mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-27 17:22:53 +03:00
741 lines
27 KiB
Python
741 lines
27 KiB
Python
import gc
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import unittest
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import numpy as np
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import torch
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import torch.nn as nn
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from diffusers import (
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AuraFlowPipeline,
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AuraFlowTransformer2DModel,
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DiffusionPipeline,
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FluxControlPipeline,
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FluxPipeline,
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FluxTransformer2DModel,
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GGUFQuantizationConfig,
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HiDreamImageTransformer2DModel,
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SD3Transformer2DModel,
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StableDiffusion3Pipeline,
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WanTransformer3DModel,
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WanVACETransformer3DModel,
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)
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from diffusers.utils import load_image
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from ...testing_utils import (
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Expectations,
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backend_empty_cache,
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backend_max_memory_allocated,
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backend_reset_peak_memory_stats,
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enable_full_determinism,
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is_gguf_available,
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nightly,
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numpy_cosine_similarity_distance,
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require_accelerate,
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require_accelerator,
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require_big_accelerator,
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require_gguf_version_greater_or_equal,
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require_kernels_version_greater_or_equal,
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require_peft_backend,
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require_torch_version_greater,
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torch_device,
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)
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from ..test_torch_compile_utils import QuantCompileTests
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if is_gguf_available():
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import gguf
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from diffusers.quantizers.gguf.utils import GGUFLinear, GGUFParameter
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enable_full_determinism()
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@nightly
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@require_accelerate
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@require_accelerator
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@require_gguf_version_greater_or_equal("0.10.0")
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@require_kernels_version_greater_or_equal("0.9.0")
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class GGUFCudaKernelsTests(unittest.TestCase):
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def setUp(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def test_cuda_kernels_vs_native(self):
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if torch_device != "cuda":
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self.skipTest("CUDA kernels test requires CUDA device")
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from diffusers.quantizers.gguf.utils import GGUFLinear, can_use_cuda_kernels
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if not can_use_cuda_kernels:
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self.skipTest("CUDA kernels not available (compute capability < 7 or kernels not installed)")
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test_quant_types = ["Q4_0", "Q4_K"]
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test_shape = (1, 64, 512) # batch, seq_len, hidden_dim
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compute_dtype = torch.bfloat16
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for quant_type in test_quant_types:
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qtype = getattr(gguf.GGMLQuantizationType, quant_type)
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in_features, out_features = 512, 512
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torch.manual_seed(42)
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float_weight = torch.randn(out_features, in_features, dtype=torch.float32)
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quantized_data = gguf.quants.quantize(float_weight.numpy(), qtype)
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weight_data = torch.from_numpy(quantized_data).to(device=torch_device)
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weight = GGUFParameter(weight_data, quant_type=qtype)
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x = torch.randn(test_shape, dtype=compute_dtype, device=torch_device)
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linear = GGUFLinear(in_features, out_features, bias=True, compute_dtype=compute_dtype)
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linear.weight = weight
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linear.bias = nn.Parameter(torch.randn(out_features, dtype=compute_dtype))
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linear = linear.to(torch_device)
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with torch.no_grad():
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output_native = linear.forward_native(x)
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output_cuda = linear.forward_cuda(x)
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assert torch.allclose(output_native, output_cuda, 1e-2), (
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f"GGUF CUDA Kernel Output is different from Native Output for {quant_type}"
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)
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@nightly
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@require_big_accelerator
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@require_accelerate
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@require_gguf_version_greater_or_equal("0.10.0")
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class GGUFSingleFileTesterMixin:
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ckpt_path = None
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model_cls = None
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torch_dtype = torch.bfloat16
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expected_memory_use_in_gb = 5
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def test_gguf_parameters(self):
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quant_storage_type = torch.uint8
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config)
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for param_name, param in model.named_parameters():
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if isinstance(param, GGUFParameter):
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assert hasattr(param, "quant_type")
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assert param.dtype == quant_storage_type
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def test_gguf_linear_layers(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config)
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear) and hasattr(module.weight, "quant_type"):
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assert module.weight.dtype == torch.uint8
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if module.bias is not None:
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assert module.bias.dtype == self.torch_dtype
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def test_gguf_memory_usage(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(
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self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype
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)
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model.to(torch_device)
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assert (model.get_memory_footprint() / 1024**3) < self.expected_memory_use_in_gb
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inputs = self.get_dummy_inputs()
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backend_reset_peak_memory_stats(torch_device)
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backend_empty_cache(torch_device)
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with torch.no_grad():
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model(**inputs)
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max_memory = backend_max_memory_allocated(torch_device)
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assert (max_memory / 1024**3) < self.expected_memory_use_in_gb
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def test_keep_modules_in_fp32(self):
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r"""
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A simple tests to check if the modules under `_keep_in_fp32_modules` are kept in fp32.
