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https://github.com/huggingface/diffusers.git
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f1a93c765f
* Improve incorrect LoRA format error message * Add flag in PeftLoraLoaderMixinTests to disable text encoder LoRA tests * Apply changes to LTX2LoraTests * Further improve incorrect LoRA format error msg following review --------- Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
272 lines
9.0 KiB
Python
272 lines
9.0 KiB
Python
# Copyright 2025 HuggingFace Inc.
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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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import sys
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import unittest
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import torch
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from transformers import AutoTokenizer, Gemma3ForConditionalGeneration
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from diffusers import (
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AutoencoderKLLTX2Audio,
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AutoencoderKLLTX2Video,
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FlowMatchEulerDiscreteScheduler,
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LTX2Pipeline,
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LTX2VideoTransformer3DModel,
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)
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from diffusers.pipelines.ltx2 import LTX2TextConnectors
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from diffusers.pipelines.ltx2.vocoder import LTX2Vocoder
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from diffusers.utils.import_utils import is_peft_available
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from ..testing_utils import floats_tensor, require_peft_backend
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if is_peft_available():
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from peft import LoraConfig
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sys.path.append(".")
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from .utils import PeftLoraLoaderMixinTests # noqa: E402
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@require_peft_backend
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class LTX2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
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pipeline_class = LTX2Pipeline
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scheduler_cls = FlowMatchEulerDiscreteScheduler
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scheduler_kwargs = {}
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transformer_kwargs = {
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"in_channels": 4,
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"out_channels": 4,
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"patch_size": 1,
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"patch_size_t": 1,
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"num_attention_heads": 2,
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"attention_head_dim": 8,
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"cross_attention_dim": 16,
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"audio_in_channels": 4,
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"audio_out_channels": 4,
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"audio_num_attention_heads": 2,
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"audio_attention_head_dim": 4,
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"audio_cross_attention_dim": 8,
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"num_layers": 1,
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"qk_norm": "rms_norm_across_heads",
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"caption_channels": 32,
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"rope_double_precision": False,
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"rope_type": "split",
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}
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transformer_cls = LTX2VideoTransformer3DModel
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vae_kwargs = {
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"in_channels": 3,
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"out_channels": 3,
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"latent_channels": 4,
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"block_out_channels": (8,),
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"decoder_block_out_channels": (8,),
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"layers_per_block": (1,),
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"decoder_layers_per_block": (1, 1),
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"spatio_temporal_scaling": (True,),
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"decoder_spatio_temporal_scaling": (True,),
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"decoder_inject_noise": (False, False),
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"downsample_type": ("spatial",),
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"upsample_residual": (False,),
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"upsample_factor": (1,),
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"timestep_conditioning": False,
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"patch_size": 1,
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"patch_size_t": 1,
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"encoder_causal": True,
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"decoder_causal": False,
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}
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vae_cls = AutoencoderKLLTX2Video
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audio_vae_kwargs = {
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"base_channels": 4,
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"output_channels": 2,
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"ch_mult": (1,),
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"num_res_blocks": 1,
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"attn_resolutions": None,
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"in_channels": 2,
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"resolution": 32,
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"latent_channels": 2,
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"norm_type": "pixel",
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"causality_axis": "height",
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"dropout": 0.0,
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"mid_block_add_attention": False,
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"sample_rate": 16000,
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"mel_hop_length": 160,
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"is_causal": True,
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"mel_bins": 8,
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}
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audio_vae_cls = AutoencoderKLLTX2Audio
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vocoder_kwargs = {
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"in_channels": 16, # output_channels * mel_bins = 2 * 8
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"hidden_channels": 32,
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"out_channels": 2,
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"upsample_kernel_sizes": [4, 4],
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"upsample_factors": [2, 2],
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"resnet_kernel_sizes": [3],
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"resnet_dilations": [[1, 3, 5]],
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"leaky_relu_negative_slope": 0.1,
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"output_sampling_rate": 16000,
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}
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vocoder_cls = LTX2Vocoder
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connectors_kwargs = {
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"caption_channels": 32, # Will be set dynamically from text_encoder
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"text_proj_in_factor": 2, # Will be set dynamically from text_encoder
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"video_connector_num_attention_heads": 4,
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"video_connector_attention_head_dim": 8,
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"video_connector_num_layers": 1,
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"video_connector_num_learnable_registers": None,
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"audio_connector_num_attention_heads": 4,
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"audio_connector_attention_head_dim": 8,
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"audio_connector_num_layers": 1,
