Upload 2 files

api_app.py

@@ -0,0 +1,313 @@
+
+import os
+import random
+import sys
+from typing import Sequence, Mapping, Any, Union
+import torch
+import gradio as gr
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import spaces
+from comfy import model_management
+
+hf_hub_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", filename="flux1-redux-dev.safetensors", local_dir="models/style_models")
+hf_hub_download(repo_id="black-forest-labs/FLUX.1-Depth-dev", filename="flux1-depth-dev.safetensors", local_dir="models/diffusion_models")
+hf_hub_download(repo_id="Comfy-Org/sigclip_vision_384", filename="sigclip_vision_patch14_384.safetensors", local_dir="models/clip_vision")
+hf_hub_download(repo_id="Kijai/DepthAnythingV2-safetensors", filename="depth_anything_v2_vitl_fp32.safetensors", local_dir="models/depthanything")
+hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev", filename="ae.safetensors", local_dir="models/vae/FLUX1")
+hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="clip_l.safetensors", local_dir="models/text_encoders")
+t5_path = hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp16.safetensors", local_dir="models/text_encoders/t5")
+
+# Import all the necessary functions from the original script
+def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
+    try:
+        return obj[index]
+    except KeyError:
+        return obj["result"][index]
+
+# Add all the necessary setup functions from the original script
+def find_path(name: str, path: str = None) -> str:
+    if path is None:
+        path = os.getcwd()
+    if name in os.listdir(path):
+        path_name = os.path.join(path, name)
+        print(f"{name} found: {path_name}")
+        return path_name
+    parent_directory = os.path.dirname(path)
+    if parent_directory == path:
+        return None
+    return find_path(name, parent_directory)
+
+def add_comfyui_directory_to_sys_path() -> None:
+    comfyui_path = find_path("ComfyUI")
+    if comfyui_path is not None and os.path.isdir(comfyui_path):
+        sys.path.append(comfyui_path)
+        print(f"'{comfyui_path}' added to sys.path")
+
+def add_extra_model_paths() -> None:
+    try:
+        from main import load_extra_path_config
+    except ImportError:
+        from utils.extra_config import load_extra_path_config
+    extra_model_paths = find_path("extra_model_paths.yaml")
+    if extra_model_paths is not None:
+        load_extra_path_config(extra_model_paths)
+    else:
+        print("Could not find the extra_model_paths config file.")
+
+# Initialize paths
+add_comfyui_directory_to_sys_path()
+add_extra_model_paths()
+
+def import_custom_nodes() -> None:
+    import asyncio
+    import execution
+    from nodes import init_extra_nodes
+    import server
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+    init_extra_nodes()
+
+# Import all necessary nodes
+from nodes import (
+    StyleModelLoader,
+    VAEEncode,
+    NODE_CLASS_MAPPINGS,
+    LoadImage,
+    CLIPVisionLoader,
+    SaveImage,
+    VAELoader,
+    CLIPVisionEncode,
+    DualCLIPLoader,
+    EmptyLatentImage,
+    VAEDecode,
+    UNETLoader,
+    CLIPTextEncode,
+)
+
+# Initialize all constant nodes and models in global context
+import_custom_nodes()
+
+# Global variables for preloaded models and constants
+#with torch.inference_mode():
+    # Initialize constants
+intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
+CONST_1024 = intconstant.get_value(value=1024)
+
+# Load CLIP
+dualcliploader = DualCLIPLoader()
+CLIP_MODEL = dualcliploader.load_clip(
+    clip_name1="t5/t5xxl_fp16.safetensors",
+    clip_name2="clip_l.safetensors",
+    type="flux",
+)
+
+# Load VAE
+vaeloader = VAELoader()
+VAE_MODEL = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
+
+# Load UNET
+unetloader = UNETLoader()
+UNET_MODEL = unetloader.load_unet(
+    unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
+)
+
+# Load CLIP Vision
+clipvisionloader = CLIPVisionLoader()
+CLIP_VISION_MODEL = clipvisionloader.load_clip(
+    clip_name="sigclip_vision_patch14_384.safetensors"
+)
+
+# Load Style Model
+stylemodelloader = StyleModelLoader()
+STYLE_MODEL = stylemodelloader.load_style_model(
+    style_model_name="flux1-redux-dev.safetensors"
+)
+
+# Initialize samplers
+ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
+SAMPLER = ksamplerselect.get_sampler(sampler_name="euler")
+
+# Initialize depth model
+cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
+downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS["DownloadAndLoadDepthAnythingV2Model"]()
