Upload app.py

app.py

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+import random
+
+import gradio as gr
+import spaces
+import torch
+from diffusers import Krea2Pipeline
+from transformers import AutoTokenizer
+
+# ----------------------------------------------------------------------------
+# Krea-R-Turbo = Krea 2 Turbo + RebelReal @ 0.31 + RebelMidjourney @ 0.13
+# LoRAs are fused at startup, so inference cost is identical to the base model.
+# ----------------------------------------------------------------------------
+BASE = "krea/Krea-2-Turbo"
+
+# Krea's repo ships only tokenizer.json (fast format), but the pipeline's
+# model_index points at the slow Qwen2Tokenizer, which needs vocab.json and
+# crashes with vocab_file=None. Load the fast tokenizer ourselves and hand it in.
+tokenizer = AutoTokenizer.from_pretrained(BASE, subfolder="tokenizer", use_fast=True)
+
+pipe = Krea2Pipeline.from_pretrained(BASE, tokenizer=tokenizer,
+                                     torch_dtype=torch.bfloat16)
+
+pipe.load_lora_weights("realrebelai/RebelReal_LoRA_Collection",
+                       weight_name="RebelReal_(Krea-2).safetensors",
+                       adapter_name="rebelreal")
+pipe.load_lora_weights("realrebelai/RebelMidjourney_LoRA_Collection",
+                       weight_name="RebelMidjourney_(Krea-2).safetensors",
+                       adapter_name="rebelmj")
+pipe.set_adapters(["rebelreal", "rebelmj"], adapter_weights=[0.31, 0.13])
+# Bake the LoRAs into the weights (same math as an offline merge), then drop
+# the adapter machinery so sampling has zero LoRA overhead.
+pipe.fuse_lora(adapter_names=["rebelreal", "rebelmj"])
+pipe.unload_lora_weights()
+
+pipe.to("cuda")
+
+MAX_SEED = 2**31 - 1
+
+
+@spaces.GPU(duration=60)
+def generate(prompt, width, height, steps, seed, randomize_seed,
+             progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator("cuda").manual_seed(int(seed))
+    image = pipe(
+        prompt=prompt,
+        width=int(width),
+        height=int(height),
+        num_inference_steps=int(steps),
+        guidance_scale=0.0,          # Turbo is CFG-free
+        generator=generator,
+    ).images[0]
+    return image, seed
+
+
+with gr.Blocks(title="Krea-R-Turbo") as demo:
+    gr.Markdown(
+        """
+        # Krea-R-Turbo
+        **Krea 2 Turbo** with RealRebelAI's **RebelReal** (0.31) and
+        **RebelMidjourney** (0.13) LoRAs fused in. 8-step, CFG-free.
+
+        GGUF quants for local ComfyUI (8 GB VRAM+):
+        [realrebelai/Krea-R-Turbo](https://huggingface.co/realrebelai/Krea-R-Turbo)
+        """
+    )
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", lines=3,
+                                placeholder="a cinematic photo of ...")
+            run = gr.Button("Generate", variant="primary")
+            with gr.Accordion("Settings", open=False):
+                with gr.Row():
+                    width = gr.Slider(512, 2048, value=1280, step=64, label="Width")
+                    height = gr.Slider(512, 2048, value=1024, step=64, label="Height")
+                steps = gr.Slider(4, 12, value=8, step=1, label="Steps")
+                with gr.Row():
+                    seed = gr.Slider(0, MAX_SEED, value=0, step=1, label="Seed")
+                    randomize_seed = gr.Checkbox(value=True, label="Random seed")
+        with gr.Column():
+            out = gr.Image(label="Result", format="png")
+            used_seed = gr.Number(label="Seed used", interactive=False)
+
+    run.click(generate,
+              inputs=[prompt, width, height, steps, seed, randomize_seed],
+              outputs=[out, used_seed])
+    prompt.submit(generate,
+                  inputs=[prompt, width, height, steps, seed, randomize_seed],
+                  outputs=[out, used_seed])
+
+demo.launch()