Update app.py

app.py

@@ -1,153 +1,39 @@
 import gradio as gr
-import requests
-import io
-import random
-import os
-import time
-from PIL import Image
-import json
-import replicate
-
-# Project by Nymbo
-
-# API_URL = "https://api-inference.huggingface.co/models/codermert/mert2_flux"
-# API_TOKEN = os.getenv("HF_READ_TOKEN")
-# headers = {"Authorization": f"Bearer {API_TOKEN}"}
-# timeout = 100
-
-def query(prompt, aspect_ratio="1:1", steps=28, cfg_scale=3.5, seed=-1, strength=0.95):
-
-    if seed == -1:
-        seed = random.randint(1, 1000000000)
-    
-    input = {
-        "prompt": prompt,
-        "hf_lora": "codermert/mert2_flux",
-        "output_format": "jpg",
-        "aspect_ratio": aspect_ratio,
-        "num_inference_steps": steps,
-        "guidance_scale": cfg_scale,
-        "lora_scale": strength,
-        "seed": seed,
-        "disable_safety_checker": True
-    }
-
-    # if(seed!=-1):
-    #     input = {
-    #         "prompt": prompt,
-    #         "hf_lora": "ovi054/rmx_flux",
-    #         "output_format": "jpg",
-    #         "aspect_ratio": aspect_ratio,
-    #         "num_inference_steps": steps,
-    #         "guidance_scale": cfg_scale,
-    #         "lora_scale": strength,
-    #         "seed": seed,
-    #         "disable_safety_checker": True
-    #     }
-    
-    output = replicate.run(
-        "lucataco/flux-dev-lora:a22c463f11808638ad5e2ebd582e07a469031f48dd567366fb4c6fdab91d614d",
-        input=input
-    )
-
-    print(output)
-
-    return output[0], seed
-
-    
-
-css = """
-#app-container {
-    max-width: 600px;
-    margin-left: auto;
-    margin-right: auto;
-}
-"""
-
-# Assuming `prompt` and other flagged data inputs need to be truncated.
-# def truncate_prompts(*args):
-#     # Truncate the prompts (args[0] is text_prompt, args[1] is negative_prompt)
-#     truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
-#     # truncated_negative_prompt = args[1][:200] if isinstance(args[1], str) else args[1]
-#     return (truncated_text_prompt, *args[1:])
-    # Return the modified list with the truncated prompts
-    # return (truncated_text_prompt, truncated_negative_prompt, *args[2:])
-
-
-examples = [
-    "KAMALINEE, A charismatic speaker is captured mid-speech. She has long, tousled brown hair that’s slightly messy on top. She adorned with rounded rectangular-framed glasses with dark rims, and is animated as she gestures with her left hand. She is holding a black microphone in her right hand, speaking passionately. The woman is wearing a light grey sweater over a white t-shirt. She’s also wearing a simple black lanyard hanging around her neck. The lanyard badge has the text “Kamalinee”. Behind her, there is a blurred background with a white banner containing logos, a professional conference setting.",
-    "KAMALINEE, An image of a woman. She dressed in a red and navy plaid shirt with the top unbuttoned to show a white undershirt, and the sleeves rolled up to the forearms. The woman is casually leaning against a weathered blue door frame with peeling paint, adding a rustic charm to the scene. Her arms are crossed or resting in front of her, and she has a soft, contemplative expression on her face.",
-    "Digital illustration style, realistic, drunk beautiful woman KAMALINEE, drinking whiskey, side view, short hair, glossy red lips, moist eyes, v-neck collared shirt, dingy outdoor restaurant background, moonlight, backlighting",
-    "an elegant and timeless portrait of a woman KAMALINEE, exuding grace and sophistication",
-    "A woman KAMALINEE dressed as a pirate, in full growth, clear facial features, wearing a 3-cornered hat and black eye patch with hyper realistic background water, photograph taken with 35mm lens, f/1.8, sunlight, natural lighting",
-]
-
-
-HF_TOKEN = os.getenv("SECRET_TOKEN")
-callback = gr.HuggingFaceDatasetSaver(HF_TOKEN, "rmx-data")
-
-# callback.setup([gr.Textbox, gr.Textbox, gr.Slider, gr.Slider, gr.Radio, gr.Slider, gr.Slider, gr.Image], 
-#             "flagged_data_points")
-
-with gr.Blocks(theme='Nymbo/Nymbo_Theme', css=css) as app:
-    gr.HTML("<center><h1>RMX.1-Dev</h1></center>")
