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import torch
from optimum.quanto import freeze, qfloat8, quantize
from transformers.modeling_utils import PreTrainedModel
from diffusers import (
FlowMatchEulerDiscreteScheduler,
AutoencoderKL,
AutoencoderTiny,
FluxImg2ImgPipeline,
FluxPipeline,
)
from diffusers import (
FluxImg2ImgPipeline,
FluxPipeline,
FluxTransformer2DModel,
GGUFQuantizationConfig,
)
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
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(BaseModel):
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(
256, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
256, 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")
# ckpt_path: str = "https://huggingface.co/city96/FLUX.1-schnell-gguf/blob/main/flux1-schnell-Q6_K.gguf"
ckpt_path: str = "https://huggingface.co/city96/FLUX.1-schnell-gguf/blob/main/flux1-schnell-Q4_K_S.gguf"
transformer = FluxTransformer2DModel.from_single_file(
ckpt_path,
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
torch_dtype=torch.bfloat16,
)
# else:
pipe = FluxImg2ImgPipeline.from_pretrained(
# "black-forest-labs/FLUX.1-dev",
"black-forest-labs/FLUX.1-Schnell",
transformer=transformer,
torch_dtype=torch.bfloat16,
)
if args.taesd:
pipe.vae = AutoencoderTiny.from_pretrained(
taesd_path, torch_dtype=torch.bfloat16, use_safetensors=True
)
# pipe.enable_model_cpu_offload()
pipe = pipe.to(device)
# pipe.enable_model_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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