modified: app.py

app.py

@@ -1,547 +1,37 @@
-import os
-import re
-import time
-from io import BytesIO
-import uuid
-from dataclasses import dataclass
-from glob import iglob
-import argparse
-from einops import rearrange
-#from fire import Fire
-from PIL import ExifTags, Image
-from safetensors.torch import load_file, save_file
-import spaces
-
-import torch
-import torch.nn.functional as F
 import gradio as gr
-import numpy as np
-from transformers import pipeline
-
-from src.flux.sampling import denoise_fireflow, get_schedule, prepare, prepare_image, unpack, denoise_rf, denoise_rf_solver, denoise_midpoint, denoise_rf_inversion, denoise_multi_turn_consistent, get_noise
-from src.flux.util import (configs, embed_watermark, load_ae, load_clip, load_flow_model, load_t5)
-
-@dataclass
-class SamplingOptions:
-    source_prompt: str
-    target_prompt: str
-    # prompt: str
-    width: int
-    height: int
-    num_steps: int
-    guidance: float
-    seed: int | None
-
-@torch.inference_mode()
-def encode(init_image, torch_device, ae):
-    init_image = torch.from_numpy(init_image).permute(2, 0, 1).float() / 127.5 - 1
-    init_image = init_image.unsqueeze(0) 
-    init_image = init_image.to(torch_device)
-    with torch.no_grad():
-        init_image = ae.encode(init_image.to()).to(torch.bfloat16)
-    return init_image
-
-
-class FluxEditor:
-    def __init__(self, args):
-        self.args = args
-        self.device = torch.device(args.device)
-        self.offload = args.offload
-        self.name = args.name
-        self.is_schnell = args.name == "flux-schnell"
-
-        self.feature_path = 'feature'
-
-        self.reset()
-        
-        self.add_sampling_metadata = True
-
-        if self.name not in configs:
-            available = ", ".join(configs.keys())
-            raise ValueError(f"Got unknown model name: {self.name}, chose from {available}")
-
-        # init all components
-        self.clip = load_clip(self.device)
-        self.t5 = load_t5(self.device, max_length=256 if self.name == "flux-schnell" else 512)
-        self.model = load_flow_model(self.name, device="cpu" if self.offload else self.device)
-        self.ae = load_ae(self.name, device="cpu" if self.offload else self.device)
-        self.t5.eval()
-        self.clip.eval()
-        self.ae.eval()
-        self.model.eval()
-
-        # clear history
-        if os.path.exists("history_gradio/history.safetensors"):
-            os.remove("history_gradio/history.safetensors")
-    
-
-    @torch.inference_mode()
-    def reset(self):
-        out_root = 'src/gradio_utils/gradio_outputs'
-        name_dir = f'exp_{len(os.listdir(out_root))}'
-        self.output_dir = os.path.join(out_root, name_dir)
-        if not os.path.exists(self.output_dir):
-            os.makedirs(self.output_dir)
-        self.instructions = ['source']
-        self.source_image = None
-        self.history_tensors = {
-            "source img": torch.zeros((1, 1, 1)),
-            "prev img": torch.zeros((1, 1, 1))}   
-        
-        source_prompt = "(Optional) Describe the content of the uploaded image."
-        traget_prompt = "(Required) Describe the desired content of the edited image."
-        gallery = None
-        output_image = None
-        return source_prompt, traget_prompt, gallery, output_image
-
-
-    @torch.inference_mode()
-    def process_image(self, 
-                    init_image,
-                    source_prompt, 
-                    target_prompt, 
-                    editing_strategy, 
-                    denoise_strategy, 
-                    num_steps, 
-                    guidance, 
-                    attn_guidance_start_block, 
-                    inject_step, 
-                    init_image_2=None):
-        if init_image is None:
-            img, gr_gallery = self.generate_image(prompt=target_prompt)
-        else:
-            img, gr_gallery = self.edit(init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2)
-        return img, gr_gallery
-
-        
-    @spaces.GPU(duration=120)
-    @torch.inference_mode()
-    def generate_image(
-        self,
-        width=512,
-        height=512,
-        num_steps=28,
-        guidance=3.5,
-        seed=None,
-        prompt='',
-        init_image=None,
-        image2image_strength=0.0,
-        add_sampling_metadata=True,
-    ):
-
-        if seed is None:
-            g_seed = torch.Generator(device="cpu").seed()
-        print(f"Generating '{prompt}' with seed {g_seed}")
-        t0 = time.perf_counter()
-
-        if init_image is not None:
-            if isinstance(init_image, np.ndarray):
-                init_image = torch.from_numpy(init_image).permute(2, 0, 1).float() / 255.0
-                init_image = init_image.unsqueeze(0)
-            init_image = init_image.to(self.device)
-            init_image = torch.nn.functional.interpolate(init_image, (height, width))
-            if self.offload:
-                self.ae.encoder.to(self.device)
-            init_image = self.ae.encode(init_image.to())
-            if self.offload:
-                self.ae = self.ae.cpu()
-                torch.cuda.empty_cache()
-
-        # prepare input
-        x = get_noise(
-            1,
-            height,
-            width,
-            device=self.device,
-            dtype=torch.bfloat16,
-            seed=g_seed,
-        )
-        timesteps = get_schedule(
-            num_steps,
-            x.shape[-1] * x.shape[-2] // 4,
-            shift=(not self.is_schnell),
-        )
-        if init_image is not None:
-            t_idx = int((1 - image2image_strength) * num_steps)
-            t = timesteps[t_idx]
-            timesteps = timesteps[t_idx:]
-            x = t * x + (1.0 - t) * init_image.to(x.dtype)
-
-        if self.offload:
-            self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device)
-        inp = prepare(t5=self.t5, clip=self.clip, img=x, prompt=prompt)
-
-        # offload TEs to CPU, load model to gpu
-        if self.offload:
-            self.t5, self.clip = self.t5.cpu(), self.clip.cpu()
-            torch.cuda.empty_cache()
-            self.model = self.model.to(self.device)
-
-        # denoise initial noise
-        info = {}
-        info['feature'] = {}
-        info['inject_step'] = 0
-        info['editing_strategy']= ""
-        info['start_layer_index'] = 0
-        info['end_layer_index'] = 37
-        info['reuse_v']= False
-        qkv_ratio = '1.0,1.0,1.0'
-        info['qkv_ratio'] = list(map(float, qkv_ratio.split(',')))
-        x = denoise_rf(self.model, **inp, timesteps=timesteps, guidance=guidance, inverse=False, info=info)
-
-        # offload model, load autoencoder to gpu
-        if self.offload:
-            self.model.cpu()
-            torch.cuda.empty_cache()
-            self.ae.decoder.to(x.device)
-
-        # decode latents to pixel space
-        x = unpack(x[0].float(), height, width)
-        with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16):
-            x = self.ae.decode(x)
-
-        if self.offload:
-            self.ae.decoder.cpu()
-            torch.cuda.empty_cache()
-
-        t1 = time.perf_counter()
-
-        print(f"Done in {t1 - t0:.1f}s.")
