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## What does this PR do? **Type of change:** New feature <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> **Overview:** This PR adds support for exporting quantized diffusers models (DiT, Flux, SD3, UNet, etc.) to HuggingFace checkpoint format, enabling deployment to inference frameworks like SGLang, vLLM, and TensorRT-LLM. **Changes** New file: `diffusers_utils.py` - Dummy input generation for various diffusion models - Pipeline component extraction helpers - QKV projection detection and grouping - `hide_quantizers_from_state_dict()` context manager for clean saves Refactored: `unified_export_hf.py` - New `_fuse_qkv_linears_diffusion()` for QKV amax fusion - `_export_diffusers_checkpoint()` to export full pipelines (models + tokenizers + schedulers etc.) Plans - [x] [1/3] Add the basic functionalities to support limited image models with NVFP4 + FP8, with some refactoring on the previous LLM code and the diffusers example. PIC: @jingyu-ml - [ ] [2/3] Add support to more video gen modelsPIC: @jingyu-ml - [ ] [3/3] Add test cases, refactor on the doc, and all related README. PIC: @jingyu-ml ## Usage <!-- You can potentially add a usage example below. --> ``` mtq.quantize(pipe, quant_config, forward_call) export_hf_checkpoint(pipe, export_dir=hf_ckpt_dir) ``` ## Testing <!-- Mention how have you tested your change if applicable. --> ## Before your PR is "*Ready for review*" <!-- If you haven't finished some of the above items you can still open `Draft` PR. --> - **Make sure you read and follow [Contributor guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)** and your commits are signed. - **Is this change backward compatible?**: Yes <!--- If No, explain why. --> - **Did you write any new necessary tests?**:No - **Did you add or update any necessary documentation?**:No - **Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:No <!--- Only for new features, API changes, critical bug fixes or bw breaking changes. --> ## Additional Information <!-- E.g. related issue. --> <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## New Features * Added HuggingFace checkpoint export support for quantized diffusion models with configurable output directory * Introduced new `--hf-ckpt-dir` CLI argument for specifying checkpoint export destination * Extended export functionality to support selective component exports from diffusion pipelines * Enhanced quantized model export with improved component handling and multi-stage checkpoint generation <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Jingyu Xin <jingyux@nvidia.com>
195 lines
6.5 KiB
Python
195 lines
6.5 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Callable
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from enum import Enum
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from typing import Any
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from diffusers import (
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DiffusionPipeline,
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FluxPipeline,
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LTXConditionPipeline,
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StableDiffusion3Pipeline,
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WanPipeline,
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)
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from utils import (
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filter_func_default,
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filter_func_flux_dev,
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filter_func_ltx_video,
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filter_func_wan_video,
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)
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class ModelType(str, Enum):
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"""Supported model types."""
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SDXL_BASE = "sdxl-1.0"
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SDXL_TURBO = "sdxl-turbo"
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SD3_MEDIUM = "sd3-medium"
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SD35_MEDIUM = "sd3.5-medium"
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FLUX_DEV = "flux-dev"
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FLUX_SCHNELL = "flux-schnell"
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LTX_VIDEO_DEV = "ltx-video-dev"
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WAN22_T2V_14b = "wan2.2-t2v-14b"
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WAN22_T2V_5b = "wan2.2-t2v-5b"
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def get_model_filter_func(model_type: ModelType) -> Callable[[str], bool]:
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"""
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Get the appropriate filter function for a given model type.
