Z-Image-Turbo-Text-Encoder-Heretic-NVFP4

Duplicate from catplusplus/Z-Image-Turbo-Text-Encoder-Heretic-NVFP4

d00f17b 5 months ago

10.9 kB

import argparse
import base64
import io
import time
import torch
import uvicorn
import numpy as np
import gc
import asyncio
from fastapi import FastAPI, HTTPException, Request
from accelerate import infer_auto_device_map, dispatch_model
from pydantic import BaseModel
from diffusers import (
    Flux2Pipeline,
    Flux2Transformer2DModel,
    AutoencoderKLFlux2,
    FlowMatchEulerDiscreteScheduler
)
from diffusers.pipelines.flux2.pipeline_flux2 import compute_empirical_mu, retrieve_timesteps
from diffusers.pipelines.flux2.image_processor import Flux2ImageProcessor
from transformers import Mistral3ForConditionalGeneration, AutoProcessor

# Argument parsing
parser = argparse.ArgumentParser(description="Flux2 Image Generation Server")
parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to bind to")
parser.add_argument("--port", type=int, default=8000, help="Port to bind to")
parser.add_argument("--model", type=str, default="black-forest-labs/FLUX.1-dev", help="Path or Repo ID of the model")
args = parser.parse_args()

app = FastAPI()

# Global components
text_encoder = None
tokenizer = None
transformer = None
vae = None
scheduler = None
image_processor = None
request_lock = asyncio.Lock()

# Device maps
text_encoder_map = None
transformer_map = None
vae_map = None

GPU_MEMORY_FRACTION = 0.90

def load_model():
    global text_encoder, tokenizer, transformer, vae, scheduler, image_processor
    global text_encoder_map, transformer_map, vae_map
    
    print(f"Loading model from {args.model}...")
    
    try:
        print("Loading Flux2 components...")

        # Calculate max memory per GPU
        #max_memory = {}
        #if torch.cuda.is_available():
        #    for i in range(torch.cuda.device_count()):
        #        total_mem = torch.cuda.get_device_properties(i).total_memory
        #        max_memory[i] = int(total_mem * GPU_MEMORY_FRACTION)

        max_memory = {
          0: "5GB",   # leave a little headroom
 #        1: "10GB",
          "cpu": "120GB"  # your 128GB RAM minus OS
        }
        
        # Load Text Encoder (Mistral3) on CPU
        print("Loading Text Encoder on CPU...")
        text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
            args.model,
            subfolder="text_encoder",
            torch_dtype=torch.bfloat16,
            device_map="cpu"
        )
        print("Calculating Text Encoder device map...")
        text_encoder_map = infer_auto_device_map(text_encoder, max_memory=max_memory)

        # Load Tokenizer on CPU
        print("Loading Tokenizer on CPU...")
        tokenizer = AutoProcessor.from_pretrained(
            args.model,
            subfolder="tokenizer",
            device_map="cpu"
        )

        # Load Transformer on CPU
        print("Loading Transformer on CPU...")
        transformer = Flux2Transformer2DModel.from_pretrained(
            args.model,
            subfolder="transformer",
            torch_dtype=torch.bfloat16,
            device_map="cpu"
        )
        print("Calculating Transformer device map...")
        transformer_map = infer_auto_device_map(transformer, max_memory=max_memory)

        # Load VAE on CPU
        print("Loading VAE on CPU...")
        vae = AutoencoderKLFlux2.from_pretrained(
            args.model,
            subfolder="vae",
            torch_dtype=torch.bfloat16,
            device_map="cpu"
        )
        print("Calculating VAE device map...")
        vae_map = infer_auto_device_map(vae, max_memory=max_memory)

        # Initialize Scheduler
        print("Initializing Scheduler...")
        scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
            args.model,
            subfolder="scheduler"
        )

