Update app.py

app.py

@@ -1,290 +1,288 @@
-import gradio as gr
-import numpy as np
-import random
-import spaces
-import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL, StableDiffusionUpscalePipeline
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
-from huggingface_hub import hf_hub_download
-import os
-import requests
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
-
-# Performance optimizations
-if hasattr(pipe, "enable_model_cpu_offload"):
-    pipe.enable_model_cpu_offload()
-if hasattr(pipe, "enable_attention_slicing"):
-    pipe.enable_attention_slicing(1)
-if hasattr(pipe, "enable_vae_slicing"):
-    pipe.enable_vae_slicing()
-if hasattr(pipe, "enable_vae_tiling"):
-    pipe.enable_vae_tiling()
-
-# Compile transformer for faster inference (if supported)
-try:
-    pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
-    print("✓ Transformer compiled for faster inference")
-except Exception as e:
-    print(f"Warning: Could not compile transformer: {e}")
-
-# Load upscaler pipeline with optimizations
-upscaler = StableDiffusionUpscalePipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", torch_dtype=dtype).to(device)
-if hasattr(upscaler, "enable_model_cpu_offload"):
-    upscaler.enable_model_cpu_offload()
-if hasattr(upscaler, "enable_attention_slicing"):
-    upscaler.enable_attention_slicing(1)
-if hasattr(upscaler, "enable_vae_slicing"):
-    upscaler.enable_vae_slicing()
-
-# Available LoRAs
-LORAS = {
-    "None": None,
-    "AntiBlur": "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur",
-    "Add Details": "Shakker-Labs/FLUX.1-dev-LoRA-add-details",
-    "Ultra Realism": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/Canopus-LoRA-Flux-UltraRealism.safetensors",
-    "Face Realism": "https://huggingface.co/its-magick/merlin-test-loras/resolve/main/Canopus-LoRA-Flux-FaceRealism.safetensors"
-}
-
-# Store loaded LoRA paths
-loaded_loras = {}
-
-def download_lora_from_url(url, filename):
-    """Download LoRA file from direct URL"""
-    if not os.path.exists(filename):
-        print(f"Downloading {filename}...")
-        response = requests.get(url)
-        with open(filename, 'wb') as f:
-            f.write(response.content)
-        print(f"Downloaded {filename}")
-    return filename
-
-def preload_and_apply_all_loras():
-    """Download and apply all LoRAs simultaneously at startup"""
-    global loaded_loras
-    
-    print("Downloading and applying all LoRAs...")
-    
-    for lora_name, lora_path in LORAS.items():
-        if lora_name == "None" or lora_path is None:
-            continue
-            
-        # Handle direct URL downloads
-        if lora_path.startswith('http'):
-            filename = f"{lora_name.lower().replace(' ', '_')}_lora.safetensors"
-            lora_path = download_lora_from_url(lora_path, filename)
-        
-        loaded_loras[lora_name] = lora_path
-        print(f"Downloaded {lora_name}")
-        
-        # Apply each LoRA with optimal scaling
-        try:
-            optimal_scale = get_optimal_lora_scale(lora_name)
-            pipe.load_lora_weights(lora_path, adapter_name=lora_name.lower().replace(' ', '_'))
-            print(f"Applied {lora_name} with scale {optimal_scale}")
-        except Exception as e:
-            print(f"Failed to apply {lora_name}: {e}")
-    
-    print(f"All {len(loaded_loras)} LoRAs downloaded and applied!")