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Args:
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model_type: The model type enum
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Returns:
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A filter function appropriate for the model type
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"""
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filter_func_map = {
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ModelType.FLUX_DEV: filter_func_flux_dev,
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ModelType.FLUX_SCHNELL: filter_func_default,
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ModelType.SDXL_BASE: filter_func_default,
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ModelType.SDXL_TURBO: filter_func_default,
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ModelType.SD3_MEDIUM: filter_func_default,
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ModelType.SD35_MEDIUM: filter_func_default,
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ModelType.LTX_VIDEO_DEV: filter_func_ltx_video,
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ModelType.WAN22_T2V_14b: filter_func_wan_video,
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ModelType.WAN22_T2V_5b: filter_func_wan_video,
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}
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return filter_func_map.get(model_type, filter_func_default)
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# Model registry with HuggingFace model IDs
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MODEL_REGISTRY: dict[ModelType, str] = {
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ModelType.SDXL_BASE: "stabilityai/stable-diffusion-xl-base-1.0",
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ModelType.SDXL_TURBO: "stabilityai/sdxl-turbo",
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ModelType.SD3_MEDIUM: "stabilityai/stable-diffusion-3-medium-diffusers",
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ModelType.SD35_MEDIUM: "stabilityai/stable-diffusion-3.5-medium",
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ModelType.FLUX_DEV: "black-forest-labs/FLUX.1-dev",
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ModelType.FLUX_SCHNELL: "black-forest-labs/FLUX.1-schnell",
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ModelType.LTX_VIDEO_DEV: "Lightricks/LTX-Video-0.9.7-dev",
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ModelType.WAN22_T2V_14b: "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
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ModelType.WAN22_T2V_5b: "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
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}
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MODEL_PIPELINE: dict[ModelType, type[DiffusionPipeline]] = {
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ModelType.SDXL_BASE: DiffusionPipeline,
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ModelType.SDXL_TURBO: DiffusionPipeline,
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ModelType.SD3_MEDIUM: StableDiffusion3Pipeline,
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ModelType.SD35_MEDIUM: StableDiffusion3Pipeline,
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ModelType.FLUX_DEV: FluxPipeline,
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ModelType.FLUX_SCHNELL: FluxPipeline,
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ModelType.LTX_VIDEO_DEV: LTXConditionPipeline,
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ModelType.WAN22_T2V_14b: WanPipeline,
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ModelType.WAN22_T2V_5b: WanPipeline,
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}
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# Shared dataset configurations
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_SD_PROMPTS_DATASET = {
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"name": "Gustavosta/Stable-Diffusion-Prompts",
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"split": "train",
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"column": "Prompt",
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}
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_OPENVID_DATASET = {
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"name": "nkp37/OpenVid-1M",
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"split": "train",
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"column": "caption",
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}
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# Model family base configurations
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_SDXL_BASE_CONFIG: dict[str, Any] = {
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"backbone": "unet",
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"dataset": _SD_PROMPTS_DATASET,
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}
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_SD3_BASE_CONFIG: dict[str, Any] = {
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"backbone": "transformer",
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"dataset": _SD_PROMPTS_DATASET,
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}
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_FLUX_BASE_CONFIG: dict[str, Any] = {
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"backbone": "transformer",
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"dataset": _SD_PROMPTS_DATASET,
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"inference_extra_args": {
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"height": 1024,
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"width": 1024,
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"guidance_scale": 3.5,
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"max_sequence_length": 512,
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},
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}
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_WAN_BASE_CONFIG: dict[str, Any] = {
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"backbone": "transformer",
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"dataset": _OPENVID_DATASET,
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}
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# Model-specific default arguments for calibration
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MODEL_DEFAULTS: dict[ModelType, dict[str, Any]] = {
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ModelType.SDXL_BASE: _SDXL_BASE_CONFIG,
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ModelType.SDXL_TURBO: _SDXL_BASE_CONFIG,
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ModelType.SD3_MEDIUM: _SD3_BASE_CONFIG,
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ModelType.SD35_MEDIUM: _SD3_BASE_CONFIG,
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ModelType.FLUX_DEV: _FLUX_BASE_CONFIG,
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ModelType.FLUX_SCHNELL: _FLUX_BASE_CONFIG,
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ModelType.LTX_VIDEO_DEV: {
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"backbone": "transformer",
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"dataset": _SD_PROMPTS_DATASET,
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"inference_extra_args": {
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"height": 512,
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"width": 704,
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"num_frames": 121,
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"negative_prompt": "worst quality, inconsistent motion, blurry, jittery, distorted",
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},
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},
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ModelType.WAN22_T2V_14b: {
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**_WAN_BASE_CONFIG,
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"from_pretrained_extra_args": {
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"boundary_ratio": 0.875,
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},
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"inference_extra_args": {
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"height": 720,
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"width": 1280,
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"num_frames": 81,
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"fps": 16,
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"guidance_scale": 4.0,
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"guidance_scale_2": 3.0,
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"negative_prompt": (
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"vivid colors, overexposed, static, blurry details, subtitles, style, "
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"work of art, painting, picture, still, overall grayish, worst quality, "
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"low quality, JPEG artifacts, ugly, deformed, extra fingers, poorly drawn hands, "
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"poorly drawn face, deformed, disfigured, deformed limbs, fused fingers, "
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"static image, cluttered background, three legs, many people in the background, "
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"walking backwards"
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),
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},
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},
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ModelType.WAN22_T2V_5b: {
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**_WAN_BASE_CONFIG,
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"inference_extra_args": {
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"height": 512,
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"width": 768,
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"num_frames": 81,
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"fps": 16,
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"guidance_scale": 5.0,
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"negative_prompt": (
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"色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留" # noqa: RUF001
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",丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体," # noqa: RUF001
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"手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走" # noqa: RUF001
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),
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},
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},
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}
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