+DEPTH_MODEL = downloadandloaddepthanythingv2model.loadmodel(
+    model="depth_anything_v2_vitl_fp32.safetensors"
+)
+
+cliptextencode = CLIPTextEncode()
+loadimage = LoadImage()
+vaeencode = VAEEncode()
+fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
+instructpixtopixconditioning = NODE_CLASS_MAPPINGS["InstructPixToPixConditioning"]()
+clipvisionencode = CLIPVisionEncode()
+stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
+emptylatentimage = EmptyLatentImage()
+basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
+basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()        
+randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
+samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
+vaedecode = VAEDecode()
+cr_text = NODE_CLASS_MAPPINGS["CR Text"]()
+saveimage = SaveImage()
+getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
+depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
+imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
+
+model_loaders = [CLIP_MODEL, VAE_MODEL, UNET_MODEL, CLIP_VISION_MODEL]
+
+model_management.load_models_gpu([
+    loader[0].patcher if hasattr(loader[0], 'patcher') else loader[0] for loader in model_loaders
+])
+
+@spaces.GPU
+def generate_image(prompt, structure_image, style_image, depth_strength=15, style_strength=0.5, progress=gr.Progress(track_tqdm=True)) -> str:
+    """Main generation function that processes inputs and returns the path to the generated image."""
+    with torch.inference_mode():
+        # Set up CLIP
+        clip_switch = cr_clip_input_switch.switch(
+            Input=1,
+            clip1=get_value_at_index(CLIP_MODEL, 0),
+            clip2=get_value_at_index(CLIP_MODEL, 0),
+        )
+        
+        # Encode text
+        text_encoded = cliptextencode.encode(
+            text=prompt,
+            clip=get_value_at_index(clip_switch, 0),
+        )
+        empty_text = cliptextencode.encode(
+            text="",
+            clip=get_value_at_index(clip_switch, 0),
+        )
+        
+        # Process structure image
+        structure_img = loadimage.load_image(image=structure_image)
+        
+        # Resize image
+        resized_img = imageresize.execute(
+            width=get_value_at_index(CONST_1024, 0),
+            height=get_value_at_index(CONST_1024, 0),
+            interpolation="bicubic",
+            method="keep proportion",
+            condition="always",
+            multiple_of=16,
+            image=get_value_at_index(structure_img, 0),
+        )
+        
+        # Get image size
+        size_info = getimagesizeandcount.getsize(
+            image=get_value_at_index(resized_img, 0)
+        )
+        
+        # Encode VAE
+        vae_encoded = vaeencode.encode(
+            pixels=get_value_at_index(size_info, 0),
+            vae=get_value_at_index(VAE_MODEL, 0),
+        )
+        
+        # Process depth
+        depth_processed = depthanything_v2.process(
+            da_model=get_value_at_index(DEPTH_MODEL, 0),
+            images=get_value_at_index(size_info, 0),
+        )
+        
+        # Apply Flux guidance
+        flux_guided = fluxguidance.append(
+            guidance=depth_strength,
+            conditioning=get_value_at_index(text_encoded, 0),
+        )
+        
+        # Process style image
+        style_img = loadimage.load_image(image=style_image)
+        
+        # Encode style with CLIP Vision
+        style_encoded = clipvisionencode.encode(
+            crop="center",
+            clip_vision=get_value_at_index(CLIP_VISION_MODEL, 0),
+            image=get_value_at_index(style_img, 0),
+        )
+        
+        # Set up conditioning
+        conditioning = instructpixtopixconditioning.encode(
+            positive=get_value_at_index(flux_guided, 0),
+            negative=get_value_at_index(empty_text, 0),
+            vae=get_value_at_index(VAE_MODEL, 0),
+            pixels=get_value_at_index(depth_processed, 0),
+        )
+        
+        # Apply style
+        style_applied = stylemodelapplyadvanced.apply_stylemodel(
+            strength=style_strength,
+            conditioning=get_value_at_index(conditioning, 0),
+            style_model=get_value_at_index(STYLE_MODEL, 0),
+            clip_vision_output=get_value_at_index(style_encoded, 0),
+        )
+        
+        # Set up empty latent
+        empty_latent = emptylatentimage.generate(
+            width=get_value_at_index(resized_img, 1),
+            height=get_value_at_index(resized_img, 2),
+            batch_size=1,
+        )
+        