        # Initialize Image Processor
        print("Initializing Image Processor...")
        # VAE scale factor logic from pipeline
        vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
        image_processor = Flux2ImageProcessor(vae_scale_factor=vae_scale_factor * 2)
        
    except Exception as e:
        print(f"Error loading model: {e}")
        raise e
    
    print("Model loaded successfully!")

def flush():
    gc.collect()
    torch.cuda.empty_cache()

class ImageGenerationRequest(BaseModel):
    prompt: str
    n: int = 1
    size: str = "1024x1024"
    response_format: str = "b64_json"
    quality: str = "standard"
    style: str = "vivid"

@app.on_event("startup")
async def startup_event():
    load_model()

@app.post("/v1/images/generations")
async def generate_image(request: ImageGenerationRequest):
    if not transformer:
        raise HTTPException(status_code=500, detail="Model not loaded")

    async with request_lock:
        print(f"Received request: {request.prompt}")

        # Parse size
        try:
            width, height = map(int, request.size.split("x"))
        except ValueError:
            width, height = 1024, 1024

        num_inference_steps = 28
        guidance_scale = 4.0
        max_sequence_length = 512
        device = torch.device("cuda")
        dtype = torch.bfloat16

        images = []
        
        # 1. Generate embeddings on CPU
        print("Generating embeddings...")
        flush()
        prompt_embeds = Flux2Pipeline._get_mistral_3_small_prompt_embeds(
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            prompt=request.prompt,            
#            device=torch.device("cpu"),
            max_sequence_length=max_sequence_length
        )


#       prompt_embeds = prompt_embeds.to("cuda")       
     
        # 2. Prepare Latents
        # Flux latents are turned into 2x2 patches and packed. 
        # This means the latent width and height has to be divisible by the patch size.
        # So the vae scale factor is multiplied by the patch size to account for this
        vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
        
        height = height or 1024
        width = width or 1024
        
        # Resize to be divisible by vae_scale_factor * 2
        height = 2 * (int(height) // (vae_scale_factor * 2))
        width = 2 * (int(width) // (vae_scale_factor * 2))

        num_channels_latents = transformer.config.in_channels // 4
        shape = (1, num_channels_latents * 4, height // 2, width // 2)
        
        # 3. Prepare IDs
        # We need to prepare text_ids and latent_ids
        # prompt_embeds shape: (batch_size, seq_len, hidden_dim)
        batch_size, seq_len, _ = prompt_embeds.shape
        
        # Repeat for num_images_per_prompt (assuming 1 for now per loop iteration)
        # If request.n > 1, we loop outside or handle batching. Here we loop outside.
        
        # Prepare text IDs
        text_ids = Flux2Pipeline._prepare_text_ids(prompt_embeds).to(device)
        
        for _ in range(request.n):
            # Generate random latents
            latents = torch.randn(shape, device=device, dtype=dtype)
            
            # Prepare latent IDs
            latent_ids = Flux2Pipeline._prepare_latent_ids(latents).to(device)
            
            # Pack latents
            packed_latents = Flux2Pipeline._pack_latents(latents)
            
            # 4. Prepare Timesteps
            sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
            image_seq_len = packed_latents.shape[1]
            mu = compute_empirical_mu(image_seq_len=image_seq_len, num_steps=num_inference_steps)
            timesteps, num_inference_steps = retrieve_timesteps(
                scheduler,
                num_inference_steps,
                device,
                sigmas=sigmas,
                mu=mu,
            )
            
            # --- SWAP TRANSFORMER TO CUDA ---
            print("Moving Transformer to CUDA...")
            flush()
            dispatch_model(transformer, device_map=transformer_map)
            
            # 5. Denoising Loop
            print("Starting denoising loop on CUDA...")
            scheduler.set_begin_index(0)
            
            guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
            guidance = guidance.expand(packed_latents.shape[0])
            
            for i, t in enumerate(timesteps):
                start_time = time.time()
                # broadcast to batch dimension
                timestep = t.expand(packed_latents.shape[0]).to(packed_latents.dtype)
                
                noise_pred = transformer(
                    hidden_states=packed_latents,
                    timestep=timestep / 1000,
                    guidance=guidance,
                    encoder_hidden_states=prompt_embeds,
                    txt_ids=text_ids,
                    img_ids=latent_ids,
                    return_dict=False,
                )[0]

