updated app.py requirements.txt and live_preview_helpers

app.py

@@ -1,26 +1,29 @@
 from datasets import load_dataset
 import gradio as gr, json, os, random, torch, spaces
-from diffusers import FluxPipeline, AutoencoderKL
+from diffusers import StableDiffusionPipeline, AutoencoderKL
 from gradio_client import Client
 from live_preview_helpers import (
     flux_pipe_call_that_returns_an_iterable_of_images as flux_iter,
 )
 
+# Device selection
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16
+
+# PUBLIC Stable Diffusion pipeline setup
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
 ).to(device)
 good_vae = AutoencoderKL.from_pretrained(
-    "black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16
+    "runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16
 ).to(device)
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_iter.__get__(pipe)
 
+# LLM client config (Zephyr or Gemma fallback)
 LLM_SPACES = [
     "https://huggingfaceh4-zephyr-chat.hf.space",
     "huggingface-projects/gemma-2-9b-it",
 ]
-
-def first_live_space(space_ids: list[str]) -> Client:
+def first_live_space(space_ids):
     for sid in space_ids:
         try:
             print(f"[info] probing {sid}")
@@ -31,11 +34,9 @@ def first_live_space(space_ids: list[str]) -> Client:
         except Exception as e:
             print(f"[warn] {sid} unusable → {e}")
     raise RuntimeError("No live chat Space found!")
-
 llm_client = first_live_space(LLM_SPACES)
-CHAT_API   = "/chat"
-
-def call_llm(prompt: str, max_tokens: int = 256, temperature: float = 0.6, top_p: float = 0.9) -> str:
+CHAT_API = "/chat"
+def call_llm(prompt, max_tokens=256, temperature=0.6, top_p=0.9):
     try:
         return llm_client.predict(
             prompt, max_tokens, temperature, top_p, api_name=CHAT_API
@@ -46,16 +47,14 @@ def call_llm(prompt: str, max_tokens: int = 256, temperature: float = 0.6, top_p
 
 # Datasets and prompt templates
 ds = load_dataset("MohamedRashad/FinePersonas-Lite", split="train")
-def random_persona() -> str:
+def random_persona():
     return ds[random.randint(0, len(ds) - 1)]["persona"]
-
 WORLD_PROMPT = (
     "Invent a short, unique and vivid world description. Respond with the description only."
 )
-def random_world() -> str:
+def random_world():
     return call_llm(WORLD_PROMPT, max_tokens=120)
 
-# Standard single character prompt (optional)
 PROMPT_TEMPLATE = """Generate a character with this persona description:
 {persona_description}
 In a world with this description:
@@ -64,7 +63,7 @@ Write the character in JSON with keys:
 name, background, appearance, personality, skills_and_abilities, goals, conflicts, backstory, current_situation, spoken_lines (list of strings).
 Respond with JSON only (no markdown)."""
 
-def generate_character(world_desc: str, persona_desc: str, progress=gr.Progress(track_tqdm=True)):
+def generate_character(world_desc, persona_desc, progress=gr.Progress(track_tqdm=True)):
     raw = call_llm(
         PROMPT_TEMPLATE.format(
             persona_description=persona_desc,
@@ -113,7 +112,7 @@ Each character must include:
 Respond with pure JSON array.
 """
 
-def generate_connected_characters(world_desc: str, persona_desc: str, progress=gr.Progress(track_tqdm=True)):
+def generate_connected_characters(world_desc, persona_desc, progress=gr.Progress(track_tqdm=True)):
     raw = call_llm(
         CHAIN_PROMPT_TEMPLATE.format(
             world_description=world_desc,
@@ -136,7 +135,7 @@ def generate_connected_characters(world_desc: str, persona_desc: str, progress=g
 # Gradio UI
 DESCRIPTION = """
 * Generates a trio of connected character sheets for a world + persona.
-* Images via **FLUX-dev**; story text via Zephyr-chat or Gemma fallback.
+* Images via **Stable Diffusion**; story text via Zephyr-chat or Gemma fallback.
 * Personas sampled from **FinePersonas-Lite**.
 Tip → Shuffle the world then persona for rapid inspiration.
 """
@@ -162,10 +161,18 @@ with gr.Blocks(title="Connected Character Chain Generator", theme="Nymbo/Nymbo_T
         json_out = gr.JSON(label="Character Description")
     chained_out = gr.JSON(label="Connected Characters (Protagonist, Ally, Nemesis)")
 
+    jls_extract_var = lambda character: next(pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+                prompt=character["appearance"],
+                guidance_scale=7.5,
+                num_inference_steps=25,
+                width=512,
+                height=512,
+                output_type="pil"
+            ))
     btn_generate.click(
         generate_character, [world_tb, persona_tb], [json_out]
     ).then(
-        lambda character: infer_flux(character), [json_out], [img_out]
+        jls_extract_var, [json_out], [img_out]
     )
     btn_chain.click(
         generate_connected_characters, [world_tb, persona_tb], [chained_out]

live_preview_helpers.py

@@ -3,26 +3,23 @@
 import torch
 from typing import Iterator
 
+# live_preview_helpers.py
+
 def flux_pipe_call_that_returns_an_iterable_of_images(
     self,
-    prompt: str,
-    guidance_scale: float = 3.5,
-    num_inference_steps: int = 28,
-    width: int = 1024,
-    height: int = 1024,
+    prompt,
+    guidance_scale=7.5,
+    num_inference_steps=20,
+    width=512,
+    height=512,
     generator=None,
-    output_type: str = "pil",
-    good_vae=None,
-) -> Iterator:
+    output_type="pil",
+    good_vae=None
+):
     """
-    Streams an iterable of images as generated by the FLUX pipeline, for use with Gradio's live preview.
+    For StableDiffusion: yields a single image for now.
     """
-    # You may use your actual FLUX pipeline API if different.
-    pipe = self  # usually the FLUX pipeline instance
-
-    if generator is None:
-        generator = torch.Generator(device="cpu").manual_seed(0)
-    images = pipe(
+    image = self(
         prompt=prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
@@ -30,8 +27,5 @@ def flux_pipe_call_that_returns_an_iterable_of_images(
         height=height,
         generator=generator,
         output_type=output_type,
-        vae=good_vae,
-    ).images
-
-    for img in images:
-        yield img
+    ).images[0]
+    yield image

requirements.txt

@@ -1,6 +1,10 @@
-gradio
-torch
-diffusers
-datasets
-transformers
-Pillow
+gradio>=4.21.0
+gradio_client>=0.4.0
+transformers>=4.39.0
+datasets>=2.18.0
+huggingface_hub>=0.23.0
+accelerate>=0.27.0
+diffusers>=0.27.0
+torch>=2.1.0
+Pillow>=10.0.0
+spaces>=0.26.0