224 lines
9.5 KiB
Python
224 lines
9.5 KiB
Python
import os
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import sys
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import torch
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from diffusers import FlowMatchEulerDiscreteScheduler
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current_file_path = os.path.abspath(__file__)
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project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))]
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for project_root in project_roots:
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sys.path.insert(0, project_root) if project_root not in sys.path else None
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from videox_fun.dist import set_multi_gpus_devices, shard_model
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from videox_fun.models import (AutoencoderKLQwenImage21,
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Qwen3VLForConditionalGeneration,
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Qwen3VLProcessor, QwenImage21Transformer2DModel)
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from videox_fun.pipeline import QwenImage21Pipeline
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from videox_fun.utils import (register_auto_device_hook,
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safe_enable_group_offload)
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from videox_fun.utils.fp8_optimization import (convert_model_weight_to_float8,
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convert_weight_dtype_wrapper)
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from videox_fun.utils.lora_utils import merge_lora, unmerge_lora
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# GPU memory mode, which can be chosen in [model_full_load, model_full_load_and_qfloat8, model_cpu_offload, model_cpu_offload_and_qfloat8, model_group_offload, sequential_cpu_offload].
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# model_full_load means that the entire model will be moved to the GPU.
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#
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# model_full_load_and_qfloat8 means that the entire model will be moved to the GPU,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory.
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#
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# model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# model_group_offload transfers internal layer groups between CPU/CUDA,
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# balancing memory efficiency and speed between full-module and leaf-level offloading methods.
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#
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# sequential_cpu_offload means that each layer of the model will be moved to the CPU after use,
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# resulting in slower speeds but saving a large amount of GPU memory.
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GPU_memory_mode = "model_group_offload"
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# Multi GPUs config
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# Qwen-Image 2.1 uses a block-causal single-stream transformer with a prefix KV cache, which is not
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# compatible with the sequence-parallel attention used by the other families. Please run it on a single
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# GPU (ulysses_degree = 1 and ring_degree = 1).
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ulysses_degree = 1
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ring_degree = 1
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# Use FSDP to save more GPU memory in multi gpus.
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fsdp_dit = False
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fsdp_text_encoder = False
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# Compile will give a speedup in fixed resolution and need a little GPU memory.
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# The compile_dit is not compatible with the fsdp_dit and sequential_cpu_offload.
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compile_dit = False
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# model path
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model_name = "models/Diffusion_Transformer/Qwen-Image-2.1"
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# Choose the sampler. Qwen-Image 2.1 is a flow-matching model sampled with the Euler discrete scheduler.
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sampler_name = "Flow"
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# Load pretrained model if need
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transformer_path = None
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vae_path = None
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lora_path = None
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# Other params
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# sample_size is the output canvas in pixels as [height, width]; the pipeline rounds it down to a
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# multiple of 32. Leave it as None to fall back to the pipeline's default square resolution.
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sample_size = [1024, 1024]
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# Cache the text and condition-image keys/values after the first denoising step. Valid because the
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# transformer modulates those tokens from t = 0, making their activations step-independent.
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use_kv_cache = True
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# Use torch.float16 if GPU does not support torch.bfloat16
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# Some graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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# Please use as detailed a prompt as possible to describe the object that needs to be generated.
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prompts = ["a young girl with flowing long hair, wearing a white halter dress and smiling sweetly. The background features a blue seaside where seagulls fly freely."]
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negative_prompt = " "
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guidance_scale = 1.0
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seed = 43
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num_inference_steps = 40
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lora_weight = 0.55
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save_path = "samples/qwenimage21-t2i"
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device = set_multi_gpus_devices(ulysses_degree, ring_degree)
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transformer = QwenImage21Transformer2DModel.from_pretrained(
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model_name,
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subfolder="transformer",
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low_cpu_mem_usage=True,
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torch_dtype=weight_dtype,
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).to(weight_dtype)
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if transformer_path is not None:
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print(f"From checkpoint: {transformer_path}")
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if transformer_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(transformer_path)
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else:
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state_dict = torch.load(transformer_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = transformer.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get Vae
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vae = AutoencoderKLQwenImage21.from_pretrained(
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model_name,
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subfolder="vae"
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).to(weight_dtype)
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if vae_path is not None:
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print(f"From checkpoint: {vae_path}")
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if vae_path.endswith("safetensors"):
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from safetensors.torch import load_file, safe_open
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state_dict = load_file(vae_path)
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else:
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state_dict = torch.load(vae_path, map_location="cpu")
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state_dict = state_dict["state_dict"] if "state_dict" in state_dict else state_dict
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m, u = vae.load_state_dict(state_dict, strict=False)
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print(f"missing keys: {len(m)}, unexpected keys: {len(u)}")
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# Get processor and text_encoder. Qwen-Image 2.1 encodes the prompt (and any condition images) with a
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# Qwen3-VL model, so a processor replaces the plain tokenizer used by the earlier Qwen-Image families.
