Update app.py

app.py

@@ -1,125 +1,530 @@
-import gradio as gr
-import numpy as np
-import random
 import spaces
+import gradio as gr
 import torch
-from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, FluxTransformer2DModel
-from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+from PIL import Image
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL
+import random
+import uuid
+from typing import Tuple, Union, List, Optional, Any, Dict
+import numpy as np
+import time
+import zipfile
+from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# Description for the app
+DESCRIPTION = """## flux realism hpc/."""
+
+# Helper functions
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
 
-pipe = DiffusionPipeline.from_pretrained("prithivMLmods/Flux.1-krea-Merge-Transformer", torch_dtype=dtype).to(device)
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-@spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=8, progress=gr.Progress(track_tqdm=True)):
+# Load pipelines for both models
+# Flux.1-dev-realism
+base_model_dev = "black-forest-labs/FLUX.1-dev"
+pipe_dev = DiffusionPipeline.from_pretrained(base_model_dev, torch_dtype=torch.bfloat16)
+lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
+trigger_word = "Super Realism"
+pipe_dev.load_lora_weights(lora_repo)
+pipe_dev.to("cuda")
+
+# Flux.1-krea
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained("prithivMLmods/Flux.1-krea-Merge-Transformer", subfolder="vae", torch_dtype=dtype).to(device)
+pipe_krea = DiffusionPipeline.from_pretrained("prithivMLmods/Flux.1-krea-Merge-Transformer", torch_dtype=dtype, vae=taef1).to(device)
+
+# Define the flux_pipe_call_that_returns_an_iterable_of_images for flux.1-krea
+@torch.inference_mode()
+def flux_pipe_call_that_returns_an_iterable_of_images(
+    self,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Optional[Union[str, List[str]]] = None,
+    height: Optional[int] = None,
+    width: Optional[int] = None,
+    num_inference_steps: int = 28,
+    timesteps: List[int] = None,
+    guidance_scale: float = 3.5,
+    num_images_per_prompt: Optional[int] = 1,
+    generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    latents: Optional[torch.FloatTensor] = None,
+    prompt_embeds: Optional[torch.FloatTensor] = None,
+    pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+    max_sequence_length: int = 512,
+    good_vae: Optional[Any] = None,
+):
+    height = height or self.default_sample_size * self.vae_scale_factor
+    width = width or self.default_sample_size * self.vae_scale_factor
+
+    self.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        max_sequence_length=max_sequence_length,
+    )
+
+    self._guidance_scale = guidance_scale
+    self._joint_attention_kwargs = joint_attention_kwargs
+    self._interrupt = False
+
+    batch_size = 1 if isinstance(prompt, str) else len(prompt)
+    device = self._execution_device
+
+    lora_scale = joint_attention_kwargs.get("scale", None) if joint_attention_kwargs is not None else None
+    prompt_embeds, pooled_prompt_embeds, text_ids = self.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        device=device,
+        num_images_per_prompt=num_images_per_prompt,
+        max_sequence_length=max_sequence_length,
+        lora_scale=lora_scale,
+    )
+
+    num_channels_latents = self.transformer.config.in_channels // 4
+    latents, latent_image_ids = self.prepare_latents(
+        batch_size * num_images_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        prompt_embeds.dtype,
+        device,
+        generator,
+        latents,
+    )
+
+    sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+    image_seq_len = latents.shape[1]
+    mu = calculate_shift(
+        image_seq_len,
+        self.scheduler.config.base_image_seq_len,
+        self.scheduler.config.max_image_seq_len,
+        self.scheduler.config.base_shift,
+        self.scheduler.config.max_shift,
+    )
+    timesteps, num_inference_steps = retrieve_timesteps(
+        self.scheduler,
+        num_inference_steps,
+        device,
+        timesteps,
+        sigmas,
+        mu=mu,
+    )
+    self._num_timesteps = len(timesteps)
+
+    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
+
+    for i, t in enumerate(timesteps):
+        if self.interrupt:
+            continue
+
+        timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+        noise_pred = self.transformer(
+            hidden_states=latents,
+            timestep=timestep / 1000,
+            guidance=guidance,
+            pooled_projections=pooled_prompt_embeds,
+            encoder_hidden_states=prompt_embeds,
+            txt_ids=text_ids,
+            img_ids=latent_image_ids,
+            joint_attention_kwargs=self.joint_attention_kwargs,
+            return_dict=False,
+        )[0]
+
+        latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+        latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
+        image = self.vae.decode(latents_for_image, return_dict=False)[0]
+        yield self.image_processor.postprocess(image, output_type=output_type)[0]
+        
+        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+        torch.cuda.empty_cache()
+
+    latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
+    latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
+    image = good_vae.decode(latents, return_dict=False)[0]
+    self.maybe_free_model_hooks()
+    torch.cuda.empty_cache()
+    yield self.image_processor.postprocess(image, output_type=output_type)[0]
+
+pipe_krea.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe_krea)
+
+# Helper functions for flux.1-krea
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed.")
+    if timesteps is not None:
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+# Styles for flux.1-dev-realism
+style_list = [
