Add files via upload
This commit is contained in:
kpsss34
+360
@@ -0,0 +1,360 @@
|
||||
import os
|
||||
import gc
|
||||
import json
|
||||
import torch
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
import folder_paths
|
||||
import comfy.model_management as model_management
|
||||
import comfy.utils
|
||||
import tempfile
|
||||
import inspect
|
||||
import atexit
|
||||
import weakref
|
||||
from typing import Any, Callable, Dict, List, Optional, Union
|
||||
try:
|
||||
from safetensors import safe_open
|
||||
from safetensors.torch import load_file
|
||||
from diffusers.image_processor import VaeImageProcessor
|
||||
from diffusers.loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
|
||||
from diffusers.models.autoencoders import AutoencoderKL
|
||||
from diffusers.models.transformers import SD3Transformer2DModel
|
||||
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
|
||||
from diffusers.utils import scale_lora_layers, unscale_lora_layers, USE_PEFT_BACKEND
|
||||
from diffusers.utils.torch_utils import randn_tensor
|
||||
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
|
||||
from diffusers.pipelines.stable_diffusion_3.pipeline_output import StableDiffusion3PipelineOutput
|
||||
from transformers import (
|
||||
CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel, T5TokenizerFast,
|
||||
CLIPTextConfig, T5Config, PreTrainedModel, PretrainedConfig
|
||||
)
|
||||
print("SD3.5s Node: All required libraries loaded successfully.")
|
||||
except ImportError as e:
|
||||
print(f"SD3.5 Node ERROR: A required library is missing: {e}")
|
||||
|
||||
class MemoryManager:
|
||||
"""Enhanced memory management for SD3.5s models"""
|
||||
_instance = None
|
||||
_cached_objects = []
|
||||
|
||||
def __new__(cls):
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
# Register cleanup on exit
|
||||
atexit.register(cls._instance.cleanup_all)
|
||||
return cls._instance
|
||||
|
||||
def register_object(self, obj):
|
||||
"""Register object for cleanup tracking"""
|
||||
self._cached_objects.append(weakref.ref(obj))
|
||||
|
||||
def cleanup_all(self):
|
||||
"""Comprehensive cleanup of all cached objects and memory"""
|
||||
print("SD3.5s: Starting comprehensive memory cleanup...")
|
||||
|
||||
# Clear all weak references
|
||||
for ref in self._cached_objects:
|
||||
obj = ref()
|
||||
if obj is not None:
|
||||
try:
|
||||
if hasattr(obj, 'to'):
|
||||
obj.to('cpu')
|
||||
del obj
|
||||
except:
|
||||
pass
|
||||
self._cached_objects.clear()
|
||||
|
||||
# Force garbage collection multiple times
|
||||
for _ in range(3):
|
||||
gc.collect()
|
||||
|
||||
# Clear CUDA cache if available
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
print(f"CUDA cache cleared. Available VRAM: {torch.cuda.get_device_properties(0).total_memory - torch.cuda.memory_allocated():.2f} MB")
|
||||
|
||||
print("SD3.5s: Memory cleanup completed")
|
||||
|
||||
def cleanup_torch_memory(self):
|
||||
"""Specifically clean up PyTorch memory"""
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
# Force garbage collection
|
||||
gc.collect()
|
||||
|
||||
# Global memory manager instance
|
||||
memory_manager = MemoryManager()
|
||||
|
||||
class StableDiffusion3SPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin):
|
||||
_optional_components = ["text_encoder_3", "tokenizer_3"]
|
||||
|
||||
def __init__(self, transformer, scheduler, vae, text_encoder, tokenizer, text_encoder_2, tokenizer_2, text_encoder_3, tokenizer_3):
|
||||
super().__init__()
|
||||
self.register_modules(vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, text_encoder_3=text_encoder_3, tokenizer=tokenizer, tokenizer_2=tokenizer_2, tokenizer_3=tokenizer_3, transformer=transformer, scheduler=scheduler)
