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import torch
from optimum.quanto import freeze, qfloat8, quantize
from transformers.modeling_utils import PreTrainedModel
from diffusers import AutoencoderTiny
from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
from diffusers.pipelines.flux.pipeline_flux_img2img import FluxImg2ImgPipeline
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL
from pruna import smash, SmashConfig
from pruna.telemetry import set_telemetry_metrics
set_telemetry_metrics(False) # disable telemetry for current session
set_telemetry_metrics(False, set_as_default=True) # disable telemetry globally
try:
import intel_extension_for_pytorch as ipex # type: ignore
except:
pass
import psutil
from config import Args
from pydantic import BaseModel, Field
from PIL import Image
from pathlib import Path
from util import ParamsModel
import math
import gc
# model_path = "black-forest-labs/FLUX.1-dev"
model_path = "black-forest-labs/FLUX.1-schnell"
base_model_path = "black-forest-labs/FLUX.1-schnell"
taesd_path = "madebyollin/taef1"
subfolder = "transformer"
transformer_path = model_path
models_path = Path("models")
default_prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
page_content = """
<h1 class="text-3xl font-bold">Real-Time FLUX</h1>
"""
def flush():
torch.cuda.empty_cache()
gc.collect()
class Pipeline:
class Info(BaseModel):
name: str = "img2img"
title: str = "Image-to-Image SDXL"
description: str = "Generates an image from a text prompt"
input_mode: str = "image"
page_content: str = page_content
class InputParams(ParamsModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
seed: int = Field(
2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
)
steps: int = Field(
1, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
)
width: int = Field(
1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
strength: float = Field(
0.5,
min=0.25,
max=1.0,
step=0.001,
title="Strength",
field="range",
hide=True,
id="strength",
)
guidance: float = Field(
3.5,
min=0,
max=20,
step=0.001,
title="Guidance",
hide=True,
field="range",
id="guidance",
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
# ckpt_path = (
# "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
# )
print("Loading model")
model_id = "black-forest-labs/FLUX.1-schnell"
model_revision = "refs/pr/1"
text_model_id = "openai/clip-vit-large-patch14"
model_data_type = torch.bfloat16
tokenizer = CLIPTokenizer.from_pretrained(
text_model_id, torch_dtype=model_data_type
)
text_encoder = CLIPTextModel.from_pretrained(
text_model_id, torch_dtype=model_data_type
)
# 2
tokenizer_2 = T5TokenizerFast.from_pretrained(
model_id,
subfolder="tokenizer_2",
torch_dtype=model_data_type,
revision=model_revision,
)
text_encoder_2 = T5EncoderModel.from_pretrained(
model_id,
subfolder="text_encoder_2",
torch_dtype=model_data_type,
revision=model_revision,
)
# Transformers
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
model_id, subfolder="scheduler", revision=model_revision
)
transformer = FluxTransformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
torch_dtype=model_data_type,
revision=model_revision,
)
# VAE
# vae = AutoencoderKL.from_pretrained(
# model_id,
# subfolder="vae",
# torch_dtype=model_data_type,
# revision=model_revision,
# )
vae = AutoencoderTiny.from_pretrained(
"madebyollin/taef1", torch_dtype=torch.bfloat16
)
# Initialize the SmashConfig
smash_config = SmashConfig()
smash_config["quantizer"] = "quanto"
smash_config["quanto_calibrate"] = False
smash_config["quanto_weight_bits"] = "qint4"
# (
# "qint4" # "qfloat8" # or "qint2", "qint4", "qint8"
# )
transformer = smash(
model=transformer,
smash_config=smash_config,
)
text_encoder_2 = smash(
model=text_encoder_2,
smash_config=smash_config,
)
pipe = FluxImg2ImgPipeline(
scheduler=scheduler,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
vae=vae,
transformer=transformer,
)
# if args.taesd:
# pipe.vae = AutoencoderTiny.from_pretrained(
# taesd_path, torch_dtype=torch.bfloat16, use_safetensors=True
# )
# pipe.enable_model_cpu_offload()
pipe.text_encoder.to(device)
pipe.vae.to(device)
pipe.transformer.to(device)
pipe.text_encoder_2.to(device)
# pipe.enable_model_cpu_offload()
# For added memory savings run this block, there is however a trade-off with speed.
# vae.enable_tiling()
# vae.enable_slicing()
# pipe.enable_sequential_cpu_offload()
self.pipe = pipe
self.pipe.set_progress_bar_config(disable=True)
# vae = AutoencoderKL.from_pretrained(
# base_model_path, subfolder="vae", torch_dtype=torch_dtype
# )
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
steps = params.steps
strength = params.strength
prompt = params.prompt
guidance = params.guidance
results = self.pipe(
image=params.image,
prompt=prompt,
generator=generator,
strength=strength,
num_inference_steps=steps,
guidance_scale=guidance,
width=params.width,
height=params.height,
)
return results.images[0]
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