                # step
                packed_latents = scheduler.step(noise_pred, t, packed_latents, return_dict=False)[0]
                
                step_time = time.time() - start_time
                print(f"Step {i+1}/{num_inference_steps}: {step_time:.2f}s")

            # --- SWAP TRANSFORMER TO CPU ---
            print("Moving Transformer to CPU...")
            transformer.to("cpu")
            flush()

            # --- SWAP VAE TO CUDA ---
            print("Moving VAE to CUDA...")
            dispatch_model(vae, device_map=vae_map)

            # 6. Decode
            print("Decoding on CUDA...")
            # Move packed_latents to CUDA for decoding (already there, but ensuring)
            packed_latents = packed_latents.to(device)
            latent_ids = latent_ids.to(device)
            
            latents = Flux2Pipeline._unpack_latents_with_ids(packed_latents, latent_ids)
            
            latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
            latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps).to(
                latents.device, latents.dtype
            )
            latents = latents * latents_bn_std + latents_bn_mean
            latents = Flux2Pipeline._unpatchify_latents(latents)

            image = vae.decode(latents, return_dict=False)[0]
            image = image_processor.postprocess(image, output_type="pil")[0]
            
            # --- SWAP VAE TO CPU ---
            print("Moving VAE to CPU...")
            vae.to("cpu")
            
            # Convert to base64
            buffered = io.BytesIO()
            image.save(buffered, format="PNG")
            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
            images.append({"b64_json": img_str})

        return {
            "created": int(time.time()),
            "data": images
        }

if __name__ == "__main__":
    uvicorn.run(app, host=args.host, port=args.port)

	import argparse
	import base64
	import io
	import time
	import torch
	import uvicorn
	import numpy as np
	import gc
	import asyncio
	from fastapi import FastAPI, HTTPException, Request
	from accelerate import infer_auto_device_map, dispatch_model
	from pydantic import BaseModel
	from diffusers import (
	Flux2Pipeline,
	Flux2Transformer2DModel,
	AutoencoderKLFlux2,
	FlowMatchEulerDiscreteScheduler
	)
	from diffusers.pipelines.flux2.pipeline_flux2 import compute_empirical_mu, retrieve_timesteps
	from diffusers.pipelines.flux2.image_processor import Flux2ImageProcessor
	from transformers import Mistral3ForConditionalGeneration, AutoProcessor

	# Argument parsing
	parser = argparse.ArgumentParser(description="Flux2 Image Generation Server")
	parser.add_argument("--host", type=str, default="0.0.0.0", help="Host to bind to")
	parser.add_argument("--port", type=int, default=8000, help="Port to bind to")
	parser.add_argument("--model", type=str, default="black-forest-labs/FLUX.1-dev", help="Path or Repo ID of the model")
	args = parser.parse_args()

	app = FastAPI()

	# Global components
	text_encoder = None
	tokenizer = None
	transformer = None
	vae = None
	scheduler = None
	image_processor = None
	request_lock = asyncio.Lock()

	# Device maps
	text_encoder_map = None
	transformer_map = None
	vae_map = None

	GPU_MEMORY_FRACTION = 0.90

	def load_model():
	global text_encoder, tokenizer, transformer, vae, scheduler, image_processor
	global text_encoder_map, transformer_map, vae_map

	print(f"Loading model from {args.model}...")

	try:
	print("Loading Flux2 components...")

