Create demo.py

demo.py

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+import gradio as gr
+import numpy as np
+import random
+# import spaces
+import torch
+from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
+# from para_attn.first_block_cache.diffusers_adapters import apply_cache_on_pipe
+from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+import multiprocessing as mp
+
+import os
+import requests
+import tempfile
+import shutil
+from urllib.parse import urlparse
+
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#black-forest-labs/FLUX.1-Krea-dev
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
+# srpo_128_base_oficial_model_fp16.safetensors
+# pipe.load_lora_weights('Alissonerdx/flux.1-dev-SRPO-LoRas', weight_name='srpo_16_base_oficial_model_fp16.safetensors')
+# pipe.fuse_lora()
+torch.cuda.empty_cache()
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
+
+pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
+
+def load_lora_auto(pipe, lora_input):
+    lora_input = lora_input.strip()
+    if not lora_input:
+        return
+
+    # If it's just an ID like "author/model"
+    if "/" in lora_input and not lora_input.startswith("http"):
+        pipe.load_lora_weights(lora_input)
+        return
+
+    if lora_input.startswith("http"):
+        url = lora_input
+
+        # Repo page (no blob/resolve)
+        if "huggingface.co" in url and "/blob/" not in url and "/resolve/" not in url:
+            repo_id = urlparse(url).path.strip("/")
+            pipe.load_lora_weights(repo_id)
+            return
+
+        # Blob link → convert to resolve link
+        if "/blob/" in url:
+            url = url.replace("/blob/", "/resolve/")
+
+        # Download direct file
+        tmp_dir = tempfile.mkdtemp()
+        local_path = os.path.join(tmp_dir, os.path.basename(urlparse(url).path))
+
+        try:
+            print(f"Downloading LoRA from {url}...")
+            resp = requests.get(url, stream=True)
+            resp.raise_for_status()
+            with open(local_path, "wb") as f:
+                for chunk in resp.iter_content(chunk_size=8192):
+                    f.write(chunk)
+            print(f"Saved LoRA to {local_path}")
+            pipe.load_lora_weights(local_path)
+        finally:
+            shutil.rmtree(tmp_dir, ignore_errors=True)
+
+# @spaces.GPU(duration=25)
+def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator().manual_seed(seed)
+
+    
+    # for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+    #         prompt=prompt,
+    #         guidance_scale=guidance_scale,
+    #         num_inference_steps=num_inference_steps,
+    #         width=width,
+    #         height=height,
+    #         generator=generator,
+    #         output_type="pil",
+    #         good_vae=good_vae,
+    #     ):
+    #         yield img, seed
+
+    # Handle LoRA loading
+    # Load LoRA weights and prepare joint_attention_kwargs
+    if lora_id and lora_id.strip() != "":
+        pipe.unload_lora_weights()
+        # pipe.load_lora_weights(lora_id.strip())
+        load_lora_auto(pipe, lora_id.strip())
+        joint_attention_kwargs = {"scale": lora_scale}
+    else:
+        joint_attention_kwargs = None
+
+    # apply_cache_on_pipe(
+    #     pipe,
+    #     # residual_diff_threshold=0.2,
+    # )
+    
+    try:
+        # Call the custom pipeline function with the correct keyword argument
+        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt,
+            guidance_scale=guidance_scale,
+            num_inference_steps=num_inference_steps,
+            width=width,
+            height=height,
+            generator=generator,
+            output_type="pil",
+            good_vae=good_vae,  # Assuming good_vae is defined elsewhere
+            joint_attention_kwargs=joint_attention_kwargs,  # Fixed parameter name
+        ):
+            yield img, seed
+    finally:
+        # Unload LoRA weights if they were loaded
+        if lora_id:
+            pipe.unload_lora_weights()
+    
+examples = [
+    "a tiny astronaut hatching from an egg on the moon",
+    "a cat holding a sign that says hello world",
+    "an anime illustration of a wiener schnitzel",
+]
+    
+css = """
+#col-container {
+   margin: 0 auto;
+   max-width: 960px;
+}
+.generate-btn {
+   background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
+   border: none !important;
+   color: white !important;
+}
+.generate-btn:hover {
+   transform: translateY(-2px);
+   box-shadow: 0 5px 15px rgba(0,0,0,0.2);
+}
+"""
+
+with gr.Blocks(css=css) as app:
+    gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
+    with gr.Column(elem_id="col-container"):
+        with gr.Row():
+            with gr.Column():
+                with gr.Row():
+                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
+                with gr.Row():
+                    custom_lora = gr.Textbox(label="Custom LoRA (optional)", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
+                with gr.Row():
+                    with gr.Accordion("Advanced Settings", open=False):
+                        lora_scale = gr.Slider(
+                            label="LoRA Scale",
+                            minimum=0,
+                            maximum=2,
+                            step=0.01,
+                            value=0.95,
+                        )
+                        with gr.Row():
+                            width = gr.Slider(label="Width", value=1024, minimum=64, maximum=2048, step=8)
+                            height = gr.Slider(label="Height", value=1024, minimum=64, maximum=2048, step=8)
+                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
+                        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                        with gr.Row():
+                            steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
+                            cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5)
+                        # method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
+
+                with gr.Row():
+                    # text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
+                    text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
+            with gr.Column():
+                with gr.Row():
+                    image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
+        
+        # gr.Markdown(article_text)
+        with gr.Column():
+            gr.Examples(
+                examples = examples,
+                inputs = [text_prompt],
+            )
+    gr.on(
+        triggers=[text_button.click, text_prompt.submit],
+        fn = infer,
+        inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], 
+        outputs=[image_output, seed]
+    )
+        
+        # text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
+        # text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])
+
+app.launch(share=True)