+        # Set up guidance
+        guided = basicguider.get_guider(
+            model=get_value_at_index(UNET_MODEL, 0),
+            conditioning=get_value_at_index(style_applied, 0),
+        )
+        
+        # Set up scheduler
+        schedule = basicscheduler.get_sigmas(
+            scheduler="simple",
+            steps=28,
+            denoise=1,
+            model=get_value_at_index(UNET_MODEL, 0),
+        )
+        
+        # Generate random noise
+        noise = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
+        
+        # Sample
+        sampled = samplercustomadvanced.sample(
+            noise=get_value_at_index(noise, 0),
+            guider=get_value_at_index(guided, 0),
+            sampler=get_value_at_index(SAMPLER, 0),
+            sigmas=get_value_at_index(schedule, 0),
+            latent_image=get_value_at_index(empty_latent, 0),
+        )
+        
+        # Decode VAE
+        decoded = vaedecode.decode(
+            samples=get_value_at_index(sampled, 0),
+            vae=get_value_at_index(VAE_MODEL, 0),
+        )
+        
+        # Save image
+        prefix = cr_text.text_multiline(text="Flux_BFL_Depth_Redux")
+        
+        saved = saveimage.save_images(
+            filename_prefix=get_value_at_index(prefix, 0),
+            images=get_value_at_index(decoded, 0),
+        )
+        saved_path = f"output/{saved['ui']['images'][0]['filename']}"
+        return saved_path
+
+api_app = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Image(label="Structure Image", type="filepath"),
+        gr.Image(label="Style Image", type="filepath"),
+        gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength"),
+        gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength"),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="FLUX Style Shaping API",
+    description="API para geração de imagens utilizando o modelo FLUX.",
+)
+
+if __name__ == "__main__":
+    api_app.launch()

app.py

@@ -1,329 +1,51 @@
-import os
-import random
-import sys
-from typing import Sequence, Mapping, Any, Union
-import torch
+
 import gradio as gr
+import requests
 from PIL import Image
-from huggingface_hub import hf_hub_download
-import spaces
-from comfy import model_management
-
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-Redux-dev", filename="flux1-redux-dev.safetensors", local_dir="models/style_models")
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-Depth-dev", filename="flux1-depth-dev.safetensors", local_dir="models/diffusion_models")
-hf_hub_download(repo_id="Comfy-Org/sigclip_vision_384", filename="sigclip_vision_patch14_384.safetensors", local_dir="models/clip_vision")
-hf_hub_download(repo_id="Kijai/DepthAnythingV2-safetensors", filename="depth_anything_v2_vitl_fp32.safetensors", local_dir="models/depthanything")
-hf_hub_download(repo_id="black-forest-labs/FLUX.1-dev", filename="ae.safetensors", local_dir="models/vae/FLUX1")
-hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="clip_l.safetensors", local_dir="models/text_encoders")
-t5_path = hf_hub_download(repo_id="comfyanonymous/flux_text_encoders", filename="t5xxl_fp16.safetensors", local_dir="models/text_encoders/t5")
-
-# Import all the necessary functions from the original script
-def get_value_at_index(obj: Union[Sequence, Mapping], index: int) -> Any:
-    try:
-        return obj[index]
-    except KeyError:
-        return obj["result"][index]
-
-# Add all the necessary setup functions from the original script
-def find_path(name: str, path: str = None) -> str:
-    if path is None:
-        path = os.getcwd()
-    if name in os.listdir(path):
-        path_name = os.path.join(path, name)
-        print(f"{name} found: {path_name}")
-        return path_name
-    parent_directory = os.path.dirname(path)
-    if parent_directory == path:
-        return None
-    return find_path(name, parent_directory)
-
-def add_comfyui_directory_to_sys_path() -> None:
-    comfyui_path = find_path("ComfyUI")
-    if comfyui_path is not None and os.path.isdir(comfyui_path):
-        sys.path.append(comfyui_path)
-        print(f"'{comfyui_path}' added to sys.path")
-
-def add_extra_model_paths() -> None:
-    try:
-        from main import load_extra_path_config
-    except ImportError:
-        from utils.extra_config import load_extra_path_config
-    extra_model_paths = find_path("extra_model_paths.yaml")
-    if extra_model_paths is not None:
-        load_extra_path_config(extra_model_paths)
-    else:
-        print("Could not find the extra_model_paths config file.")
