Rename quad app.py to app.py

quad app.py → app.py

@@ -293,24 +293,30 @@ def remove_custom_lora(selected_indices, current_loras, gallery):
         lora_image_2
     )
 
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
-    print("Generating image...")
+def generate_images(prompt_mash, steps, cfg_scale, width, height, progress):
+    print("Generating multiple images...")
     pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    with calculateDuration("Generating image"):
-        # Generate image
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt_mash,
-            num_inference_steps=steps,
-            guidance_scale=cfg_scale,
-            width=width,
-            height=height,
-            generator=generator,
-            joint_attention_kwargs={"scale": 1.0},
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img
+    images = []
+
+    for _ in range(4):  # Generate 4 images
+        seed = random.randint(0, MAX_SEED)
+        generator = torch.Generator(device="cuda").manual_seed(seed)
+        with calculateDuration("Generating image"):
+            for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+                prompt=prompt_mash,
+                num_inference_steps=steps,
+                guidance_scale=cfg_scale,
+                width=width,
+                height=height,
+                generator=generator,
+                joint_attention_kwargs={"scale": 1.0},
+                output_type="pil",
+                good_vae=good_vae,
+            ):
+                images.append((img, seed))  # Store image and its seed
+                break  # Only take the first generated image
+
+    return images
 
 #def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
 #    pipe_i2i.to("cuda")
@@ -331,7 +337,7 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
     return img
 
 @spaces.GPU(duration=75)
-def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
+def run_lora_multi(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
     if not selected_indices:
         raise gr.Error("You must select at least one LoRA before proceeding.")
 
@@ -349,6 +355,7 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
                 appends.append(trigger_word)
     prompt_mash = " ".join(prepends + [prompt] + appends)
     print("Prompt Mash: ", prompt_mash)
+
     # Unload previous LoRA weights
     with calculateDuration("Unloading LoRA"):
         pipe.unload_lora_weights()
@@ -377,6 +384,8 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
 #            else: pipe = pipe_to_use
         print("Loaded LoRAs:", lora_names)
         print("Adapter weights:", lora_weights)
+        print("cfg_scale:", cfg_scale)
+        print("steps:", steps)
 #        if image_input is not None:
 #            pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
 #        else:
@@ -393,6 +402,7 @@ def run_lora(prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scal
 #        yield final_image, seed, gr.update(visible=False)
 #    else:
         image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
+        return [(img, seed) for img, seed in images]
 # Consume the generator to get the final image
 #        final_image = None
         step_counter = 0
@@ -628,7 +638,11 @@ with gr.Blocks(css=css, delete_cache=(240, 240)) as app:
         triggers=[generate_button.click, prompt.submit],
         fn=run_lora,
         inputs=[prompt, cfg_scale, steps, selected_indices, lora_scale_1, lora_scale_2, randomize_seed, seed, width, height, loras_state],
-        outputs=[result, seed, progress_bar]
+        outputs=[
+            result, seed, progress_bar,
+            gr.Gallery(label="Generated Images"),  # Display 4 images
+            gr.Markdown(label="Seeds")            # Display seeds used
+        ]
     )#.then(
      #   fn=lambda x, history: update_history(x, history),
      #   inputs=[result, history_gallery],