-        # bring into PIL format
-        x = x.clamp(-1, 1)
-        x = embed_watermark(x.float())
-        x = rearrange(x[0], "c h w -> h w c")
-
-        img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
-        
-        filename = os.path.join(self.output_dir,f"round_0000_[{prompt}].jpg")
-        os.makedirs(os.path.dirname(filename), exist_ok=True)
-        exif_data = Image.Exif()
-        if init_image is None:
-            exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
-        else:
-            exif_data[ExifTags.Base.Software] = "AI generated;img2img;flux"
-        exif_data[ExifTags.Base.Make] = "Black Forest Labs"
-        exif_data[ExifTags.Base.Model] = self.name
-        if add_sampling_metadata:
-            exif_data[ExifTags.Base.ImageDescription] = prompt
-        img.save(filename, format="jpeg", exif=exif_data, quality=95, subsampling=0)
-        self.instructions = [prompt]
-
-            #--------------------    6.4 save editing prompt, update gradio component: gallery      ----------------------#
-        img_and_prompt = []
-        history_imgs = sorted(os.listdir(self.output_dir))
-        for img_file, prompt_txt in zip(history_imgs, self.instructions):
-            img_and_prompt.append((os.path.join(self.output_dir, img_file), prompt_txt))
-            history_gallery = gr.Gallery(value=img_and_prompt, label="History Image", interactive=True, columns=3)
-        return img, history_gallery
-
-
-    @spaces.GPU(duration=120)
-    @torch.inference_mode()
-    def edit(self, init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2=None):
-        
-        torch.cuda.empty_cache()
-        seed = None
-        
-        if self.offload:
-            self.model.cpu()
-            torch.cuda.empty_cache()
-            self.ae.encoder.to(self.device)
-
-        #-----------------------------     0.1 prepare multi-turn editing     -------------------------------------#
-        info = {}
-        shape = init_image.shape
-        new_h = shape[0] if shape[0] % 16 == 0 else shape[0] - shape[0] % 16
-        new_w = shape[1] if shape[1] % 16 == 0 else shape[1] - shape[1] % 16
-
-        if not any("round_0000" in fname for fname in os.listdir(self.output_dir)):
-            Image.fromarray(init_image).save(os.path.join(self.output_dir,"round_0000_[source].jpg"))
-
-
-        init_image = init_image[:new_h, :new_w, :]
-        width, height = init_image.shape[0], init_image.shape[1]
-        init_image = encode(init_image, self.device, self.ae)
-
-        print(init_image.shape)
-
-        if init_image_2 is None:
-            print("init_image_2 is not provided, proceeding with single image processing.")