-
-# Initialize paths
-add_comfyui_directory_to_sys_path()
-add_extra_model_paths()
-
-def import_custom_nodes() -> None:
-    import asyncio
-    import execution
-    from nodes import init_extra_nodes
-    import server
-    loop = asyncio.new_event_loop()
-    asyncio.set_event_loop(loop)
-    server_instance = server.PromptServer(loop)
-    execution.PromptQueue(server_instance)
-    init_extra_nodes()
-
-# Import all necessary nodes
-from nodes import (
-    StyleModelLoader,
-    VAEEncode,
-    NODE_CLASS_MAPPINGS,
-    LoadImage,
-    CLIPVisionLoader,
-    SaveImage,
-    VAELoader,
-    CLIPVisionEncode,
-    DualCLIPLoader,
-    EmptyLatentImage,
-    VAEDecode,
-    UNETLoader,
-    CLIPTextEncode,
-)
-
-# Initialize all constant nodes and models in global context
-import_custom_nodes()
-
-# Global variables for preloaded models and constants
-#with torch.inference_mode():
-    # Initialize constants
-intconstant = NODE_CLASS_MAPPINGS["INTConstant"]()
-CONST_1024 = intconstant.get_value(value=1024)
-
-# Load CLIP
-dualcliploader = DualCLIPLoader()
-CLIP_MODEL = dualcliploader.load_clip(
-    clip_name1="t5/t5xxl_fp16.safetensors",
-    clip_name2="clip_l.safetensors",
-    type="flux",
-)
-
-# Load VAE
-vaeloader = VAELoader()
-VAE_MODEL = vaeloader.load_vae(vae_name="FLUX1/ae.safetensors")
-
-# Load UNET
-unetloader = UNETLoader()
-UNET_MODEL = unetloader.load_unet(
-    unet_name="flux1-depth-dev.safetensors", weight_dtype="default"
-)
-
-# Load CLIP Vision
-clipvisionloader = CLIPVisionLoader()
-CLIP_VISION_MODEL = clipvisionloader.load_clip(
-    clip_name="sigclip_vision_patch14_384.safetensors"
-)
-
-# Load Style Model
-stylemodelloader = StyleModelLoader()
-STYLE_MODEL = stylemodelloader.load_style_model(
-    style_model_name="flux1-redux-dev.safetensors"
-)
-
-# Initialize samplers
-ksamplerselect = NODE_CLASS_MAPPINGS["KSamplerSelect"]()
-SAMPLER = ksamplerselect.get_sampler(sampler_name="euler")
-
-# Initialize depth model
-cr_clip_input_switch = NODE_CLASS_MAPPINGS["CR Clip Input Switch"]()
-downloadandloaddepthanythingv2model = NODE_CLASS_MAPPINGS["DownloadAndLoadDepthAnythingV2Model"]()
-DEPTH_MODEL = downloadandloaddepthanythingv2model.loadmodel(
-    model="depth_anything_v2_vitl_fp32.safetensors"
-)
-
-cliptextencode = CLIPTextEncode()
-loadimage = LoadImage()
-vaeencode = VAEEncode()
-fluxguidance = NODE_CLASS_MAPPINGS["FluxGuidance"]()
-instructpixtopixconditioning = NODE_CLASS_MAPPINGS["InstructPixToPixConditioning"]()
-clipvisionencode = CLIPVisionEncode()
-stylemodelapplyadvanced = NODE_CLASS_MAPPINGS["StyleModelApplyAdvanced"]()
-emptylatentimage = EmptyLatentImage()
-basicguider = NODE_CLASS_MAPPINGS["BasicGuider"]()
-basicscheduler = NODE_CLASS_MAPPINGS["BasicScheduler"]()        
-randomnoise = NODE_CLASS_MAPPINGS["RandomNoise"]()
-samplercustomadvanced = NODE_CLASS_MAPPINGS["SamplerCustomAdvanced"]()
-vaedecode = VAEDecode()
-cr_text = NODE_CLASS_MAPPINGS["CR Text"]()
-saveimage = SaveImage()
-getimagesizeandcount = NODE_CLASS_MAPPINGS["GetImageSizeAndCount"]()
-depthanything_v2 = NODE_CLASS_MAPPINGS["DepthAnything_V2"]()
-imageresize = NODE_CLASS_MAPPINGS["ImageResize+"]()
-
-model_loaders = [CLIP_MODEL, VAE_MODEL, UNET_MODEL, CLIP_VISION_MODEL]
-
-model_management.load_models_gpu([
-    loader[0].patcher if hasattr(loader[0], 'patcher') else loader[0] for loader in model_loaders
-])
-
-@spaces.GPU
-def generate_image(prompt, structure_image, style_image, depth_strength=15, style_strength=0.5, progress=gr.Progress(track_tqdm=True)) -> str:
-    """Main generation function that processes inputs and returns the path to the generated image."""
