Hyper-FLUX-8Steps-LoRA_CPU

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import spaces
import argparse
import os
import time
from os import path
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path

import gradio as gr
import torch
from diffusers import FluxPipeline

torch.backends.cuda.matmul.allow_tf32 = True



base_model_id = "Freepik/flux.1-lite-8B-alpha"
torch_dtype = torch.bfloat16
device = "cpu"

# Load the pipe
model_id = "Freepik/flux.1-lite-8B-alpha"
pipe = FluxPipeline.from_pretrained(
    model_id, torch_dtype=torch_dtype
).to(device)

# Inference
prompt = "A close-up image of a green alien with fluorescent skin in the middle of a dark purple forest"

guidance_scale = 3.5  # Keep guidance_scale at 3.5
n_steps = 8
seed = 11

with torch.inference_mode():
    image = pipe(
        prompt=prompt,
        generator=torch.Generator(device="cpu").manual_seed(seed),
        num_inference_steps=n_steps,
        guidance_scale=guidance_scale,
        height=512,
        width=512,
    ).images[0]
image.save("output.png")










class timer:
    def __init__(self, method_name="timed process"):
        self.method = method_name
    def __enter__(self):
        self.start = time.time()
        print(f"{self.method} starts")
    def __exit__(self, exc_type, exc_val, exc_tb):
        end = time.time()
        print(f"{self.method} took {str(round(end - self.start, 2))}s")

if not path.exists(cache_path):
    os.makedirs(cache_path, exist_ok=True)
from huggingface_hub import hf_hub_download
import torch

from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig
os.system("wget https://huggingface.co/city96/flux.1-lite-8B-alpha-gguf/flux.1-lite-8B-alpha-Q3_K_S.gguf")
ckpt_path = (
    "flux.1-lite-8B-alpha-Q3_K_S.gguf"
)
transformer = FluxTransformer2DModel.from_single_file(
    ckpt_path,
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16,
)
pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
)
# https://huggingface.co/martintomov/Hyper-FLUX.1-dev-gguf/resolve/main/hyper-flux-16step-Q3_K_M.gguf
#pipe = FluxPipeline.from_pretrained("flux1-schnell-Q3_K_S.gguf")
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
pipe.fuse_lora(lora_scale=0.125)

pipe.enable_model_cpu_offload()
prompt = "A cat holding a sign that says hello world"
image = pipe(prompt, generator=torch.manual_seed(0)).images[0]
image.save("flux-gguf.png")


#pipe.to(device="cpu", dtype=torch.bfloat16)

#hf_hub_download(repo_id="city96/FLUX.1-schnell-gguf", filename="flux1-schnell-Q3_K_S.gguf")

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        <div style="text-align: center; max-width: 650px; margin: 0 auto;">
            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1>
            <p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p>
        </div>
        """
    )

    with gr.Row():
        with gr.Column(scale=3):
            with gr.Group():
                prompt = gr.Textbox(
                    label="Your Image Description",
                    placeholder="E.g., A serene landscape with mountains and a lake at sunset",
                    lines=3
                )
                
                with gr.Accordion("Advanced Settings", open=False):
                    with gr.Group():
                        with gr.Row():
                            height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
                            width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
                        
                        with gr.Row():
                            steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=16)
                            scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
                        
                        seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
                
                generate_btn = gr.Button("Generate Image", variant="primary", scale=1)

        with gr.Column(scale=4):
            output = gr.Image(label="Your Generated Image")
    
    gr.Markdown(
        """
        <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
            <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
            <ol style="padding-left: 1.5rem;">
                <li>Enter a detailed description of the image you want to create.</li>
                <li>Adjust advanced settings if desired (tap to expand).</li>
                <li>Tap "Generate Image" and wait for your creation!</li>
            </ol>
            <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
        </div>
        """
    )

    @spaces.GPU
    def process_image(height, width, steps, scales, prompt, seed):
        global pipe
        with torch.inference_mode(), torch.autocast("cpu", dtype=torch.bfloat16), timer("inference"):
            return pipe(
                prompt=[prompt],
                generator=torch.Generator().manual_seed(int(seed)),
                num_inference_steps=int(steps),
                guidance_scale=float(scales),
                height=int(height),
                width=int(width),
                max_sequence_length=256
            ).images[0]

    generate_btn.click(
        process_image,
        inputs=[height, width, steps, scales, prompt, seed],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()
'''
import spaces
import argparse
import os
import time
from os import path
from safetensors.torch import load_file
from huggingface_hub import hf_hub_download

# Setting up cache directories
cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
os.environ["TRANSFORMERS_CACHE"] = cache_path
os.environ["HF_HUB_CACHE"] = cache_path
os.environ["HF_HOME"] = cache_path

import gradio as gr
import torch
from diffusers import FluxPipeline

# Remove CUDA-specific settings since this will run on CPU
# torch.backends.cuda.matmul.allow_tf32 = True

class timer:
    def __init__(self, method_name="timed process"):
        self.method = method_name
    def __enter__(self):
        self.start = time.time()
        print(f"{self.method} starts")
    def __exit__(self, exc_type, exc_val, exc_tb):
        end = time.time()
        print(f"{self.method} took {str(round(end - self.start, 2))}s")

if not path.exists(cache_path):
    os.makedirs(cache_path, exist_ok=True)

# Load the model in a CPU-friendly format (use float32 to save memory)
pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.float32)
pipe.load_lora_weights(hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"))
pipe.fuse_lora(lora_scale=0.125)

# Switch to CPU and use float32 for inference
pipe.to(device="cpu", dtype=torch.float32)

# Gradio UI setup
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        <div style="text-align: center; max-width: 650px; margin: 0 auto;">
            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1>
            <p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p>
        </div>
        """
    )

    with gr.Row():
        with gr.Column(scale=3):
            with gr.Group():
                prompt = gr.Textbox(
                    label="Your Image Description",
                    placeholder="E.g., A serene landscape with mountains and a lake at sunset",
                    lines=3
                )
                
                with gr.Accordion("Advanced Settings", open=False):
                    with gr.Group():
                        with gr.Row():
                            height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=512)
                            width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=512)
                        
                        with gr.Row():
                            steps = gr.Slider(label="Inference Steps", minimum=6, maximum=25, step=1, value=8)
                            scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
                        
                        seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
                
                generate_btn = gr.Button("Generate Image", variant="primary", scale=1)

        with gr.Column(scale=4):
            output = gr.Image(label="Your Generated Image")
    
    gr.Markdown(
        """
        <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
            <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
            <ol style="padding-left: 1.5rem;">
                <li>Enter a detailed description of the image you want to create.</li>
                <li>Adjust advanced settings if desired (tap to expand).</li>
                <li>Tap "Generate Image" and wait for your creation!</li>
            </ol>
            <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
        </div>
        """
    )

    # Processing function for CPU execution
    def process_image(height, width, steps, scales, prompt, seed):
        global pipe
        with torch.inference_mode(), timer("inference"):
            return pipe(
                prompt=[prompt],
                generator=torch.Generator().manual_seed(int(seed)),
                num_inference_steps=int(steps),
                guidance_scale=float(scales),
                height=int(height),
                width=int(width),
                max_sequence_length=256
            ).images[0]

    generate_btn.click(
        process_image,
        inputs=[height, width, steps, scales, prompt, seed],
        outputs=output
    )

if __name__ == "__main__":
    demo.launch()'''