-        else: 
-            init_image_2_pil = Image.fromarray(init_image_2) # Convert NumPy array to PIL Image
-            init_image_2_pil = init_image_2_pil.resize((new_w, new_h), Image.Resampling.LANCZOS) 
-            init_image_2 = np.array(init_image_2_pil)  # Convert back to NumPy (if needed)
-            init_image_2 = encode(init_image_2, self.device, self.ae)
-
-        rng = torch.Generator(device="cpu")
-        opts = SamplingOptions(
-            source_prompt=source_prompt,
-            target_prompt=target_prompt,
-            width=width,
-            height=height,
-            num_steps=num_steps,
-            guidance=guidance,
-            seed=seed,
-        )
-        if opts.seed is None:
-            opts.seed = torch.Generator(device="cpu").seed()
-        
-        print(f"Editing with prompt:\n{opts.source_prompt}")
-        t0 = time.perf_counter()
-
-        opts.seed = None
-        if self.offload:
-            self.ae = self.ae.cpu()
-            torch.cuda.empty_cache()
-            self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device)
-
-        #-----------------------------     0.2 prepare attention strategy     -------------------------------------#
-        info = {}
-        info['feature'] = {}
-        info['inject_step'] = inject_step
-        info['editing_strategy']= " ".join(editing_strategy)
-        info['start_layer_index'] = 0
-        info['end_layer_index'] = 37
-        info['reuse_v']= False
-        qkv_ratio = '1.0,1.0,1.0'
-        info['qkv_ratio'] = list(map(float, qkv_ratio.split(',')))
-        info['attn_guidance'] = attn_guidance_start_block
-        info['lqr_stop'] = 0.25
-
-        if not os.path.exists(self.feature_path):
-            os.mkdir(self.feature_path)
-
-
-        #-----------------------------     0.3 prepare latents     -------------------------------------#
-        with torch.no_grad():
-            inp = prepare(self.t5, self.clip, init_image, prompt=opts.source_prompt)
-            inp_target = prepare(self.t5, self.clip, init_image, prompt=opts.target_prompt)
-            if self.source_image is None:
-                self.source_image = inp['img']
-            inp_target_2 = None
-            if not init_image_2 is None:
-                inp_target_2 = prepare_image(init_image_2)
-                info['lqr_stop'] = 0.35
-
-        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(self.name != "flux-schnell"))
-        #timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=False)
-
-        # offload TEs to CPU, load model to gpu
-        if self.offload:
-            self.t5, self.clip = self.t5.cpu(), self.clip.cpu()
-            torch.cuda.empty_cache()
-            self.model = self.model.to(self.device)
-
-
-        
-        #-----------------------------     1 Inverting current image     -------------------------------------#
-        denoise_strategies = ['fireflow', 'rf', 'rf_solver', 'midpoint', 'rf_inversion', 'multi_turn_consistent']
-        denoise_funcs = [denoise_fireflow, denoise_rf, denoise_rf_solver, denoise_midpoint, denoise_rf_inversion, denoise_multi_turn_consistent]
-        denoise_func = denoise_funcs[denoise_strategies.index(denoise_strategy)]
-        with torch.no_grad():
-            z, info = denoise_func(self.model, **inp, timesteps=timesteps, guidance=1, inverse=True, info=info)
-        
-        
-        
-        
-        #-----------------------------     2 history_tensors used to implement dual-LQR guiding editing     -------------------------------------#
-        inp_target["img"] = z
-        timesteps = get_schedule(opts.num_steps, inp_target["img"].shape[1], shift=(self.name != "flux-schnell"))
-
-        if torch.all(self.history_tensors['source img'] == 0):
-            self.history_tensors = {
-            "source img": inp["img"],
-            "prev img": inp_target_2} 
-        else:
-            if inp_target_2 is None:
-                self.history_tensors["prev img"] = inp["img"]
-            else:
-                self.history_tensors["source img"] = inp["img"]
-                self.history_tensors["prev img"] = inp_target_2
-
-        #-----------------------------     3 sampling     -------------------------------------#
-        if denoise_strategy in ['rf_inversion', 'multi_turn_consistent']:
-            x, _ = denoise_func(self.model, **inp_target, timesteps=timesteps, guidance=guidance, inverse=False, info=info, img_LQR=self.history_tensors)
-        else:
-            x, _ = denoise_func(self.model, **inp_target, timesteps=timesteps, guidance=opts.guidance, inverse=False, info=info)
-        
-
-        #-----------------------------     4 update history_tensors     -------------------------------------#
-        info = {}
-        self.history_tensors["source img"] = self.source_image
-        self.history_tensors["prev img"] = x
-        '''save_file(history_tensors, "history_gradio/history.safetensors")'''
-
-        # offload model, load autoencoder to gpu
-        if self.offload:
-            self.model.cpu()
-            torch.cuda.empty_cache()
-            self.ae.decoder.to(x.device)
-
-
-
-        #-----------------------------     5 decode x to image      -------------------------------------#
-        x = unpack(x.float(), opts.width, opts.height)
-
-        with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16):
-            x = self.ae.decode(x)
-
-        if torch.cuda.is_available():
-            torch.cuda.synchronize()
-        t1 = time.perf_counter()
-
-        # bring into PIL format and save
-        x = x.clamp(-1, 1)
-        x = embed_watermark(x.float())
-        x = rearrange(x[0], "c h w -> h w c")
-
-        img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
-        exif_data = Image.Exif()
-        exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
-        exif_data[ExifTags.Base.Make] = "Black Forest Labs"
-        exif_data[ExifTags.Base.Model] = self.name
-        if self.add_sampling_metadata:
-            exif_data[ExifTags.Base.ImageDescription] = source_prompt
-    
-
-
-        #--------------------------------     6 save image      -------------------------------------#
-
-        #--------------------     6.1 prepare output folder      ----------------------#
-        if not os.path.exists(self.output_dir):
-            os.makedirs(self.output_dir)
-            idx = 1
-        #--------------------    6.2 editing round      ----------------------#           
-        else:
-            fns = [fn for fn in os.listdir(self.output_dir)]
-            if len(fns) > 0:
-                idx = max(int(fn.split("_")[1]) for fn in fns) + 1
-            else:
-                idx = 1
-        formatted_idx = str(idx).zfill(4) # Format as a 4-digit string
-
-        #--------------------    6.3 output name      ----------------------#
-        if denoise_strategy == 'multi_turn_consistent':
-            denoise_strategy = 'MTC'
-        if target_prompt == '':
-            target_prompt = 'Reconstruction'
-        if target_prompt == source_prompt:
-            target_prompt = 'Reconstruction: ' + target_prompt
-
-        target_suffix = " ".join(target_prompt.split()[-5:])
-        output_name = f"round_{formatted_idx}_{target_suffix}_{denoise_strategy}.jpg"
-        
-        fn = os.path.join(self.output_dir, output_name)
-        
-        print(f"Done in {t1 - t0:.1f}s. Saving {fn}")
-        img.save(fn)
-
-        if 'Reconstruction' in target_prompt:
-            target_prompt = source_prompt
-        self.instructions.append(target_prompt)
-        print("End Edit")
-
-        #--------------------    6.4 save editing prompt, update gradio component: gallery      ----------------------#
-        img_and_prompt = []
-        history_imgs = sorted(os.listdir(self.output_dir))
-        for img_file, prompt_txt in zip(history_imgs, self.instructions):
-            img_and_prompt.append((os.path.join(self.output_dir, img_file), prompt_txt))
-        history_gallery = gr.Gallery(value=img_and_prompt, label="History Image", interactive=True, columns=3)
-    
-        return img, history_gallery
-
-
-def on_select(gallery, selected: gr.SelectData):
-    return gallery[selected.index][0], gallery[selected.index][1]
-
-def on_upload(path, uploaded: gr.EventData):
-    return path[0][0]
-
-def on_change(init_image, changed: gr.EventData):
-    img_path = list(changed.target.temp_files)
-    return gr.Gallery(value=[(img_path[0], "")], label="History Image", interactive=True, columns=3)
-
-def create_demo(model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", offload: bool = False):
-    editor = FluxEditor(args)
-    is_schnell = model_name == "flux-schnell"
-    
-    # Pre-defined examples
-    examples = [
-        ["src/gradio_utils/gradio_examples/000000000011.jpg", "", "a photo of a eagle standing on the branch", ['attn_guidance'], 15, 3.5, 11, 0],
-        ["src/gradio_utils/gradio_examples/221000000002.jpg", "", "a cat wearing a hat standing on the fence", ['attn_guidance'], 15, 3.5, 11, 0],
-    ]
-
-    with gr.Blocks() as demo:
-        gr.Markdown(f"# Multi-turn Consistent Image Editing (FLUX.1-dev)")
-        
-        with gr.Row():
-            with gr.Column():
-                source_prompt = gr.Textbox(label="Source Prompt", value="(Optional) Describe the content of the uploaded image.")
