149 lines
6.1 KiB
Python
149 lines
6.1 KiB
Python
import torch
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import numpy as np
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from diffusers import LuminaText2ImgPipeline, FlowMatchEulerDiscreteScheduler
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from transformers import AutoModel, AutoTokenizer
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import comfy.model_management as mm
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import os
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import traceback
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import math
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from .utils import get_2d_rotary_pos_embed_lumina, REGIONAL_PROMPT
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import inspect
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class LuminaDiffusersNode:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model_path": ("STRING", {"default": "Lumina-Next-SFT-diffusers"}),
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"prompt": ("STRING", {"multiline": True}),
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"negative_prompt": ("STRING", {"multiline": True}),
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"num_inference_steps": ("INT", {"default": 30, "min": 1, "max": 200}),
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"guidance_scale": ("FLOAT", {"default": 4.0, "min": 0.1, "max": 20.0}),
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"width": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
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"height": ("INT", {"default": 1024, "min": 512, "max": 2048, "step": 64}),
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"seed": ("INT", {"default": -1}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 4}),
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"scaling_watershed": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0}),
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"proportional_attn": ("BOOLEAN", {"default": True}),
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"clean_caption": ("BOOLEAN", {"default": True}),
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"max_sequence_length": ("INT", {"default": 256, "min": 64, "max": 512}),
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},
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"optional": {
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"regional_prompts": ("REGIONAL_PROMPTS",),
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"mask": ("MASK",),
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"latents": ("LATENT",),
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}
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}
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RETURN_TYPES = ("IMAGE", "LATENT")
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FUNCTION = "generate"
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CATEGORY = "LuminaWrapper"
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def __init__(self):
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self.pipe = None
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def load_model(self, model_path):
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try:
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device = mm.get_torch_device()
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dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
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print(f"Loading Lumina model from: {model_path}")
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print(f"Device: {device}, Dtype: {dtype}")
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full_path = os.path.join(os.path.dirname(__file__), model_path)
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if not os.path.exists(full_path):
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print(f"Model not found. Downloading Lumina model to: {full_path}")
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self.pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=dtype)
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self.pipe.save_pretrained(full_path)
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else:
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self.pipe = LuminaText2ImgPipeline.from_pretrained(full_path, torch_dtype=dtype)
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self.pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(self.pipe.scheduler.config)
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self.pipe.to(device)
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print("Pipeline successfully loaded and moved to device.")
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except Exception as e:
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print(f"Error in load_model: {str(e)}")
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traceback.print_exc()
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def generate(self, model_path, prompt, negative_prompt, num_inference_steps, guidance_scale, width, height, seed,
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batch_size, scaling_watershed, proportional_attn, clean_caption, max_sequence_length):
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try:
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if self.pipe is None:
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self.load_model(model_path)
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device = mm.get_torch_device()
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if seed == -1:
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seed = int.from_bytes(os.urandom(4), "big")
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generator = torch.Generator(device=device).manual_seed(seed)
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default_image_size = self.pipe.transformer.config.sample_size * self.pipe.vae_scale_factor
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if proportional_attn:
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self.pipe.cross_attention_kwargs = {"base_sequence_length": (default_image_size // 16) ** 2}
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else:
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self.pipe.cross_attention_kwargs = None
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print(f"Starting generation with seed: {seed}")
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pipe_args = {
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"prompt": prompt,
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"negative_prompt": negative_prompt,
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"height": height,
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"width": width,
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"num_inference_steps": num_inference_steps,
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"guidance_scale": guidance_scale,
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"num_images_per_prompt": batch_size,
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"generator": generator,
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"output_type": "pt",
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"clean_caption": clean_caption,
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"max_sequence_length": max_sequence_length,
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"scaling_watershed": scaling_watershed,
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}
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# Generate images
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output = self.pipe(**pipe_args)
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processed_images = self.process_output(output.images)
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latents_dict = self.process_latents(output.images)
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return (processed_images, latents_dict)
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except Exception as e:
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print(f"Error in generate: {str(e)}")
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traceback.print_exc()
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return (torch.zeros((batch_size, height, width, 3), dtype=torch.float32),
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{"samples": torch.zeros((batch_size, 4, height // 8, width // 8), dtype=torch.float32)})
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def process_output(self, images):
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print(f"Raw output images shape: {images.shape}")
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print(f"Raw output images dtype: {images.dtype}")
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print(f"Raw output images min: {images.min()}, max: {images.max()}")
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images = images.float()
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images = images.clamp(0, 1)
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images = images.permute(0, 2, 3, 1)
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print(f"Processed images shape: {images.shape}")
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print(f"Processed images dtype: {images.dtype}")
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print(f"Processed images min: {images.min()}, max: {images.max()}")
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return images
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def process_latents(self, images):
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latents = images.clone().detach()
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latents = latents.to(dtype=torch.float32)
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print(f"Latents shape before processing: {latents.shape}")
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print(f"Latents dtype: {latents.dtype}")
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print(f"Latents min: {latents.min()}, max: {latents.max()}")
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return {"samples": latents}
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NODE_CLASS_MAPPINGS = {
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"LuminaDiffusersNode": LuminaDiffusersNode
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LuminaDiffusersNode": "Lumina-Next-SFT Diffusers"
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}
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