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Also ensures if inference works.
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"""
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_keep_in_fp32_modules = self.model_cls._keep_in_fp32_modules
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self.model_cls._keep_in_fp32_modules = ["proj_out"]
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config)
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for name, module in model.named_modules():
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if isinstance(module, torch.nn.Linear):
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if name in model._keep_in_fp32_modules:
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assert module.weight.dtype == torch.float32
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self.model_cls._keep_in_fp32_modules = _keep_in_fp32_modules
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def test_dtype_assignment(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config)
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with self.assertRaises(ValueError):
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# Tries with a `dtype`
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model.to(torch.float16)
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with self.assertRaises(ValueError):
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# Tries with a `device` and `dtype`
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device_0 = f"{torch_device}:0"
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model.to(device=device_0, dtype=torch.float16)
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with self.assertRaises(ValueError):
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# Tries with a cast
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model.float()
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with self.assertRaises(ValueError):
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# Tries with a cast
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model.half()
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# This should work
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model.to(torch_device)
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def test_dequantize_model(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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model = self.model_cls.from_single_file(self.ckpt_path, quantization_config=quantization_config)
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model.dequantize()
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def _check_for_gguf_linear(model):
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has_children = list(model.children())
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if not has_children:
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return
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for name, module in model.named_children():
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if isinstance(module, nn.Linear):
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assert not isinstance(module, GGUFLinear), f"{name} is still GGUFLinear"
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assert not isinstance(module.weight, GGUFParameter), f"{name} weight is still GGUFParameter"
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for name, module in model.named_children():
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_check_for_gguf_linear(module)
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class FluxGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
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ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
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diffusers_ckpt_path = "https://huggingface.co/sayakpaul/flux-diffusers-gguf/blob/main/model-Q4_0.gguf"
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torch_dtype = torch.bfloat16
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model_cls = FluxTransformer2DModel
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expected_memory_use_in_gb = 5
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def setUp(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def get_dummy_inputs(self):
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return {
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"hidden_states": torch.randn((1, 4096, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
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torch_device, self.torch_dtype
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),
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"encoder_hidden_states": torch.randn(
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(1, 512, 4096),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
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"pooled_projections": torch.randn(
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(1, 768),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
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"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
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"img_ids": torch.randn((4096, 3), generator=torch.Generator("cpu").manual_seed(0)).to(
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torch_device, self.torch_dtype
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),
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"txt_ids": torch.randn((512, 3), generator=torch.Generator("cpu").manual_seed(0)).to(
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torch_device, self.torch_dtype
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),
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"guidance": torch.tensor([3.5]).to(torch_device, self.torch_dtype),
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}
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def test_pipeline_inference(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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transformer = self.model_cls.from_single_file(
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self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype
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)
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pipe = FluxPipeline.from_pretrained(
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"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=self.torch_dtype
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)
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pipe.enable_model_cpu_offload()
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prompt = "a cat holding a sign that says hello"
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output = pipe(
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prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np"
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).images[0]
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output_slice = output[:3, :3, :].flatten()