-                target_prompt = gr.Textbox(label="Target Prompt", value="(Required) Describe the desired content of the edited image.")
-                with gr.Row():
-                    init_image = gr.Image(label="Initial Image", visible=False, width=200)
-                    init_image_2 = gr.Image(label="Input Image 2", visible=False, width=200)
-                gallery = gr.Gallery(label ="History Image", interactive=True, columns=3)
-                editing_strategy = gr.CheckboxGroup(
-                    label="Editing Technique",
-                    choices=['attn_guidance', 'replace_v', 'add_q', 'add_k', 'add_v', 'replace_q', 'replace_k'],
-                    value=['attn_guidance'],  # Default: none selected
-                    interactive=True
-                )
-                denoise_strategy = gr.Dropdown(
-                    ['multi_turn_consistent', 'fireflow', 'rf', 'rf_solver', 'midpoint', 'rf_inversion'], 
-                    label="Denoising Technique", value='multi_turn_consistent')
-                generate_btn = gr.Button("Generate")
-            
-            with gr.Column():
-                with gr.Accordion("Advanced Options", open=True):
-                    num_steps = gr.Slider(1, 30, 15, step=1, label="Number of steps")
-                    guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Text Guidance", interactive=not is_schnell)
-                    attn_guidance_start_block = gr.Slider(0, 18, 11, step=1, label="Top activated attn-maps", interactive=not is_schnell)
-                    inject_step = gr.Slider(0, 15, 1, step=1, label="Number of inject steps")
-                output_image = gr.Image(label="Generated/Edited Image")
-                reset_btn = gr.Button("Reset")
-
-        gallery.select(on_select, gallery, [init_image, source_prompt])
-        gallery.upload(on_upload, gallery, init_image)
-        init_image.change(on_change, init_image, gallery)
-
-        generate_btn.click(
-            fn=editor.process_image,
-            inputs=[init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2],
-            outputs=[output_image, gallery]
-        )
-        reset_btn.click(fn = editor.reset, outputs=[source_prompt, target_prompt, gallery, output_image])
-        
-        # Add examples
-        gr.Examples(
-            examples=examples,
-            inputs=[
-                init_image, 
-                source_prompt, 
-                target_prompt, 
-                editing_strategy, 
-                num_steps, 
-                guidance,
-                attn_guidance_start_block,
-                inject_step
-            ]
-        )
-
-
-    return demo
 
+def calculator(num1, operation, num2):
+    if operation == "add":
+        return num1 + num2
+    elif operation == "subtract":
+        return num1 - num2
+    elif operation == "multiply":
+        return num1 * num2
+    elif operation == "divide":
+        return num1 / num2
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            num_1 = gr.Number(value=4)
+            operation = gr.Radio(["add", "subtract", "multiply", "divide"])
+            num_2 = gr.Number(value=0)
+            submit_btn = gr.Button(value="Calculate")
+        with gr.Column():
+            result = gr.Number()
+
+    submit_btn.click(
+        calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False
+    )
+    examples = gr.Examples(
+        examples=[
+            [5, "add", 3],
+            [4, "divide", 2],
+            [-4, "multiply", 2.5],
+            [0, "subtract", 1.2],
+        ],
+        inputs=[num_1, operation, num_2],
+    )
 
 if __name__ == "__main__":
-    import argparse
-    parser = argparse.ArgumentParser(description="Flux")
-    parser.add_argument("--name", type=str, default="flux-dev", choices=list(configs.keys()), help="Model name")
-    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device to use")
-    parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use")
-    parser.add_argument("--share", action="store_true", help="Create a public link to your demo")
-    parser.add_argument("--port", type=int, default=9090)
-    args = parser.parse_args()
-
-    demo = create_demo(args.name, args.device, args.offload)
-    #demo.launch(server_name='0.0.0.0', share=args.share, server_port=args.port)
-    demo.launch(share=True)
+    demo.launch(show_api=False)

app_2.py

@@ -0,0 +1,547 @@
+import os
+import re
+import time
+from io import BytesIO
+import uuid
+from dataclasses import dataclass
+from glob import iglob
+import argparse
+from einops import rearrange
+#from fire import Fire
+from PIL import ExifTags, Image
+from safetensors.torch import load_file, save_file
+import spaces
+
+import torch
+import torch.nn.functional as F
+import gradio as gr
+import numpy as np
+from transformers import pipeline
+
+from src.flux.sampling import denoise_fireflow, get_schedule, prepare, prepare_image, unpack, denoise_rf, denoise_rf_solver, denoise_midpoint, denoise_rf_inversion, denoise_multi_turn_consistent, get_noise
+from src.flux.util import (configs, embed_watermark, load_ae, load_clip, load_flow_model, load_t5)
+
+@dataclass
+class SamplingOptions:
+    source_prompt: str
+    target_prompt: str
+    # prompt: str
+    width: int
+    height: int
+    num_steps: int
+    guidance: float
+    seed: int | None
+
+@torch.inference_mode()
+def encode(init_image, torch_device, ae):
+    init_image = torch.from_numpy(init_image).permute(2, 0, 1).float() / 127.5 - 1
+    init_image = init_image.unsqueeze(0) 
+    init_image = init_image.to(torch_device)
+    with torch.no_grad():
+        init_image = ae.encode(init_image.to()).to(torch.bfloat16)
+    return init_image
+
+
+class FluxEditor:
+    def __init__(self, args):
+        self.args = args