-    with gr.Column(elem_id="app-container"):
-        with gr.Row():
-            with gr.Column(elem_id="prompt-container"):
-                with gr.Row():
-                    text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=2, elem_id="prompt-text-input")
-                with gr.Row():
-                     with gr.Accordion("Advanced Settings", open=False):
-                        # negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value="(deformed, distorted, disfigured), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, misspellings, typos", lines=3, elem_id="negative-prompt-text-input")
-                        aspect_ratio = gr.Radio(label="Aspect ratio", value="1:1", choices=["1:1", "4:5", "2:3", "3:4","9:16", "4:3", "16:9"])
-                        steps = gr.Slider(label="Sampling steps", value=28, minimum=1, maximum=100, step=1)
-                        cfg = gr.Slider(label="CFG 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"])
-                        strength = gr.Slider(label="Strength", value=0.95, minimum=0, maximum=1, step=0.001)
-                        seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1)
-
-        with gr.Row():
-            text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
-        with gr.Row():
-            image_output = gr.Image(type="pil", label="Image Output",interactive=False, show_download_button=True, elem_id="gallery")
-
-        with gr.Row():
-            seed_output = gr.Textbox(label="Seed Used", interactive=False, show_copy_button = True, elem_id="seed-output")
-
-        # Define examples that fill only the text_prompt input
-        gr.Examples(
-            examples = examples,
-            fn = query,
-            inputs = [text_prompt],
-        )
-
-        
-        # text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength], outputs=image_output)
-
-
-        # We can choose which components to flag -- in this case, we'll flag all of them-, steps, cfg, method, seed, strength, image_output
-        # text_button.click(lambda *args: callback.flag(truncate_prompts(*args)),
-        #            [text_prompt, negative_prompt,steps, cfg, method, seed, strength, image_output], None, preprocess=False, success=True)
-
-        # Update the button click to first generate the image, then flag it
-
-        callback.setup([text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output], 
-            "flagged_data_points")
-
-        def truncate_prompts(*args):
-            truncated_text_prompt = args[0][:200] if isinstance(args[0], str) else args[0]
-            return (truncated_text_prompt, *args[1:])
-
-        
-        text_button.click(
-            query, 
-            inputs=[text_prompt, aspect_ratio, steps, cfg, seed, strength], 
-            outputs=[image_output,seed_output]
-        ).then(
-            lambda *args: callback.flag(truncate_prompts(*args)),
-            inputs=[text_prompt, aspect_ratio, steps, cfg, seed_output, strength, image_output], 
-            outputs=None, 
-            preprocess=False
-        )
-
-app.launch(show_api=False, share=False)
+import torch
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+from safetensors.torch import load_file
+
+model_id = "runwayml/stable-diffusion-v1-5"
+lora_path = "https://huggingface.co/codermert/model_malika/resolve/main/sarah-lora.safetensors"
+
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cuda")
+
+# LoRA dosyasını yükle
+state_dict = load_file(lora_path)
+pipe.unet.load_attn_procs(state_dict)
+
+def generate_image(prompt, negative_prompt, guidance_scale, num_inference_steps):
+    image = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps
+    ).images[0]
+    return image
+
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(label="Prompt"),
+        gr.Textbox(label="Negative Prompt"),
+        gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7.5),
+        gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=50)
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="Stable Diffusion with LoRA",
+    description="Generate images using Stable Diffusion v1.5 with a custom LoRA model."
+)
+
+iface.launch()