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expected_slice = np.array(
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[
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0.47265625,
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0.43359375,
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0.359375,
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0.47070312,
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0.421875,
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0.34375,
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0.46875,
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0.421875,
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0.34765625,
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0.46484375,
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0.421875,
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0.34179688,
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0.47070312,
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0.42578125,
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0.34570312,
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0.46875,
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0.42578125,
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0.3515625,
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0.45507812,
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0.4140625,
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0.33984375,
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0.4609375,
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0.41796875,
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0.34375,
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0.45898438,
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0.41796875,
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0.34375,
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]
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)
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max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice)
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assert max_diff < 1e-4
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def test_loading_gguf_diffusers_format(self):
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model = self.model_cls.from_single_file(
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self.diffusers_ckpt_path,
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subfolder="transformer",
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quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
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config="black-forest-labs/FLUX.1-dev",
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)
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model.to(torch_device)
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model(**self.get_dummy_inputs())
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class SD35LargeGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
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ckpt_path = "https://huggingface.co/city96/stable-diffusion-3.5-large-gguf/blob/main/sd3.5_large-Q4_0.gguf"
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torch_dtype = torch.bfloat16
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model_cls = SD3Transformer2DModel
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expected_memory_use_in_gb = 5
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def setUp(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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gc.collect()
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backend_empty_cache(torch_device)
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def get_dummy_inputs(self):
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return {
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"hidden_states": torch.randn((1, 16, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
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torch_device, self.torch_dtype
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),
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"encoder_hidden_states": torch.randn(
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(1, 512, 4096),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
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"pooled_projections": torch.randn(
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(1, 2048),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
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"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
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}
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def test_pipeline_inference(self):
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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transformer = self.model_cls.from_single_file(
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self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype
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)
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pipe = StableDiffusion3Pipeline.from_pretrained(
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"stabilityai/stable-diffusion-3.5-large", transformer=transformer, torch_dtype=self.torch_dtype
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)
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pipe.enable_model_cpu_offload()
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prompt = "a cat holding a sign that says hello"
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output = pipe(
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prompt=prompt,
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num_inference_steps=2,
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generator=torch.Generator("cpu").manual_seed(0),
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output_type="np",
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).images[0]
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output_slice = output[:3, :3, :].flatten()
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expected_slices = Expectations(
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{
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("xpu", 3): np.array(
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[
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0.16796875,
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0.27929688,
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0.28320312,
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0.11328125,
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0.27539062,
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0.26171875,
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0.10742188,
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0.26367188,
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0.26171875,
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0.1484375,
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0.2734375,