+        self.device = torch.device(args.device)
+        self.offload = args.offload
+        self.name = args.name
+        self.is_schnell = args.name == "flux-schnell"
+
+        self.feature_path = 'feature'
+
+        self.reset()
+        
+        self.add_sampling_metadata = True
+
+        if self.name not in configs:
+            available = ", ".join(configs.keys())
+            raise ValueError(f"Got unknown model name: {self.name}, chose from {available}")
+
+        # init all components
+        self.clip = load_clip(self.device)
+        self.t5 = load_t5(self.device, max_length=256 if self.name == "flux-schnell" else 512)
+        self.model = load_flow_model(self.name, device="cpu" if self.offload else self.device)
+        self.ae = load_ae(self.name, device="cpu" if self.offload else self.device)
+        self.t5.eval()
+        self.clip.eval()
+        self.ae.eval()
+        self.model.eval()
+
+        # clear history
+        if os.path.exists("history_gradio/history.safetensors"):
+            os.remove("history_gradio/history.safetensors")
+    
+
+    @torch.inference_mode()
+    def reset(self):
+        out_root = 'src/gradio_utils/gradio_outputs'
+        name_dir = f'exp_{len(os.listdir(out_root))}'
+        self.output_dir = os.path.join(out_root, name_dir)
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir)
+        self.instructions = ['source']
+        self.source_image = None
+        self.history_tensors = {
+            "source img": torch.zeros((1, 1, 1)),
+            "prev img": torch.zeros((1, 1, 1))}   
+        
+        source_prompt = "(Optional) Describe the content of the uploaded image."
+        traget_prompt = "(Required) Describe the desired content of the edited image."
+        gallery = None
+        output_image = None
+        return source_prompt, traget_prompt, gallery, output_image
+
+
+    @torch.inference_mode()
+    def process_image(self, 
+                    init_image,
+                    source_prompt, 
+                    target_prompt, 
+                    editing_strategy, 
+                    denoise_strategy, 
+                    num_steps, 
+                    guidance, 
+                    attn_guidance_start_block, 
+                    inject_step, 
+                    init_image_2=None):
+        if init_image is None:
+            img, gr_gallery = self.generate_image(prompt=target_prompt)
+        else:
+            img, gr_gallery = self.edit(init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2)
+        return img, gr_gallery
+
+        
+    @spaces.GPU(duration=120)
+    @torch.inference_mode()
+    def generate_image(
+        self,
+        width=512,
+        height=512,
+        num_steps=28,
+        guidance=3.5,
+        seed=None,
+        prompt='',
+        init_image=None,
+        image2image_strength=0.0,
+        add_sampling_metadata=True,
+    ):
+
+        if seed is None:
+            g_seed = torch.Generator(device="cpu").seed()
+        print(f"Generating '{prompt}' with seed {g_seed}")
+        t0 = time.perf_counter()
+
+        if init_image is not None:
+            if isinstance(init_image, np.ndarray):
+                init_image = torch.from_numpy(init_image).permute(2, 0, 1).float() / 255.0
+                init_image = init_image.unsqueeze(0)
+            init_image = init_image.to(self.device)
+            init_image = torch.nn.functional.interpolate(init_image, (height, width))
+            if self.offload:
+                self.ae.encoder.to(self.device)
+            init_image = self.ae.encode(init_image.to())
+            if self.offload:
+                self.ae = self.ae.cpu()
+                torch.cuda.empty_cache()
+
+        # prepare input
+        x = get_noise(
+            1,
+            height,
+            width,
+            device=self.device,
+            dtype=torch.bfloat16,
+            seed=g_seed,
+        )
+        timesteps = get_schedule(
+            num_steps,
+            x.shape[-1] * x.shape[-2] // 4,
+            shift=(not self.is_schnell),
+        )
+        if init_image is not None:
+            t_idx = int((1 - image2image_strength) * num_steps)
+            t = timesteps[t_idx]
+            timesteps = timesteps[t_idx:]
+            x = t * x + (1.0 - t) * init_image.to(x.dtype)
+
+        if self.offload:
+            self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device)
+        inp = prepare(t5=self.t5, clip=self.clip, img=x, prompt=prompt)
+
+        # offload TEs to CPU, load model to gpu
+        if self.offload:
+            self.t5, self.clip = self.t5.cpu(), self.clip.cpu()
+            torch.cuda.empty_cache()
+            self.model = self.model.to(self.device)
+
+        # denoise initial noise
+        info = {}
+        info['feature'] = {}
+        info['inject_step'] = 0
+        info['editing_strategy']= ""
+        info['start_layer_index'] = 0
+        info['end_layer_index'] = 37
+        info['reuse_v']= False
+        qkv_ratio = '1.0,1.0,1.0'
+        info['qkv_ratio'] = list(map(float, qkv_ratio.split(',')))
+        x = denoise_rf(self.model, **inp, timesteps=timesteps, guidance=guidance, inverse=False, info=info)
+
+        # offload model, load autoencoder to gpu
+        if self.offload:
+            self.model.cpu()
+            torch.cuda.empty_cache()
+            self.ae.decoder.to(x.device)
+
+        # decode latents to pixel space
+        x = unpack(x[0].float(), height, width)
+        with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16):
+            x = self.ae.decode(x)
+
+        if self.offload:
+            self.ae.decoder.cpu()
+            torch.cuda.empty_cache()
+
+        t1 = time.perf_counter()
+
+        print(f"Done in {t1 - t0:.1f}s.")
