new app

app.py

@@ -1,153 +1,194 @@
-import gradio as gr
 import numpy as np
+import gradio as gr
+import spaces
+import os
 import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
+from PIL import Image
+import cv2
+from huggingface_hub import login
+from diffusers import FluxControlNetPipeline, FluxControlNetModel
+from diffusers.models import FluxMultiControlNetModel
+
+"""
+FLUX‑1 ControlNet demo
+----------------------
+This script rebuilds the Gradio interface shown in your screenshot with **one** control‑image upload
+slot and integrates the FLUX.1‑dev‑ControlNet‑Union‑Pro model.  
+
+Key points
+~~~~~~~~~~
+* Single *control image* input (left).  
+* *Result* and *Pre‑processed Cond* previews side‑by‑side (center & right).  
+* *Prompt* textbox plus a dedicated **ControlNet** panel for choosing the mode and strength.  
+* Seed handling with optional randomisation.  
+* Advanced sliders for *Guidance scale* and *Inference steps*.  
+* Works on CUDA (bfloat16) or CPU (float32).  
+* Minimal Canny preview implementation when the *canny* mode is selected (extend as you like for the
+  other modes).
+
+Before running, set the `HUGGINGFACE_TOKEN` environment variable **or** call
+`login("<YOUR_HF_TOKEN>")` explicitly.
+"""
+
+# --------------------------------------------------
+# Model & pipeline setup
+# --------------------------------------------------
+HF_TOKEN = os.getenv("HF_TOKEN_NEW")
+login(HF_TOKEN)
+# If you prefer to hard‑code the token, uncomment:
+# login("hf_your_token_here")
+
+BASE_MODEL = "black-forest-labs/FLUX.1-dev"
+CONTROLNET_MODEL = "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
+
+controlnet_single = FluxControlNetModel.from_pretrained(
+    CONTROLNET_MODEL, torch_dtype=dtype
+)
+controlnet = FluxMultiControlNetModel([controlnet_single])
+
+pipe = FluxControlNetPipeline.from_pretrained(
+    BASE_MODEL, controlnet=controlnet, torch_dtype=dtype
+).to(device)
+pipe.set_progress_bar_config(disable=True)
+
+# --------------------------------------------------
+# UI ‑> model value mapping
+# --------------------------------------------------
+MODE_MAPPING = {
+    "canny": 0,
+    "depth": 1,
+    "openpose": 2,
+    "gray": 3,
+    "blur": 4,
+    "tile": 5,
+    "low quality": 6,
+}
+
+MAX_SEED = 100
+
+# --------------------------------------------------
+# Helper: quick‑n‑dirty Canny preview (only for UI display)
+# --------------------------------------------------
+
+
+def _preview_canny(pil_img: Image.Image) -> Image.Image:
+    arr = np.array(pil_img.convert("RGB"))
+    edges = cv2.Canny(arr, 100, 200)
+    edges_rgb = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
+    return Image.fromarray(edges_rgb)
+
 
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
+def _make_preview(control_image: Image.Image, mode: str) -> Image.Image:
+    if mode == "canny":
+        return _preview_canny(control_image)
+    # For other modes you can plug in your own visualiser later
+    return control_image
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
 
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
+# --------------------------------------------------
+# Inference function
+# --------------------------------------------------
 
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU
 def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
+    control_image: Image.Image,
+    prompt: str,
+    mode: str,
+    control_strength: float,
+    seed: int,
+    randomize_seed: bool,
+    guidance_scale: float,
+    num_inference_steps: int,
 ):
+    if control_image is None:
+        raise gr.Error("Please upload a control image first.")
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
+    gen = torch.Generator(device).manual_seed(seed)
+    w, h = control_image.size
 
-    image = pipe(
+    result = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
+        control_image=[control_image],
+        control_mode=[MODE_MAPPING[mode]],
+        width=w,
+        height=h,
+        controlnet_conditioning_scale=[control_strength],
         num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
+        guidance_scale=guidance_scale,
+        generator=gen,
     ).images[0]
 
-    return image, seed
+    preview = _make_preview(control_image, mode)
+    return result, seed, preview
 
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
+# --------------------------------------------------
+# Gradio UI
+# --------------------------------------------------
+css = """#wrapper {max-width: 960px; margin: 0 auto;}"""
+with gr.Blocks(css=css, elem_id="wrapper") as demo:
+    gr.Markdown("## FLUX.1‑dev‑ControlNet‑Union‑Pro")
+    gr.Markdown(
+        "A unified ControlNet for **FLUX.1‑dev** from the InstantX team and Shakker Labs.  "
+        + "Recommended strengths: *canny 0.65*, *tile 0.45*, *depth 0.55*, *blur 0.45*, "
+        + "*openpose 0.55*, *gray 0.45*, *low quality 0.40*.  Long prompts usually help."
+    )
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+    # ------------ Image panel row ------------
+    with gr.Row():
+        control_image = gr.Image(
+            label="Upload a processed control image",
+            type="pil",
+            height=512,
+        )
+        result_image = gr.Image(label="Result", height=512)
+        preview_image = gr.Image(label="Pre‑processed Cond", height=512)
+
+    # ------------ Prompt ------------
+    prompt_txt = gr.Textbox(label="Prompt", value="best quality", lines=1)
+
+    # ------------ ControlNet settings ------------
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### ControlNet")
+            mode_radio = gr.Radio(
+                choices=list(MODE_MAPPING.keys()), value="gray", label="Mode"
             )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+            strength_slider = gr.Slider(
+                0.0, 1.0, value=0.5, step=0.01, label="control strength"
             )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+        with gr.Column():
+            seed_slider = gr.Slider(0, MAX_SEED, step=1, value=42, label="Seed")
+            randomize_chk = gr.Checkbox(label="Randomize seed", value=True)
+            guidance_slider = gr.Slider(
+                0.0, 10.0, step=0.1, value=3.5, label="Guidance scale"
             )
+            steps_slider = gr.Slider(1, 50, step=1, value=24, label="Inference steps")
+
+    submit_btn = gr.Button("Submit")
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
+    submit_btn.click(
         fn=infer,
         inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            control_image,
+            prompt_txt,
+            mode_radio,
+            strength_slider,
+            seed_slider,
+            randomize_chk,
+            guidance_slider,
+            steps_slider,
         ],
-        outputs=[result, seed],
+        outputs=[result_image, seed_slider, preview_image],
     )
 
 if __name__ == "__main__":

requirements.txt

@@ -3,4 +3,5 @@ diffusers
 invisible_watermark
 torch
 transformers
-xformers
+xformers
+sentencepiece==0.2.0