Upload app.py

app.py

@@ -0,0 +1,242 @@
+import gradio as gr
+import json
+import logging
+import argparse
+import torch
+import os
+from os import path
+from PIL import Image
+import numpy as np
+import spaces
+import copy
+import random
+import time
+from typing import Any, Dict, List, Optional, Union
+from huggingface_hub import hf_hub_download
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoPipelineForImage2Image
+import safetensors.torch
+from safetensors.torch import load_file
+from pipeline import FluxWithCFGPipeline
+from transformers import CLIPModel, CLIPProcessor, CLIPConfig
+import gc
+import warnings
+
+warnings.filterwarnings("ignore", category=FutureWarning) 
+
+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
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+torch._inductor.config.conv_1x1_as_mm = True
+torch._inductor.config.coordinate_descent_tuning = True
+torch._inductor.config.epilogue_fusion = False
+torch._inductor.config.coordinate_descent_check_all_directions = True
+
+dtype = torch.bfloat16
+pipe = FluxWithCFGPipeline.from_pretrained("ostris/OpenFLUX.1", torch_dtype=dtype, text_encoder_3=None, tokenizer_3=None
+).to("cuda")
+pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to("cuda")
+
+pipe.to("cuda")
+clipmodel = 'norm'
+if clipmodel == "long":
+    model_id = "zer0int/LongCLIP-GmP-ViT-L-14"
+    config = CLIPConfig.from_pretrained(model_id)
+    maxtokens = 77
+if clipmodel == "norm":
+    model_id = "zer0int/CLIP-GmP-ViT-L-14"
+    config = CLIPConfig.from_pretrained(model_id)
+    maxtokens = 77
+clip_model = CLIPModel.from_pretrained(model_id, torch_dtype=torch.bfloat16, config=config, ignore_mismatched_sizes=True).to("cuda")
+clip_processor = CLIPProcessor.from_pretrained(model_id, padding="max_length", max_length=maxtokens, ignore_mismatched_sizes=True, return_tensors="pt", truncation=True)
+config.text_config.max_position_embeddings = 77
+
+pipe.tokenizer = clip_processor.tokenizer
+pipe.text_encoder = clip_model.text_model
+pipe.tokenizer_max_length = maxtokens
+pipe.text_encoder.dtype = torch.bfloat16
+torch.cuda.empty_cache()
+
+pipe.transformer.to(memory_format=torch.channels_last)
+pipe.vae.to(memory_format=torch.channels_last)
+
+pipe.transformer = torch.compile(pipe.transformer, mode="max-autotune", fullgraph=True)
+pipe.vae.decode = torch.compile(pipe.vae.decode, mode="max-autotune", fullgraph=True)
+
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
+    
+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"Type a prompt for {selected_lora['title']}"
+    lora_repo = selected_lora["repo"]
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
+    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=70)
+def generate_image(prompt, negative_prompt, width, height, steps, seed, lora_scale=1.0, cfg_scale=3.5):
+    pipe.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    
+    with calculateDuration("Generating image"):
+        # Generate image
+        image = pipe(
+            prompt=f"{prompt}",
+            negative_prompt=negative_prompt,
+            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, negative_prompt, lora_scale, cfg_scale, steps, selected_lora, seed, width, height):
+    if negative_prompt == "":
+        negative_prompt = None
+    if selected_index is None:
+        raise gr.Error("Select a LoRA adapter square before proceeding.")