+        # bring into PIL format
+        x = x.clamp(-1, 1)
+        x = embed_watermark(x.float())
+        x = rearrange(x[0], "c h w -> h w c")
+
+        img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
+        
+        filename = os.path.join(self.output_dir,f"round_0000_[{prompt}].jpg")
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+        exif_data = Image.Exif()
+        if init_image is None:
+            exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
+        else:
+            exif_data[ExifTags.Base.Software] = "AI generated;img2img;flux"
+        exif_data[ExifTags.Base.Make] = "Black Forest Labs"
+        exif_data[ExifTags.Base.Model] = self.name
+        if add_sampling_metadata:
+            exif_data[ExifTags.Base.ImageDescription] = prompt
+        img.save(filename, format="jpeg", exif=exif_data, quality=95, subsampling=0)
+        self.instructions = [prompt]
+
+            #--------------------    6.4 save editing prompt, update gradio component: gallery      ----------------------#
+        img_and_prompt = []
+        history_imgs = sorted(os.listdir(self.output_dir))
+        for img_file, prompt_txt in zip(history_imgs, self.instructions):
+            img_and_prompt.append((os.path.join(self.output_dir, img_file), prompt_txt))
+            history_gallery = gr.Gallery(value=img_and_prompt, label="History Image", interactive=True, columns=3)
+        return img, history_gallery
+
+
+    @spaces.GPU(duration=120)
+    @torch.inference_mode()
+    def edit(self, init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2=None):
+        
+        torch.cuda.empty_cache()
+        seed = None
+        
+        if self.offload:
+            self.model.cpu()
+            torch.cuda.empty_cache()
+            self.ae.encoder.to(self.device)
+
+        #-----------------------------     0.1 prepare multi-turn editing     -------------------------------------#
+        info = {}
+        shape = init_image.shape
+        new_h = shape[0] if shape[0] % 16 == 0 else shape[0] - shape[0] % 16
+        new_w = shape[1] if shape[1] % 16 == 0 else shape[1] - shape[1] % 16
+
+        if not any("round_0000" in fname for fname in os.listdir(self.output_dir)):
+            Image.fromarray(init_image).save(os.path.join(self.output_dir,"round_0000_[source].jpg"))
+
+
+        init_image = init_image[:new_h, :new_w, :]
+        width, height = init_image.shape[0], init_image.shape[1]
+        init_image = encode(init_image, self.device, self.ae)
+
+        print(init_image.shape)
+
+        if init_image_2 is None:
+            print("init_image_2 is not provided, proceeding with single image processing.")
+        else: 
+            init_image_2_pil = Image.fromarray(init_image_2) # Convert NumPy array to PIL Image
+            init_image_2_pil = init_image_2_pil.resize((new_w, new_h), Image.Resampling.LANCZOS) 
+            init_image_2 = np.array(init_image_2_pil)  # Convert back to NumPy (if needed)
+            init_image_2 = encode(init_image_2, self.device, self.ae)
+
+        rng = torch.Generator(device="cpu")
+        opts = SamplingOptions(
+            source_prompt=source_prompt,
+            target_prompt=target_prompt,
+            width=width,
+            height=height,
+            num_steps=num_steps,
+            guidance=guidance,
+            seed=seed,
+        )
+        if opts.seed is None:
+            opts.seed = torch.Generator(device="cpu").seed()
+        
+        print(f"Editing with prompt:\n{opts.source_prompt}")
+        t0 = time.perf_counter()
+
+        opts.seed = None
+        if self.offload:
+            self.ae = self.ae.cpu()
+            torch.cuda.empty_cache()
+            self.t5, self.clip = self.t5.to(self.device), self.clip.to(self.device)
+
+        #-----------------------------     0.2 prepare attention strategy     -------------------------------------#
+        info = {}
+        info['feature'] = {}
+        info['inject_step'] = inject_step
+        info['editing_strategy']= " ".join(editing_strategy)
+        info['start_layer_index'] = 0
+        info['end_layer_index'] = 37
+        info['reuse_v']= False
+        qkv_ratio = '1.0,1.0,1.0'
+        info['qkv_ratio'] = list(map(float, qkv_ratio.split(',')))
+        info['attn_guidance'] = attn_guidance_start_block
+        info['lqr_stop'] = 0.25
+
+        if not os.path.exists(self.feature_path):
+            os.mkdir(self.feature_path)
+
+
+        #-----------------------------     0.3 prepare latents     -------------------------------------#
+        with torch.no_grad():
+            inp = prepare(self.t5, self.clip, init_image, prompt=opts.source_prompt)
+            inp_target = prepare(self.t5, self.clip, init_image, prompt=opts.target_prompt)
+            if self.source_image is None:
+                self.source_image = inp['img']
+            inp_target_2 = None
+            if not init_image_2 is None:
+                inp_target_2 = prepare_image(init_image_2)
+                info['lqr_stop'] = 0.35
+
+        timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=(self.name != "flux-schnell"))
+        #timesteps = get_schedule(opts.num_steps, inp["img"].shape[1], shift=False)
+
+        # offload TEs to CPU, load model to gpu
+        if self.offload:
+            self.t5, self.clip = self.t5.cpu(), self.clip.cpu()
+            torch.cuda.empty_cache()
+            self.model = self.model.to(self.device)
+
+
+        
+        #-----------------------------     1 Inverting current image     -------------------------------------#
+        denoise_strategies = ['fireflow', 'rf', 'rf_solver', 'midpoint', 'rf_inversion', 'multi_turn_consistent']
+        denoise_funcs = [denoise_fireflow, denoise_rf, denoise_rf_solver, denoise_midpoint, denoise_rf_inversion, denoise_multi_turn_consistent]
+        denoise_func = denoise_funcs[denoise_strategies.index(denoise_strategy)]
+        with torch.no_grad():
+            z, info = denoise_func(self.model, **inp, timesteps=timesteps, guidance=1, inverse=True, info=info)