	# Calculate max memory per GPU
	#max_memory = {}
	#if torch.cuda.is_available():
	# for i in range(torch.cuda.device_count()):
	# total_mem = torch.cuda.get_device_properties(i).total_memory
	# max_memory[i] = int(total_mem * GPU_MEMORY_FRACTION)

	max_memory = {
	0: "5GB", # leave a little headroom
	# 1: "10GB",
	"cpu": "120GB" # your 128GB RAM minus OS
	}

	# Load Text Encoder (Mistral3) on CPU
	print("Loading Text Encoder on CPU...")
	text_encoder = Mistral3ForConditionalGeneration.from_pretrained(
	args.model,
	subfolder="text_encoder",
	torch_dtype=torch.bfloat16,
	device_map="cpu"
	)
	print("Calculating Text Encoder device map...")
	text_encoder_map = infer_auto_device_map(text_encoder, max_memory=max_memory)

	# Load Tokenizer on CPU
	print("Loading Tokenizer on CPU...")
	tokenizer = AutoProcessor.from_pretrained(
	args.model,
	subfolder="tokenizer",
	device_map="cpu"
	)

	# Load Transformer on CPU
	print("Loading Transformer on CPU...")
	transformer = Flux2Transformer2DModel.from_pretrained(
	args.model,
	subfolder="transformer",
	torch_dtype=torch.bfloat16,
	device_map="cpu"
	)
	print("Calculating Transformer device map...")
	transformer_map = infer_auto_device_map(transformer, max_memory=max_memory)

	# Load VAE on CPU
	print("Loading VAE on CPU...")
	vae = AutoencoderKLFlux2.from_pretrained(
	args.model,
	subfolder="vae",
	torch_dtype=torch.bfloat16,
	device_map="cpu"
	)
	print("Calculating VAE device map...")
	vae_map = infer_auto_device_map(vae, max_memory=max_memory)

	# Initialize Scheduler
	print("Initializing Scheduler...")
	scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
	args.model,
	subfolder="scheduler"
	)

	# Initialize Image Processor
	print("Initializing Image Processor...")
	# VAE scale factor logic from pipeline
	vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)
	image_processor = Flux2ImageProcessor(vae_scale_factor=vae_scale_factor * 2)

	except Exception as e:
	print(f"Error loading model: {e}")
	raise e

	print("Model loaded successfully!")

	def flush():
	gc.collect()
	torch.cuda.empty_cache()

	class ImageGenerationRequest(BaseModel):
	prompt: str
	n: int = 1
	size: str = "1024x1024"
	response_format: str = "b64_json"
	quality: str = "standard"
	style: str = "vivid"

	@app.on_event("startup")
	async def startup_event():
	load_model()

	@app.post("/v1/images/generations")
	async def generate_image(request: ImageGenerationRequest):
	if not transformer:
	raise HTTPException(status_code=500, detail="Model not loaded")

	async with request_lock:
	print(f"Received request: {request.prompt}")

	# Parse size
	try:
	width, height = map(int, request.size.split("x"))
	except ValueError:
	width, height = 1024, 1024

	num_inference_steps = 28
	guidance_scale = 4.0
	max_sequence_length = 512
	device = torch.device("cuda")
	dtype = torch.bfloat16

	images = []

	# 1. Generate embeddings on CPU
	print("Generating embeddings...")
	flush()
	prompt_embeds = Flux2Pipeline._get_mistral_3_small_prompt_embeds(
	text_encoder=text_encoder,
	tokenizer=tokenizer,
	prompt=request.prompt,
	# device=torch.device("cpu"),
	max_sequence_length=max_sequence_length
	)


	# prompt_embeds = prompt_embeds.to("cuda")

	# 2. Prepare Latents
	# Flux latents are turned into 2x2 patches and packed.
	# This means the latent width and height has to be divisible by the patch size.
	# So the vae scale factor is multiplied by the patch size to account for this
	vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1)

	height = height or 1024
	width = width or 1024

	# Resize to be divisible by vae_scale_factor * 2
	height = 2 * (int(height) // (vae_scale_factor * 2))
	width = 2 * (int(width) // (vae_scale_factor * 2))

	num_channels_latents = transformer.config.in_channels // 4
	shape = (1, num_channels_latents * 4, height // 2, width // 2)

	# 3. Prepare IDs
	# We need to prepare text_ids and latent_ids
	# prompt_embeds shape: (batch_size, seq_len, hidden_dim)
	batch_size, seq_len, _ = prompt_embeds.shape

	# Repeat for num_images_per_prompt (assuming 1 for now per loop iteration)
	# If request.n > 1, we loop outside or handle batching. Here we loop outside.

