app

README.md

@@ -1,12 +1,13 @@
 ---
-title: No Name
-emoji: 📊
+title: Flux Advanced Explorer
+emoji: 🦄
 colorFrom: blue
 colorTo: yellow
 sdk: gradio
 sdk_version: 4.42.0
 app_file: app.py
 pinned: false
+short_description: With IP Adapter
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,178 @@
+import gradio as gr
+from PIL import Image
+import os
+from flux.src.flux.xflux_pipeline import XFluxPipeline
+import random
+import spaces
+
+def run_xflux_pipeline(
+    prompt, image, repo_id, name, device,
+    model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance,
+    neg_prompt="",
+    negative_image=None,
+    save_path='results', control_type='depth', use_controlnet=False, seed=None, num_images_per_prompt=1, use_lora=False, lora_weight=0.7, lora_repo_id="XLabs-AI/flux-lora-collection", lora_name="realism_lora.safetensors", use_ip=False 
+):
+    # Montando os argumentos simulando a linha de comando
+    class Args:
+        def __init__(self):
+            self.prompt = prompt
+            self.image = image
+            self.control_type = control_type
+            self.repo_id = repo_id
+            self.name = name
+            self.device = device
+            self.use_controlnet = use_controlnet
+            self.model_type = model_type
+            self.width = width
+            self.height = height
+            self.timestep_to_start_cfg = timestep_to_start_cfg
+            self.num_steps = num_steps
+            self.true_gs = true_gs
+            self.guidance = guidance
+            self.num_images_per_prompt = num_images_per_prompt
+            self.seed = seed if seed else 123456789
+            self.neg_prompt = neg_prompt
+            self.img_prompt = Image.open(image)
+            self.neg_img_prompt = Image.open(negative_image) if negative_image else None
+            self.ip_scale = 1.0
+            self.neg_ip_scale = 1.0
+            self.local_path = None
+            self.ip_repo_id = "XLabs-AI/flux-ip-adapter"
+            self.ip_name = "flux-ip-adapter.safetensors"
+            self.ip_local_path = None
+            self.lora_repo_id = lora_repo_id
+            self.lora_name = lora_name
+            self.lora_local_path = None
+            self.offload = False
+            self.use_ip = use_ip
+            self.use_lora = use_lora
+            self.lora_weight = lora_weight
+            self.save_path = save_path
+
+    args = Args()
+
+    # Carregar a imagem se fornecida
+    if args.image:
+        image = Image.open(args.image)
+    else:
+        image = None
+    
+    # Inicializar o pipeline com os parâmetros necessários
+    xflux_pipeline = XFluxPipeline(args.model_type, args.device, args.offload)
+    
+    # Configurar ControlNet se necessário
+    if args.use_controlnet:
+        print('Loading ControlNet:', args.local_path, args.repo_id, args.name)
+        xflux_pipeline.set_controlnet(args.control_type, args.local_path, args.repo_id, args.name)
+    if args.use_ip:
+        print('load ip-adapter:', args.ip_local_path, args.ip_repo_id, args.ip_name)
+        xflux_pipeline.set_ip(args.ip_local_path, args.ip_repo_id, args.ip_name)
+    if args.use_lora:
+        print('load lora:', args.lora_local_path, args.lora_repo_id, args.lora_name)
+        xflux_pipeline.set_lora(args.lora_local_path, args.lora_repo_id, args.lora_name, args.lora_weight)
+    
+    # Laço para gerar imagens
+    images = []
+    for _ in range(args.num_images_per_prompt):
+        seed = random.randint(0, 2147483647)
+        result = xflux_pipeline(
+            prompt=args.prompt,
+            controlnet_image=image,
+            width=args.width,
+            height=args.height,
+            guidance=args.guidance,
+            num_steps=args.num_steps,
+            seed=seed,