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0.296875,
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0.13476562,
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0.2890625,
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0.30078125,
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0.1171875,
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0.28125,
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0.28125,
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0.16015625,
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0.31445312,
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0.30078125,
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0.15625,
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0.32421875,
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0.296875,
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0.14453125,
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0.30859375,
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0.2890625,
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]
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),
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("cuda", 7): np.array(
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[
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0.17578125,
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0.27539062,
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|
0.27734375,
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0.11914062,
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|
0.26953125,
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|
0.25390625,
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|
0.109375,
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|
0.25390625,
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|
0.25,
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|
0.15039062,
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|
0.26171875,
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|
0.28515625,
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|
0.13671875,
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|
0.27734375,
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|
0.28515625,
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|
0.12109375,
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|
0.26757812,
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|
0.265625,
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|
0.16210938,
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|
0.29882812,
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|
0.28515625,
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|
0.15625,
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|
0.30664062,
|
|
0.27734375,
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|
0.14648438,
|
|
0.29296875,
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|
0.26953125,
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]
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),
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}
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)
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expected_slice = expected_slices.get_expectation()
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max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice)
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assert max_diff < 1e-4
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|
|
|
|
class SD35MediumGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
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ckpt_path = "https://huggingface.co/city96/stable-diffusion-3.5-medium-gguf/blob/main/sd3.5_medium-Q3_K_M.gguf"
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|
torch_dtype = torch.bfloat16
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model_cls = SD3Transformer2DModel
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|
expected_memory_use_in_gb = 2
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|
|
def setUp(self):
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
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|
|
def tearDown(self):
|
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gc.collect()
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|
backend_empty_cache(torch_device)
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|
|
def get_dummy_inputs(self):
|
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return {
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"hidden_states": torch.randn((1, 16, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
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torch_device, self.torch_dtype
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|
),
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"encoder_hidden_states": torch.randn(
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(1, 512, 4096),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
|
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"pooled_projections": torch.randn(
|
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(1, 2048),
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generator=torch.Generator("cpu").manual_seed(0),
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).to(torch_device, self.torch_dtype),
|
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"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
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}
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|
|
def test_pipeline_inference(self):
|
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quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
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transformer = self.model_cls.from_single_file(
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self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype
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)
|
|
pipe = StableDiffusion3Pipeline.from_pretrained(
|
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"stabilityai/stable-diffusion-3.5-medium", transformer=transformer, torch_dtype=self.torch_dtype
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)
|
|
pipe.enable_model_cpu_offload()
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|
|
prompt = "a cat holding a sign that says hello"
|
|
output = pipe(
|
|
prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np"
|
|
).images[0]
|
|
output_slice = output[:3, :3, :].flatten()
|
|
expected_slice = np.array(
|
|
[
|
|
0.625,
|
|
0.6171875,
|
|
0.609375,
|
|
0.65625,
|
|
0.65234375,
|
|
0.640625,
|
|
0.6484375,
|
|
0.640625,
|
|
0.625,
|
|
0.6484375,
|
|
0.63671875,
|
|
0.6484375,
|
|
0.66796875,
|
|
0.65625,
|
|
0.65234375,
|
|
0.6640625,
|
|
0.6484375,
|
|
0.6328125,
|
|
0.6640625,
|
|
0.6484375,
|
|
0.640625,
|
|
0.67578125,
|
|
0.66015625,
|
|
0.62109375,
|
|
0.671875,
|
|
0.65625,
|
|
0.62109375,
|
|
]
|
|
)
|
|
max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice)
|
|
assert max_diff < 1e-4
|
|
|
|
|
|
class AuraFlowGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