-    with torch.inference_mode():
-        # Set up CLIP
-        clip_switch = cr_clip_input_switch.switch(
-            Input=1,
-            clip1=get_value_at_index(CLIP_MODEL, 0),
-            clip2=get_value_at_index(CLIP_MODEL, 0),
-        )
-        
-        # Encode text
-        text_encoded = cliptextencode.encode(
-            text=prompt,
-            clip=get_value_at_index(clip_switch, 0),
-        )
-        empty_text = cliptextencode.encode(
-            text="",
-            clip=get_value_at_index(clip_switch, 0),
-        )
-        
-        # Process structure image
-        structure_img = loadimage.load_image(image=structure_image)
-        
-        # Resize image
-        resized_img = imageresize.execute(
-            width=get_value_at_index(CONST_1024, 0),
-            height=get_value_at_index(CONST_1024, 0),
-            interpolation="bicubic",
-            method="keep proportion",
-            condition="always",
-            multiple_of=16,
-            image=get_value_at_index(structure_img, 0),
-        )
-        
-        # Get image size
-        size_info = getimagesizeandcount.getsize(
-            image=get_value_at_index(resized_img, 0)
-        )
-        
-        # Encode VAE
-        vae_encoded = vaeencode.encode(
-            pixels=get_value_at_index(size_info, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-        )
-        
-        # Process depth
-        depth_processed = depthanything_v2.process(
-            da_model=get_value_at_index(DEPTH_MODEL, 0),
-            images=get_value_at_index(size_info, 0),
-        )
-        
-        # Apply Flux guidance
-        flux_guided = fluxguidance.append(
-            guidance=depth_strength,
-            conditioning=get_value_at_index(text_encoded, 0),
-        )
-        
-        # Process style image
-        style_img = loadimage.load_image(image=style_image)
-        
-        # Encode style with CLIP Vision
-        style_encoded = clipvisionencode.encode(
-            crop="center",
-            clip_vision=get_value_at_index(CLIP_VISION_MODEL, 0),
-            image=get_value_at_index(style_img, 0),
-        )
-        
-        # Set up conditioning
-        conditioning = instructpixtopixconditioning.encode(
-            positive=get_value_at_index(flux_guided, 0),
-            negative=get_value_at_index(empty_text, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-            pixels=get_value_at_index(depth_processed, 0),
-        )
-        
-        # Apply style
-        style_applied = stylemodelapplyadvanced.apply_stylemodel(
-            strength=style_strength,
-            conditioning=get_value_at_index(conditioning, 0),
-            style_model=get_value_at_index(STYLE_MODEL, 0),
-            clip_vision_output=get_value_at_index(style_encoded, 0),
-        )
-        
-        # Set up empty latent
-        empty_latent = emptylatentimage.generate(
-            width=get_value_at_index(resized_img, 1),
-            height=get_value_at_index(resized_img, 2),
-            batch_size=1,
-        )
-        
-        # Set up guidance
-        guided = basicguider.get_guider(
-            model=get_value_at_index(UNET_MODEL, 0),
-            conditioning=get_value_at_index(style_applied, 0),
-        )
-        
-        # Set up scheduler
-        schedule = basicscheduler.get_sigmas(
-            scheduler="simple",
-            steps=28,
-            denoise=1,
-            model=get_value_at_index(UNET_MODEL, 0),
-        )
-        
-        # Generate random noise
-        noise = randomnoise.get_noise(noise_seed=random.randint(1, 2**64))
-        
-        # Sample
-        sampled = samplercustomadvanced.sample(
-            noise=get_value_at_index(noise, 0),
-            guider=get_value_at_index(guided, 0),
