import gradio as gr import numpy as np import spaces import torch import random import os import subprocess import logging import safetensors ##################################################### # Forced Diffusers upgrade when cache was being stubborn; probably not needed now # force = subprocess.run("pip install -U diffusers", shell=True) # force = subprocess.run("pip install git+https://github.com/huggingface/diffusers.git", shell=True) # force = subprocess.run("pip install git+https://github.com/huggingface/transformers.git", shell=True) force = subprocess.run("git lfs install", shell=True) ##################################################### import transformers import diffusers from diffusers import DiffusionPipeline import bitsandbytes from diffusers.quantizers import PipelineQuantizationConfig from diffusers.utils import load_image from diffusers import FluxKontextPipeline from PIL import Image from huggingface_hub import hf_hub_download from huggingface_hub import create_repo, upload_folder from huggingface_hub.utils._runtime import dump_environment_info from safetensors import safe_open ##################################################### MAX_SEED = np.iinfo(np.int32).max API_TOKEN = os.environ['HF_TOKEN'] DEVICE = "cuda" if torch.cuda.is_available() else "cpu" os.environ.setdefault('GRADIO_ANALYTICS_ENABLED', 'False') os.environ.setdefault('HF_HUB_DISABLE_TELEMETRY', '1') dump_environment_info() logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger(__name__) ##################################################### # TESTING TWO QUANTIZATION METHODS # 1) If FP8 is supported; `torchao` for quantization # quant_config = PipelineQuantizationConfig( # quant_backend="torchao", # quant_kwargs={"quant_type": "float8dq_e4m3_row"}, # components_to_quantize=["transformer"] # ) # 2) Otherwise, standard 4-bit quantization with bitsandbytes # quant_config = PipelineQuantizationConfig( # quant_backend="bitsandbytes_4bit", # quant_kwargs={"load_in_4bit": True, "bnb_4bit_compute_dtype": torch.bfloat16, "bnb_4bit_quant_type": "nf4"}, # components_to_quantize=["transformer"] # ) try: # Set max memory usage for ZeroGPU torch.cuda.set_per_process_memory_fraction(1.0) torch.set_float32_matmul_precision("high") except Exception as e: print(f"Error setting memory usage: {e}") ##################################################### # Load the pipeline with the specified quantization configuration. # We use bfloat16 as the base dtype for mixed-precision inference. # HF Spaces VRAM (50 GB) is sufficient to hold the entire pipeline (31.424 GB), # Leave the entire pipeline to the GPU for the best performance. # FLUX.1 Dev Kontext Lightning Model / 8-Steps kontext_model = "LPX55/FLUX.1_Kontext-Lightning" pipe = FluxKontextPipeline.from_pretrained( "LPX55/FLUX.1_Kontext-Lightning", torch_dtype=torch.float16 ).to("cuda") # Save as a single `.safetensors` file pipe.save_pretrained( "./flux_16bit", safe_serialization=True, max_shard_size="100GB" # Forces all shards into one file (no split files) ) local_folder = "./flux_16bit" hub_repo_name = "LPX55/FLUX.1_Kontext-Lightning" # create_repo(hub_repo_name, exist_ok=True, private=False) # with safe_open("./flux_16bit/model.safetensors", framework="pt", device="cuda") as f: # for k in f.keys(): # print(k, f.get_slice(k).shape) upload_folder( folder_path=local_folder, path_in_repo="float16", repo_id=hub_repo_name, repo_type="model", commit_message="Upload half-precision FLUX.1 Kontext Lightning model", token=API_TOKEN ) ################################################### # SECTION FOR LORA(S); SKIP FOR NOW # try: # repo_name = "" # ckpt_name = "" # pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name), adapter_name="A1") # pipe.set_adapters(["A1"], adapter_weights=[0.5]) # pipe.fuse_lora(adapter_names=["A1"], lora_scale=1.0) # pipe.unload_lora_weights() # except Exception as e: # print(f"Error while loading Lora: {e}") ##################################################### def concatenate_images(images, direction="horizontal"): """ Concatenate multiple PIL images either horizontally or vertically. Args: images: List of PIL Images direction: "horizontal" or "vertical" Returns: PIL Image: Concatenated image """ if not images: return None # Filter out None images valid_images = [img for img in images if img is not None] if not valid_images: return None if len(valid_images) == 1: return valid_images[0].convert("RGB") # Convert all images to RGB valid_images = [img.convert("RGB") for img in valid_images] if direction == "horizontal": # Calculate total width and max height total_width = sum(img.width for img in valid_images) max_height = max(img.height for img in valid_images) # Create new image concatenated = Image.new('RGB', (total_width, max_height), (255, 255, 255)) # Paste images x_offset = 0 for img in valid_images: # Center image vertically if heights differ y_offset = (max_height - img.height) // 2 concatenated.paste(img, (x_offset, y_offset)) x_offset += img.width else: # vertical # Calculate max width and total height max_width = max(img.width for img in valid_images) total_height = sum(img.height for img in valid_images) # Create new image concatenated = Image.new('RGB', (max_width, total_height), (255, 255, 255)) # Paste images y_offset = 0 for img in valid_images: # Center image horizontally if widths differ x_offset = (max_width - img.width) // 2 concatenated.paste(img, (x_offset, y_offset)) y_offset += img.height return concatenated @spaces.GPU @torch.no_grad() def infer(input_images, prompt, seed=42, randomize_seed=False, guidance_scale=2.5, steps=8, width=1024, height=1024, progress=gr.Progress(track_tqdm=True)): if randomize_seed: seed = random.randint(0, MAX_SEED) # Handle input_images - it could be a single image or a list of images if input_images is None: raise gr.Error("Please upload at least one image.") # If it's a single image (not a list), convert to list if not isinstance(input_images, list): input_images = [input_images] # Filter out None images valid_images = [img[0] for img in input_images if img is not None] if not valid_images: raise gr.Error("Please upload at least one valid image.") # Concatenate images horizontally concatenated_image = concatenate_images(valid_images, "horizontal") if concatenated_image is None: raise gr.Error("Failed to process the input images.") # original_width, original_height = concatenated_image.size # if original_width >= original_height: # new_width = 1024 # new_height = int(original_height * (new_width / original_width)) # new_height = round(new_height / 64) * 64 # else: # new_height = 1024 # new_width = int(original_width * (new_height / original_height)) # new_width = round(new_width / 64) * 64 #concatenated_image_resized = concatenated_image.resize((new_width, new_height), Image.LANCZOS) final_prompt = f"From the provided reference images, create a unified, cohesive image such that {prompt}. Maintain the identity and characteristics of each subject while adjusting their proportions, scale, and positioning to create a harmonious, naturally balanced composition. Blend and integrate all elements seamlessly with consistent lighting, perspective, and style.the final result should look like a single naturally captured scene where all subjects are properly sized and positioned relative to each other, not assembled from multiple sources." image = pipe( image=concatenated_image, prompt=final_prompt, guidance_scale=guidance_scale, width=width, height=height, max_area=width * height, num_inference_steps=steps, generator=torch.Generator().manual_seed(seed), ).images[0] return image, seed, gr.update(visible=True) css=""" #col-container { margin: 0 auto; max-width: 86vw; } """ with gr.Blocks(css=css) as demo: with gr.Column(elem_id="col-container"): gr.Markdown(f"""# FLUX.1 Kontext | Lightning 8-Step Model ⚡ """) with gr.Row(): with gr.Column(): input_images = gr.Gallery( label="Upload image(s) for editing", show_label=True, elem_id="gallery_input", columns=3, rows=2, object_fit="contain", height="auto", file_types=['image'], type='pil' ) with gr.Row(): prompt = gr.Text( label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')", container=False, ) run_button = gr.Button("Run", scale=0) with gr.Accordion("Advanced Settings", open=True): with gr.Group(): width = gr.Slider( label="W", minimum=512, maximum=2560, step=64, value=1024, ) height = gr.Slider( label="H", minimum=512, maximum=2560, step=64, value=1024, ) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, ) randomize_seed = gr.Checkbox(label="Randomize seed", value=True) guidance_scale = gr.Slider( label="Guidance Scale", minimum=1, maximum=10, step=0.1, value=2.5, ) input_steps = gr.Slider( label="Steps", minimum=1, maximum=30, step=1, value=16, ) with gr.Column(): result = gr.Image(label="Result", show_label=False, interactive=False) reuse_button = gr.Button("Reuse this image", visible=False) gr.on( triggers=[run_button.click, prompt.submit], fn = infer, inputs = [input_images, prompt, seed, randomize_seed, guidance_scale, input_steps, width, height], outputs = [result, seed, reuse_button] ) reuse_button.click( fn = lambda image: [image] if image is not None else [], # Convert single image to list for gallery inputs = [result], outputs = [input_images] ) demo.queue().launch()