+
+    lora_path = selected_lora["repo"]
+        
+    # 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"], adapter_name=selected_lora["repo"], lora_scale=[1.0], adapter_weights=lora_scale)
+        else:
+            pipe.load_lora_weights(lora_path, adapter_name=selected_lora["repo"], lora_scale=[1.0], adapter_weights=["scale"])
+    # Load LoRA weights
+ #   with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
+    #    if "weights" in selected_lora:
+    #        pipe.load_lora_weights("ostris/OpenFLUX.1", weight_name="openflux1-v0.1.0-fast-lora.safetensors", adapter_name="soon", adapter_weights=[1.0], lora_scale=[1.0])
+    #        pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"], adapter_name=selected_lora["repo"], lora_scale=[1.0])
+    #        pipe.set_adapters(["fast", selected_lora["repo"]], adapter_weights=[1.0, 1.0])
+    #    else:
+    #        pipe.load_lora_weights("ostris/OpenFLUX.1", weight_name="openflux1-v0.1.0-fast-lora.safetensors", adapter_name="soon", adapter_weights=[1.0], lora_scale=[1.0])
+    #        pipe.load_lora_weights(lora_path, adapter_name=selected_lora["repo"], lora_scale=[1.0])
+     #       pipe.set_adapters(["fast", selected_lora["repo"]], adapter_weights=[1.0, 1.0])
+
+    image = generate_image(prompt, width, height, steps, negative_prompt, seed, lora_scale, cfg_scale)
+    pipe.to("cpu")
+    pipe.unload_lora_weights()
+    return image, seed  
+
+run_lora.zerogpu = True
+#pipe.load_lora_weights("ostris/OpenFLUX.1", weight_name="openflux1-v0.1.0-fast-lora.safetensors", adapter_name="fast")
+#pipe.set_adapters("fast")
+#pipe.set_adapters(["fast", "toy"], adapter_weights=[0.5, 1.0])
+#pipe.fuse_lora(adapter_names=["fast"], lora_scale=1.0)
+
+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/AlekseyCalvin/HSTklimbimOPENfluxLora/resolve/main/acs62iv.png" alt="LoRA">OpenFlux LoRAsoon®</h1>""",
+        elem_id="title",
+    )
+    	    # Info blob stating what the app is running
+    info_blob = gr.HTML(
+        """<div id="info_blob"> SOON®'s curated LoRa Gallery & Art Manufactory Space.|Runs on Ostris' OpenFLUX.1 model + fast-gen LoRA & Zer0int's fine-tuned CLIP-GmP-ViT-L-14*! (*'normal' 77 tokens)| Largely stocked w/our trained LoRAs: Historic Color, Silver Age Poets, Sots Art, more!|</div>"""
+    )
+
+        # Info blob stating what the app is running
+    info_blob = gr.HTML(
+        """<div id="info_blob"> *Auto-planting of prompts with a choice LoRA trigger errors out in this space over flaws yet unclear. In its stead, we pose numbered LoRA-box rows & a matched token cheat-sheet: ungainly & free. So, prephrase your prompts w/: 1-2. HST style autochrome |3. RCA style Communist poster |4. SOTS art |5. HST Austin Osman Spare style |6. Vladimir Mayakovsky |7-8. Marina Tsvetaeva Tsvetaeva_02.CR2 |9. Anna Akhmatova |10. Osip Mandelshtam |11-12. Alexander Blok |13. Blok_02.CR2 |14. LEN Lenin |15. Leon Trotsky |16. Rosa Fluxemburg |17. HST Peterhof photo |18-19. HST |20. HST portrait |21. HST |22. HST 80s Perestroika-era Soviet photo |23-30. HST |31. How2Draw a__ |32. propaganda poster |33. TOK hybrid photo of__ with cartoon of__ |34. 2004 IMG_1099.CR2 photo |35. unexpected photo of |36. flmft |37. 80s yearbook photo |38. TOK portra |39. pficonics |40. retrofuturism |41. wh3r3sw4ld0 |42. amateur photo |43. crisp |44-45. IMG_1099.CR2 |46. FilmFotos |47. ff-collage |48. HST |49-50. AOS |51. cover </div>"""
+    )
+    selected_index = gr.State(None)
+    with gr.Row():
+        with gr.Column(scale=3):
+            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Select LoRa/Style & type prompt! What do you want to see?")
+    with gr.Row():
+        with gr.Column(scale=3):
+            negative_prompt = gr.Textbox(label="Negative Prompt", lines=1, placeholder="List unwanted conditions, open-fluxedly!")
+        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=1, maximum=20, step=0.5, value=3)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=20)
+                
+                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():
+                    seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
+                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0.2, maximum=2.0, 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, seed, width, height, steps, negative_prompt, lora_scale, cfg_scale, selected_index],
+        outputs=[result, seed]
+    )
+
+app.queue(default_concurrency_limit=None).launch(show_error=True)
+app.launch()