edit

app.py

@@ -2,33 +2,60 @@ import gradio as gr
 import numpy as np
 import random
 from diffusers import DiffusionPipeline
+from diffusers import EulerAncestralDiscreteScheduler
+from compel import Compel, ReturnedEmbeddingsType
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = DiffusionPipeline.from_pretrained("John6666/mala-anime-mix-nsfw-pony-xl-v6-sdxl", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
 else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = DiffusionPipeline.from_pretrained("John6666/mala-anime-mix-nsfw-pony-xl-v6-sdxl", use_safetensors=True)
     pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
+
+pipe.safety_checker = None
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+compel = Compel(
+  tokenizer=[pipe.tokenizer, pipe.tokenizer_2] ,
+  text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
+  returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+  requires_pooled=[False, True]
+)
+
+pipe.load_lora_weights("xenov2/pony", weight_name="style_cogecha_pony_1.safetensors", adapter_name="chochega")
+
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_weight):
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator().manual_seed(seed)
+
+    conditioning, pooled = compel(prompt)
+    negative_conditioning, negative_pooled = compel(neg)
+
+    [conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])
+
     
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
+        # prompt = prompt, 
+        # negative_prompt = negative_prompt,
+        prompt_embeds=conditioning,
+        pooled_prompt_embeds=pooled,
+        negative_propmt_embeds=negative_conditioning,
+        negative_pooled_prompt_embeds=negative_pooled,
         guidance_scale = guidance_scale, 
+        cross_attention_kwargs={"scale": lora_weight},
         num_inference_steps = num_inference_steps, 
         width = width, 
         height = height,
@@ -95,6 +122,14 @@ with gr.Blocks(css=css) as demo:
             )
             
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+            lora_weight = gr.Slider(
+                label="Lora weight",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=0.0,
+            )
             
             with gr.Row():
                 
@@ -139,7 +174,7 @@ with gr.Blocks(css=css) as demo:
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_weight],
         outputs = [result]
     )
 

requirements.txt

@@ -3,4 +3,5 @@ diffusers
 invisible_watermark
 torch
 transformers
-xformers
+xformers
+compel