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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 | |
| 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 | |
| ckpt_path = ( | |
| "https://huggingface.co/city96/flux.1-lite-8B-alpha-gguf/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> | |
| """ | |
| ) | |
| 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()''' |