Update img2imgalt.py

Fix with documentation

scripts/img2imgalt.py

@@ -11,6 +11,10 @@
 import torch
 import k_diffusion as K
 
+# Debugging notes - the original method apply_model is being called for sd1.5 is in modules.sd_hijack_utils and is ldm.models.diffusion.ddpm.LatentDiffusion
+# For sdxl - OpenAIWrapper will be called, which will call the underlying diffusion_model 
+
+
 def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
     x = p.init_latent
 
@@ -30,15 +34,25 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigmas[i] * s_in] * 2)
-        cond_in = torch.cat([uncond, cond])
+
+        if shared.sd_model.is_sdxl:
+            cond_tensor = cond['crossattn']
+            uncond_tensor = uncond['crossattn']
+            cond_in = torch.cat([uncond_tensor, cond_tensor])
+        else:
+            cond_in = torch.cat([uncond, cond])
 
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
 
         c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)[skip:]]
         t = dnw.sigma_to_t(sigma_in)
+
+        if shared.sd_model.is_sdxl:
+            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )            
+        else:
+            eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
 
-        eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
         denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
 
         denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale
@@ -64,6 +78,13 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps):
 
 # Based on changes suggested by briansemrau in https://github.com/AUTOMATIC1111/stable-diffusion-webui/issues/736
 def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
+    if shared.sd_model.is_sdxl:
+        cond_tensor = cond['crossattn']
+        uncond_tensor = uncond['crossattn']
+        cond_in = torch.cat([uncond_tensor, cond_tensor])
+    else:
+        cond_in = torch.cat([uncond, cond])
+
     x = p.init_latent
 
     s_in = x.new_ones([x.shape[0]])
@@ -82,7 +103,14 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
 
         x_in = torch.cat([x] * 2)
         sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2)
-        cond_in = torch.cat([uncond, cond])
+
+
+        if shared.sd_model.is_sdxl:
+            cond_tensor = cond['crossattn']
+            uncond_tensor = uncond['crossattn']
+            cond_in = torch.cat([uncond_tensor, cond_tensor])
+        else:
+            cond_in = torch.cat([uncond, cond])
 
         image_conditioning = torch.cat([p.image_conditioning] * 2)
         cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]}
@@ -94,7 +122,12 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps):
         else:
             t = dnw.sigma_to_t(sigma_in)
 
-        eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
+
+        if shared.sd_model.is_sdxl:
+            eps = shared.sd_model.model(x_in * c_in, t, {"crossattn": cond_in["c_crossattn"][0]} )            
+        else:
+            eps = shared.sd_model.apply_model(x_in * c_in, t, cond=cond_in)
+
         denoised_uncond, denoised_cond = (x_in + eps * c_out).chunk(2)
 
         denoised = denoised_uncond + (denoised_cond - denoised_uncond) * cfg_scale