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processor = Qwen3VLProcessor.from_pretrained(
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model_name, subfolder="processor"
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)
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text_encoder = Qwen3VLForConditionalGeneration.from_pretrained(
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model_name, subfolder="text_encoder", torch_dtype=weight_dtype
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)
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# Get Scheduler
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Chosen_Scheduler = {
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"Flow": FlowMatchEulerDiscreteScheduler,
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}[sampler_name]
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scheduler = Chosen_Scheduler.from_pretrained(
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model_name,
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subfolder="scheduler"
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)
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pipeline = QwenImage21Pipeline(
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vae=vae,
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text_encoder=text_encoder,
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processor=processor,
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transformer=transformer,
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scheduler=scheduler,
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)
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if ulysses_degree > 1 or ring_degree > 1:
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from functools import partial
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transformer.enable_multi_gpus_inference()
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if fsdp_dit:
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype, module_to_wrapper=list(transformer.transformer_blocks))
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pipeline.transformer = shard_fn(pipeline.transformer)
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print("Add FSDP DIT")
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if fsdp_text_encoder:
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from functools import partial
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from videox_fun.dist import set_multi_gpus_devices, shard_model
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shard_fn = partial(shard_model, device_id=device, param_dtype=weight_dtype, module_to_wrapper=text_encoder.model.language_model.layers)
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text_encoder = shard_fn(text_encoder)
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print("Add FSDP TEXT ENCODER")
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if compile_dit:
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for i in range(len(pipeline.transformer.transformer_blocks)):
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pipeline.transformer.transformer_blocks[i] = torch.compile(pipeline.transformer.transformer_blocks[i])
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print("Add Compile")
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if GPU_memory_mode == "sequential_cpu_offload":
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pipeline.enable_sequential_cpu_offload(device=device)
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elif GPU_memory_mode == "model_group_offload":
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register_auto_device_hook(pipeline.transformer)
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safe_enable_group_offload(pipeline, onload_device=device, offload_device="cpu", offload_type="leaf_level", use_stream=True)
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elif GPU_memory_mode == "model_cpu_offload_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["img_in", "txt_in", "time_text_embed", "modulation"], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_cpu_offload":
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pipeline.enable_model_cpu_offload(device=device)
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elif GPU_memory_mode == "model_full_load_and_qfloat8":
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convert_model_weight_to_float8(transformer, exclude_module_name=["img_in", "txt_in", "time_text_embed", "modulation"], device=device)
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convert_weight_dtype_wrapper(transformer, weight_dtype)
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pipeline.to(device=device)
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else:
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pipeline.to(device=device)
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for prompt in prompts:
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generator = torch.Generator(device=device).manual_seed(seed)
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if lora_path is not None:
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pipeline = merge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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with torch.no_grad():
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sample = pipeline(
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prompt,
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negative_prompt = negative_prompt,
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height = sample_size[0] if sample_size is not None else None,
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width = sample_size[1] if sample_size is not None else None,
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generator = generator,
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true_cfg_scale = guidance_scale,
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num_inference_steps = num_inference_steps,
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use_kv_cache = use_kv_cache,
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).images
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if lora_path is not None:
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pipeline = unmerge_lora(pipeline, lora_path, lora_weight, device=device, dtype=weight_dtype)
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def save_results():
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if not os.path.exists(save_path):
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os.makedirs(save_path, exist_ok=True)
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index = len([path for path in os.listdir(save_path)]) + 1
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prefix = str(index).zfill(8)
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image_path = os.path.join(save_path, prefix + ".png")
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image = sample[0]
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image.save(image_path)
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if ulysses_degree * ring_degree > 1:
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import torch.distributed as dist
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if dist.get_rank() == 0:
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save_results()
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else:
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save_results()
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