Update app.py

app.py

@@ -18,22 +18,22 @@ with open('loras.json', 'r') as f:
     loras = json.load(f)
 
 # Initialize the base model
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float32
+device = "cpu"
 base_model = "black-forest-labs/FLUX.1-dev"
 
 taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
 good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
 pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
 pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
-                                                      vae=good_vae,
-                                                      transformer=pipe.transformer,
-                                                      text_encoder=pipe.text_encoder,
-                                                      tokenizer=pipe.tokenizer,
-                                                      text_encoder_2=pipe.text_encoder_2,
-                                                      tokenizer_2=pipe.tokenizer_2,
-                                                      torch_dtype=dtype
-                                                     )
+    vae=good_vae,
+    transformer=pipe.transformer,
+    text_encoder=pipe.text_encoder,
+    tokenizer=pipe.tokenizer,
+    text_encoder_2=pipe.text_encoder_2,
+    tokenizer_2=pipe.tokenizer_2,
+    torch_dtype=dtype
+)
 
 MAX_SEED = 2**32-1
 
@@ -46,7 +46,7 @@ class calculateDuration:
     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
@@ -78,263 +78,4 @@ def update_selection(evt: gr.SelectData, width, height):
         height,
     )
 
-@spaces.GPU(duration=70)
-def generate_image(prompt_mash, 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
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt_mash,
-            num_inference_steps=steps,
-            guidance_scale=cfg_scale,
-            width=width,
-            height=height,
-            generator=generator,
-            joint_attention_kwargs={"scale": lora_scale},
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img
-
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    pipe_i2i.to("cuda")
-    image_input = load_image(image_input_path)
-    final_image = pipe_i2i(
-        prompt=prompt_mash,
-        image=image_input,
-        strength=image_strength,
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-        output_type="pil",
-    ).images[0]
-    return final_image 
-
-@spaces.GPU(duration=70)
-def run_lora(prompt, image_input, image_strength, 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"]
-    if(trigger_word):
-        if "trigger_position" in selected_lora:
-            if selected_lora["trigger_position"] == "prepend":
-                prompt_mash = f"{trigger_word} {prompt}"
-            else:
-                prompt_mash = f"{prompt} {trigger_word}"
-        else:
-            prompt_mash = f"{trigger_word} {prompt}"
-    else:
-        prompt_mash = prompt
-
-    with calculateDuration("Unloading LoRA"):
-        pipe.unload_lora_weights()
-        pipe_i2i.unload_lora_weights()
-        
-    # Load LoRA weights
-    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
-        pipe_to_use = pipe_i2i if image_input is not None else pipe
-        weight_name = selected_lora.get("weights", None)
-        
-        pipe_to_use.load_lora_weights(
-            lora_path, 
-            weight_name=weight_name, 
-            low_cpu_mem_usage=True
-        )
-                
-    # Set random seed for reproducibility
-    with calculateDuration("Randomizing seed"):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-            
-    if(image_input is not None):
-        
-        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
-        yield final_image, seed, gr.update(visible=False)
-    else:
-        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
-    
-        # Consume the generator to get the final image
-        final_image = None
-        step_counter = 0
-        for image in image_generator:
-            step_counter+=1
-            final_image = image
-            progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-            yield image, seed, gr.update(value=progress_bar, visible=True)
-            
-        yield final_image, seed, gr.update(value=progress_bar, visible=False)
-        
-def get_huggingface_safetensors(link):
-  split_link = link.split("/")
-  if(len(split_link) == 2):
-            model_card = ModelCard.load(link)
-            base_model = model_card.data.get("base_model")
-            print(base_model)
-            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
-                raise Exception("Not a FLUX LoRA!")
-            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
-            fs = HfFileSystem()
-            try:
-                list_of_files = fs.ls(link, detail=False)
-                for file in list_of_files:
-                    if(file.endswith(".safetensors")):
-                        safetensors_name = file.split("/")[-1]
-                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
-                      image_elements = file.split("/")
-                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
-            except Exception as e:
-              print(e)
-              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
-              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
-            return split_link[1], link, safetensors_name, trigger_word, image_url
-
-def check_custom_model(link):
-    if(link.startswith("https://")):
-        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
-            link_split = link.split("huggingface.co/")
-            return get_huggingface_safetensors(link_split[1])
-    else: 
-        return get_huggingface_safetensors(link)
-
-def add_custom_lora(custom_lora):
-    global loras
-    if(custom_lora):
-        try:
-            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
-            print(f"Loaded custom LoRA: {repo}")
-            card = f'''
-            <div class="custom_lora_card">
-              <span>Loaded custom LoRA:</span>
-              <div class="card_internal">
-                <img src="{image}" />
-                <div>
-                    <h3>{title}</h3>
-                    <small>{"Using: <code><b>"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
-                </div>
-              </div>
-            </div>
-            '''
-            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
-            if(not existing_item_index):
-                new_item = {
-                    "image": image,
-                    "title": title,
-                    "repo": repo,
-                    "weights": path,
-                    "trigger_word": trigger_word
-                }
-                print(new_item)
-                existing_item_index = len(loras)
-                loras.append(new_item)
-        
-            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
-        except Exception as e:
-            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
-            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
-    else:
-        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
-
-def remove_custom_lora():
-    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
-
-run_lora.zerogpu = True
-
-css = '''
-#gen_btn{height: 100%}
-#gen_column{align-self: stretch}
-#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}
-#lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
-.card_internal{display: flex;height: 100px;margin-top: .5em}
-.card_internal img{margin-right: 1em}
-.styler{--form-gap-width: 0px !important}
-#progress{height:30px}
-#progress .generating{display:none}
-.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
-.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
-'''
-font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]
-with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(60, 60)) as app:
-    title = gr.HTML(
-        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""",
-        elem_id="title",
-    )
-    selected_index = gr.State(None)
-    with gr.Row():
-        with gr.Column(scale=3):
-            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
-        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():
-            selected_info = gr.Markdown("")
-            gallery = gr.Gallery(
-                [(item["image"], item["title"]) for item in loras],
-                label="LoRA Gallery",
-                allow_preview=False,
-                columns=3,
-                elem_id="gallery",
-                show_share_button=False
-            )
-            with gr.Group():
-                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
-                gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
-            custom_lora_info = gr.HTML(visible=False)
-            custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
-        with gr.Column():
-            progress_bar = gr.Markdown(elem_id="progress",visible=False)
-            result = gr.Image(label="Generated Image")
-
-    with gr.Row():
-        with gr.Accordion("Advanced Settings", open=False):
-            with gr.Row():
-                input_image = gr.Image(label="Input image", type="filepath")
-                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
-            with gr.Column():
-                with gr.Row():
-                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-                
-                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=3, step=0.01, value=0.95)
-
-    gallery.select(
-        update_selection,
-        inputs=[width, height],
-        outputs=[prompt, selected_info, selected_index, width, height]
-    )
-    custom_lora.input(
-        add_custom_lora,
-        inputs=[custom_lora],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
-    )
-    custom_lora_button.click(
-        remove_custom_lora,
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
-    )
-    gr.on(
-        triggers=[generate_button.click, prompt.submit],
-        fn=run_lora,
-        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
-        outputs=[result, seed, progress_bar]
-    )
-
-app.queue()
-app.launch()
+def generate_image(prompt_mash, steps, seed, cfg_scale, width