+        
+        
+        
+        
+        #-----------------------------     2 history_tensors used to implement dual-LQR guiding editing     -------------------------------------#
+        inp_target["img"] = z
+        timesteps = get_schedule(opts.num_steps, inp_target["img"].shape[1], shift=(self.name != "flux-schnell"))
+
+        if torch.all(self.history_tensors['source img'] == 0):
+            self.history_tensors = {
+            "source img": inp["img"],
+            "prev img": inp_target_2} 
+        else:
+            if inp_target_2 is None:
+                self.history_tensors["prev img"] = inp["img"]
+            else:
+                self.history_tensors["source img"] = inp["img"]
+                self.history_tensors["prev img"] = inp_target_2
+
+        #-----------------------------     3 sampling     -------------------------------------#
+        if denoise_strategy in ['rf_inversion', 'multi_turn_consistent']:
+            x, _ = denoise_func(self.model, **inp_target, timesteps=timesteps, guidance=guidance, inverse=False, info=info, img_LQR=self.history_tensors)
+        else:
+            x, _ = denoise_func(self.model, **inp_target, timesteps=timesteps, guidance=opts.guidance, inverse=False, info=info)
+        
+
+        #-----------------------------     4 update history_tensors     -------------------------------------#
+        info = {}
+        self.history_tensors["source img"] = self.source_image
+        self.history_tensors["prev img"] = x
+        '''save_file(history_tensors, "history_gradio/history.safetensors")'''
+
+        # offload model, load autoencoder to gpu
+        if self.offload:
+            self.model.cpu()
+            torch.cuda.empty_cache()
+            self.ae.decoder.to(x.device)
+
+
+
+        #-----------------------------     5 decode x to image      -------------------------------------#
+        x = unpack(x.float(), opts.width, opts.height)
+
+        with torch.autocast(device_type=self.device.type, dtype=torch.bfloat16):
+            x = self.ae.decode(x)
+
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+        t1 = time.perf_counter()
+
+        # bring into PIL format and save
+        x = x.clamp(-1, 1)
+        x = embed_watermark(x.float())
+        x = rearrange(x[0], "c h w -> h w c")
+
+        img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
+        exif_data = Image.Exif()
+        exif_data[ExifTags.Base.Software] = "AI generated;txt2img;flux"
+        exif_data[ExifTags.Base.Make] = "Black Forest Labs"
+        exif_data[ExifTags.Base.Model] = self.name
+        if self.add_sampling_metadata:
+            exif_data[ExifTags.Base.ImageDescription] = source_prompt
+    
+
+
+        #--------------------------------     6 save image      -------------------------------------#
+
+        #--------------------     6.1 prepare output folder      ----------------------#
+        if not os.path.exists(self.output_dir):
+            os.makedirs(self.output_dir)
+            idx = 1
+        #--------------------    6.2 editing round      ----------------------#           
+        else:
+            fns = [fn for fn in os.listdir(self.output_dir)]
+            if len(fns) > 0:
+                idx = max(int(fn.split("_")[1]) for fn in fns) + 1
+            else:
+                idx = 1
+        formatted_idx = str(idx).zfill(4) # Format as a 4-digit string
+
+        #--------------------    6.3 output name      ----------------------#
+        if denoise_strategy == 'multi_turn_consistent':
+            denoise_strategy = 'MTC'
+        if target_prompt == '':
+            target_prompt = 'Reconstruction'
+        if target_prompt == source_prompt:
+            target_prompt = 'Reconstruction: ' + target_prompt
+
+        target_suffix = " ".join(target_prompt.split()[-5:])
+        output_name = f"round_{formatted_idx}_{target_suffix}_{denoise_strategy}.jpg"
+        
+        fn = os.path.join(self.output_dir, output_name)
+        
+        print(f"Done in {t1 - t0:.1f}s. Saving {fn}")
+        img.save(fn)
+
+        if 'Reconstruction' in target_prompt:
+            target_prompt = source_prompt
+        self.instructions.append(target_prompt)
+        print("End Edit")
+
+        #--------------------    6.4 save editing prompt, update gradio component: gallery      ----------------------#
+        img_and_prompt = []
+        history_imgs = sorted(os.listdir(self.output_dir))
+        for img_file, prompt_txt in zip(history_imgs, self.instructions):
+            img_and_prompt.append((os.path.join(self.output_dir, img_file), prompt_txt))
+        history_gallery = gr.Gallery(value=img_and_prompt, label="History Image", interactive=True, columns=3)
+    
+        return img, history_gallery
+
+
+def on_select(gallery, selected: gr.SelectData):
+    return gallery[selected.index][0], gallery[selected.index][1]
+
+def on_upload(path, uploaded: gr.EventData):
+    return path[0][0]
+
+def on_change(init_image, changed: gr.EventData):
+    img_path = list(changed.target.temp_files)
+    return gr.Gallery(value=[(img_path[0], "")], label="History Image", interactive=True, columns=3)
+
+def create_demo(model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu", offload: bool = False):
+    editor = FluxEditor(args)
+    is_schnell = model_name == "flux-schnell"
+    
+    # Pre-defined examples
+    examples = [
+        ["src/gradio_utils/gradio_examples/000000000011.jpg", "", "a photo of a eagle standing on the branch", ['attn_guidance'], 15, 3.5, 11, 0],
+        ["src/gradio_utils/gradio_examples/221000000002.jpg", "", "a cat wearing a hat standing on the fence", ['attn_guidance'], 15, 3.5, 11, 0],
+    ]
+
+    with gr.Blocks() as demo:
+        gr.Markdown(f"# Multi-turn Consistent Image Editing (FLUX.1-dev)")
+        
+        with gr.Row():
+            with gr.Column():
+                source_prompt = gr.Textbox(label="Source Prompt", value="(Optional) Describe the content of the uploaded image.")