-
-def get_optimal_lora_scale(lora_name):
-    """Return optimal LoRA scale based on LoRA type for better quality/speed balance"""
-    lora_scales = {
-        "AntiBlur": 0.8,  # Slightly lower for better balance
-        "Add Details": 1.2,  # Higher for more detail enhancement
-        "Ultra Realism": 0.9,  # Balanced for realism
-        "Face Realism": 1.1,  # Optimized for facial features
-    }
-    return lora_scales.get(lora_name, 1.0)
-
-# Download and apply all LoRAs at startup
-preload_and_apply_all_loras()
-
-torch.cuda.empty_cache()
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 2048
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
-@spaces.GPU(duration=75)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, enable_upscale=False, progress=gr.Progress(track_tqdm=True)):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator().manual_seed(seed)
-    
-    # All LoRAs are already loaded and active
-    
-    try:
-        final_img = None
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-                prompt=prompt,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                width=width,
-                height=height,
-                generator=generator,
-                output_type="pil",
-                good_vae=good_vae,
-            ):
-                final_img = img
-                yield img, seed
-        
-        # Apply upscaling if enabled with optimized settings
-        if enable_upscale and final_img is not None:
-            try:
-                # Use fewer steps for faster upscaling with minimal quality loss
-                upscaled_img = upscaler(
-                    prompt=prompt,
-                    image=final_img,
-                    num_inference_steps=15,  # Reduced from 20 for speed
-                    guidance_scale=6.0,  # Slightly lower for faster convergence
-                    generator=generator,
-                ).images[0]
-                yield upscaled_img, seed
-            except Exception as e:
-                print(f"Error during upscaling: {e}")
-                yield final_img, seed
-                
-    except Exception as e:
-        print(f"Error during generation: {e}")
-        # Fallback to basic generation
-        img = pipe(
-            prompt=prompt,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            width=width,
-            height=height,
-            generator=generator,
-        ).images[0]
-        
-        # Apply upscaling if enabled
-        if enable_upscale:
-            try:
-                img = upscaler(
-                    prompt=prompt,
-                    image=img,
-                    num_inference_steps=20,
-                    guidance_scale=7.5,
-                    generator=generator,
-                ).images[0]
-            except Exception as e:
-                print(f"Error during upscaling: {e}")
-                
-        yield img, seed
-    
-examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
-[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            gr.Markdown("**LoRAs Active:** All LoRAs are loaded and active simultaneously")
-            
-            enable_upscale = gr.Checkbox(
-                label="Enable 4x Upscaling",
-                value=False,
-                info="Upscale final image using Stable Diffusion 4x upscaler"
-            )
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                    info="Lower values = faster generation, higher values = more prompt adherence"
-                )
-  
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=4,
-                    maximum=50,
-                    step=1,
-                    value=20,
-                    info="Lower values = faster generation, higher values = better quality"
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
-        )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, enable_upscale],
-        outputs = [result, seed]
-    )
-
+import gradio as gr
+import numpy as np
+import random
+import spaces
+import torch
+from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL, StableDiffusionUpscalePipeline
+from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from huggingface_hub import hf_hub_download
+import os
+import requests
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
+
+# Performance optimizations
+if hasattr(pipe, "enable_model_cpu_offload"):
+    pipe.enable_model_cpu_offload()
+if hasattr(pipe, "enable_attention_slicing"):
+    pipe.enable_attention_slicing(1)
+if hasattr(pipe, "enable_vae_slicing"):
+    pipe.enable_vae_slicing()
+if hasattr(pipe, "enable_vae_tiling"):
+    pipe.enable_vae_tiling()
+
+# Compile transformer for faster inference (if supported)
+try:
+    pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
+    print("✓ Transformer compiled for faster inference")
+except Exception as e:
+    print(f"Warning: Could not compile transformer: {e}")
+
+# Load upscaler pipeline with optimizations
+upscaler = StableDiffusionUpscalePipeline.from_pretrained("stabilityai/stable-diffusion-x4-upscaler", torch_dtype=dtype).to(device)
+if hasattr(upscaler, "enable_model_cpu_offload"):
+    upscaler.enable_model_cpu_offload()
+if hasattr(upscaler, "enable_attention_slicing"):
+    upscaler.enable_attention_slicing(1)
+if hasattr(upscaler, "enable_vae_slicing"):
+    upscaler.enable_vae_slicing()
+
+# Available LoRAs
+LORAS = {
+    "None": None,
+    "AntiBlur": "Shakker-Labs/FLUX.1-dev-LoRA-AntiBlur",
+    "Add Details": "Shakker-Labs/FLUX.1-dev-LoRA-add-details"
+}
+
+# Store loaded LoRA paths
+loaded_loras = {}
+
+def download_lora_from_url(url, filename):
+    """Download LoRA file from direct URL"""
+    if not os.path.exists(filename):
+        print(f"Downloading {filename}...")
+        response = requests.get(url)
+        with open(filename, 'wb') as f:
+            f.write(response.content)
+        print(f"Downloaded {filename}")
+    return filename
+
+def preload_and_apply_all_loras():
+    """Download and apply all LoRAs simultaneously at startup"""
+    global loaded_loras
+    
+    print("Downloading and applying all LoRAs...")