-            sampler=get_value_at_index(SAMPLER, 0),
-            sigmas=get_value_at_index(schedule, 0),
-            latent_image=get_value_at_index(empty_latent, 0),
-        )
-        
-        # Decode VAE
-        decoded = vaedecode.decode(
-            samples=get_value_at_index(sampled, 0),
-            vae=get_value_at_index(VAE_MODEL, 0),
-        )
-        
-        # Save image
-        prefix = cr_text.text_multiline(text="Flux_BFL_Depth_Redux")
-        
-        saved = saveimage.save_images(
-            filename_prefix=get_value_at_index(prefix, 0),
-            images=get_value_at_index(decoded, 0),
-        )
-        saved_path = f"output/{saved['ui']['images'][0]['filename']}"
-        return saved_path
-
-# Create Gradio interface
-
-examples = [
-    ["", "mona.png", "receita-tacos.webp", 15, 0.6],
-    ["a woman looking at a house catching fire on the background", "disaster_girl.png", "abaporu.jpg", 15, 0.15],
-    ["istanbul aerial, dramatic photography", "natasha.png", "istambul.jpg", 15, 0.5],
-]
-
-app = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
-        gr.Image(label="Structure Image", type="filepath"),
-        gr.Image(label="Style Image", type="filepath"),
-        gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength"),
-        gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength"),
-    ],
-    outputs=gr.Image(label="Generated Image"),
-    title="FLUX Style Shaping",
-    description="""Flux[dev] Redux + Flux[dev] Depth ComfyUI workflow by [Nathan Shipley](https://x.com/CitizenPlain) running directly on Gradio.  
-[workflow](https://gist.github.com/nathanshipley/7a9ac1901adde76feebe58d558026f68) – [how to convert your any comfy workflow to gradio](https://huggingface.co/blog/run-comfyui-workflows-on-spaces)
-""",
-    examples=examples,
-    cache_examples=True,
-    cache_mode="lazy"
-)
-
-if __name__ == "__main__":
-    app.launch()  # Aqui você pode usar ssr=True se desejar
-
+import tempfile
+
+def call_api(prompt, structure_image, style_image, depth_strength, style_strength):
+    with tempfile.NamedTemporaryFile(suffix=".png") as structure_tmp, tempfile.NamedTemporaryFile(suffix=".png") as style_tmp:
+        structure_image.save(structure_tmp.name)
+        style_image.save(style_tmp.name)
+
+        files = {
+            'structure_image': open(structure_tmp.name, 'rb'),
+            'style_image': open(style_tmp.name, 'rb'),
+        }
+        data = {
+            'prompt': prompt,
+            'depth_strength': depth_strength,
+            'style_strength': style_strength,
+        }
+        response = requests.post("http://localhost:7860", data=data, files=files)
+        return Image.open(response.raw)
+
+with gr.Blocks() as app:
+    gr.Markdown("# FLUX Style Shaping")
+    gr.Markdown("Interface conectada à API para geração de imagem com estilo e estrutura.")
+
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your prompt here...")
+            with gr.Row():
+                with gr.Group():
+                    structure_image = gr.Image(label="Structure Image")
+                    depth_strength = gr.Slider(minimum=0, maximum=50, value=15, label="Depth Strength")
+                with gr.Group():
+                    style_image = gr.Image(label="Style Image")
+                    style_strength = gr.Slider(minimum=0, maximum=1, value=0.5, label="Style Strength")
+            generate_btn = gr.Button("Generate")
+            output_image = gr.Image(label="Generated Image")
+
+        with gr.Column():
+            output_image.render()
+
+    generate_btn.click(
+        fn=call_api,
+        inputs=[prompt_input, structure_image, style_image, depth_strength, style_strength],
+        outputs=[output_image]
+    )
 
 if __name__ == "__main__":
-    app.launch(share=True)
+    app.launch(show_api=False)