|
||||
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
|
||||
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
|
||||
self.tokenizer_max_length = 77
|
||||
self.tokenizer_3_max_length = 256
|
||||
self.default_sample_size = self.transformer.config.sample_size
|
||||
self.patch_size = self.transformer.config.patch_size
|
||||
|
||||
# Register with memory manager
|
||||
memory_manager.register_object(self)
|
||||
|
||||
def _get_clip_prompt_embeds(self, prompt, num_images_per_prompt, device, clip_model_index):
|
||||
clip_tokenizers = [self.tokenizer, self.tokenizer_2]
|
||||
clip_text_encoders = [self.text_encoder, self.text_encoder_2]
|
||||
tokenizer, text_encoder = clip_tokenizers[clip_model_index], clip_text_encoders[clip_model_index]
|
||||
text_inputs = tokenizer(prompt, padding="max_length", max_length=self.tokenizer_max_length, truncation=True, return_tensors="pt")
|
||||
outputs = text_encoder(text_inputs.input_ids.to(device), output_hidden_states=True)
|
||||
return outputs.hidden_states[-2].repeat_interleave(num_images_per_prompt, dim=0), outputs[0].repeat_interleave(num_images_per_prompt, dim=0)
|
||||
|
||||
def encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt, lora_scale):
|
||||
if lora_scale is not None:
|
||||
for model in [self.transformer, self.text_encoder, self.text_encoder_2]:
|
||||
if model and USE_PEFT_BACKEND: scale_lora_layers(model, lora_scale)
|
||||
|
||||
prompts = [prompt] if isinstance(prompt, str) else prompt
|
||||
|
||||
pos_embeds_1, pos_pooled_1 = self._get_clip_prompt_embeds(prompts, num_images_per_prompt, device, 0)
|
||||
pos_embeds_2, pos_pooled_2 = self._get_clip_prompt_embeds(prompts, num_images_per_prompt, device, 1)
|
||||
t5_inputs = self.tokenizer_3(prompts, padding="max_length", max_length=self.tokenizer_3_max_length, truncation=True, return_tensors="pt")
|
||||
t5_embeds = self.text_encoder_3(t5_inputs.input_ids.to(device))[0].repeat_interleave(num_images_per_prompt, dim=0)
|
||||
|
||||
pooled_prompt_embeds = torch.cat([pos_pooled_1, pos_pooled_2], dim=-1)
|
||||
clip_embeds = torch.cat([pos_embeds_1, pos_embeds_2], dim=-1)
|
||||
|
||||
target_dim = self.transformer.config.joint_attention_dim
|
||||
if clip_embeds.shape[-1] < target_dim: clip_embeds = torch.nn.functional.pad(clip_embeds, (0, target_dim - clip_embeds.shape[-1]))
|
||||
if t5_embeds.shape[-1] < target_dim: t5_embeds = torch.nn.functional.pad(t5_embeds, (0, target_dim - t5_embeds.shape[-1]))
|
||||
prompt_embeds = torch.cat([clip_embeds, t5_embeds], dim=1)
|
||||
|
||||
if do_classifier_free_guidance:
|
||||
negative_prompts = [negative_prompt or ""] * len(prompts)
|
||||
neg_embeds_1, neg_pooled_1 = self._get_clip_prompt_embeds(negative_prompts, num_images_per_prompt, device, 0)
|
||||
neg_embeds_2, neg_pooled_2 = self._get_clip_prompt_embeds(negative_prompts, num_images_per_prompt, device, 1)
|
||||
t5_inputs_neg = self.tokenizer_3(negative_prompts, padding="max_length", max_length=self.tokenizer_3_max_length, truncation=True, return_tensors="pt")
|
||||
t5_embeds_neg = self.text_encoder_3(t5_inputs_neg.input_ids.to(device))[0].repeat_interleave(num_images_per_prompt, dim=0)
|
||||
|
||||
negative_pooled_prompt_embeds = torch.cat([neg_pooled_1, neg_pooled_2], dim=-1)
|
||||
neg_clip_embeds = torch.cat([neg_embeds_1, neg_embeds_2], dim=-1)
|
||||
if neg_clip_embeds.shape[-1] < target_dim: neg_clip_embeds = torch.nn.functional.pad(neg_clip_embeds, (0, target_dim - neg_clip_embeds.shape[-1]))
|
||||
if t5_embeds_neg.shape[-1] < target_dim: t5_embeds_neg = torch.nn.functional.pad(t5_embeds_neg, (0, target_dim - t5_embeds_neg.shape[-1]))
|
||||
negative_prompt_embeds = torch.cat([neg_clip_embeds, t5_embeds_neg], dim=1)