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"audio_connector_num_learnable_registers": None,
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"connector_rope_base_seq_len": 32,
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"rope_theta": 10000.0,
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"rope_double_precision": False,
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"causal_temporal_positioning": False,
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"rope_type": "split",
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}
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connectors_cls = LTX2TextConnectors
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tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-gemma3"
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text_encoder_cls, text_encoder_id = (
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Gemma3ForConditionalGeneration,
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"hf-internal-testing/tiny-gemma3",
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)
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denoiser_target_modules = ["to_q", "to_k", "to_out.0"]
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supports_text_encoder_loras = False
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@property
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def output_shape(self):
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return (1, 5, 32, 32, 3)
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def get_dummy_inputs(self, with_generator=True):
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batch_size = 1
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sequence_length = 16
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num_channels = 4
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num_frames = 5
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num_latent_frames = 2
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latent_height = 8
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latent_width = 8
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_latent_frames, num_channels, latent_height, latent_width))
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input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
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pipeline_inputs = {
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"prompt": "a robot dancing",
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"num_frames": num_frames,
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"num_inference_steps": 2,
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"guidance_scale": 1.0,
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"height": 32,
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"width": 32,
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"frame_rate": 25.0,
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"max_sequence_length": sequence_length,
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"output_type": "np",
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}
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if with_generator:
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pipeline_inputs.update({"generator": generator})
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return noise, input_ids, pipeline_inputs
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def get_dummy_components(self, scheduler_cls=None, use_dora=False, lora_alpha=None):
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# Override to instantiate LTX2-specific components (connectors, audio_vae, vocoder)
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torch.manual_seed(0)
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text_encoder = self.text_encoder_cls.from_pretrained(self.text_encoder_id)
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tokenizer = self.tokenizer_cls.from_pretrained(self.tokenizer_id)
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# Update caption_channels and text_proj_in_factor based on text_encoder config
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transformer_kwargs = self.transformer_kwargs.copy()
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transformer_kwargs["caption_channels"] = text_encoder.config.text_config.hidden_size
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connectors_kwargs = self.connectors_kwargs.copy()
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connectors_kwargs["caption_channels"] = text_encoder.config.text_config.hidden_size
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connectors_kwargs["text_proj_in_factor"] = text_encoder.config.text_config.num_hidden_layers + 1
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torch.manual_seed(0)
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transformer = self.transformer_cls(**transformer_kwargs)
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torch.manual_seed(0)
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vae = self.vae_cls(**self.vae_kwargs)
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vae.use_framewise_encoding = False
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vae.use_framewise_decoding = False
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torch.manual_seed(0)
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audio_vae = self.audio_vae_cls(**self.audio_vae_kwargs)
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torch.manual_seed(0)
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vocoder = self.vocoder_cls(**self.vocoder_kwargs)
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torch.manual_seed(0)
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connectors = self.connectors_cls(**connectors_kwargs)
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if scheduler_cls is None:
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scheduler_cls = self.scheduler_cls
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scheduler = scheduler_cls(**self.scheduler_kwargs)
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rank = 4
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lora_alpha = rank if lora_alpha is None else lora_alpha
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text_lora_config = LoraConfig(
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r=rank,
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lora_alpha=lora_alpha,
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target_modules=self.text_encoder_target_modules,
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init_lora_weights=False,
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use_dora=use_dora,
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)
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denoiser_lora_config = LoraConfig(
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r=rank,
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lora_alpha=lora_alpha,
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target_modules=["to_q", "to_k", "to_v", "to_out.0"],
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init_lora_weights=False,
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use_dora=use_dora,
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)
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pipeline_components = {
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"transformer": transformer,
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"vae": vae,
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"audio_vae": audio_vae,
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"scheduler": scheduler,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"connectors": connectors,
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"vocoder": vocoder,
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}
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return pipeline_components, text_lora_config, denoiser_lora_config
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def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
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super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
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def test_simple_inference_with_text_denoiser_lora_unfused(self):
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super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
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@unittest.skip("Not supported in LTX2.")
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def test_simple_inference_with_text_denoiser_block_scale(self):
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pass
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@unittest.skip("Not supported in LTX2.")
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def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
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pass
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@unittest.skip("Not supported in LTX2.")
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def test_modify_padding_mode(self):
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pass
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