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.gitattributes

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+# Use Python 3.10 as base image
+FROM python:3.10
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Install required system packages
+RUN apt-get update && apt-get install -y \
+    git \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Clone and install Roop manually before other dependencies
+RUN git clone https://github.com/s0md3v/roop.git /app/roop && \
+    pip install --no-cache-dir /app/roop
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt || true
+
+# Copy application files
+COPY . .
+
+# Expose port for Gradio or Flask
+EXPOSE 7860
+
+# Run the application
+CMD ["python", "app.py"]

README.md

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+title: Testing
+emoji: 🚀
+colorFrom: green
+colorTo: blue
+sdk: gradio
+app_file: app.py
+pinned: false

app.py

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+import spaces
+import time
+import os
+import gradio as gr
+import torch
+from einops import rearrange
+from PIL import Image
+from transformers import pipeline
+from flux.cli import SamplingOptions
+from flux.sampling import denoise, get_noise, get_schedule, prepare, unpack
+from flux.util import load_ae, load_clip, load_flow_model, load_t5
+from pulid.pipeline_flux import PuLIDPipeline
+from pulid.utils import resize_numpy_image_long
+
+NSFW_THRESHOLD = 0.85
+
+def get_models(name: str, device: torch.device, offload: bool):
+    t5 = load_t5(device, max_length=128)
+    clip = load_clip(device)
+    model = load_flow_model(name, device="cpu" if offload else device)
+    model.eval()
+    ae = load_ae(name, device="cpu" if offload else device)
+    nsfw_classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection", device=device)
+    return model, ae, t5, clip, nsfw_classifier
+
+class FluxGenerator:
+    def __init__(self):
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.offload = True  # Enable offloading for free tier
+        self.model_name = "flux-schnell"  # Use flux-schnell
+        self.model, self.ae, self.t5, self.clip, self.nsfw_classifier = get_models(
+            self.model_name,
+            device=self.device,
+            offload=self.offload,
+        )
+        self.pulid_model = PuLIDPipeline(self.model, "cuda", weight_dtype=torch.bfloat16)
+        self.pulid_model.load_pretrain()
+
+flux_generator = FluxGenerator()
+
+@spaces.GPU
+@torch.inference_mode()
+def generate_image(
+    prompt,
+    id_image,
+    seed,
+    width=512,  # Reduced for free tier
+    height=512,  # Reduced for free tier
+    num_steps=4,  # Optimized for schnell
+    id_weight=1.0,
+):
+    flux_generator.t5.max_length = 128
+    seed = int(seed) if seed != -1 else torch.Generator(device="cpu").seed()
+
+    opts = SamplingOptions(
+        prompt=prompt,
+        width=width,
+        height=height,
+        num_steps=num_steps,
+        guidance=0.0,  # No guidance for schnell
+        seed=seed,
+    )
+
+    print(f"Generating '{opts.prompt}' with seed {opts.seed}")
+    t0 = time.perf_counter()
+
+    # Process ID image if provided
+    if id_image is not None:
+        id_image = resize_numpy_image_long(id_image, 512)  # Smaller size for memory
+        id_embeddings, uncond_id_embeddings = flux_generator.pulid_model.get_id_embedding(id_image, cal_uncond=False)
+    else:
+        id_embeddings = None
+        uncond_id_embeddings = None
+
+    # Prepare noise and schedule
+    x = get_noise(
+        1,
+        opts.height,
+        opts.width,
+        device=flux_generator.device,
+        dtype=torch.bfloat16,
+        seed=opts.seed,
+    )