|
||||
else:
|
||||
negative_prompt_embeds, negative_pooled_prompt_embeds = None, None
|
||||
|
||||
return prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(self, prompt, height=1024, width=1024, num_inference_steps=28, guidance_scale=7.0, negative_prompt=None, generator=None, callback_on_step_end=None):
|
||||
device = self.device
|
||||
prompt_embeds, pooled_prompt_embeds, negative_prompt_embeds, negative_pooled_prompt_embeds = self.encode_prompt(
|
||||
prompt, device, 1, True, negative_prompt, lora_scale=None)
|
||||
|
||||
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
||||
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds])
|
||||
|
||||
shape = (1, self.transformer.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor)
|
||||
latents = randn_tensor(shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
|
||||
|
||||
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
||||
timesteps = self.scheduler.timesteps
|
||||
|
||||
for i, t in enumerate(timesteps):
|
||||
latent_model_input = torch.cat([latents] * 2)
|
||||
timestep = t.expand(latent_model_input.shape[0])
|
||||
|
||||
noise_pred = self.transformer(
|
||||
hidden_states=latent_model_input,
|
||||
timestep=timestep,
|
||||
encoder_hidden_states=prompt_embeds,
|
||||
pooled_projections=pooled_prompt_embeds
|
||||
)[0]
|
||||
|
||||
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
|
||||
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
|
||||
latents = self.scheduler.step(noise_pred, t, latents).prev_sample
|
||||
if callback_on_step_end is not None: callback_on_step_end(self, i, t, {})
|
||||
|
||||
latents /= self.vae.config.scaling_factor if hasattr(self.vae.config, 'scaling_factor') else 0.13025
|
||||
image = self.vae.decode(latents.to(self.vae.dtype)).sample
|
||||
image = self.image_processor.postprocess(image, output_type="pil")
|
||||
|
||||
return StableDiffusion3PipelineOutput(images=image)
|
||||
|
||||
class SD35RepackedLoaderSampler:
|
||||
_pipeline_cache = {}
|
||||
def __init__(self):
|
||||
self._pipeline_cache = {}
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
checkpoints_path = folder_paths.get_folder_paths("checkpoints")[0]
|
||||
files = [f for f in os.listdir(checkpoints_path) if f.endswith(".safetensors")]
|
||||
return { "required": {
|
||||
"model_name": (files,),
|
||||
"positive_prompt": ("STRING", {"multiline": True, "default": ""}),
|
||||
"negative_prompt": ("STRING", {"multiline": True, "default": "bad hands, bad finger, worst quality, low quality, jpeg artifacts, cartoon, painting, doll, ugly, disfigured, deformed, mutated, extra limbs, extra fingers, missing fingers, long neck, bad anatomy, bad proportions, unrealistic face, cloned face, blurred, watermark, text"}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"steps": ("INT", {"default": 35, "min": 1, "max": 100}),
|
||||
"cfg": ("FLOAT", {"default": 5.5, "min": 0.0, "max": 20.0, "step": 0.1}),
|
||||
"width": ("INT", {"default": 768, "min": 256, "max": 4096, "step": 64}),
|
||||
"height": ("INT", {"default": 1024, "min": 256, "max": 4096, "step": 64}),
|
||||
"clear_cache": ("BOOLEAN", {"default": False}), # เพิ่มตัวเลือกการล้าง cache
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE",)
|
||||
FUNCTION = "generate"
|
||||
CATEGORY = "loaders/sd35s"
|
||||
|
||||
@classmethod
|
||||
def VALIDATE_INPUTS(cls, **kwargs):
|
||||
"""Validate inputs and clear cache if requested"""
|
||||
if kwargs.get("clear_cache", False):
|
||||
cls.clear_all_caches()
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def clear_all_caches(cls):
|
||||
"""Clear all pipeline caches and perform memory cleanup"""
|
||||
print("SD3.5s: Clearing all caches and cleaning memory...")