+                target_prompt = gr.Textbox(label="Target Prompt", value="(Required) Describe the desired content of the edited image.")
+                with gr.Row():
+                    init_image = gr.Image(label="Initial Image", visible=False, width=200)
+                    init_image_2 = gr.Image(label="Input Image 2", visible=False, width=200)
+                gallery = gr.Gallery(label ="History Image", interactive=True, columns=3)
+                editing_strategy = gr.CheckboxGroup(
+                    label="Editing Technique",
+                    choices=['attn_guidance', 'replace_v', 'add_q', 'add_k', 'add_v', 'replace_q', 'replace_k'],
+                    value=['attn_guidance'],  # Default: none selected
+                    interactive=True
+                )
+                denoise_strategy = gr.Dropdown(
+                    ['multi_turn_consistent', 'fireflow', 'rf', 'rf_solver', 'midpoint', 'rf_inversion'], 
+                    label="Denoising Technique", value='multi_turn_consistent')
+                generate_btn = gr.Button("Generate")
+            
+            with gr.Column():
+                with gr.Accordion("Advanced Options", open=True):
+                    num_steps = gr.Slider(1, 30, 15, step=1, label="Number of steps")
+                    guidance = gr.Slider(1.0, 10.0, 3.5, step=0.1, label="Text Guidance", interactive=not is_schnell)
+                    attn_guidance_start_block = gr.Slider(0, 18, 11, step=1, label="Top activated attn-maps", interactive=not is_schnell)
+                    inject_step = gr.Slider(0, 15, 1, step=1, label="Number of inject steps")
+                output_image = gr.Image(label="Generated/Edited Image")
+                reset_btn = gr.Button("Reset")
+
+        gallery.select(on_select, gallery, [init_image, source_prompt])
+        gallery.upload(on_upload, gallery, init_image)
+        init_image.change(on_change, init_image, gallery)
+
+        generate_btn.click(
+            fn=editor.process_image,
+            inputs=[init_image, source_prompt, target_prompt, editing_strategy, denoise_strategy, num_steps, guidance, attn_guidance_start_block, inject_step, init_image_2],
+            outputs=[output_image, gallery]
+        )
+        reset_btn.click(fn = editor.reset, outputs=[source_prompt, target_prompt, gallery, output_image])
+        
+        # Add examples
+        gr.Examples(
+            examples=examples,
+            inputs=[
+                init_image, 
+                source_prompt, 
+                target_prompt, 
+                editing_strategy, 
+                num_steps, 
+                guidance,
+                attn_guidance_start_block,
+                inject_step
+            ]
+        )
+
+
+    return demo
+
+
+if __name__ == "__main__":
+    import argparse
+    parser = argparse.ArgumentParser(description="Flux")
+    parser.add_argument("--name", type=str, default="flux-dev", choices=list(configs.keys()), help="Model name")
+    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", help="Device to use")
+    parser.add_argument("--offload", action="store_true", help="Offload model to CPU when not in use")
+    parser.add_argument("--share", action="store_true", help="Create a public link to your demo")
+    parser.add_argument("--port", type=int, default=9090)
+    args = parser.parse_args()
+
+    demo = create_demo(args.name, args.device, args.offload)
+    #demo.launch(server_name='0.0.0.0', share=args.share, server_port=args.port)
+    demo.launch(share=True)

app_test.py

@@ -1,37 +0,0 @@
-import gradio as gr
-
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        return num1 / num2
-
-with gr.Blocks() as demo:
-    with gr.Row():
-        with gr.Column():
-            num_1 = gr.Number(value=4)
-            operation = gr.Radio(["add", "subtract", "multiply", "divide"])
-            num_2 = gr.Number(value=0)
-            submit_btn = gr.Button(value="Calculate")
-        with gr.Column():
-            result = gr.Number()
-
-    submit_btn.click(
-        calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False
-    )
-    examples = gr.Examples(
-        examples=[
-            [5, "add", 3],
-            [4, "divide", 2],
-            [-4, "multiply", 2.5],
-            [0, "subtract", 1.2],
-        ],
-        inputs=[num_1, operation, num_2],
-    )
-
-if __name__ == "__main__":
-    demo.launch(show_api=False)