|
|
ckpt_path = "https://huggingface.co/city96/AuraFlow-v0.3-gguf/blob/main/aura_flow_0.3-Q2_K.gguf"
|
|
torch_dtype = torch.bfloat16
|
|
model_cls = AuraFlowTransformer2DModel
|
|
expected_memory_use_in_gb = 4
|
|
|
|
def setUp(self):
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def tearDown(self):
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"hidden_states": torch.randn((1, 4, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
|
|
torch_device, self.torch_dtype
|
|
),
|
|
"encoder_hidden_states": torch.randn(
|
|
(1, 512, 2048),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
|
|
}
|
|
|
|
def test_pipeline_inference(self):
|
|
quantization_config = GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
|
|
transformer = self.model_cls.from_single_file(
|
|
self.ckpt_path, quantization_config=quantization_config, torch_dtype=self.torch_dtype
|
|
)
|
|
pipe = AuraFlowPipeline.from_pretrained(
|
|
"fal/AuraFlow-v0.3", transformer=transformer, torch_dtype=self.torch_dtype
|
|
)
|
|
pipe.enable_model_cpu_offload()
|
|
|
|
prompt = "a pony holding a sign that says hello"
|
|
output = pipe(
|
|
prompt=prompt, num_inference_steps=2, generator=torch.Generator("cpu").manual_seed(0), output_type="np"
|
|
).images[0]
|
|
output_slice = output[:3, :3, :].flatten()
|
|
expected_slice = np.array(
|
|
[
|
|
0.46484375,
|
|
0.546875,
|
|
0.64453125,
|
|
0.48242188,
|
|
0.53515625,
|
|
0.59765625,
|
|
0.47070312,
|
|
0.5078125,
|
|
0.5703125,
|
|
0.42773438,
|
|
0.50390625,
|
|
0.5703125,
|
|
0.47070312,
|
|
0.515625,
|
|
0.57421875,
|
|
0.45898438,
|
|
0.48632812,
|
|
0.53515625,
|
|
0.4453125,
|
|
0.5078125,
|
|
0.56640625,
|
|
0.47851562,
|
|
0.5234375,
|
|
0.57421875,
|
|
0.48632812,
|
|
0.5234375,
|
|
0.56640625,
|
|
]
|
|
)
|
|
max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice)
|
|
assert max_diff < 1e-4
|
|
|
|
|
|
@require_peft_backend
|
|
@nightly
|
|
@require_big_accelerator
|
|
@require_accelerate
|
|
@require_gguf_version_greater_or_equal("0.10.0")
|
|
class FluxControlLoRAGGUFTests(unittest.TestCase):
|
|
def test_lora_loading(self):
|
|
ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
|
|
transformer = FluxTransformer2DModel.from_single_file(
|
|
ckpt_path,
|
|
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
|
|
torch_dtype=torch.bfloat16,
|
|
)
|
|
pipe = FluxControlPipeline.from_pretrained(
|
|
"black-forest-labs/FLUX.1-dev",
|
|
transformer=transformer,
|
|
torch_dtype=torch.bfloat16,
|
|
).to(torch_device)
|
|
pipe.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora")
|
|
|
|
prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
|
|
control_image = load_image(
|
|
"https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/control_image_robot_canny.png"
|
|
)
|
|
|
|
output = pipe(
|
|
prompt=prompt,
|
|
control_image=control_image,
|
|
height=256,
|
|
width=256,
|
|
num_inference_steps=10,
|
|
guidance_scale=30.0,
|
|
output_type="np",
|
|
generator=torch.manual_seed(0),
|
|
).images
|
|
|
|
out_slice = output[0, -3:, -3:, -1].flatten()
|
|
expected_slice = np.array([0.8047, 0.8359, 0.8711, 0.6875, 0.7070, 0.7383, 0.5469, 0.5820, 0.6641])
|
|
|
|
max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
|
|
self.assertTrue(max_diff < 1e-3)
|
|
|
|
|
|
class HiDreamGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
|
|
ckpt_path = "https://huggingface.co/city96/HiDream-I1-Dev-gguf/blob/main/hidream-i1-dev-Q2_K.gguf"
|
|
torch_dtype = torch.bfloat16
|
|
model_cls = HiDreamImageTransformer2DModel
|
|
expected_memory_use_in_gb = 8
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"hidden_states": torch.randn((1, 16, 128, 128), generator=torch.Generator("cpu").manual_seed(0)).to(
|
|
torch_device, self.torch_dtype
|
|
),
|
|
"encoder_hidden_states_t5": torch.randn(
|
|
(1, 128, 4096),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"encoder_hidden_states_llama3": torch.randn(
|
|
(32, 1, 128, 4096),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"pooled_embeds": torch.randn(
|
|
(1, 2048),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"timesteps": torch.tensor([1]).to(torch_device, self.torch_dtype),
|
|
}
|
|
|
|
|
|
class WanGGUFTexttoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
|
|
ckpt_path = "https://huggingface.co/city96/Wan2.1-T2V-14B-gguf/blob/main/wan2.1-t2v-14b-Q3_K_S.gguf"
|
|
torch_dtype = torch.bfloat16
|
|
model_cls = WanTransformer3DModel
|
|
expected_memory_use_in_gb = 9
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
|
|
torch_device, self.torch_dtype
|
|
),
|
|
"encoder_hidden_states": torch.randn(
|
|
(1, 512, 4096),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
|
|
}
|
|
|
|
|
|
class WanGGUFImagetoVideoSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
|
|
ckpt_path = "https://huggingface.co/city96/Wan2.1-I2V-14B-480P-gguf/blob/main/wan2.1-i2v-14b-480p-Q3_K_S.gguf"
|
|
torch_dtype = torch.bfloat16
|
|
model_cls = WanTransformer3DModel
|
|
expected_memory_use_in_gb = 9
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"hidden_states": torch.randn((1, 36, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
|
|
torch_device, self.torch_dtype
|
|
),
|
|
"encoder_hidden_states": torch.randn(
|
|
(1, 512, 4096),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"encoder_hidden_states_image": torch.randn(
|
|
(1, 257, 1280), generator=torch.Generator("cpu").manual_seed(0)
|
|
).to(torch_device, self.torch_dtype),
|
|
"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
|
|
}
|
|
|
|
|
|
class WanVACEGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
|
|
ckpt_path = "https://huggingface.co/QuantStack/Wan2.1_14B_VACE-GGUF/blob/main/Wan2.1_14B_VACE-Q3_K_S.gguf"
|
|
torch_dtype = torch.bfloat16
|
|
model_cls = WanVACETransformer3DModel
|
|
expected_memory_use_in_gb = 9
|
|
|
|
def get_dummy_inputs(self):
|
|
return {
|
|
"hidden_states": torch.randn((1, 16, 2, 64, 64), generator=torch.Generator("cpu").manual_seed(0)).to(
|
|
torch_device, self.torch_dtype
|
|
),
|
|
"encoder_hidden_states": torch.randn(
|
|
(1, 512, 4096),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"control_hidden_states": torch.randn(
|
|
(1, 96, 2, 64, 64),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"control_hidden_states_scale": torch.randn(
|
|
(8,),
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
).to(torch_device, self.torch_dtype),
|
|
"timestep": torch.tensor([1]).to(torch_device, self.torch_dtype),
|
|
}
|
|
|
|
|
|
@require_torch_version_greater("2.7.1")
|
|
class GGUFCompileTests(QuantCompileTests, unittest.TestCase):
|
|
torch_dtype = torch.bfloat16
|
|
gguf_ckpt = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
|
|
|
|
@property
|
|
def quantization_config(self):
|
|
return GGUFQuantizationConfig(compute_dtype=self.torch_dtype)
|
|
|
|
def _init_pipeline(self, *args, **kwargs):
|
|
transformer = FluxTransformer2DModel.from_single_file(
|
|
self.gguf_ckpt, quantization_config=self.quantization_config, torch_dtype=self.torch_dtype
|
|
)
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"black-forest-labs/FLUX.1-dev", transformer=transformer, torch_dtype=self.torch_dtype
|
|
)
|
|
return pipe
|