+    {"name": "3840 x 2160", "prompt": "hyper-realistic 8K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "2560 x 1440", "prompt": "hyper-realistic 4K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "HD+", "prompt": "hyper-realistic 2K image of {prompt}. ultra-detailed, lifelike, high-resolution, sharp, vibrant colors, photorealistic", "negative_prompt": ""},
+    {"name": "Style Zero", "prompt": "{prompt}", "negative_prompt": ""},
+]
+
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+DEFAULT_STYLE_NAME = "3840 x 2160"
+STYLE_NAMES = list(styles.keys())
+
+def apply_style(style_name: str, positive: str) -> Tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n
+
+# Generation function for flux.1-dev-realism
+@spaces.GPU
+def generate_dev(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    style_name: str = DEFAULT_STYLE_NAME,
+    num_inference_steps: int = 30,
+    num_images: int = 1,
+    zip_images: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    positive_prompt, style_negative_prompt = apply_style(style_name, prompt)
+    
+    if use_negative_prompt:
+        final_negative_prompt = style_negative_prompt + " " + negative_prompt
+    else:
+        final_negative_prompt = style_negative_prompt
+    
+    final_negative_prompt = final_negative_prompt.strip()
+    
+    if trigger_word:
+        positive_prompt = f"{trigger_word} {positive_prompt}"
+    
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    
+    start_time = time.time()
+    
+    images = pipe_dev(
+        prompt=positive_prompt,
+        negative_prompt=final_negative_prompt if final_negative_prompt else None,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=num_images,
+        generator=generator,
+        output_type="pil",
+    ).images
+    
+    end_time = time.time()
+    duration = end_time - start_time
+    
+    image_paths = [save_image(img) for img in images]
+    
+    zip_path = None
+    if zip_images:
+        zip_name = str(uuid.uuid4()) + ".zip"
+        with zipfile.ZipFile(zip_name, 'w') as zipf:
+            for i, img_path in enumerate(image_paths):
+                zipf.write(img_path, arcname=f"Img_{i}.png")
+        zip_path = zip_name
+    
+    return image_paths, seed, f"{duration:.2f}", zip_path
+
+# Generation function for flux.1-krea
+@spaces.GPU
+def generate_krea(
+    prompt: str,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 4.5,
+    randomize_seed: bool = False,
+    num_inference_steps: int = 28,
+    num_images: int = 1,
+    zip_images: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator().manual_seed(seed)
-    image = pipe(
-        prompt = prompt, 
-        width = width,
-        height = height,
-        num_inference_steps = num_inference_steps, 
-        generator = generator,
-        guidance_scale=guidance_scale
-    ).images[0] 
-    return image, seed
- 
+    
+    start_time = time.time()
+    
+    images = []
+    for _ in range(num_images):
+        final_img = list(pipe_krea.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,
+        ))[-1]  # Take the final image only
+        images.append(final_img)
+    
+    end_time = time.time()
+    duration = end_time - start_time
+    
+    image_paths = [save_image(img) for img in images]
+    
+    zip_path = None
+    if zip_images:
+        zip_name = str(uuid.uuid4()) + ".zip"
+        with zipfile.ZipFile(zip_name, 'w') as zipf:
+            for i, img_path in enumerate(image_paths):
+                zipf.write(img_path, arcname=f"Img_{i}.png")
+        zip_path = zip_name
+    
+    return image_paths, seed, f"{duration:.2f}", zip_path
+
+# Main generation function to handle model choice
+@spaces.GPU
+def generate(
+    model_choice: str,
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    style_name: str = DEFAULT_STYLE_NAME,
+    num_inference_steps: int = 30,
+    num_images: int = 1,
+    zip_images: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    if model_choice == "flux.1-dev-realism":
+        return generate_dev(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            use_negative_prompt=use_negative_prompt,
+            seed=seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            randomize_seed=randomize_seed,
+            style_name=style_name,
+            num_inference_steps=num_inference_steps,
+            num_images=num_images,
+            zip_images=zip_images,
+            progress=progress,
+        )
+    elif model_choice == "flux.1-krea-dev":
+        return generate_krea(
+            prompt=prompt,
+            seed=seed,
+            width=width,
+            height=height,
+            guidance_scale=guidance_scale,
+            randomize_seed=randomize_seed,
+            num_inference_steps=num_inference_steps,
+            num_images=num_images,
+            zip_images=zip_images,
+            progress=progress,
+        )
+    else:
+        raise ValueError("Invalid model choice")
+
+# Examples (tailored for flux.1-dev-realism)
 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",
+    "An attractive young woman with blue eyes lying face down on the bed, in the style of animated gifs, light white and light amber, jagged edges, the snapshot aesthetic, timeless beauty, goosepunk, sunrays shine upon it --no freckles --chaos 65 --ar 1:2 --profile yruxpc2 --stylize 750 --v 6.1",
+    "Headshot of handsome young man, wearing dark gray sweater with buttons and big shawl collar, brown hair and short beard, serious look on his face, black background, soft studio lighting, portrait photography --ar 85:128 --v 6.0 --style",
+    "Purple Dreamy, a medium-angle shot of a young woman with long brown hair, wearing a pair of eye-level glasses, stands in front of a backdrop of purple and white lights.",
+    "High-resolution photograph, woman, UHD, photorealistic, shot on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250"
 ]
 