+    
+    for lora_name, lora_path in LORAS.items():
+        if lora_name == "None" or lora_path is None:
+            continue
+            
+        # Handle direct URL downloads
+        if lora_path.startswith('http'):
+            filename = f"{lora_name.lower().replace(' ', '_')}_lora.safetensors"
+            lora_path = download_lora_from_url(lora_path, filename)
+        
+        loaded_loras[lora_name] = lora_path
+        print(f"Downloaded {lora_name}")
+        
+        # Apply each LoRA with optimal scaling
+        try:
+            optimal_scale = get_optimal_lora_scale(lora_name)
+            pipe.load_lora_weights(lora_path, adapter_name=lora_name.lower().replace(' ', '_'))
+            print(f"Applied {lora_name} with scale {optimal_scale}")
+        except Exception as e:
+            print(f"Failed to apply {lora_name}: {e}")
+    
+    print(f"All {len(loaded_loras)} LoRAs downloaded and applied!")
+
+def get_optimal_lora_scale(lora_name):
+    """Return optimal LoRA scale based on LoRA type for better quality/speed balance"""
+    lora_scales = {
+        "AntiBlur": 0.8,  # Slightly lower for better balance
+        "Add Details": 1.2,  # Higher for more detail enhancement
+        "Ultra Realism": 0.9,  # Balanced for realism
+        "Face Realism": 1.1,  # Optimized for facial features
+    }
+    return lora_scales.get(lora_name, 1.0)
+
+# Download and apply all LoRAs at startup
+preload_and_apply_all_loras()
+
+torch.cuda.empty_cache()
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+
+@spaces.GPU(duration=75)
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, enable_upscale=False, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+    
+    # All LoRAs are already loaded and active
+    
+    try:
+        final_img = None
+        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+                prompt=prompt,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_inference_steps,
+                width=width,
+                height=height,
+                generator=generator,
+                output_type="pil",
+                good_vae=good_vae,
+            ):
+                final_img = img
+                yield img, seed
+        
+        # Apply upscaling if enabled with optimized settings
+        if enable_upscale and final_img is not None:
+            try:
+                # Use fewer steps for faster upscaling with minimal quality loss
+                upscaled_img = upscaler(
+                    prompt=prompt,
+                    image=final_img,
+                    num_inference_steps=15,  # Reduced from 20 for speed
+                    guidance_scale=6.0,  # Slightly lower for faster convergence
+                    generator=generator,
+                ).images[0]
+                yield upscaled_img, seed
+            except Exception as e:
+                print(f"Error during upscaling: {e}")
+                yield final_img, seed
+                
+    except Exception as e:
+        print(f"Error during generation: {e}")
+        # Fallback to basic generation
+        img = pipe(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+        ).images[0]
+        
+        # Apply upscaling if enabled
+        if enable_upscale:
+            try:
+                img = upscaler(
+                    prompt=prompt,
+                    image=img,
+                    num_inference_steps=20,
+                    guidance_scale=7.5,
+                    generator=generator,
+                ).images[0]
+            except Exception as e:
+                print(f"Error during upscaling: {e}")
+                
+        yield img, seed
+    
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+
+css="""
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""# FLUX.1 [dev]
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
+        """)
+        
+        with gr.Row():
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            gr.Markdown("**LoRAs Active:** All LoRAs are loaded and active simultaneously")
+            
+            enable_upscale = gr.Checkbox(
+                label="Enable 4x Upscaling",
+                value=False,
+                info="Upscale final image using Stable Diffusion 4x upscaler"
+            )
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+            
+            with gr.Row():
+
+                guidance_scale = gr.Slider(
+                    label="Guidance Scale",
+                    minimum=1,
+                    maximum=15,
+                    step=0.1,
+                    value=3.5,
+                    info="Lower values = faster generation, higher values = more prompt adherence"
+                )
+  
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=4,
+                    maximum=50,
+                    step=1,
+                    value=20,
+                    info="Lower values = faster generation, higher values = better quality"
+                )
+        
+        gr.Examples(
+            examples = examples,
+            fn = infer,
+            inputs = [prompt],
+            outputs = [result, seed],
+            cache_examples="lazy"
+        )
+
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn = infer,
+        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, enable_upscale],
+        outputs = [result, seed]
+    )
+
 demo.launch(share=True)