	# Prepare text IDs
	text_ids = Flux2Pipeline._prepare_text_ids(prompt_embeds).to(device)

	for _ in range(request.n):
	# Generate random latents
	latents = torch.randn(shape, device=device, dtype=dtype)

	# Prepare latent IDs
	latent_ids = Flux2Pipeline._prepare_latent_ids(latents).to(device)

	# Pack latents
	packed_latents = Flux2Pipeline._pack_latents(latents)

	# 4. Prepare Timesteps
	sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
	image_seq_len = packed_latents.shape[1]
	mu = compute_empirical_mu(image_seq_len=image_seq_len, num_steps=num_inference_steps)
	timesteps, num_inference_steps = retrieve_timesteps(
	scheduler,
	num_inference_steps,
	device,
	sigmas=sigmas,
	mu=mu,
	)

	# --- SWAP TRANSFORMER TO CUDA ---
	print("Moving Transformer to CUDA...")
	flush()
	dispatch_model(transformer, device_map=transformer_map)

	# 5. Denoising Loop
	print("Starting denoising loop on CUDA...")
	scheduler.set_begin_index(0)

	guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32)
	guidance = guidance.expand(packed_latents.shape[0])

	for i, t in enumerate(timesteps):
	start_time = time.time()
	# broadcast to batch dimension
	timestep = t.expand(packed_latents.shape[0]).to(packed_latents.dtype)

	noise_pred = transformer(
	hidden_states=packed_latents,
	timestep=timestep / 1000,
	guidance=guidance,
	encoder_hidden_states=prompt_embeds,
	txt_ids=text_ids,
	img_ids=latent_ids,
	return_dict=False,
	)[0]

	# step
	packed_latents = scheduler.step(noise_pred, t, packed_latents, return_dict=False)[0]

	step_time = time.time() - start_time
	print(f"Step {i+1}/{num_inference_steps}: {step_time:.2f}s")

	# --- SWAP TRANSFORMER TO CPU ---
	print("Moving Transformer to CPU...")
	transformer.to("cpu")
	flush()

	# --- SWAP VAE TO CUDA ---
	print("Moving VAE to CUDA...")
	dispatch_model(vae, device_map=vae_map)

	# 6. Decode
	print("Decoding on CUDA...")
	# Move packed_latents to CUDA for decoding (already there, but ensuring)
	packed_latents = packed_latents.to(device)
	latent_ids = latent_ids.to(device)

	latents = Flux2Pipeline._unpack_latents_with_ids(packed_latents, latent_ids)

	latents_bn_mean = vae.bn.running_mean.view(1, -1, 1, 1).to(latents.device, latents.dtype)
	latents_bn_std = torch.sqrt(vae.bn.running_var.view(1, -1, 1, 1) + vae.config.batch_norm_eps).to(
	latents.device, latents.dtype
	)
	latents = latents * latents_bn_std + latents_bn_mean
	latents = Flux2Pipeline._unpatchify_latents(latents)

	image = vae.decode(latents, return_dict=False)[0]
	image = image_processor.postprocess(image, output_type="pil")[0]

	# --- SWAP VAE TO CPU ---
	print("Moving VAE to CPU...")
	vae.to("cpu")

	# Convert to base64
	buffered = io.BytesIO()
	image.save(buffered, format="PNG")
	img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
	images.append({"b64_json": img_str})

	return {
	"created": int(time.time()),
	"data": images
	}

	if __name__ == "__main__":
	uvicorn.run(app, host=args.host, port=args.port)