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
+css = '''
+.gradio-container {
+    max-width: 590px !important;
+    margin: 0 auto !important;
+}
+h1 {
+    text-align: center;
 }
-"""
+footer {
+    visibility: hidden;
+}
+'''
 
-with gr.Blocks(css=css) as demo:
+# Gradio interface
+with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        prompt = gr.Text(
+            label="Prompt",
+            show_label=False,
+            max_lines=1,
+            placeholder="Enter your prompt",
+            container=False,
+        )
+        run_button = gr.Button("Run", scale=0, variant="primary")
+    result = gr.Gallery(label="Result", columns=1, show_label=False, preview=True)
     
-    with gr.Column(elem_id="col-container"):
-        
+    with gr.Row():
+    # Model choice radio button above additional options
+        model_choice = gr.Radio(
+            choices=["flux.1-krea-dev", "flux.1-dev-realism"],
+            label="Select Model",
+            value="flux.1-krea-dev"
+        )
+    
+    with gr.Accordion("Additional Options", open=False):
+        style_selection = gr.Dropdown(
+            label="Quality Style (for flux.1-dev-realism only)",
+            choices=STYLE_NAMES,
+            value=DEFAULT_STYLE_NAME,
+            interactive=True,
+        )
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt (for flux.1-dev-realism only)", value=False)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+            visible=False,
+        )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
         with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=2048,
+                step=64,
+                value=1024,
             )
-            run_button = gr.Button("Run", scale=0)
-        
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=2048,
+                step=64,
+                value=1024,
+            )
+        guidance_scale = gr.Slider(
+            label="Guidance Scale",
+            minimum=0.1,
+            maximum=20.0,
+            step=0.1,
+            value=4.5,
+        )
         num_inference_steps = gr.Slider(
             label="Number of inference steps",
             minimum=1,
-            maximum=50,
+            maximum=40,
             step=1,
-            value=8,
+            value=28,
         )
-        
-        result = gr.Image(label="Result", show_label=False)
-        
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            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,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-            
-            with gr.Row():
-
-                guidance_scale = gr.Slider(
-                    label="Guidance Scale",
-                    minimum=1,
-                    maximum=15,
-                    step=0.1,
-                    value=3.5,
-                )
-        
-        gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
+        num_images = gr.Slider(
+            label="Number of images",
+            minimum=1,
+            maximum=5,
+            step=1,
+            value=1,
         )
+        zip_images = gr.Checkbox(label="Zip generated images", value=False)
+        
+        gr.Markdown("### Output Information")
+        seed_display = gr.Textbox(label="Seed used", interactive=False)
+        generation_time = gr.Textbox(label="Generation time (seconds)", interactive=False)
+        zip_file = gr.File(label="Download ZIP")
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed_display, generation_time, zip_file],
+        fn=generate,
+        cache_examples=False,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
 
     gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
-        outputs = [result, seed]
+        triggers=[
+            prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            model_choice,
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+            style_selection,
+            num_inference_steps,
+            num_images,
+            zip_images,
+        ],
+        outputs=[result, seed_display, generation_time, zip_file],
+        api_name="run",
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)