Create app.py

app.py

@@ -0,0 +1,221 @@
+import os
+import gradio as gr
+import json
+import logging
+import torch
+from PIL import Image
+from os import path
+from torchvision import transforms
+from dataclasses import dataclass
+import math
+from typing import Callable
+import spaces
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from transformers import CLIPModel, CLIPProcessor, CLIPTextModel, CLIPTokenizer, CLIPConfig, T5EncoderModel, T5Tokenizer
+from diffusers.models.transformers import FluxTransformer2DModel
+import copy
+import random
+import time
+import safetensors.torch
+from tqdm import tqdm
+from safetensors.torch import load_file
+from huggingface_hub import HfFileSystem, ModelCard
+from huggingface_hub import login, hf_hub_download
+hf_token = os.environ.get("HF_TOKEN")
+login(token=hf_token)
+from sdnq import SDNQConfig # import sdnq to register it into diffusers and transformers
+
+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
+
+#torch.set_float32_matmul_precision("medium")
+
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+
+# Initialize the base model
+#dtype = torch.bfloat16
+#base_model = "AlekseyCalvin/Artsy_Lite_Flux_v1_by_jurdn_Diffusers"
+#pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to("cuda")
+##pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.float16).to("cuda")
+
+pipe = diffusers.ZImagePipeline.from_pretrained("Disty0/Z-Image-Turbo-SDNQ-uint4-svd-r32", torch_dtype=torch.bfloat16)
+torch.cuda.empty_cache()
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+pipe.enable_model_cpu_offload()
+
+
+#model_id = ("zer0int/LongCLIP-GmP-ViT-L-14")
+#config = CLIPConfig.from_pretrained(model_id)
+#config.text_config.max_position_embeddings = 248
+#clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True)
+#clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=248)
+#pipe.tokenizer = clip_processor.tokenizer
+#pipe.text_encoder = clip_model.text_model
+#pipe.tokenizer_max_length = 248
+#pipe.text_encoder.dtype = torch.bfloat16
+#pipe.text_encoder_2 = t5.text_model
+
+MAX_SEED = 2**32-1
+
+class calculateDuration:
+    def __init__(self, activity_name=""):
+        self.activity_name = activity_name
+
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
+    
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time = self.end_time - self.start_time
+        if self.activity_name:
+            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
+        else:
+            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
+
+
+def update_selection(evt: gr.SelectData, width, height):
+    selected_lora = loras[evt.index]
+    new_placeholder = f"Prompt with activator word(s): '{selected_lora['trigger_word']}'! "
+    lora_repo = selected_lora["repo"]
+    lora_trigger = selected_lora['trigger_word']
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}). Prompt using: '{lora_trigger}'!"
+    if "aspect" in selected_lora:
+        if selected_lora["aspect"] == "portrait":
+            width = 768
+            height = 1024
+        elif selected_lora["aspect"] == "landscape":
+            width = 1024
+            height = 768
+    return (
+        gr.update(placeholder=new_placeholder),
+        updated_text,
+        evt.index,
+        width,
+        height,
+    )
+
+@spaces.GPU(duration=50)
+def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress): 
+    pipe.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    
+    with calculateDuration("Generating image"):
+        # Generate image
+        image = pipe(
+            prompt=f"{prompt} {trigger_word}",
+            num_inference_steps=steps,
+            guidance_scale=cfg_scale,
+            width=width,
+            height=height,
+            generator=generator,
+            joint_attention_kwargs={"scale": lora_scale},
+        ).images[0]
+    return image
+
+def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    if selected_index is None:
+        raise gr.Error("You must select a LoRA before proceeding.")
+
+    selected_lora = loras[selected_index]
+    lora_path = selected_lora["repo"]
+    trigger_word = selected_lora['trigger_word']
+
+    # Load LoRA weights
+    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
+        if "weights" in selected_lora:
+            pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
+        else:
+            pipe.load_lora_weights(lora_path)
+        
+    # Set random seed for reproducibility
+    with calculateDuration("Randomizing seed"):
+        if randomize_seed:
+            seed = random.randint(0, MAX_SEED)
+    
+    image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
+    pipe.to("cpu")
+    pipe.unload_lora_weights()
+    return image, seed  
+
+run_lora.zerogpu = True
+
+css = '''
+#gen_btn{height: 100%}
+#title{text-align: center}
+#title h1{font-size: 3em; display:inline-flex; align-items:center}
+#title img{width: 100px; margin-right: 0.5em}
+#gallery .grid-wrap{height: 10vh}
+'''
+with gr.Blocks(theme=gr.themes.Soft(), css=css) as app:
+    title = gr.HTML(
+        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> SOONfactory </h1>""",
+        elem_id="title",
+    )
+    	    # Info blob stating what the app is running
+    info_blob = gr.HTML(
+        """<div id="info_blob"> Hosted Gallery of Custom-Trained Text2Image Generative Low-Rank Adaptors (LoRAs) for FLUX models. Running On: ArtsyLite FLUX base variant. First 4 gallery rows are adapters fine-tuned for the use of RCA (Revolutionary Communists of America at [https://CommunistUSA.org/]), & other activists/artists. Below them are adapters trained on works of Soviet Avant-Garde, Dada, Surrealism, & other radical styles + some original conceptions/fusions. Under those are identity models of notable revolutionaries & poets. Click squares to switch adapters & see links to their pages, many of them offering more info/resources. </div>"""
+    )
+
+        # Info blob stating what the app is running
+    info_blob = gr.HTML(
+        """<div id="info_blob"> To reinforce/focus a selected adapter style, add its pre-encoded “trigger" word/phrase to your prompt. Corresponding activator info &/or prompt template appears once an adapter square is clicked. Copy/Paste these into prompt box as a starting point.  </div>"""
+    )
+    selected_index = gr.State(None)
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(label="Prompt", lines=2, placeholder="Select LoRa/Style & type prompt!")
+        with gr.Column(scale=1, elem_id="gen_column"):
+            generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
+    with gr.Row():
+        with gr.Column(scale=3):
+            selected_info = gr.Markdown("")
+            gallery = gr.Gallery(
+                [(item["image"], item["title"]) for item in loras],
+                label="LoRA Inventory",
+                allow_preview=False,
+                columns=3,
+                elem_id="gallery"
+            )
+            
+        with gr.Column(scale=4):
+            result = gr.Image(label="Generated Image")
+
+    with gr.Row():
+        with gr.Accordion("Advanced Settings", open=True):
+            with gr.Column():
+                with gr.Row():
+                    cfg_scale = gr.Slider(label="CFG Scale", minimum=0, maximum=20, step=.1, value=1.0)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=9)
+                
+                with gr.Row():
+                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
+                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+                
+                with gr.Row():
+                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
+                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
+                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=2.5, step=0.01, value=1.0)
+
+    gallery.select(
+        update_selection,
+        inputs=[width, height],
+        outputs=[prompt, selected_info, selected_index, width, height]
+    )
+
+    gr.on(
+        triggers=[generate_button.click, prompt.submit],
+        fn=run_lora,
+        inputs=[prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
+        outputs=[result, seed]
+    )
+
+app.queue(default_concurrency_limit=2).launch(show_error=True)
+app.launch()