|
||||
|
||||
# Clear pipeline cache
|
||||
for pipe in cls._pipeline_cache.values():
|
||||
try:
|
||||
if hasattr(pipe, 'to'):
|
||||
pipe.to('cpu')
|
||||
del pipe
|
||||
except:
|
||||
pass
|
||||
cls._pipeline_cache.clear()
|
||||
|
||||
# Use global memory manager for cleanup
|
||||
memory_manager.cleanup_all()
|
||||
|
||||
print("SD3.5s: All caches cleared and memory cleaned")
|
||||
|
||||
def _load_pipeline(self, model_path, dtype):
|
||||
if model_path in self._pipeline_cache:
|
||||
return self._pipeline_cache[model_path]
|
||||
|
||||
print(f"Unpacking and loading SD3.5 model from: {os.path.basename(model_path)}")
|
||||
with safe_open(model_path, framework="pt", device="cpu") as f: metadata = f.metadata()
|
||||
if metadata is None: raise ValueError(f"Model '{model_path}' missing metadata.")
|
||||
state_dict = load_file(model_path, device="cpu")
|
||||
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
def load_component(cls, name):
|
||||
config_dict = json.loads(metadata.get(f"{name}_config"))
|
||||
if issubclass(cls, PreTrainedModel):
|
||||
config = (CLIPTextConfig if cls is CLIPTextModelWithProjection else T5Config).from_dict(config_dict)
|
||||
model = cls(config)
|
||||
else:
|
||||
model = cls.from_config(config_dict)
|
||||
|
||||
component_sd = {k.replace(f"{name}.", ""): v for k, v in state_dict.items() if k.startswith(f"{name}.")}
|
||||
if name == "text_encoder_3" and "encoder.embed_tokens.weight" not in component_sd and "shared.weight" in component_sd:
|
||||
component_sd["encoder.embed_tokens.weight"] = component_sd["shared.weight"]
|
||||
model.load_state_dict(component_sd)
|
||||
|
||||
# Register each component with memory manager
|
||||
memory_manager.register_object(model)
|
||||
return model
|
||||
|
||||
def load_tokenizer(cls, name):
|
||||
tokenizer_dir = os.path.join(temp_dir, name)
|
||||
os.makedirs(tokenizer_dir, exist_ok=True)
|
||||
for key, value in metadata.items():
|
||||
if key.startswith(f"{name}_") and not key.endswith("_b64"):
|
||||
with open(os.path.join(tokenizer_dir, key.replace(f"{name}_", "")), 'w', encoding='utf-8') as f: f.write(value)
|
||||
return cls.from_pretrained(tokenizer_dir)
|
||||
|
||||
try:
|
||||
scheduler = FlowMatchEulerDiscreteScheduler.from_config(json.loads(metadata["scheduler_config"]))
|
||||
except:
|
||||
scheduler = FlowMatchEulerDiscreteScheduler()
|
||||
|
||||
pipe = StableDiffusion3SPipeline(
|
||||
vae=load_component(AutoencoderKL, "vae").to(dtype=dtype),
|
||||
transformer=load_component(SD3Transformer2DModel, "transformer").to(dtype=dtype),
|
||||
text_encoder=load_component(CLIPTextModelWithProjection, "text_encoder").to(dtype=dtype),
|
||||
text_encoder_2=load_component(CLIPTextModelWithProjection, "text_encoder_2").to(dtype=dtype),
|
||||
text_encoder_3=load_component(T5EncoderModel, "text_encoder_3").to(dtype=dtype),
|
||||
tokenizer=load_tokenizer(CLIPTokenizer, "tokenizer"),
