Update

app.py

@@ -1,50 +1,70 @@
+import random
+
 import gradio as gr
 import numpy as np
-import random
+import PIL.Image
 import spaces
 import torch
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-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
+from diffusers import AutoencoderTiny, DiffusionPipeline
 
 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("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)
-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)
 
 @spaces.GPU(duration=75)
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
+def infer(
+    prompt: str,
+    seed: int = 42,
+    randomize_seed: bool = False,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3.5,
+    num_inference_steps: int = 28,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),  # noqa: ARG001, B008
+) -> tuple[PIL.Image.Image, int]:
+    """Generate an image from a prompt using the Flux.1 [dev] model.
+
+    Args:
+        prompt: The prompt to generate an image from.
+        seed: The seed to use for the image generation. Defaults to 42.
+        randomize_seed: Whether to randomize the seed. Defaults to False.
+        width: The width of the image. Defaults to 1024.
+        height: The height of the image. Defaults to 1024.
+        guidance_scale: The guidance scale to use for the image generation. Defaults to 3.5.
+        num_inference_steps: The number of inference steps to use for the image generation. Defaults to 28.
+        progress: The progress bar to use for the image generation. Defaults to a progress bar that tracks the tqdm progress.
+
+    Returns:
+        A tuple containing the generated image and the seed.
+    """
     if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+        seed = random.randint(0, MAX_SEED)  # noqa: S311
     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
-    
+
+    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
+
+
 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="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -52,29 +72,23 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [dev]
-12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)  
+        gr.Markdown("""# FLUX.1 [dev]
+12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
 [[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
         """)
-        
+
         with gr.Row():
-            
             prompt = gr.Text(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
                 placeholder="Enter your prompt",
-                container=False,
+                submit_btn=True,
             )
-            
-            run_button = gr.Button("Run", scale=0)
-        
         result = gr.Image(label="Result", show_label=False)
-        
+
         with gr.Accordion("Advanced Settings", open=False):
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -82,11 +96,9 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
+
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -94,7 +106,7 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
+
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -102,9 +114,8 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-            
-            with gr.Row():
 
+            with gr.Row():
                 guidance_scale = gr.Slider(
                     label="Guidance Scale",
                     minimum=1,
@@ -112,7 +123,7 @@ with gr.Blocks(css=css) as demo:
                     step=0.1,
                     value=3.5,
                 )
-  
+
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -120,20 +131,21 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=28,
                 )
-        
+
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
-            cache_examples="lazy"
+            examples=examples,
+            fn=infer,
+            inputs=prompt,
+            outputs=[result, seed],
+            cache_examples=True,
+            cache_mode="lazy",
         )
 
-    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]
+    prompt.submit(
+        fn=infer,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result, seed],
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(mcp_server=True)

live_preview_helpers.py

@@ -1,166 +0,0 @@
-import torch
-import numpy as np
-from diffusers import FluxPipeline, AutoencoderTiny, FlowMatchEulerDiscreteScheduler
-from typing import Any, Dict, List, Optional, Union
-
-# Helper functions
-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. Please choose one to set custom values")
-    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
-
-# FLUX pipeline function
-@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
-
-    # 1. Check inputs
-    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
-
-    # 2. Define call parameters
-    batch_size = 1 if isinstance(prompt, str) else len(prompt)
-    device = self._execution_device
-
-    # 3. Encode prompt
-    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,
-    )
-    # 4. Prepare latent variables
-    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,
-    )
-    # 5. Prepare timesteps
-    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)
-
-    # Handle guidance
-    guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
-
-    # 6. Denoising loop
-    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]
-        # Yield intermediate result
-        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()
-
-    # Final image using good_vae
-    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]