, "inversion")
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lora_dest = path.join(ctx.model_path, "lora")
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for inversion in model.get("inversions", []):
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if "text_encoder" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "text_encoder", "model.onnx"))
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for inversion in model.get("inversions", []):
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if "text_encoder" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "text_encoder", "model.onnx"))
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if "tokenizer" not in blend_models:
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blend_models["tokenizer"] = CLIPTokenizer.from_pretrained(path.join(ctx.model_path, model), subfolder="tokenizer")
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if "tokenizer" not in blend_models:
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blend_models["tokenizer"] = CLIPTokenizer.from_pretrained(path.join(ctx.model_path, model), subfolder="tokenizer")
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inversion_name = inversion["name"]
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inversion_source = inversion["source"]
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inversion_format = inversion.get("format", "embeddings")
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inversion_source = fetch_model(
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ctx,
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f"{name}-inversion-{inversion_name}",
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inversion_source,
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dest=inversion_dest,
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)
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inversion_token = inversion.get("token", inversion_name)
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inversion_weight = inversion.get("weight", 1.0)
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blend_textual_inversions(
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ctx,
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blend_models["text_encoder"],
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blend_models["tokenizer"],
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[inversion_source],
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[inversion_format],
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base_token=inversion_token,
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inversion_weights=[inversion_weight],
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)
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for lora in model.get("loras", []):
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if "text_encoder" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "text_encoder", "model.onnx"))
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if "unet" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "unet", "model.onnx"))
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# load models if not loaded yet
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lora_name = lora["name"]
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lora_source = lora["source"]
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lora_source = fetch_model(
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ctx,
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f"{name}-lora-{lora_name}",
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lora_source
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dest=lora_dest,
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)
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lora_weight = lora.get("weight", 1.0)
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blend_loras(
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ctx,
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blend_models["text_encoder"],
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[lora_name],
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[lora_source],
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"text_encoder",
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lora_weights=[lora_weight],
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)
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if "tokenizer" in blend_models:
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dest_path = path.join(ctx.model_path, model, "tokenizer")
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logger.debug("saving blended tokenizer to %s", dest_path)
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blend_models["tokenizer"].save_pretrained(dest_path)
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for name in ["text_encoder", "unet"]:
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if name in blend_models:
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dest_path = path.join(ctx.model_path, model, name, "model.onnx")
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logger.debug("saving blended %s model to %s", name, dest_path)
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save_model(
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blend_models[name],
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dest_path,
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save_as_external_data=True,
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all_tensors_to_one_file=True,
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location="weights.pb",
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inversion_name = inversion["name"]
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inversion_source = inversion["source"]
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inversion_format = inversion.get("format", "embeddings")
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inversion_source = fetch_model(
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ctx,
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f"{name}-inversion-{inversion_name}",
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inversion_source,
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dest=inversion_dest,
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)
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inversion_token = inversion.get("token", inversion_name)
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inversion_weight = inversion.get("weight", 1.0)
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blend_textual_inversions(
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ctx,
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blend_models["text_encoder"],
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blend_models["tokenizer"],
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[inversion_source],
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[inversion_format],
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base_token=inversion_token,
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inversion_weights=[inversion_weight],
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)
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for lora in model.get("loras", []):
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if "text_encoder" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "text_encoder", "model.onnx"))
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if "unet" not in blend_models:
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blend_models["text_encoder"] = load_model(path.join(ctx.model_path, model, "unet", "model.onnx"))
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# load models if not loaded yet
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lora_name = lora["name"]
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lora_source = lora["source"]
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lora_source = fetch_model(
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ctx,
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f"{name}-lora-{lora_name}",
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lora_source,
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dest=lora_dest,
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)
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lora_weight = lora.get("weight", 1.0)
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blend_loras(
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ctx,
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blend_models["text_encoder"],
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[lora_name],
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[lora_source],
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"text_encoder",
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lora_weights=[lora_weight],
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)
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if "tokenizer" in blend_models:
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dest_path = path.join(ctx.model_path, model, "tokenizer")
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logger.debug("saving blended tokenizer to %s", dest_path)
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blend_models["tokenizer"].save_pretrained(dest_path)
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for name in ["text_encoder", "unet"]:
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if name in blend_models:
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dest_path = path.join(ctx.model_path, model, name, "model.onnx")
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logger.debug("saving blended %s model to %s", name, dest_path)
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save_model(
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blend_models[name],
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dest_path,
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save_as_external_data=True,
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all_tensors_to_one_file=True,
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location="weights.pb",
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)
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except Exception:
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@ -88,7 +88,7 @@ def convert_diffusion_diffusers(
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ctx: ConversionContext,
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model: Dict,
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source: str,
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):
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) -> Tuple[bool, str]:
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"""
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From https://github.com/huggingface/diffusers/blob/main/scripts/convert_stable_diffusion_checkpoint_to_onnx.py
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"""
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@ -111,7 +111,7 @@ def convert_diffusion_diffusers(
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if path.exists(dest_path) and path.exists(model_index):
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logger.info("ONNX model already exists, skipping")
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return
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return (False, dest_path)
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pipeline = StableDiffusionPipeline.from_pretrained(
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source,
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@ -328,3 +328,5 @@ def convert_diffusion_diffusers(
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)
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logger.info("ONNX pipeline is loadable")
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return (True, dest_path)
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@ -18,7 +18,7 @@ import os
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import re
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import shutil
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from logging import getLogger
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from typing import Dict, List
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from typing import Dict, List, Tuple
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import torch
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from diffusers import (
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@ -1658,7 +1658,7 @@ def convert_diffusion_original(
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ctx: ConversionContext,
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model: ModelDict,
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source: str,
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):
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) -> Tuple[bool, str]:
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name = model["name"]
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source = source or model["source"]
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@ -1670,7 +1670,7 @@ def convert_diffusion_original(
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if os.path.exists(dest_path) and os.path.exists(dest_index):
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logger.info("ONNX pipeline already exists, skipping")
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return
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return (False, dest_path)
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torch_name = name + "-torch"
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torch_path = os.path.join(ctx.cache_path, torch_name)
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@ -1698,10 +1698,12 @@ def convert_diffusion_original(
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if "vae" in model:
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del model["vae"]
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convert_diffusion_diffusers(ctx, model, working_name)
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result = convert_diffusion_diffusers(ctx, model, working_name)
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if "torch" in ctx.prune:
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logger.info("removing intermediate Torch models: %s", torch_path)
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shutil.rmtree(torch_path)
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logger.info("ONNX pipeline saved to %s", name)
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return result
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