|
||||
tokenizer_2=load_tokenizer(CLIPTokenizer, "tokenizer_2"),
|
||||
tokenizer_3=load_tokenizer(T5TokenizerFast, "tokenizer_3"),
|
||||
scheduler=scheduler,
|
||||
)
|
||||
|
||||
# Clean up state_dict and force garbage collection
|
||||
del state_dict
|
||||
gc.collect()
|
||||
memory_manager.cleanup_torch_memory()
|
||||
|
||||
self._pipeline_cache[model_path] = pipe
|
||||
return pipe
|
||||
|
||||
def generate(self, model_name, positive_prompt, negative_prompt, seed, steps, cfg, width, height, clear_cache=False):
|
||||
# Clear cache if requested
|
||||
if clear_cache:
|
||||
self.clear_all_caches()
|
||||
|
||||
model_path = folder_paths.get_full_path("checkpoints", model_name)
|
||||
device = model_management.get_torch_device()
|
||||
dtype = torch.bfloat16 if model_management.should_use_bf16() else torch.float16
|
||||
|
||||
pbar = comfy.utils.ProgressBar(steps)
|
||||
def callback(pipe, i, t, kwargs):
|
||||
pbar.update(1)
|
||||
# Cleanup intermediate tensors during generation
|
||||
if i % 5 == 0: # Every 5 steps
|
||||
memory_manager.cleanup_torch_memory()
|
||||
return kwargs
|
||||
|
||||
pipe = self._load_pipeline(model_path, dtype)
|
||||
pipe.to(device)
|
||||
generator = torch.manual_seed(seed)
|
||||
|
||||
try:
|
||||
image_pil = pipe(positive_prompt, height, width, steps, cfg, negative_prompt, generator=generator, callback_on_step_end=callback).images[0]
|
||||
|
||||
image_np = np.array(image_pil).astype(np.float32) / 255.0
|
||||
image_tensor = torch.from_numpy(image_np)[None,]
|
||||
|
||||
finally:
|
||||
# Always cleanup after generation
|
||||
pipe.to("cpu")
|
||||
del generator
|
||||
gc.collect()
|
||||
memory_manager.cleanup_torch_memory()
|
||||
|
||||
return (image_tensor,)
|
||||
|
||||
# Hook into ComfyUI's cleanup system
|
||||
def on_comfyui_reset():
|
||||
"""Called when ComfyUI resets - clean up all memory"""
|
||||
print("ComfyUI Reset detected - cleaning SD3.5s memory...")
|
||||
SD35RepackedLoaderSampler.clear_all_caches()
|
||||
memory_manager.cleanup_all()
|
||||
|
||||
# Try to register with ComfyUI's cleanup system if available
|
||||
try:
|
||||
import comfy.model_management
|
||||
# Register cleanup callback if ComfyUI supports it
|
||||
if hasattr(comfy.model_management, 'cleanup_models'):
|
||||
original_cleanup = comfy.model_management.cleanup_models
|
||||
def enhanced_cleanup(*args, **kwargs):
|
||||
result = original_cleanup(*args, **kwargs)
|
||||
on_comfyui_reset()
|
||||
return result
|
||||
comfy.model_management.cleanup_models = enhanced_cleanup
|
||||
print("SD3.5s: Registered with ComfyUI cleanup system")
|
||||
except:
|
||||
print("SD3.5s: Could not register with ComfyUI cleanup system")
|
||||
|
||||
NODE_CLASS_MAPPINGS = { "SD35sLoaderSampler": SD35RepackedLoaderSampler }
|
||||
NODE_DISPLAY_NAME_MAPPINGS = { "SD35sLoaderSampler": "Load and Sample SD3.5s" }
|
||||
Reference in New Issue
Block a user