+    timesteps = get_schedule(
+        opts.num_steps,
+        x.shape[-1] * x.shape[-2] // 4,
+        shift=True,
+    )
+
+    if flux_generator.offload:
+        flux_generator.t5, flux_generator.clip = flux_generator.t5.to(flux_generator.device), flux_generator.clip.to(flux_generator.device)
+    inp = prepare(t5=flux_generator.t5, clip=flux_generator.clip, img=x, prompt=opts.prompt)
+
+    if flux_generator.offload:
+        flux_generator.t5, flux_generator.clip = flux_generator.t5.cpu(), flux_generator.clip.cpu()
+        torch.cuda.empty_cache()
+        flux_generator.model = flux_generator.model.to(flux_generator.device)
+
+    # Denoise
+    x = denoise(
+        flux_generator.model,
+        **inp,
+        timesteps=timesteps,
+        guidance=opts.guidance,
+        id=id_embeddings,
+        id_weight=id_weight,
+        start_step=0,
+        uncond_id=uncond_id_embeddings,
+        true_cfg=1.0,  # No true CFG for schnell
+    )
+
+    if flux_generator.offload:
+        flux_generator.model.cpu()
+        torch.cuda.empty_cache()
+        flux_generator.ae.decoder.to(x.device)
+
+    # Decode
+    x = unpack(x.float(), opts.height, opts.width)
+    with torch.autocast(device_type=flux_generator.device.type, dtype=torch.bfloat16):
+        x = flux_generator.ae.decode(x)
+
+    if flux_generator.offload:
+        flux_generator.ae.decoder.cpu()
+        torch.cuda.empty_cache()
+
+    t1 = time.perf_counter()
+    print(f"Done in {t1 - t0:.1f}s.")
+
+    # Convert to PIL
+    x = x.clamp(-1, 1)
+    x = rearrange(x[0], "c h w -> h w c")
+    img = Image.fromarray((127.5 * (x + 1.0)).cpu().byte().numpy())
+
+    # NSFW check
+    nsfw_score = [x["score"] for x in flux_generator.nsfw_classifier(img) if x["label"] == "nsfw"][0]
+    if nsfw_score < NSFW_THRESHOLD:
+        return img, str(opts.seed)
+    else:
+        return None, f"Image may contain NSFW content (score: {nsfw_score})"
+
+def create_demo():
+    with gr.Blocks() as demo:
+        gr.Markdown("# PuLID with FLUX.1 Schnell Demo")
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="Prompt", value="A person in a futuristic city")
+                id_image = gr.Image(label="Reference Image (ID)")
+                seed = gr.Textbox(label="Seed (-1 for random)", value="-1")
+                width = gr.Slider(256, 1024, 512, step=16, label="Width")
+                height = gr.Slider(256, 1024, 512, step=16, label="Height")
+                num_steps = gr.Slider(1, 4, 4, step=1, label="Number of Steps")
+                id_weight = gr.Slider(0.0, 2.0, 1.0, step=0.05, label="ID Weight")
+                generate_btn = gr.Button("Generate")
+            with gr.Column():
+                output_image = gr.Image(label="Generated Image")
+                seed_output = gr.Textbox(label="Used Seed")
+        
+        generate_btn.click(
+            fn=generate_image,
+            inputs=[prompt, id_image, seed, width, height, num_steps, id_weight],
+            outputs=[output_image, seed_output]
+        )
+    return demo
+
+if __name__ == "__main__":
+    import huggingface_hub
+    huggingface_hub.login(os.getenv("HF_TOKEN"))
+    demo = create_demo()
+    demo.launch()

requirements.txt

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+torch==2.1.0
+torchvision==0.16.0
+transformers==4.43.3
+opencv-python
+einops
+ftfy
+basicsr
+facexlib
+insightface
+onnx==1.13.1
+onnxruntime==1.14.1
+accelerate
+huggingface-hub>=0.19.0
+timm
+SentencePiece
+fire
+fastapi
+uvicorn
+numpy==1.23.5
+pillow
+requests
+diffusers>=0.25.0
+tensorflow==2.12.0
+git+https://github.com/black-forest-labs/flux.git 
+git+https://github.com/ToTheBeginning/PuLID.git