moved generation to FAL

app.py

@@ -129,8 +129,11 @@ async def run_virtual_tryon_pipeline(
         #lets use openai for this. Also, return errors where needed
         r.set(request_id, "Creating base image")
         # STEP 1: make base image
+
+        ## analyse the garment images
+        garment_description = describe_garment(front_final_image)
         try:
-            base_image = base_generator.create_image(talent_lora_url, talent_trigger_word, "", 1, num_images)
+            base_image = base_generator.create_image(talent_lora_url, talent_trigger_word, garment_description, 1, num_images)
             print(f"base_image: {base_image}")
         except Exception as e:
             r.set(request_id, "error generating base image")

base_generator.py

@@ -1,6 +1,7 @@
 import os
 import random
 import requests
+import fal_client
 
 #Todo
 # Something to select the pose from the list
@@ -30,7 +31,7 @@ def run_workflow(body):
     response = requests.post(url, headers=headers, json=body)
     return response.json()
 
-def create_image(character_lora, character_keyword, outfit_desc, pose_id, num_outputs): 
+def comfy_create_image(character_lora, character_keyword, outfit_desc, pose_id, num_outputs): 
     seed = random.randint(0, 1000000)
     print(f"seed: {seed}")
     prompt = {"56": {"_meta": {"title": "CLIP Text Encode (Prompt)"}, "inputs": {"clip": ["365", 1], "text": ""}, "class_type": "CLIPTextEncode"}, "159": {"_meta": {"title": "Load VAE"}, "inputs": {"vae_name": "flux1-ae.safetensors"}, "class_type": "VAELoader"}, "175": {"_meta": {"title": "Apply ControlNet"}, "inputs": {"vae": ["159", 0], "image": ["369", 0], "negative": ["56", 0], "positive": ["199", 0], "strength": 0.7000000000000001, "control_net": ["260", 0], "end_percent": 0.5, "start_percent": 0}, "class_type": "ControlNetApplyAdvanced"}, "199": {"_meta": {"title": "CLIP Text Encode (Prompt)"}, "inputs": {"clip": ["365", 1], "text": f"Fashion model {character_keyword} wearing {outfit_desc}. posing in front of white background"}, "class_type": "CLIPTextEncode"}, "260": {"_meta": {"title": "Load ControlNet Model"}, "inputs": {"control_net_name": "flux.1-dev-controlnet-union.safetensors"}, "class_type": "ControlNetLoader"}, "263": {"_meta": {"title": "Save Image"}, "inputs": {"images": ["311", 0], "filename_prefix": "ControlNet"}, "class_type": "SaveImage"}, "307": {"_meta": {"title": "FluxGuidance"}, "inputs": {"guidance": 3.5, "conditioning": ["175", 0]}, "class_type": "FluxGuidance"}, "308": {"_meta": {"title": "KSampler"}, "inputs": {"cfg": 1, "seed": seed, "model": ["365", 0], "steps": 20, "denoise": 1, "negative": ["335", 0], "positive": ["307", 0], "scheduler": "simple", "latent_image": ["344", 0], "sampler_name": "euler"}, "class_type": "KSampler"}, "310": {"_meta": {"title": "DualCLIPLoader"}, "inputs": {"type": "flux", "device": "default", "clip_name1": "t5xxl_fp8_e4m3fn.safetensors", "clip_name2": "clip_l.safetensors"}, "class_type": "DualCLIPLoader"}, "311": {"_meta": {"title": "VAE Decode"}, "inputs": {"vae": ["159", 0], "samples": ["308", 0]}, "class_type": "VAEDecode"}, "335": {"_meta": {"title": "FluxGuidance"}, "inputs": {"guidance": 3.5, "conditioning": ["175", 1]}, "class_type": "FluxGuidance"}, "344": {"_meta": {"title": "Empty Latent Image"}, "inputs": {"width": 544, "height": 960, "batch_size": num_outputs}, "class_type": "EmptyLatentImage"}, "363": {"_meta": {"title": "Load Diffusion Model"}, "inputs": {"unet_name": "flux1-dev-fp8-e4m3fn.safetensors", "weight_dtype": "fp8_e4m3fn"}, "class_type": "UNETLoader"}, "365": {"_meta": {"title": "Load LoRA"}, "inputs": {"clip": ["310", 0], "model": ["363", 0], "lora_name": character_lora, "strength_clip": 0.99, "strength_model": 0.84}, "class_type": "LoraLoader"}, "369": {"_meta": {"title": "Load Image"}, "inputs": {"image": "Pose_Female_Front_full_standing_02.webp_00001_.png", "upload": "image"}, "class_type": "LoadImage"}}
@@ -43,6 +44,52 @@ def create_image(character_lora, character_keyword, outfit_desc, pose_id, num_ou
     print(run)
     return(run)
 
+def on_queue_update(update):
+    if isinstance(update, fal_client.InProgress):
+        for log in update.logs:
+           print(log["message"])
+
+def create_image(character_lora, character_keyword, outfit_desc, pose_id, num_outputs): 
+    seed = random.randint(0, 1000000)
+    print(f"seed: {seed}")
+    prompt = "Fashion model {character_keyword} wearing {outfit_desc}. posing in front of white background"
+
+    result = fal_client.subscribe(
+        "fal-ai/flux-general",
+        arguments={
+            "seed": seed,
+            "loras": [{
+                "path": character_lora,
+                "scale": "1"
+            }],
+            "prompt": prompt,
+            "image_size": "portrait_9_16",
+            "num_images": num_outputs,
+            "controlnets": [],
+            "guidance_scale": 3.5,
+            "controlnet_unions": [{
+                "path": "InstantX/FLUX.1-dev-Controlnet-Union",
+                "variant": None,
+                "controls": [{
+                    "control_mode": "pose",
+                    "control_image_url": "blob:https://fal.ai/2fdf0ff3-73e0-445c-b521-51b3dcf7f2ff",
+                    "end_percentage": 0.8
+                }]
+            }],
+            "num_inference_steps": 28,
+            "enable_safety_checker": True,
+            "control_loras": [],
+            "use_beta_schedule": True
+        },
+        with_logs=True,
+        on_queue_update=on_queue_update,
+    )
+    print(result)
+
+
+
+
+
 if __name__ == "__main__":
     character_lora = "yOA2a06KWEMR-ewq3j8io_pytorch_lora_weights.safetensors"
     character_keyword = "crxter_scandi"