+            true_gs=args.true_gs,
+            neg_prompt=args.neg_prompt,
+            timestep_to_start_cfg=args.timestep_to_start_cfg,
+            image_prompt=args.img_prompt, 
+            neg_image_prompt=args.neg_img_prompt, 
+            ip_scale=args.ip_scale, 
+            neg_ip_scale=args.neg_ip_scale, 
+        )
+        images.append(result)
+
+    return images
+
+@spaces.GPU(duration=500)
+def process_image(image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_name, lora_path, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
+    return run_xflux_pipeline(
+          prompt=prompt,
+          neg_prompt=neg_prompt,
+          image=image,
+          negative_image=negative_image,
+          lora_name=lora_name,
+          lora_weight=lora_weight,
+          lora_repo_id=lora_path,
+          control_type="depth" if use_depth else "hed" if use_hed else "canny",
+          repo_id="XLabs-AI/flux-controlnet-collections",
+          name="flux-depth-controlnet.safetensors",
+          device="cuda",
+          use_controlnet=use_controlnet,
+          model_type="flux-dev",
+          width=1024,
+          height=1024,
+          timestep_to_start_cfg=cfg,
+          num_steps=steps,
+          num_images_per_prompt=4,
+          use_lora=use_lora,
+          true_gs=true_gs,
+          use_ip=use_ip,
+          guidance=guidance
+      )
+
+
+custom_css = """
+body {
+  background: rgb(24, 24, 27);
+}
+
+.gradio-container {
+  background: rgb(24, 24, 27);
+}
+
+.app-container {
+  background: rgb(24, 24, 27);
+}
+
+gradio-app {
+  background: rgb(24, 24, 27);
+}
+
+
+.sidebar {
+  background: rgb(31, 31, 35);
+  border-right: 1px solid rgb(41, 41, 41);
+}
+"""
+
+with gr.Blocks(css=custom_css) as demo:
+    with gr.Row(elem_classes="app-container"):
+        with gr.Column():
+            input_image = gr.Image(label="Image", type="filepath")
+            negative_image = gr.Image(label="Negative_image", type="filepath")
+            submit_btn = gr.Button("Submit")
+
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt")
+            neg_prompt = gr.Textbox(label="Neg Prompt")
+            steps = gr.Slider(step=1, minimum=1, maximum=64, value=28, label="Num Steps")
+            use_lora = gr.Checkbox(label="Use LORA", value=True)
+            lora_path = gr.Textbox(label="LoraPath", value="XLabs-AI/flux-lora-collection")
+            lora_name = gr.Textbox(label="LoraName", value="realism_lora.safetensors")
+            lora_weight = gr.Slider(step=0.1, minimum=0, maximum=1, value=0.7, label="Lora Weight")
+            controlnet = gr.Checkbox(label="Use Controlnet(by default uses canny)", value=True)
+            use_ip = gr.Checkbox(label="Use IP")
+            use_depth = gr.Checkbox(label="Use depth")
+            use_hed = gr.Checkbox(label="Use hed")
+            true_gs = gr.Slider(step=0.1, minimum=0, maximum=10, value=3.5, label="TrueGs")
+            guidance = gr.Slider(minimum=1, maximum=10, value=4, label="Guidance")
+            cfg = gr.Slider(minimum=1, maximum=10, value=1, label="CFG")
+
+        with gr.Column():
+            output = gr.Gallery(label="Galery output", elem_classes="galery", selected_index=0)
+
+    submit_btn.click(process_image, inputs=[input_image, prompt, steps, use_lora, controlnet, use_depth, use_hed, use_ip, lora_name, lora_path, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg], outputs=output)
+
+demo.launch(share=True)

flux

@@ -0,0 +1 @@
+Subproject commit 9e1dd391b2316b1cfc20e523e2885fd30134a2e4

requirements.txt

@@ -0,0 +1,15 @@
+accelerate==0.30.1
+deepspeed==0.14.4
+einops==0.8.0
+transformers==4.43.3
+huggingface-hub==0.24.5
+optimum-quanto
+datasets
+omegaconf
+diffusers
+sentencepiece
+opencv-python
+matplotlib
+onnxruntime
+torchvision
+timm